Repository: patrickvonplaten/notebooks Branch: master Commit: 5d18f174e637 Files: 31 Total size: 14.1 MB Directory structure: gitextract_qeul1y2r/ ├── ADD_NEW_MODEL.md ├── BERT2BERT_for_CNN_Dailymail.ipynb ├── Boosting_Wav2Vec2_with_n_grams_in_Transformers.ipynb ├── Diffusers.ipynb ├── Distil_Whisper_Benchmark.ipynb ├── Encoder_Decoder_Model.ipynb ├── Evaluating_Big_Bird_on_TriviaQA.ipynb ├── Fine_Tune_MMS_on_Common_Voice.ipynb ├── Fine_Tune_XLSR_Wav2Vec2_on_Common_Voice.ipynb ├── Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_🤗_Transformers.ipynb ├── Fine_Tune_XLS_R_on_Common_Voice.ipynb ├── Fine_tune_Longformer_Encoder_Decoder_(LED)_for_Summarization_on_pubmed.ipynb ├── Fine_tuning_Wav2Vec2_for_English_ASR.ipynb ├── Getting_the_most_out_of_LLMs.ipynb ├── How_to_evaluate_Longformer_on_TriviaQA_using_NLP.ipynb ├── LED_on_Arxiv.ipynb ├── Leveraging_Pre_trained_Checkpoints_for_Encoder_Decoder_Models.ipynb ├── ODSC_2022_🤗_Transformers_Workshop.ipynb ├── PyTorch_Reformer.ipynb ├── README.md ├── Reformer_1_4.ipynb ├── Reformer_2_4.ipynb ├── Reformer_3_4.ipynb ├── Reformer_4_4.ipynb ├── Reformer_For_Masked_LM.ipynb ├── RoBERTaShared_for_BBC_XSum.ipynb ├── Text_Classification_on_GLUE_on_TPU_using_Jax_Flax.ipynb ├── Using_🤗_Transformers_and_🤗_Datasets_filter_customer_feedback_filtering.ipynb ├── examples/ │ └── multi_lingual_speech_recognition.ipynb ├── getting_the_most_out_of_LLMs.md └── osdc.ipynb ================================================ FILE CONTENTS ================================================ ================================================ FILE: ADD_NEW_MODEL.md ================================================ # *TEMPLATE* *search & replace the following keywords:* -[name of model] -[name of teacher] -[start date] -[end date] -[camelcase name of mode] # How to add [name of model] to 🤗Transformers Teacher: [name of hugging face teacher] Begin: [start date] Estimated time: [end date] The following sections explain in detail how to add [name of model] to 🤗Transformers. You will work closely with [name of hugging face teacher] to integrate [name of model] into 🤗Transformers. By doing so, you will both gain a theoretical and deep practical understanding of [name of model]. But more importantly, you will have made a major open-source contribution to 🤗Transformers. Along the way, you will: - get insights into open-source best practices, - understand the design principles of one of the most popular NLP libraries, - learn how to do efficiently test large NLP models, - learn how to integrate python utilities like `black`, `isort`, `make fix-copies` into a library to always ensure clean and readable code. The following list is a summary of everything that has to be done to add a model and can be used by you as a To-Do List: - [ ] Understood theoretical aspects - [ ] Prepared environment - [ ] Set up debugging environment of original repository - [ ] Created script that successfully runs forward pass using original repository and checkpoint - [ ] Successfully converted original checkpoint to Transformers checkpoint - [ ] Successfully ran forward pass in Transformers that gives identical output to original checkpoint - [ ] Finished model tests in Transformers - [ ] Succesfully added Tokenizer in Transformers - [ ] Finished docs - [ ] Uploaded model weights to the hub - [ ] Merged the pull request - [ ] (Optionally) added a notebook To begin with, you should start by getting a good understanding of the model. ## Theoretical aspects of [name of model] ### Paper You should take some time to read [[name of model]'s paper]([link to paper]). There might be large sections of the paper that are difficult to understand. If this is the case, this is fine - don't worry! The goal is not to get a deep theoretical understanding of the paper, but to extract the necessary information required to effectively re-implement the model to 🤗Transformers. That being said, you don't have to spend too much time on the theoretical aspects, but rather focus on the practical ones, namely: - What time of model is [name of model]? BERT-like encoder-only model? GPT2-like decoder-only model? BART-like encoder-decoder model? - What are the applications of [name of model]? Text classification? Text generation? Seq2Seq tasks, *e.g.* summarization? - What is the novel feature of the model making it different from BERT or BART (if it's an encoder-decoder model)? - Which of the already existing [🤗Transformers models](https://huggingface.co/transformers/#contents) is most similar to [name of model]? - What type of tokenizer is used? A sentence piece tokenizer? Word piece tokenizer? Is it the same tokenizer as used for BERT or BART? After you feel like you have gotten a good overview of the architecture of the model, you might want to write [name of hugging face teacher] for any questions you might have. This might include questions regarding the model's architecture, its attention layer, etc. [name of hugging face teacher] will be more than happy to help you. ### Additional resources Before diving into the code, here are some additional resources that might be worth taking a look at: - [link 1] - [link 2] - [link 3] - ... ### Make sure you've understood the fundamental aspects of [name of model] Alright, now you should be ready to take a closer look into the actual code of [name of model]. You should have understood the following aspects of [name of model] by now: - [characteristic 1 of [name of model]] - [characteristic 2 of [name of model]] - ... If any of the mentioned aspects above are **not** clear to you, now is a great time to talk to [name of hugging face teacher] again! ## Next prepare your environment 1. Fork the [repository](https://github.com/huggingface/transformers) by clicking on the 'Fork' button on the repository's page. This creates a copy of the code under your GitHub user account. 2. Clone your fork to your local disk, and add the base repository as a remote: ```bash git clone https://github.com/[your Github handle]/transformers.git cd transformers git remote add upstream https://github.com/huggingface/transformers.git ``` 3. Set up a development environment, for instance by running the following command: ```bash conda create -n env python=3.7 --y conda activate env pip install -e ".[dev]" ``` and return to the parent directory ```bash cd .. ``` 4. We recommend to add the PyTorch version of [name of model] to Transformers. To install PyTorch, please follow the instructions on https://pytorch.org/. **Note:** You don't need to have CUDA install. It is sufficient to just be working on CPU. 5. To port [name of model], you will also need access to its [original repository]([link to original repo]): ```bash git clone [clone link to original repo] cd [name of repo] pip install -e . ``` Now you have set up a development environment to port [name of model] to 🤗Transformers. ## Run a pretrained checkpoint using the original repository At first, you will work on the original repository. Often, the original implementation is very "researchy" meaning that documentation might be lacking and the code can be difficult to understand. But this should be exactly your motivation to reimplement [name of the model]. At Hugging Face, one of our main goals is to *make people stand on the shoulders of giants* which translates here very well into taking a working model and rewriting it to make it as **accessible, user-friendly, and beautiful** as possible. This is the #1 motivation to re-implement models into 🤗Transformers - trying to maximize access to a complex new NLP technology for **everybody**. You should start thereby by diving into the original repository. ### Get familiar with the original repository. Successfully running the official pre-trained model in the original repository is often **the most difficult** step. From our experience, it is very important to spend some time to get familiar with the [original codebase]([link to original repo]). You should find out - Where to find the pre-trained weights - How to load the pre-trained weights into its corresponding model - Trace one forward pass so that you know which classes and functions are required for a simple forward pass . Usually, you only have to reimplement those functions. - Be able to locate the important components of the model: Where is the model class? Are there submodel classes, *e.g.* EncoderModel, DecoderModel? Where is the self-attention layer? Are there multiple different attention layers, *e.g.* *self-attention*, *cross-attention*...? - How can you debug the model in the original environment of the repo? Do you have to set `print` statements or can you work with an interactive debugger like `ipdb`? It is very important that before you start opening a PR in 🤗Transformers that you can **efficiently** debug code in the original repository! This means that you should be able to run a forward pass and print out the actual values of the output of a layer. *I.e* you can load a pre-trained model, pass an input vector of token ids, *i.e.* `input_ids = [0, 1, 4, 5, ...]` to the model's forward function and you can print out the intermediate outputs of - let's say - the first self-attention layer that could look something like this: ```bash [[ [-0.1465, -0.6501, 0.1993, ..., 0.1451, 0.3430, 0.6024], [-0.4417, -0.5920, 0.3450, ..., -0.3062, 0.6182, 0.7132], [-0.5009, -0.7122, 0.4548, ..., -0.3662, 0.6091, 0.7648], ..., [-0.5613, -0.6332, 0.4324, ..., -0.3792, 0.7372, 0.9288], [-0.5416, -0.6345, 0.4180, ..., -0.3564, 0.6992, 0.9191], [-0.5334, -0.6403, 0.4271, ..., -0.3339, 0.6533, 0.8694]]], ``` This means that your debugging environment should consists of a short script (ideally written by you) that does the following (in pseudocode): ```bash model = [name of model]Model.load_pretrained_checkpoint(/path/to/checkpoint/) input_ids = ... # vector of input ids original_output = model.predict(input_ids) ``` By running such a script, you should be able to print out intermediate values or hit a breakpoint in the clone of the original repository that is saved locally on your computer. We expect that every model added to 🤗Transformers passes a couple of integration tests, meaning that the original model and the reimplemented version in 🤗Transformers have to give the exact same output up to a precision of 0.001! It is not enough if the model gives nearly the same output, they have to be the same. Therefore, you will certainly compare the intermediate outputs of the 🤗Transformers version multiple times against the intermediate outputs of the original implementation of [name of model] in which case an **efficient** debugging environment of the original repository is absolute key. Here is some advice is to make your debugging environment as efficient as possible. - Find the best way of debugging intermediate results. Is the original repository written in PyTorch? Then you should be able to use a simple debugger like [ipdb](https://pypi.org/project/ipdb/) to print out intermediate values. Is the original repository written in Tensorflow 1? Then you might have to rely on TensorFlow print operations like https://www.tensorflow.org/api_docs/python/tf/print to output intermediate values. Is the original repository written in Jax? Then make sure that the model is **not jitted** when running the forward pass, *e.g.* check-out [this link](https://github.com/google/jax/issues/196). - Use the smallest pre-trained checkpoint you can find. The smaller the checkpoint, the faster your debug cycle becomes. It is not efficient if your pre-trained model is so big that your forward pass takes more than 10 seconds. In case only very large checkpoints are available, it might make more sense to create a dummy model in the new environment with randomly initialized weights and save those weights for comparison with the 🤗Transformers version of your model - Make sure you are using the easiest way of calling a forward pass in the original repository. Ideally, you want to find the function in the original repository that **only** calls a single forward pass, *i.e.* that is often called `predict`, `evaluate`, `forward` or `__call__`. You don't want to debug a function that calls `forward` multiple times, *e.g.* to generate text, like `autoregressive_sample`, `generate`. - Try to separate the tokenization from the model's `forward` pass. If the original repository shows examples where you have to input a string, then try to find out where in the forward call the string input is changed to input ids and start from this point. This might mean that you have to possibly write a small script yourself or change the original code so that you can directly input the ids instead of an input string. - Make sure that the model in your debugging setup is **not** in training mode, which often causes the model to yield random outputs due to multiple dropout layers in the model. Make sure that the forward pass in your debugging environment is **deterministic** so that the dropout layers are not used. The following section gives you more specific details/tips on how you can do this for [name of model]. ### More details on how to create a debugging environment for [name of model] [Here the teacher should add very specific information on what the student should do] [to set up an efficient environment for the special requirements of this model] ## Implement [name of model] into 🤗Transformers Next, you can finally add code to 🤗Transformers. Go into the clone of your 🤗Transformers' fork: ``` cd transformers ``` ### Use the Cookiecutter to automatically generate the model's code To begin with head over to the [🤗Transformers templates](https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model) to make use of our `cookiecutter` implementation to automatically generate all the relevant files for your model. Again, we recommend only adding the PyTorch version of the model at first. Make sure you follow the instructions of the `README.md` on the [🤗Transformers templates](https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model) carefully. ### Open a Pull Request on the main `huggingface/transformers` repo Before starting to adapt the automatically generated code, now is the time to open a "Work in progress (WIP)" pull request, *e.g.* "[WIP] Add [name of model]", in 🤗Transformers so that you and [name of teacher] can work side-by-side on integrating the model into 🤗Transformers. You should do the following: 1. Commit the automatically generated code: ``` git add . git commit ``` 2. Fetch and rebase to current master ``` git fetch upstream git rebase upstream/master ``` 3. Push the changes to your account using: ``` git push -u origin a-descriptive-name-for-my-changes ``` 4. Once you are satisfied, go to the webpage of your fork on GitHub. Click on "Pull request". Make sure to add the GitHub handle of [name of teacher] as reviewer, so that [name of teacher] gets notified for future changes. 5. Change the PR into a draft by clicking on "Convert to draft" on the right of the GitHub pull request web page. In the following, whenever you have done some progress, don't forget to commit your work and push it to your account so that is shows in the pull request. Additionally, you should make sure to update your work with the current master from time to time by doing: ``` git fetch upstream git merge upstream/master ``` In general, all questions you might have regarding the model or your implementation should be asked in your PR and discussed/solved in the PR. This way, [name of teacher] will always be notified when you are committing new code or if you have a question. It is often very helpful to point [name of teacher] to your added code so that [name of teacher] can efficiently understand your problem or question. To do so, you can go to the "Files changed" tab where you see all of your changes, go to a line regarding which you want to ask a question and click on the "+" symbol to add a comment. Whenever a question or problem has been solved, you can click on the "Resolve" button of the created comment. In the same way, [name of teacher] will open comments when reviewing your code. We recommend asking most questions on GitHub on your PR. For some very general questions that are not very useful for the public, feel free to ping [name of teacher] by Slack or mail. ### Adapt the generated model's code for [name of model] At first, we will focus only on the model itself and not care about the tokenizer. All the relevant code should be found in the generated files `src/transformers/models/[lowercase name of model]/modeling_[lowercase name of model].py` and `src/transformers/models/[lowercase name of model]/configuration_[lowercase name of model].py`. Now you can finally start coding :). The generated code in `src/transformers/models/[lowercase name of model]/modeling_[lowercase name of model].py` will either has the same architecture as BERT or BART if it's an encoder-decoder model. At this point, you should remind yourself what you've learned in the beginning about the theoretical aspects of the model: *How is the model different from BERT or BART?*". Implement those changes which often means to change the *self-attention* layer, the order of the normalization layer, etc... Here it is often useful to look at the similar architecture of already existing models in Transformers. **Note** that at this point, you don't have to be very sure that your code is fully correct or clean. Rather, it is advised to add a first *unclean*, copy-pasted version of the original code to `src/transformers/models/[lowercase name of model]/modeling_[lowercase name of model].py` until you feel like all the necessary code is added. From our experience, it is much more efficient to quickly add a first version of the required code and improve/correct the code iteratively with the conversion script as described in the next section. The only thing that has to work at this point is that you can instantiate the 🤗Transformers implementation of [name of model], *i.e.* the following command works: ```python from transformers import [camelcase name of model]Model, [camelcase name of model]Config model = [camelcase name of model]Model([camelcase name of model]Config()) ``` The above command will create a model according to the default parameters as defined in `[camelcase name of model]Config()` with random weights, thus making sure that the `init()` methods of all components works. In the case of [name of model], you should at least have to do the following changes: [Here the teacher should add very specific information on what exactly has to be changed for this model] [...] [...] ### Write a conversion script Now, you should write a conversion script that lets you convert the checkpoint you used to debug [name of model] in the original repository to your just created 🤗Transformers implementation of [name of model]. Here you should not try to write the conversion script from scratch, but find similar models in 🤗Transformers that require similar conversion scripts, *i.e.* whose original repository was written with the same framework as [name of model]. In the conversion script, you should make sure that you set the parameters correctly in `[camelcase name of model]Config()` to exactly match those that were used for the checkpoint you want to convert. Also, it is very important that before you set the retrieved weights to the new weight, *e.g.* via ```python layer.weight.data = array ``` you make sure that both their **shape and name match**. If either the shape or the name doesn't match, you probably assigned the wrong checkpoint weight to a randomly initialized layer of the 🤗Transformers implementation. Therefore, it is ** necessary** to add assert statements for the shape and print out the names of the checkpoints weights. E.g. you should add statements like: ```python assert ( pointer_random_weight.shape == checkpoint_weight.shape ), f"Pointer shape of random weight {pointer_random_weight.shape} and array shape of checkpoint weight {checkpoint_weight.shape} mismatched ``` and ```python logger.info(f"Initialize PyTorch weight {pointer_random_weight.name} from {checkpoint_weight.name}") ``` for verification. Besides, you should also check that **all** required weights are initialized and print out all checkpoint weights that were not used for initialization to make sure the model is correctly converted. It is completely normal, that the conversion trials fail with either a wrong shape statement or wrong name assignment. This is most likely because either you used incorrect parameters in `[camelcase name of model]Config()`, have a wrong architecture in the 🤗Transformers implementation, you have a bug in the `init()` functions of one of the components of the 🤗Transformers implementation or you need to transpose one of the checkpoint weights. This step should be iterated with the previous step until all weights of the checkpoint are correctly loaded in the Transformers model. Having correctly loaded the checkpoint into the 🤗Transformers implementation, you can then save the model under a folder of your choice `/path/to/converted/checkpoint/folder` that should include both a `pytorch_model.bin` file and a `config.json` file. In the case of [name of model], you should probably do the following: [Here the teacher should add very specific information on what exactly has to be done for the conversion of this model] [...] [...] ### Implement the forward pass Having managed to correctly load the pretrained weights into the 🤗Transformers implementation, you should now make sure that the forward pass is correctly implemented. In *Get familiar with the original repository.*, you have already created a script that runs a forward pass of the model using the original repository. Now you should write an analogous script using the 🤗Transformers implementation instead of the original one. It should look as follows: [Here the model name might have to be adapted, *e.g.* maybe [name of model]ForConditionalGeneration instead of [name of model]Model] ```python model = [name of model]Model.from_pretrained(/path/to/converted/checkpoint/folder) input_ids = ... # the exact same vector of input ids in PyTorch as those used in the *Get familiar with the original repository.* section output = model(input_ids).last_hidden_states ``` It is very likely that the 🤗Transformers implementation and the original model implementation don't give the exact same output the very first time or that the forward pass throws an error. Don't be disappointed - it's expected! First, you should make sure that the forward pass doesn't throw any errors. It often happens that the wrong dimensions are used leading to a `Dimensionality mismatch` error or that the wrong data type object is used, *e.g.* `torch.long` instead of `torch.float32`. Don't hesitate to ask [name of teacher] for help, if you don't manage to solve certain errors. The final part to make sure the 🤗Transformers implementation works correctly is to ensure that the outputs are equivalent to a precision of `1e-3`. First, you should ensure that the output shapes are identical, *i.e.* `outputs.shape` should yield the same value for the script of the 🤗Transformers implementation and the original implementation. Next, you should make sure that the output values are identical as well. This one of the most difficult parts of adding a new model. Common mistakes why the outputs are not identical are: - Some layers were not added, *i.e.* an `activation` layer was not added, or the residual connection was forgotten - The word embedding matrix was not tied - The wrong positional embeddings are used because the original implementation uses on offset - Dropout is applied during the forward pass. To fix this make sure `model.training is False` and that no dropout layer is falsely activated during the forward pass, *i.e.* pass `self.training` to [PyTorch's functional dropout](https://pytorch.org/docs/stable/nn.functional.html?highlight=dropout#torch.nn.functional.dropout) The best way to fix the problem is usually to look at the forward pass of the original implementation and the 🤗Transformers implementation side-by-side and check if there are any differences. Ideally, you should debug/print out intermediate outputs of both implementations of the forward pass to find the exact position in the network where the 🤗Transformers implementation shows a different output than the original implementation. First, make sure that the hard-coded `input_ids` in both scripts are identical. Next, verify that the outputs of the first transformation of the `input_ids` (usually the word embeddings) are identical. And then work your way up to the very last layer of the network. At some point, you will notice a difference between the two implementations, which should point you to the bug in the 🤗Transformers implementation. From our experience, a simple and efficient way is to add many print statements in both the original implementation and 🤗Transformers implementation, at the same positions in the network respectively, and to successively remove print statements showing the same values for intermediate presentions. When you're confident that both implementations yield the same output, verifying the outputs with `torch.allclose(original_output, output, atol=1e-3)`, you're done with the most difficult part! Congratulations - the work left to be done should be a cakewalk 😊. ### Adding all necessary model tests At this point, you have successfully added a new model. However, it is very much possible that the model does not yet fully comply with the required design. To make sure, the implementation is fully compatible with 🤗Transformers, all common tests should pass. The Cookiecutter should have automatically added a test file for your model, probably under the same `tests/test_modeling_[name of model].py`. Run this test file to verify that all common tests pass: ```python pytest tests/test_modeling_[name of model].py ``` [Here the teacher should add very specific information on what tests are likely to fail after having implemented the model , e.g. given the model, it might be very likely that `test_attention_output` fails] [...] [...] Having fixed all common tests, it is now crucial to ensure that all the nice work you have done is well tested, so that - a) The community can easily understand your work by looking at specific tests of [name of model] - b) Future changes to your model will not break any important feature of the model. At first, integration tests should be added. Those integration tests essentially do the same as the debugging scripts you used earlier to implement the model to 🤗Transformers. A template of those model tests is already added by the Cookiecutter, called `[camelcase name of model]ModelIntegrationTests` and only has to be filled out by you. To ensure that those tests are passing, run ```python RUN_SLOW=1 pytest tests/test_modeling_[name of model].py::[camelcase name of model]ModelIntegrationTests ``` Second, all features that are special to [name of model] should be tested additionally in a separate test under `[camelcase name of model]ModelTester`/`[camelcase name of model]ModelTest`. This part is often forgotten, but is extremely useful in two ways: - It helps to transfer the knowledge you have acquired during the model addition to the community by showing how the special feature of [name of model] should work - Future contributors can quickly test changes to the model by running those special tests [Here the teacher should add very specific information on what special features of the model should be tested additionally] [...] [...] ### Implement the tokenizer Next, we should add the tokenizer of [name of model]. Usually, the tokenizer is equivalent or very similar to an already existing tokenizer of 🤗Transformers. [Here the teacher should add a comment whether a new tokenizer is required or if this is not the case which existing tokenizer closest resembles [name of model]'s tokenizer and how the tokenizer should be implemented] [...] [...] Next, it is very important to find/extract the original tokenizer file and to manage to load this file into the 🤗Transformers' implementation of the tokenizer. For [name of model], the tokenizer files can be found here: - [To be filled out by teacher] and having implemented the 🤗Transformers' version of the tokenizer can be loaded as follows: [To be filled out by teacher] To ensure that the tokenizer works correctly, it is recommend to analogous to the model, first create a script in the original repository that inputs a string and returns the `input_ids`. It could look similar to this (in pseudo code): ```bash input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words." model = [name of model]Model.load_pretrained_checkpoint(/path/to/checkpoint/) input_ids = model.tokenize(input_str) ``` You might have to take a deeper look again into the original repository to find the correct tokenizer function or you might even have to do changes to your clone of the original repository to only output the `input_ids`. Having written a functional tokenization script that uses the original repository, an analogous script for 🤗Transformers should be created. It should look similar to this: ```python from transformers import [camelcase name of model]Tokenizer input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words." tokenizer = [camelcase name of model]Tokenizer.from_pretrained(/path/to/tokenizer/folder/) input_ids = tokenizer(input_str).input_ids ``` When both `input_ids` yield the same values, as a final step a tokenizer test file should also be added. [Here teacher should write how to do so. Tokenizer test files strongly vary between different model implementations] Analogous to the modeling test files of [name of model], the tokenization test files of [name of model] should contain a couple of hard-coded integration tests. [Here teacher should point the student to test files of similar tokenizers] As a final step, it is recommended to add one large integration tests to `tests/test_modeling_[name of model].py` that does some end-to-end testing using both the model and the tokenizer. [Here teacher should again point to an existing similar test of another model that the student can copy & adapt] ### Add Docstring Now, all the necessary functionality for [name of model] is added - you're almost done! The only thing left to add is a nice docstring and a doc page. The Cookiecutter should have added a template file called `docs/source/model_doc/[name of model].rst` that you should fill out. Users of your model will usually first look at this page before using your model. Hence, the documentation must be understandable and concise. It is very useful for the community to add some *Tips* to show how the model should be used. Don't hesitate to ping [name of teacher] regarding the docstrings. Next, make sure that the docstring added to `src/transformers/models/[name of model]/modeling_[name of model].py` is correct and included all necessary inputs and outputs. It is always to good to remind oneself that documentation should be treated at least as carefully as the code in 🤗Transformers since the documentation is usually the first contact point of the community with the model. ### Code refactor Great, now you have added all the necessary code for [name of model]. At this point, you should correct some potential incorrect code style by running: ```bash make style ``` and verify that your coding style passes the quality check: ```bash make quality ``` There are a couple of other very strict design tests in 🤗Transformers that might still be failing, which shows up in the tests of your pull request. This is often because of some missing information in the docstring or some incorrect naming. [name of teacher] will surely help you if you're stuck here. Lastly, it is always a good idea to refactor one's code after having ensured that the code works correctly. With all tests passing, now it's a good time to go over the added code again and do some refactoring. You have now finished the coding part, congratulation! 🎉 You are Awesome! 😎 ### Upload the models to the modeling hub In this final part, you should convert and upload all checkpoints to the model hub and add a model card for each uploaded model checkpoint. You should work alongside [name of teacher] here to decide on a fitting name for each checkpoint and to get the required access rights to be able to upload the model under the author's organization of [name of model]. It is worth spending some time to create fitting model cards for each checkpoint. The model cards should highlight the specific characteristics of this particular checkpoint, *e.g.* On which dataset was the checkpoint pretrained/fine-tuned on? On what down-stream task should the model be used? and also include some code on how to correctly use the model. ### (Optional) Add notebook It is very helpful to add a notebook that showcases in-detail how [name of model] can be used for inference and/or fine-tuned on a down-stream task. This is not mandatory to merge your PR, but very useful for the community. ## Share your work!! Now, it's time to get some credit from the community for your work! Having completed a model addition is a major contribution to Transformers and the whole NLP community. Your code will certainly be used by hundreds of developers and researchers. 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"padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "H1F58j028eTV" }, "source": [ "## **Warm-starting BERT2BERT for CNN/Dailymail**\n", "\n", "***Note***: This notebook only uses a few training, validation, and test data samples for demonstration purposes. To fine-tune an encoder-decoder model on the full training data, the user should change the training and data preprocessing parameters accordingly as highlighted by the comments.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "3FO5ESocXvlK" }, "source": [ "### **Data Preprocessing**\n" ] }, { "cell_type": "code", "metadata": { "id": "w67vkz3KP9eZ" }, "source": [ "%%capture\n", "!pip install datasets==1.0.2\n", "!pip install transformers\n", "\n", "import datasets\n", "import transformers" ], "execution_count": 6, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "sgTiC0rhMb7C", "outputId": "65bde1f0-a4aa-4f47-cdb0-95f445c6a884", "colab": { "base_uri": "https://localhost:8080/", "height": 523, "referenced_widgets": [ "e13b8fdb8a564e8699c77890e29995bf", "76b62d5ac3134a46b46a1590e6918170", "10a9c887384d4197a6cb7301309a62b1", "108702a4a6b54eabbe030679908b4c58", "1c58adfad7cf4e1b975605e1f1f9cac8", "6b9652450f1541e4a000935ac3cbe4f8", "4c52ff87b6ab4c63b79979821879c2ee", "e7336a319f634ca3934683a51e10dfbf", "86808a1fa64440138b3c787781942a5f", "3e9ee7da797343199ca06a3cca4f946f", "293bd427957f4ad48066b49de5681923", "f3cfddacd0a449819ef0ae5447438592", "45530e67fabe431e968558e1f046d444", "13d45cb2884f494ebcd4ebc0719c646c", "65e8b67ade3f43759ac34b32be71d9e5", "5ff2d9364f994c07b094b71160e50ab4", "5ed3130299d64af29886ef3b3668a09a", "c36b0101bd934daaa1076b62c55134b8", "aa7e808c82f54cde95b59f6418626da1", "906b65f3129a4de7adbba7315107ba7f", "b4ffe6f38b3847f1b70f54fa1056184b", "1244629b8f414c38b5923d3df86209a0", "cd716bea01d040fc99abbb20f5ac83e3", "47ad32a9f860474cab954241527dc6fe", "5ea1872d39df4bc4b3c9b32b88102d6d", "fe462f9288624b3c873fc16a90231c80", "63f46270992c4b83b6814a5baea2e7d1", "b984044a88964297aae1eeb2f7bee4b4", "d2833c7cc97b462ab4ab93a9635543fc", "11d307de482a46059e9faadfc76bc948", "0c997d0b9d94451e8ba7e611db9a3b2d", "c14de5825dfe4b66b7023f7ff08aa1f5", "691701093591402882afeebc42ad3e12", "17e4b1b652564c33bef1cf81b03e78d1", "da6d0fa8780540509c712f75a0a5b51e", "4d9c64a8e7034261b91516b72a0cf87d", "fccd55d17f014bc98366dee5ad9323e8", "390b6e4c9b5d48bd89d548bae7d56a31", "13371f94bc6f4c9baf5976e5d6efd7c4", "be4f088cfd904553aee57a7b57def994", "a906030b9f004746a72dbd6ff9187ac8", "7f68fc0fd24a4001b1e854d63832eeb3", "94056f146bc64633a784729e5c840743", "4fa85eb5d8284857956f7ac07955d6a3", "10a68a5e77e84531bcb1d65e4409b99b", "c3a6b17e4adf4e52b34d62e033790e2f", "5b9d5c6de1a648c9a3e4b74a15e74cb9", "e9df3eef7f7f4178806718f14277ed9f", "270f174f05b9448da09f305fadc35bca", "924668445a7c4685a5bc53a23d0e2369", "5b876d26864e4139834d02256551219b", "71729ec9012b4536b732d876baa56f0b", "e2bdb032c3b74340af51c49f9af41bb9", "4f353f0d18be4f70b73abe4764f047d2", "ee0e3eebe7b64a57a1dedeaa606a34d7", "e98624ef030a407499a00e87f4aa72d5", "47177e79c37846ce83ee33394cc7eccb", "280a6bd539bc4e00be6a42412a67189c", "96d0a7094d5f41ebb701df7f27f23af4", "a0c971add6634875ad53221e3b3b5058", "0de7e7f162a3446b82a1ec3cd06b2e4b", "112878185d7444e5b166c3bf70ce386c", "9871bc09fb6f49ea88ed8d2028a47dd3", "12445adc14134f87b7cff4258585c6a9", "8566c190030d4088a11bd49bf84ab248", "98c3308baf0e4ac7a6e45d3a6c428998", "3273a149b7ba4a13b0c74b4041e49b81", "a17f3153d722497390031eed4e042002", "cf454595387e4dd29fbd321d7538619c", "e7a69811b6234785a5db7ef25bf348e4", "f1f3b62ea2b44caea526c8bb01eda888", "2a853189e40b4df88957a6fdddc88cc6", "2cfc8aa46d0d485595fa6f2d4c860a60", "3afd3007d1a74f6fb61c52f227866df3", "3456e69245944080857134ea9fa9d6d3", "c9775c6557614b80aa0b7958075eeae7", "2f2978af7ae742a7b8c0cfb79c8aa046", "d5520ec7eda34c94919e42d81b4d1461", "d07f06509065415384263fe787413564", "47c1a2195ff24e699e5ca94b51efe5f5", "9009909f771d46d086b03714e5516695", "b3f209292480421ca97f32c9e1084813", "7415380df2a441d992ce8f72a22656af", "16d8cb45df0044d7a14fb4ece2202f95", "de2a209e80e243429b129dfb53b3a8a7", "985041c01ec24d0981644a794530cc13", "e7b6affa6ccc4bf299e89959aa732760", "09d4ff28a8b34000bac1cf4d9eec67ac", "dd3fee699d6a4b818a192457900afd3f", "3b1ea16a434c465dbdfd4d9b391083c7", "7a0e1869fa86441482d85804174d5532", "1682b8d04e824a4aad13d88bb9a3e8d6", "a4890884d82e4452965333c45ef977e3", "9187459f6baa4175822c5d316414f203", "3f813f51903f4877be179c4f8b952b35", "46099c619d8c46c996881d48713bfd2c" ] } }, "source": [ "from transformers import BertTokenizerFast\n", "\n", "tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-uncased\")\n", "tokenizer.bos_token = tokenizer.cls_token\n", "tokenizer.eos_token = tokenizer.sep_token\n", "\n", "train_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\n", "val_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"validation[:10%]\")" ], "execution_count": 5, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e13b8fdb8a564e8699c77890e29995bf", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "86808a1fa64440138b3c787781942a5f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=466062.0, style=ProgressStyle(descripti…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5ed3130299d64af29886ef3b3668a09a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=3528.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5ea1872d39df4bc4b3c9b32b88102d6d", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1610.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "691701093591402882afeebc42ad3e12", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Downloading', max=1.0, style=ProgressSt…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a906030b9f004746a72dbd6ff9187ac8", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Downloading', max=1.0, style=ProgressSt…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "270f174f05b9448da09f305fadc35bca", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=572061.0, style=ProgressStyle(descripti…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { 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"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\r" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9009909f771d46d086b03714e5516695", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\r" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dd3fee699d6a4b818a192457900afd3f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\rDataset cnn_dailymail downloaded and prepared to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602. Subsequent calls will reuse this data.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "yoN2q0hZUbXN", "outputId": "0a954042-bebc-4d1e-a78c-806672ebf6ab", "colab": { "base_uri": "https://localhost:8080/", "height": 117, "referenced_widgets": [ "a17c3dfa12364f0089bbeeb84802be70", "96c733f3ba564ba5b6a06a776563ac7a", "3ae1750f7c684274b276671ed28b5540", "557901ee2556427098e5e3086445e67f", "e994714f2c6441c49694b7e82d4133b9", "cbb5626b36ce4ea2a5436177ef507cc0", "7e7f87efe3e8478caa2f6357ec273eae", "ec57b9616f66493b8f0de030c572c086", "b1d575cc621044f9958fbe64aba1109a", "832abe53dede4cd4880c44f6400b72b5", "899d4953b0cd4e5fbaeff3b0880519ed", "f7a8a4c3ec8942a9995f4e83b2706a4a", "ba2cfbbaafee4edc9740b4b903b8e94d", "e507b550c4d34882a7bde9e4750f80a3", "d04589c1c4d74894bff5a6f184414920", "a5a646dd8c514d89841bc12ad72cc8bb" ] } }, "source": [ "batch_size=4 # change to 16 for full training\n", "encoder_max_length=512\n", "decoder_max_length=128\n", "\n", "def process_data_to_model_inputs(batch):\n", " # tokenize the inputs and labels\n", " inputs = tokenizer(batch[\"article\"], padding=\"max_length\", truncation=True, max_length=encoder_max_length)\n", " outputs = tokenizer(batch[\"highlights\"], padding=\"max_length\", truncation=True, max_length=decoder_max_length)\n", "\n", " batch[\"input_ids\"] = inputs.input_ids\n", " batch[\"attention_mask\"] = inputs.attention_mask\n", " batch[\"decoder_input_ids\"] = outputs.input_ids\n", " batch[\"decoder_attention_mask\"] = outputs.attention_mask\n", " batch[\"labels\"] = outputs.input_ids.copy()\n", "\n", " # because BERT automatically shifts the labels, the labels correspond exactly to `decoder_input_ids`. \n", " # We have to make sure that the PAD token is ignored\n", " batch[\"labels\"] = [[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch[\"labels\"]]\n", "\n", " return batch\n", "\n", "# only use 32 training examples for notebook - DELETE LINE FOR FULL TRAINING\n", "train_data = train_data.select(range(32))\n", "\n", "train_data = train_data.map(\n", " process_data_to_model_inputs, \n", " batched=True, \n", " batch_size=batch_size, \n", " remove_columns=[\"article\", \"highlights\", \"id\"]\n", ")\n", "train_data.set_format(\n", " type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\n", ")\n", "\n", "\n", "# only use 16 training examples for notebook - DELETE LINE FOR FULL TRAINING\n", "val_data = val_data.select(range(16))\n", "\n", "val_data = val_data.map(\n", " process_data_to_model_inputs, \n", " batched=True, \n", " batch_size=batch_size, \n", " remove_columns=[\"article\", \"highlights\", \"id\"]\n", ")\n", "val_data.set_format(\n", " type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\n", ")" ], "execution_count": 7, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a17c3dfa12364f0089bbeeb84802be70", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=8.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b1d575cc621044f9958fbe64aba1109a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "aEjb026cNC38" }, "source": [ "### **Warm-starting the Encoder-Decoder Model**" ] }, { "cell_type": "code", "metadata": { "id": "tS0UndNoQh8t", "outputId": "745e278d-5cbb-486d-bf73-1f3449458013", "colab": { "base_uri": "https://localhost:8080/", "height": 117, "referenced_widgets": [ "73046f1e2e264e8c844a4f8b71658ca9", "83324ff551ce46dfb91a9d0124ac907e", "12839b60d5854e768d74802bf2b4c31b", "3d93773486ae4eaabfa1cb446730678b", "f99a670198c74b47ae6009ebb9dc6ddb", "ad90c49a97f44cf29ce291dbc1928e23", "46bb0baea1734bbb9afc5017d322f751", "db0f190e8bb1420aaadcb7cce92ef17a", "dd82e45c339240c2ba59f282f8137264", "19c8a174d8cd4bd1bbff56b32e7fc582", "36c5f79c12e949ff95a978d4b5107acb", "f6d67ad967964b239ff6275bc88db088", "130cb2e62508429ba1ee7e809a01748c", "0d97ed88772b431aa72ff820156e0c75", "c75d96f3a0584da79b1496448fee91cc", "bdcb5c57206448d383b3702fad22f078" ] } }, "source": [ "from transformers import EncoderDecoderModel\n", "\n", "bert2bert = EncoderDecoderModel.from_encoder_decoder_pretrained(\"bert-base-uncased\", \"bert-base-uncased\")" ], "execution_count": 8, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "73046f1e2e264e8c844a4f8b71658ca9", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dd82e45c339240c2ba59f282f8137264", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=440473133.0, style=ProgressStyle(descri…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "JD2jv3GkyjR-" }, "source": [ "# set special tokens\n", "bert2bert.config.decoder_start_token_id = tokenizer.bos_token_id\n", "bert2bert.config.eos_token_id = tokenizer.eos_token_id\n", "bert2bert.config.pad_token_id = tokenizer.pad_token_id\n", "\n", "# sensible parameters for beam search\n", "bert2bert.config.vocab_size = bert2bert.config.decoder.vocab_size\n", "bert2bert.config.max_length = 142\n", "bert2bert.config.min_length = 56\n", "bert2bert.config.no_repeat_ngram_size = 3\n", "bert2bert.config.early_stopping = True\n", "bert2bert.config.length_penalty = 2.0\n", "bert2bert.config.num_beams = 4" ], "execution_count": 9, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "u98CLZiTkgzv" }, "source": [ "### **Fine-Tuning Warm-Started Encoder-Decoder Models**" ] }, { "cell_type": "markdown", "metadata": { "id": "-gYzA-w96wCt" }, "source": [ "The `Seq2SeqTrainer` that can be found under [examples/seq2seq/seq2seq_trainer.py](https://github.com/huggingface/transformers/blob/master/examples/seq2seq/seq2seq_trainer.py) will be used to fine-tune a warm-started encoder-decoder model.\n", "\n", "Let's download the `Seq2SeqTrainer` code and import the module along with `TrainingArguments`." ] }, { "cell_type": "code", "metadata": { "id": "pyiwaF0noA5c" }, "source": [ "%%capture\n", "!rm seq2seq_trainer.py\n", "!wget https://raw.githubusercontent.com/huggingface/transformers/master/examples/seq2seq/seq2seq_trainer.py\n", "\n", "!pip install git-python==1.0.3\n", "!pip install sacrebleu==1.4.12\n", "!pip install rouge_score\n", "\n", "from seq2seq_trainer import Seq2SeqTrainer\n", "from transformers import TrainingArguments\n", "from dataclasses import dataclass, field\n", "from typing import Optional" ], "execution_count": 10, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "5nmQRT3XuHHz" }, "source": [ "We need to add some additional parameters to make `TrainingArguments` compatible with the `Seq2SeqTrainer`. Let's just copy the `dataclass` arguments as defined in [this file](https://github.com/patrickvonplaten/transformers/blob/make_seq2seq_trainer_self_contained/examples/seq2seq/finetune_trainer.py)." ] }, { "cell_type": "code", "metadata": { "id": "-zkkd66rtsnA" }, "source": [ "@dataclass\n", "class Seq2SeqTrainingArguments(TrainingArguments):\n", " label_smoothing: Optional[float] = field(\n", " default=0.0, metadata={\"help\": \"The label smoothing epsilon to apply (if not zero).\"}\n", " )\n", " sortish_sampler: bool = field(default=False, metadata={\"help\": \"Whether to SortishSamler or not.\"})\n", " predict_with_generate: bool = field(\n", " default=False, metadata={\"help\": \"Whether to use generate to calculate generative metrics (ROUGE, BLEU).\"}\n", " )\n", " adafactor: bool = field(default=False, metadata={\"help\": \"whether to use adafactor\"})\n", " encoder_layerdrop: Optional[float] = field(\n", " default=None, metadata={\"help\": \"Encoder layer dropout probability. Goes into model.config.\"}\n", " )\n", " decoder_layerdrop: Optional[float] = field(\n", " default=None, metadata={\"help\": \"Decoder layer dropout probability. Goes into model.config.\"}\n", " )\n", " dropout: Optional[float] = field(default=None, metadata={\"help\": \"Dropout probability. Goes into model.config.\"})\n", " attention_dropout: Optional[float] = field(\n", " default=None, metadata={\"help\": \"Attention dropout probability. Goes into model.config.\"}\n", " )\n", " lr_scheduler: Optional[str] = field(\n", " default=\"linear\", metadata={\"help\": f\"Which lr scheduler to use.\"}\n", " )" ], "execution_count": 11, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "dPUAgo7pxH24" }, "source": [ "Also, we need to define a function to correctly compute the ROUGE score during validation. ROUGE is a much better metric to track during training than only language modeling loss." ] }, { "cell_type": "code", "metadata": { "id": "68IHmFYLx09W", "outputId": "9ed45c24-eaea-4aac-dc88-d2820f7450de", "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "2cd3d553b6b84c079b36e4f21abb837f", "a0f82133e05f476d8b7306a991a2a720", "e60f74c273764eefa08e32375da92dcb", "12a2a2e00e1a44cea17e1c9e21bb1c34", "ae1f09968f76406caee36d7d0639a4f2", "37b1b12827f94d5f8379ff03f30bac7e", "afa0aa69d9b04d2ba8c977fa77fb8196", "1661a489cdd342c3a9a4793eec128e78" ] } }, "source": [ "# load rouge for validation\n", "rouge = datasets.load_metric(\"rouge\")\n", "\n", "def compute_metrics(pred):\n", " labels_ids = pred.label_ids\n", " pred_ids = pred.predictions\n", "\n", " # all unnecessary tokens are removed\n", " pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n", " labels_ids[labels_ids == -100] = tokenizer.pad_token_id\n", " label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\n", "\n", " rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\n", "\n", " return {\n", " \"rouge2_precision\": round(rouge_output.precision, 4),\n", " \"rouge2_recall\": round(rouge_output.recall, 4),\n", " \"rouge2_fmeasure\": round(rouge_output.fmeasure, 4),\n", " }" ], "execution_count": 12, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2cd3d553b6b84c079b36e4f21abb837f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1656.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "1ik4hZb2yV-b" }, "source": [ "Cool! Finally, we start training." ] }, { "cell_type": "code", "metadata": { "id": "LAaTxUpdzshF", "outputId": "3bf1b099-f9b9-4b29-803c-bd45331af2ae", "colab": { "base_uri": "https://localhost:8080/", "height": 345 } }, "source": [ "# set training arguments - these params are not really tuned, feel free to change\n", "training_args = Seq2SeqTrainingArguments(\n", " output_dir=\"./\",\n", " per_device_train_batch_size=batch_size,\n", " per_device_eval_batch_size=batch_size,\n", " predict_with_generate=True,\n", " evaluate_during_training=True,\n", " do_train=True,\n", " do_eval=True,\n", " logging_steps=2, # set to 1000 for full training\n", " save_steps=16, # set to 500 for full training\n", " eval_steps=4, # set to 8000 for full training\n", " warmup_steps=1, # set to 2000 for full training\n", " max_steps=16, # delete for full training\n", " overwrite_output_dir=True,\n", " save_total_limit=3,\n", " fp16=True, \n", ")\n", "\n", "# instantiate trainer\n", "trainer = Seq2SeqTrainer(\n", " model=bert2bert,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " train_dataset=train_data,\n", " eval_dataset=val_data,\n", ")\n", "trainer.train()" ], "execution_count": 13, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/transformers/training_args.py:339: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n", " FutureWarning,\n", "/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n", " return torch.tensor(x, **format_kwargs)\n", "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", " \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "text/html": [ "\n", "
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StepTraining LossValidation LossRouge2 PrecisionRouge2 RecallRouge2 Fmeasure
410.03909910.1223490.0000000.0000000.000000
87.9959647.9606260.0018000.0023000.002000
127.6755837.7566170.0055000.0151000.008000
167.4813237.7093050.0048000.0136000.007000

" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=16, training_loss=8.646506309509277)" ] }, "metadata": { "tags": [] }, "execution_count": 13 } ] }, { "cell_type": "markdown", "metadata": { "id": "ZwQIEhKOrJpl" }, "source": [ "### **Evaluation**\n", "\n", "Awesome, we finished training our dummy model. Let's now evaluated the model on the test data. We make use of the dataset's handy `.map()` function to generate a summary of each sample of the test data." ] }, { "cell_type": "code", "metadata": { "id": "oOoSrwWarJAC", "outputId": "fdbf6e2a-ed91-4902-e1b8-5938311d3af3", "colab": { "base_uri": "https://localhost:8080/", "height": 85, "referenced_widgets": [ "26e7fd38a2404fdfa35cd85aeaed344e", "13600cf404f84084b6df2398b7b1546b", "2b3d36b270ec4a27a156a5aedd1e4a49", "97470b3ec2d545ea8c3bc464a44c6528", "a1efa9a3a6c6453782063039b65de800", "c453d3e11fd644d4a90372969fd4a1ec", "8f3600b5620b45b39d7e0b940f39960e", "a2ea693d77dd483e85604dcead910fc1" ] } }, "source": [ "import datasets\n", "from transformers import BertTokenizer, EncoderDecoderModel\n", "\n", "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n", "model = EncoderDecoderModel.from_pretrained(\"./checkpoint-16\")\n", "model.to(\"cuda\")\n", "\n", "test_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"test\")\n", "\n", "# only use 16 training examples for notebook - DELETE LINE FOR FULL TRAINING\n", "test_data = test_data.select(range(16))\n", "\n", "batch_size = 16 # change to 64 for full evaluation\n", "\n", "# map data correctly\n", "def generate_summary(batch):\n", " # Tokenizer will automatically set [BOS] [EOS]\n", " # cut off at BERT max length 512\n", " inputs = tokenizer(batch[\"article\"], padding=\"max_length\", truncation=True, max_length=512, return_tensors=\"pt\")\n", " input_ids = inputs.input_ids.to(\"cuda\")\n", " attention_mask = inputs.attention_mask.to(\"cuda\")\n", "\n", " outputs = model.generate(input_ids, attention_mask=attention_mask)\n", "\n", " # all special tokens including will be removed\n", " output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n", "\n", " batch[\"pred\"] = output_str\n", "\n", " return batch\n", "\n", "results = test_data.map(generate_summary, batched=True, batch_size=batch_size, remove_columns=[\"article\"])\n", "\n", "pred_str = results[\"pred\"]\n", "label_str = results[\"highlights\"]\n", "\n", "rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\n", "\n", "print(rouge_output)" ], "execution_count": 15, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "26e7fd38a2404fdfa35cd85aeaed344e", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "Score(precision=0.005856995548236219, recall=0.020109209844549343, fmeasure=0.00887036509937936)\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "7zdm50ZotZqb" }, "source": [ "The fully trained *BERT2BERT* model is uploaded to the 🤗model hub under [patrickvonplaten/bert2bert_cnn_daily_mail](https://huggingface.co/patrickvonplaten/bert2bert_cnn_daily_mail). \n", "\n", "The model achieves a ROUGE-2 score of **18.22**, which is even a little better than reported in the paper.\n", "\n", "For some summarization examples, the reader is advised to use the online inference API of the model, [here](https://huggingface.co/patrickvonplaten/bert2bert_cnn_daily_mail)." ] } ] } ================================================ FILE: Boosting_Wav2Vec2_with_n_grams_in_Transformers.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Boosting Wav2Vec2 with n-grams in 🤗 Transformers", "provenance": [], "collapsed_sections": [], "authorship_tag": "ABX9TyPCwqLrccfLuheQ9zJG1iuO", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "7830943670c647e2acc786e9a7ef103e": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": 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null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "source": [ "# **Boosting Wav2Vec2 with n-grams in 🤗 Transformers**\n", "\n", "**Wav2Vec2** is a popular pre-trained model for speech recognition. Released in [September 2020](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by Meta AI Research, the novel architecture catalyzed progress in self-supervised pretraining for speech recognition, *e.g.* [*G. Ng et al.*, 2021](https://arxiv.org/pdf/2104.03416.pdf), [*Chen et al*, 2021](https://arxiv.org/abs/2110.13900), [*Hsu et al.*, 2021](https://arxiv.org/abs/2106.07447) and [*Babu et al.*, 2021](https://arxiv.org/abs/2111.09296). On the Hugging Face Hub, Wav2Vec2's most popular pre-trained checkpoint currently amounts to over [**250,000** monthly downloads](https://huggingface.co/facebook/wav2vec2-base-960h).\n", "\n", "Using Connectionist Temporal Classification (CTC), pre-trained Wav2Vec2-like checkpoints are extremely easy to fine-tune on downstream speech recognition tasks.\n", "In a nutshell, fine-tuning pre-trained Wav2Vec2 checkpoints works as follows: \n", "\n", "A single randomly initialized linear layer is stacked on top of the pre-trained checkpoint and trained to classify raw audio input to a sequence of letters. It does so by:\n", "\n", "1. extracting audio representations from the raw audio (using CNN layers),\n", "2. processing the sequence of audio representations with a stack of transformer layers, and,\n", "3. classifying the processed audio representations into a sequence of output letters.\n", "\n", "Previously audio classification models required an additional language model (LM) and a dictionary to transform the sequence of classified audio frames to a coherent transcription.\n", "Wav2Vec2's architecture is based on transformer layers, thus giving each processed audio representation context \n", "from all other audio representations. In addition, \n", "Wav2Vec2 leverages the [CTC algorithm](https://distill.pub/2017/ctc/) for fine-tuning, which solves the problem of alignment between a varying \"input audio length\"-to-\"output text length\" ratio.\n", "\n", "Having contextualized audio classifications and no alignment problems, Wav2Vec2 does not require \n", "an external language model or dictionary to yield acceptable audio transcriptions.\n", "\n", "As can be seen in Appendix C of the [official paper](https://arxiv.org/abs/2006.11477), Wav2Vec2 gives impressive downstream performances on [LibriSpeech]((https://huggingface.co/datasets/librispeech_asr)) without using a language model at all. However, from the appendix, it also becomes clear that using Wav2Vec2 in combination with a language model can yield a significant improvement, especially when the model was trained on only 10 minutes of transcribed audio.\n", "\n", "Until recently, the 🤗 Transformers library did not offer a simple user interface to decode audio files with a fine-tuned Wav2Vec2 **and** a language model. This has thankfully changed. 🤗 Transformers now offers an easy-to-use integration with *Kensho Technologies'* [pyctcdecode library](https://github.com/kensho-technologies/pyctcdecode). This blog post is a step-by-step **technical** guide to explain how one can create an **n-gram** language model and combine it with an existing fine-tuned Wav2Vec2 checkpoint using 🤗 Datasets and 🤗 Transformers.\n", "\n", "We start by:\n", "\n", "1. How does decoding audio with an LM differ from decoding audio without an LM?\n", "2. How to get suitable data for a language model?\n", "3. How to build an *n-gram* with KenLM?\n", "4. How to combine the *n-gram* with a fine-tuned Wav2Vec2 checkpoint?\n", "\n", "For a deep dive into how Wav2Vec2 functions - which is not necessary for this blog post - the reader is advised to consult the following material:\n", "\n", "- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477)\n", "- [Fine-Tune Wav2Vec2 for English ASR with 🤗 Transformers](https://huggingface.co/blog/fine-tune-wav2vec2-english)\n", "- [An Illustrated Tour of Wav2vec 2.0](https://jonathanbgn.com/2021/09/30/illustrated-wav2vec-2.html)" ], "metadata": { "id": "1LrJXl6HY3xO" } }, { "cell_type": "markdown", "source": [ "## **1. Decoding audio data with Wav2Vec2 and a language model**\n", "\n", "As shown in 🤗 Transformers [exemple docs of Wav2Vec2](https://huggingface.co/docs/transformers/master/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC), audio can be transcribed as follows.\n", "\n", "We install `datasets` and `transformers` as well as `pyctcdecode` and `kenLM`'s Python bindings to be able to run the language model integration.\n", "\n" ], "metadata": { "id": "nu5oeVSvprSp" } }, { "cell_type": "code", "source": [ "!pip install https://github.com/kpu/kenlm/archive/master.zip \n", "!pyctcdecode==0.3.0\n", "!pip install datasets=2.0.0\n", "!pip transformers==4.18.0" ], "metadata": { "id": "OWGc_zfyq5_T", "outputId": "dc2cc24a-48c9-4e40-bba4-29dea7b4b377", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting https://github.com/kpu/kenlm/archive/master.zip\n", " Using cached https://github.com/kpu/kenlm/archive/master.zip (542 kB)\n", "\u001b[31mERROR: Invalid requirement: 'datasets=2.0.0'\n", "Hint: = is not a valid operator. Did you mean == ?\u001b[0m\n", "ERROR: unknown command \"transformers==4.18.0\"\n" ] } ] }, { "cell_type": "markdown", "source": [ "Let's load a small excerpt of the [Librispeech dataset](https://huggingface.co/datasets/librispeech_asr) to demonstrate Wav2Vec2's speech transcription capabilities." ], "metadata": { "id": "ZzcM5yC5rICZ" } }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"hf-internal-testing/librispeech_asr_demo\", \"clean\", split=\"validation\")\n", "dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dAerOhydrNFR", "outputId": "e583d892-8ac6-4853-d93d-f4cc38df4cf5" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Reusing dataset librispeech_asr (/root/.cache/huggingface/datasets/hf-internal-testing___librispeech_asr/clean/2.1.0/d3bc4c2bc2078fcde3ad0f0f635862e4c0fef78ba94c4a34c4c250a097af240b)\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "Dataset({\n", " features: ['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'],\n", " num_rows: 73\n", "})" ] }, "metadata": {}, "execution_count": 2 } ] }, { "cell_type": "markdown", "source": [ "We can pick one of the 73 audio samples and listen to it." ], "metadata": { "id": "-AkdbAlyrszm" } }, { "cell_type": "code", "source": [ "import IPython.display as ipd\n", "\n", "audio_sample = dataset[2]\n", "print(audio_sample[\"text\"].lower())\n", "ipd.Audio(data=audio_sample[\"audio\"][\"array\"], autoplay=True, rate=audio_sample[\"audio\"][\"sampling_rate\"])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 94 }, "id": "pSomT7k_r1QX", "outputId": "3ec38ba3-addd-409b-938f-9449c97796f2" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "he tells us that at this festive season of the year with christmas and roast beef looming before us similes drawn from eating and its results occur most readily to the mind\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {}, "execution_count": 3 } ] }, { "cell_type": "markdown", "source": [ "Having chosen a data sample, we now load the fine-tuned model and processor." ], "metadata": { "id": "ZbBGOqQusdO6" } }, { "cell_type": "code", "source": [ "from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC\n", "\n", "processor = Wav2Vec2Processor.from_pretrained(\"facebook/wav2vec2-base-100h\")\n", "model = Wav2Vec2ForCTC.from_pretrained(\"facebook/wav2vec2-base-100h\")" ], "metadata": { "id": "5FUPvu0crmsY", "outputId": "51417f7d-2b29-4d62-fbb1-c2e3fb4a2bf3", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Some weights of the model checkpoint at facebook/wav2vec2-base-100h were not used when initializing Wav2Vec2ForCTC: ['wav2vec2.mask_time_emb_vector']\n", "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base-100h and are newly initialized: ['wav2vec2.masked_spec_embed']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] } ] }, { "cell_type": "markdown", "source": [ "Next, we process the data" ], "metadata": { "id": "7L8Th_yTslta" } }, { "cell_type": "code", "source": [ "inputs = processor(audio_sample[\"audio\"][\"array\"], sampling_rate=16_000, return_tensors=\"pt\")" ], "metadata": { "id": "6NoMX8qfssuw" }, "execution_count": 5, "outputs": [] }, { "cell_type": "markdown", "source": [ "forward it to the model" ], "metadata": { "id": "xyM2MqwEs1p7" } }, { "cell_type": "code", "source": [ "import torch\n", "\n", "with torch.no_grad():\n", " logits = model(**inputs).logits" ], "metadata": { "id": "4KWDRrG0s27p" }, "execution_count": 6, "outputs": [] }, { "cell_type": "markdown", "source": [ "and decode it" ], "metadata": { "id": "pEHhf_1os4rZ" } }, { "cell_type": "code", "source": [ "predicted_ids = torch.argmax(logits, dim=-1)\n", "transcription = processor.batch_decode(predicted_ids)\n", "\n", "transcription[0].lower()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "aJREUIdqs5ak", "outputId": "da21757f-7a6a-4501-9c6d-cb30fc9966f1" }, "execution_count": 7, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'he tells us that at this festive season of the year with christmaus and rose beef looming before us simalyis drawn from eating and its results occur most readily to the mind'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 7 } ] }, { "cell_type": "markdown", "source": [ "Comparing the transcription to the target transcription above, we can see that some words *sound* correct, but are not *spelled* correctly, *e.g.*:\n", "\n", "- *christmaus* vs. *christmas*\n", "- *rose* vs. *roast*\n", "- *simalyis* vs. *similes*\n", "\n", "Let's see whether combining Wav2Vec2 with an ***n-gram*** lnguage model can help here." ], "metadata": { "id": "ifBW-tM0yWhS" } }, { "cell_type": "markdown", "source": [ "For demonstration purposes, we have prepared a new model repository [patrickvonplaten/wav2vec2-base-100h-with-lm](https://huggingface.co/patrickvonplaten/wav2vec2-base-100h-with-lm) which contains the same Wav2Vec2 checkpoint but has an additional **4-gram** language model for English.\n", "\n", "Instead of using `Wav2Vec2Processor`, this time we use `Wav2Vec2ProcessorWithLM` to load the **4-gram** model in addition to the feature extractor and tokenizer." ], "metadata": { "id": "ak_X8SHqzjOY" } }, { "cell_type": "code", "source": [ "from transformers import Wav2Vec2ProcessorWithLM\n", "\n", "processor = Wav2Vec2ProcessorWithLM.from_pretrained(\"patrickvonplaten/wav2vec2-base-100h-with-lm\")" ], "metadata": { "id": "UydQ00uI0OEG", "outputId": "1f48c320-9f05-47f8-89ba-a0f8c1b5849f", "colab": { "base_uri": "https://localhost:8080/", "height": 273, "referenced_widgets": [ "96f376c685c64d8e98339400cb48863e", "c3d87748d1404cf9ade042e3a761f05a", "e1e314c10d7f4793b1e7a0b732a2e6a8", "74f1d773c7f04c64bf8624ae4d9e5a7d", "9b0852846c7d4ad7bd734d50ee3cbfbd", "c7a932912ca643eeb8c56b26533e82aa", "718fc7dd86b847a78b522872bc04ec16", "b4e3403e9d33487596531b247cb48bb3", "328f0c4b85924fa3bb02da9b2309ce91", "99138bf4facc4f74a5a0d49c895747ea", "945f7c38b28542a5994f1ffdf1829e0f", "e4b6451778094174a53313d6ff4d39b6", "010cdfa4f9ae4980828656d64207185d", "e1e2bc27655b49759bd66c5ec129757d", "6eca73d6a640428682c9a04f8f7bcaa2", "372d784034624673b4d97d0ea5907102", "a7ea1b13443b4cdb82481930bd0f319c", "f8b38a4dfe684dd7863cf1c5486d806c", "d529526b8a984edf8e8ce5ca36b9f9f2", "ab6475fb9aef4556a7eee2a0ae721a20", "edac4976ba7c40c5937ec603a9ffe83b", "df493c247f7043a1a9eca22a8b6cd0e4", "2b26ae98af2f4e5d82feece8e5234470", "1542d8fd4a8c416085bf9bae38122c3f", "02cf6c8e3bdd4bd6a89da84845b94477", "ec2398c26559439ab01f871bf8de8be4", "d3f7990e70514eee89b2d3d9ca82207b", "ea1c2c55bc0d43238a013d5233d5109f", "8ffca73d393c45879a5883217f0e21a0", "55763327bc6d4e0e9d8b55f5d9b9905e", "522851bc2aa44be9a749cae18d9b8813", "a42ffec892364b8c817ce15ff74496de", "2cc3c596d3cf4209922ef93c48572acb", "cad0cec9f90f44feb4d182cbaeb92756", "ba86e563d8ba474380422b2ec922cdd3", "328d42d807c548dfbf5e953fd324e2ca", "d392836f92d842f881c7e0d2e66e4864", "639c04787d774ab180ac0d6a840cebdc", "7872b74e2313463c8b7f61ec56114fcb", "ee776a5c5be0419c85f03864797d3778", "db88a7e261f74b22b60cb2f997cf5452", "c0b606059871498794bd1ad8dc55e23e", "3fcaf685ad764fbd9c1156d384e1bd6a", "eceaea60e83a48fe910fc988b75d4a5a", "a9b39595a29f45159381f8251664cfe1", "2ce10965f67749c3b4976a475dd5bc35", "82d868a96bf0403b935d6efac2eb7e9f", "1f5d30e2ab624afbb7589e45c1f61eca", "42c7e935d74c46aebac5277edca44db5", "d4cdeda49c354f76a385a4b9be1a2ba1", "7664b2cd2a5849088c753105b4b24bc7", "c998068ffb2a45fabf864a11687242fc", "504ecfffa2db4e03950251c44b8a7a68", "c07b6c1883324149acff30fecc197b44", "2782036729d146c786829e8ac686c8f4", "42e3199c0c5f44919af3c8875faecfee", "92b6a8e6b80a46839865a33114ce2cce", "8149dadae80d4ed996d5d796eb7d2acc", "e33ac4664d2d42f88f22e1b9c82b4b41", "9b59644acd7d4999b1f41f042effc5db", "ba4cc579763f443ebfd03bad4817544d", "783329bcbeb94f41a2894e9574b17fbc", "f68da7106d6f47c1a7340b27a8f76b5f", "0e31652ee1714df9b47cd9aa9e4a0ce9", "de0aac4c130a4ab78fdd8c8a7b0c1f16", "395969406e414589b36ffff375809095", "b801d2d42e05482984f758c1539668f6", "675a3c51a29f4c3699e372fb3f52d059", "92ac1bcd60864bfba67653ac094c89b9", "2021480fd00d4f45886b75f44e2846a5", "5c847c7b73f0433abee048173cbe8e0e", "6177fa41b07d43ca8476ec27205804eb", "db56493aa510438cbd35ce2e32eb3054", "006e30bbbe3942db9f7e5febf424679a", "99e5dc456ba2475186d8a07baffedcdd", "d61f5693575d4f7fb8525d48b2f29543", "f8e168af7df043b7853fdf63b567e022", "4b0a6722113847b6b162deb42949fb8f", "38f7c79f463640d39e7d731e1d0cf59c", "474cf37344164f3395f8e097277a2e7a", "06d2c793d75043a1ae5143f873e49484", "735dd2fb679042d39f88ae6733a515bb", "918c819e7a464c3d9062d02b91f1824a", "0cf0f93c86614050975617aa276c9573", "474d517b11114ad3af7349b1a6270241", "aa33545861d94112adcd931355c81ee1", "a55f14b3cadb400bbdddf18191c7aca8", "94cea41ffbfb42ac913daa242266ff2c" ] } }, "execution_count": 8, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading: 0%| | 0.00/263 [00:00 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z |\"" ] }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "markdown", "source": [ "Intuitively, one can understand the decoding process of `Wav2Vec2ProcessorWithLM` as applying beam search through a matrix of size 624 $\\times$ 32 probabilities while leveraging the probabilities of the next letters as given by the *n-gram* language model.\n", "\n", "OK, let's run the decoding step again. `pyctcdecode` language model decoder does not automatically convert `torch` tensors to `numpy` so we'll have to convert them ourselves before." ], "metadata": { "id": "u60UcdWL5ub-" } }, { "cell_type": "code", "source": [ "transcription = processor.batch_decode(logits.numpy()).text\n", "transcription[0].lower()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "DFRgWyuUAZI4", "outputId": "45b35f41-bc95-4297-e8a7-30d7c3980ac9" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'he tells us that at this festive season of the year with christmas and rose beef looming before us similes drawn from eating and its results occur most readily to the mind'" ] }, "metadata": {}, "execution_count": 62 } ] }, { "cell_type": "markdown", "source": [ "Cool! Recalling the words `facebook/wav2vec2-base-100h` without a language model transcribed incorrectly previously, *e.g.*,\n", "\n", "> - *christmaus* vs. *christmas*\n", "- *rose* vs. *roast*\n", "- *simalyis* vs. *similes*\n", "\n", "we can take another look at the transcription of `facebook/wav2vec2-base-100h` **with** a 4-gram language model. 2 out of 3 errors are corrected; *christmas* and *similes* have been correctly transcribed.\n", "\n", "Interestingly, the incorrect transcription of *rose* persists. However, this should not surprise us very much. Decoding audio without a language model is much more prone to yield spelling mistakes, such as *christmaus* or *similes* (those words don't exist in the English language as far as I know). This is because the speech recognition system almost solely bases its prediction on the acoustic input it was given and not really on the language modeling context of previous and successive predicted letters ${}^1$. \n", "If on the other hand, we add a language model, we can be fairly sure that the speech recognition system will heavily reduce spelling errors since a well-trained *n-gram* model will surely not predict a word that has spelling errors. But the word *rose* is a valid English word and therefore the 4-gram will predict this word with a probability that is not insignificant. \n", "\n", "The language model on its own most likely does favor the correct word *roast* since the word sequence *roast beef* is much more common in English than *rose beef*. Because the final transcription is derived from a weighted combination of `facebook/wav2vec2-base-100h` output probabilities and those of the *n-gram* language model, it is quite common to see incorrectly transcribed words such as *rose*.\n", "\n", "For more information on how you can tweak different parameters when decoding with `Wav2Vec2ProcessorWithLM`, please take a look at the official documentation [here](https://huggingface.co/docs/transformers/master/en/model_doc/wav2vec2#transformers.Wav2Vec2ProcessorWithLM.batch_decode).\n", "\n", "---\n", "${}^1$ Some research shows that a model such as `facebook/wav2vec2-base-100h` - when sufficiently large and trained on enough data - can learn language modeling dependencies between intermediate audio representations similar to a language model.\n" ], "metadata": { "id": "E4CiVIftDEMd" } }, { "cell_type": "markdown", "source": [ "Great, now that you have seen the advantages adding an *n-gram* language model can bring, let's dive into how to create an *n-gram* and `Wav2Vec2ProcessorWithLM` from scratch." ], "metadata": { "id": "9FKW9cqzKMMo" } }, { "cell_type": "markdown", "source": [ "## **2. Getting data for your language model**\n", "\n", "A language model that is useful for a speech recognition system should support the acoustic model, *e.g.* Wav2Vec2, in predicting the next word (or token, letter) and therefore model the following distribution:\n", "\n", "$\\mathbf{P}(w_n | \\mathbf{w}_0^{t-1})$ with $w_n$ being the next word and $\\mathbf{w}_0^{t-1}$ being the sequence of all previous words since the beginning of the utterance. Simply said, the language model should be good at predicting the next word given all previously transcribed words regardless of the audio input given to the speech recognition system.\n", "\n", "As always a language model is only as good as the data it is trained on. In the case of speech recognition, we should therefore ask ourselves for what kind of data, the speech recognition will be used for: *conversations*, *audiobooks*, *movies*, *speeches*, *, etc*, ...?\n", "\n", "The language model should be good at modeling language that corresponds to the \n", "target transcriptions of the speech recognition system. \n", "For demonstration purposes, we assume here that we have fine-tuned a pre-trained [`facebook/wav2vec2-xls-r-300m`](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) in Swedish. The fine-tuned checkpoint can \n", "be found [here](https://huggingface.co/hf-test/xls-r-300m-sv).\n", "Common Voice 7 is a relatively crowd-sourced read-out audio dataset and we will evaluate the model on its test data.\n", "\n", "Let's now look for suitable text data on the Hugging Face Hub. We search all datasets for those [that contain Swedish data](https://huggingface.co/datasets?languages=languages:sv&sort=downloads). \n", "Browsing a bit through the datasets, we are looking for a dataset that is similar to Common Voice's read-out audio data. The obvious choices of [oscar](https://huggingface.co/datasets/oscar) and [mc4](https://huggingface.co/datasets/mc4) might not be the most suitable here because they:\n", "\n", "- are generated from crawling the web, which might not be very clean and correspond well to spoken language\n", "- require a lot of pre-processing\n", "- are very large which is not ideal for demonstration purposes here 😉\n", "\n", "A dataset that seems sensible here and which is relatively clean and easy to pre-process is [europarl_bilingual](https://huggingface.co/datasets/europarl_bilingual) as it's a dataset that is based on discussions and talks of the European parliament. It should therefore be relatively clean and correspond well to read-out audio data. The dataset is originally designed for machine translation and can therefore only be accessed in translation pairs. We will only extract the text of the target language, Swedish (`sv`), from the *English-to-Swedish* translations." ], "metadata": { "id": "QEz599PfmPeG" } }, { "cell_type": "code", "source": [ "target_lang=\"sv\" # change to your target lang" ], "metadata": { "id": "chZw03lUVAnr" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Let's download the data." ], "metadata": { "id": "cOLruPJsVS98" } }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"europarl_bilingual\", lang1=\"en\", lang2=target_lang, split=\"train\")\n", "dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 249, "referenced_widgets": [ "7830943670c647e2acc786e9a7ef103e", "d99f9340c4934c4499f36e4ad3b6e058", "c774e4b19776448f96c52bc10c8c0f14", "5d390778f917446baa13022eac784d2b", "f3ae537bfceb4f31b51aaeaa18005375", "0ccb0717dcfd4190bd9d1eb7c7960346", "3f6896738e7747f1b98944cc1a992f2d", "bdc026a9eead4f2cbd91dc8e87d6429b", "ff5acc78c9ef4de7b3b885a99e24ca7a", "5d3ae82cc5a84f7c82d09ab2133ec27a", "f3cb069591e74375832a4a3c5f14e37f", "700d5a80528741a99a23446b754ae63b", "6b67eb50c94e4223aa19a3b7ff9e000b", "0b38debae1fe49179c0618a8345f7edd", "b92dc5b737234642992c24ab51c3a618", "db94e0efc80748fc9a250a19ed228055", "50416acba7e2414ebc038cab753810d5", "5567471b84404f4daae83ede67e4e683", "7afb72afa7ae463c9c0e2066b0d394c9", "83fe3a36a08d4fbbbdfa9fd9c8adfadb", "a19388b2d8a94eee865c7a5dcfb25ad0", "35c82d7720d54545964631502b3acea1", "6818b169b69c4c689f382449666f9090", "f59853fe53754a46a16bc483d63567ca", "5f68506b6e394d3d9cd89898032c52b4", "adb99f5d7fab4fe5bc8cb21a94b84ca1", "cc5b87a2c3804058977f281d410c03c2", "0d9493bb59f74df19801c8cee4b30f86", "cb030d4e259a4125821848c7e88b8ea4", "5fde4fda09524af8ac11b6a3b8c080a6", "4d037c9b9c0044d8a3c10781afef2ead", "6d5d8e2ca10f4dc7920a75dc7f8c2044", "842acfadced34c90a6b90e974d4de536", "32101323a7a648ff87a3348da0a1a997", "28a1463958874651b13793a404d7821e", "a80248b900504fbdbfc850a5af86a902", "d2000fb6441740179384584dafd25557", "18407378be464639995c442035481ef2", "9d8f1345c4ee4c4f8fd9863784a8287a", "c60dfab7b8cf4f489a77c687e33c3c6e", "463796a8f34b49ca8c5c55c026150fb5", "7c68506bcef74a97a4abacfa21581c88", "6fdd4d8527ae48028d7e692e8df31fe0", "27ac434d28f942f3952710ebf6a53727", "d19500bd258e45179242d1292e0c2a6a", "b35f4164c9a6424ca6cb00a4bd2b2e3f", "db4d149310d44c2990e42f1ddcd7324e", "37dcda9b3fff4a7786985a552079120b", "43305fbd971d4c8aa007e8e176b6dbf0", "eeb49043b4db4b0396b0a792d8af7d8a", "43289d434e2d412f91b9fc6dbecb9ca8", "d59a9e78205543bfad948d0cb8ac7d09", "ff99a2bf3f824cb5bd21bdfb8c7843bf", "1861bec692b84f04a0347d7181ea8b25", "eca88a05dd3944c1b6bda1564fdc752a", "1b0079ee2a9e412395267d522a016b0d", "4e46025d1b5d48acabc09fe338b48fdc", "eb39fba9160d497c98011d72b35c8d61", "08112f8edcb6424f94b9211ed931103a", "bd11c540926147b9ad7f66ad4e86f2fa", "c355e48eb29f4e668ea1c384654e4790", "d43c9ec1efac49b290981eac0f6177ac", "3bbd57969c944726964b7500dd6140bf", "5015b7be5ef6411898f3cd68c87ea531", "594540ee3f05442680ca5db326f35e72", "a3c42da7382f461c8c0aad27ea7930b5" ] }, "id": "IrAzjWc3Ok2l", "outputId": "ab3b43f5-e7f7-41a7-bff5-b1d68929b740" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7830943670c647e2acc786e9a7ef103e", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/2.60k [00:00 \"5gram.arpa\"" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_MdDNBlZrPOm", "outputId": "2fedb568-ee37-4e9c-af8f-0456ac160e45" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=== 1/5 Counting and sorting n-grams ===\n", "Reading /content/swedish_text.txt\n", "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n", "tcmalloc: large alloc 1918697472 bytes == 0x55d40d0f0000 @ 0x7fdccb1a91e7 0x55d40b2f17a2 0x55d40b28c51e 0x55d40b26b2eb 0x55d40b257066 0x7fdcc9342bf7 0x55d40b258baa\n", "tcmalloc: large alloc 8953896960 bytes == 0x55d47f6c0000 @ 0x7fdccb1a91e7 0x55d40b2f17a2 0x55d40b2e07ca 0x55d40b2e1208 0x55d40b26b308 0x55d40b257066 0x7fdcc9342bf7 0x55d40b258baa\n", "****************************************************************************************************\n", "Unigram tokens 42153890 types 360209\n", "=== 2/5 Calculating and sorting adjusted counts ===\n", "Chain sizes: 1:4322508 2:1062772928 3:1992699264 4:3188318720 5:4649631744\n", "tcmalloc: large alloc 4649631744 bytes == 0x55d40d0f0000 @ 0x7fdccb1a91e7 0x55d40b2f17a2 0x55d40b2e07ca 0x55d40b2e1208 0x55d40b26b8d7 0x55d40b257066 0x7fdcc9342bf7 0x55d40b258baa\n", "tcmalloc: large alloc 1992704000 bytes == 0x55d561ce0000 @ 0x7fdccb1a91e7 0x55d40b2f17a2 0x55d40b2e07ca 0x55d40b2e1208 0x55d40b26bcdd 0x55d40b257066 0x7fdcc9342bf7 0x55d40b258baa\n", "tcmalloc: large alloc 3188326400 bytes == 0x55d695a86000 @ 0x7fdccb1a91e7 0x55d40b2f17a2 0x55d40b2e07ca 0x55d40b2e1208 0x55d40b26bcdd 0x55d40b257066 0x7fdcc9342bf7 0x55d40b258baa\n", "Statistics:\n", "1 360208 D1=0.686222 D2=1.01595 D3+=1.33685\n", "2 5476741 D1=0.761523 D2=1.06735 D3+=1.32559\n", "3 18177681 D1=0.839918 D2=1.12061 D3+=1.33794\n", "4 30374983 D1=0.909146 D2=1.20496 D3+=1.37235\n", "5 37231651 D1=0.944104 D2=1.25164 D3+=1.344\n", "Memory estimate for binary LM:\n", "type MB\n", "probing 1884 assuming -p 1.5\n", "probing 2195 assuming -r models -p 1.5\n", "trie 922 without quantization\n", "trie 518 assuming -q 8 -b 8 quantization \n", "trie 806 assuming -a 22 array pointer compression\n", "trie 401 assuming -a 22 -q 8 -b 8 array pointer compression and quantization\n", "=== 3/5 Calculating and sorting initial probabilities ===\n", "Chain sizes: 1:4322496 2:87627856 3:363553620 4:728999592 5:1042486228\n", "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n", "####################################################################################################\n", "=== 4/5 Calculating and writing order-interpolated probabilities ===\n", "Chain sizes: 1:4322496 2:87627856 3:363553620 4:728999592 5:1042486228\n", "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n", "####################################################################################################\n", "=== 5/5 Writing ARPA model ===\n", "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n", "****************************************************************************************************\n", "Name:lmplz\tVmPeak:14181536 kB\tVmRSS:2199260 kB\tRSSMax:4160328 kB\tuser:120.598\tsys:26.6659\tCPU:147.264\treal:136.344\n" ] } ] }, { "cell_type": "markdown", "source": [ "Great, we have built a *5-gram* LM! Let's inspect the first couple of lines." ], "metadata": { "id": "1_58ktqcTBYi" } }, { "cell_type": "code", "source": [ "!head -20 5gram.arpa" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TRnV8Miusl--", "outputId": "b7a64d04-b3aa-47b9-f971-817f9e762ac2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\\data\\\n", "ngram 1=360208\n", "ngram 2=5476741\n", "ngram 3=18177681\n", "ngram 4=30374983\n", "ngram 5=37231651\n", "\n", "\\1-grams:\n", "-6.770219\t\t0\n", "0\t\t-0.11831701\n", "-4.6095004\tåterupptagande\t-1.2174699\n", "-2.2361007\tav\t-0.79668784\n", "-4.8163533\tsessionen\t-0.37327805\n", "-2.2251768\tjag\t-1.4205662\n", "-4.181505\tförklarar\t-0.56261665\n", "-3.5790775\teuropaparlamentets\t-0.63611007\n", "-4.771945\tsession\t-0.3647111\n", "-5.8043895\tåterupptagen\t-0.3058712\n", "-2.8580177\tefter\t-0.7557702\n", "-5.199537\tavbrottet\t-0.43322718\n" ] } ] }, { "cell_type": "markdown", "source": [ "There is a small problem that 🤗 Transformers will not be happy about later on.\n", "The *5-gram* correctly includes a \"Unknown\" or ``, as well as a *begin-of-sentence*, `` token, but no *end-of-sentence*, `` token.\n", "This sadly has to be corrected currently after the build.\n", "\n", "We can simply add the *end-of-sentence* token by adding the line `0 -0.11831701` below the *begin-of-sentence* token and increasing the `ngram 1` count by 1. Because the file has roughly 100 million lines, this command will take *ca.* 2 minutes." ], "metadata": { "id": "l3jfwr2RTKPn" } }, { "cell_type": "code", "source": [ "with open(\"5gram.arpa\", \"r\") as read_file, open(\"5gram_correct.arpa\", \"w\") as write_file:\n", " has_added_eos = False\n", " for line in read_file:\n", " if not has_added_eos and \"ngram 1=\" in line:\n", " count=line.strip().split(\"=\")[-1]\n", " write_file.write(line.replace(f\"{count}\", f\"{int(count)+1}\"))\n", " elif not has_added_eos and \"\" in line:\n", " write_file.write(line)\n", " write_file.write(line.replace(\"\", \"\"))\n", " has_added_eos = True\n", " else:\n", " write_file.write(line)" ], "metadata": { "id": "_7u7dVPkvyRZ" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Let's now inspect the corrected *5-gram*." ], "metadata": { "id": "u9Y8uC3VW5vc" } }, { "cell_type": "code", "source": [ "!head -20 5gram_correct.arpa" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YF1RSm-Pxst5", "outputId": "551ba880-d0b0-4e3f-8edb-c53cd9fababa" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\\data\\\n", "ngram 1=360209\n", "ngram 2=5476741\n", "ngram 3=18177681\n", "ngram 4=30374983\n", "ngram 5=37231651\n", "\n", "\\1-grams:\n", "-6.770219\t\t0\n", "0\t\t-0.11831701\n", "0\t\t-0.11831701\n", "-4.6095004\tåterupptagande\t-1.2174699\n", "-2.2361007\tav\t-0.79668784\n", "-4.8163533\tsessionen\t-0.37327805\n", "-2.2251768\tjag\t-1.4205662\n", "-4.181505\tförklarar\t-0.56261665\n", "-3.5790775\teuropaparlamentets\t-0.63611007\n", "-4.771945\tsession\t-0.3647111\n", "-5.8043895\tåterupptagen\t-0.3058712\n", "-2.8580177\tefter\t-0.7557702\n" ] } ] }, { "cell_type": "markdown", "source": [ "Great, this looks better! We're done at this point and all that is left to do is to correctly integrate the `\"ngram\"` with [`pyctcdecode`](https://github.com/kensho-technologies/pyctcdecode) and 🤗 Transformers." ], "metadata": { "id": "m7NfKtyjXCiE" } }, { "cell_type": "markdown", "source": [ "## **4. Combine an *n-gram* with Wav2Vec2**" ], "metadata": { "id": "kPImHXzDG8aH" } }, { "cell_type": "markdown", "source": [ "In a final step, we want to wrap the *5-gram* into a `Wav2Vec2ProcessorWithLM` object to make the *5-gram* boosted decoding as seamless as shown in Section 1.\n", "We start by downloading the currently \"LM-less\" processor of [`xls-r-300m-sv`](https://huggingface.co/hf-test/xls-r-300m-sv)." ], "metadata": { "id": "kfZ7qYvMXfSV" } }, { "cell_type": "code", "source": [ "from transformers import AutoProcessor\n", "\n", "processor = AutoProcessor.from_pretrained(\"hf-test/xls-r-300m-sv\")" ], "metadata": { "id": "paV71gdAtkDC" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Next, we extract the vocabulary of its tokenizer as it represents the `\"labels\"` of `pyctcdecode`'s `BeamSearchDecoder` class." ], "metadata": { "id": "sT0pDUmdYOx6" } }, { "cell_type": "code", "source": [ "vocab_dict = processor.tokenizer.get_vocab()\n", "sorted_vocab_dict = {k.lower(): v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])}" ], "metadata": { "id": "ZKwKxMoitoGS" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "The `\"labels\"` and the previously built `5gram_correct.arpa` file is all that's needed to build the decoder. " ], "metadata": { "id": "o-jUBzFXYyCZ" } }, { "cell_type": "code", "source": [ "from pyctcdecode import build_ctcdecoder\n", "\n", "decoder = build_ctcdecoder(\n", " labels=list(sorted_vocab_dict.keys()),\n", " kenlm_model_path=\"5gram_correct.arpa\",\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zTLzCLB2tQP7", "outputId": "cfe9bd47-7671-414b-9292-54e53dc6ed95" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Found entries of length > 1 in alphabet. This is unusual unless style is BPE, but the alphabet was not recognized as BPE type. Is this correct?\n", "Unigrams and labels don't seem to agree.\n" ] } ] }, { "cell_type": "markdown", "source": [ "We can safely ignore the warning and all that is left to do now is to wrap the just created `decoder`, together with the processor's `tokenizer` and `feature_extractor` into a `Wav2Vec2ProcessorWithLM` class." ], "metadata": { "id": "IzJYQsSdZQ4-" } }, { "cell_type": "code", "source": [ "from transformers import Wav2Vec2ProcessorWithLM\n", "\n", "processor_with_lm = Wav2Vec2ProcessorWithLM(\n", " feature_extractor=processor.feature_extractor,\n", " tokenizer=processor.tokenizer,\n", " decoder=decoder\n", ")" ], "metadata": { "id": "VBVf50EzZgAQ" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "We want to directly upload the LM-boosted processor into \n", "the model folder of [`xls-r-300m-sv`](https://huggingface.co/hf-test/xls-r-300m-sv) to have all relevant files in one place.\n", "\n", "Let's clone the repo, add the new decoder files and upload them afterward.\n", "First, we need to install `git-lfs`." ], "metadata": { "id": "enhS2VRNZ79c" } }, { "cell_type": "code", "source": [ "!sudo apt-get install git-lfs tree" ], "metadata": { "id": "BZZm3ECc5TMP" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Cloning and uploading of modeling files can be done conveniently with the `huggingface_hub`'s `Repository` class. \n", "\n", "More information on how to use the `huggingface_hub` to upload any files, please take a look at the [official docs](https://huggingface.co/docs/hub/how-to-upstream)." ], "metadata": { "id": "bvo2rUbfa367" } }, { "cell_type": "code", "source": [ "from huggingface_hub import Repository\n", "\n", "repo = Repository(local_dir=\"xls-r-300m-sv\", clone_from=\"hf-test/xls-r-300m-sv\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fIfcunhF4YM6", "outputId": "5e014d6c-02b4-4ef4-9342-4078aebe8075" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Cloning https://huggingface.co/hf-test/xls-r-300m-sv into local empty directory.\n" ] } ] }, { "cell_type": "markdown", "source": [ "Having cloned `xls-r-300m-sv`, let's save the new processor with LM into it." ], "metadata": { "id": "OaD20PvHbakc" } }, { "cell_type": "code", "source": [ "processor_with_lm.save_pretrained(\"xls-r-300m-sv\")" ], "metadata": { "id": "UZ1sWfPH2oce" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Let's inspect the local repository. The `tree` command conveniently can also show the size of the different files." ], "metadata": { "id": "TnkJ0iXvcITB" } }, { "cell_type": "code", "source": [ "!tree -h xls-r-300m-sv/" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ClyENOYFcC_C", "outputId": "7b6450af-b3ca-4976-965d-b47c6a4bda52" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "xls-r-300m-sv/\n", "├── [ 23] added_tokens.json\n", "├── [ 401] all_results.json\n", "├── [ 253] alphabet.json\n", "├── [2.0K] config.json\n", "├── [ 304] emissions.csv\n", "├── [ 226] eval_results.json\n", "├── [4.0K] language_model\n", "│   ├── [4.1G] 5gram_correct.arpa\n", "│   ├── [ 78] attrs.json\n", "│   └── [4.9M] unigrams.txt\n", "├── [ 240] preprocessor_config.json\n", "├── [1.2G] pytorch_model.bin\n", "├── [3.5K] README.md\n", "├── [4.0K] runs\n", "│   └── [4.0K] Jan09_22-00-50_brutasse\n", "│   ├── [4.0K] 1641765760.8871996\n", "│   │   └── [4.6K] events.out.tfevents.1641765760.brutasse.31164.1\n", "│   ├── [ 42K] events.out.tfevents.1641765760.brutasse.31164.0\n", "│   └── [ 364] events.out.tfevents.1641794162.brutasse.31164.2\n", "├── [1.2K] run.sh\n", "├── [ 30K] run_speech_recognition_ctc.py\n", "├── [ 502] special_tokens_map.json\n", "├── [ 279] tokenizer_config.json\n", "├── [ 29K] trainer_state.json\n", "├── [2.9K] training_args.bin\n", "├── [ 196] train_results.json\n", "├── [ 319] vocab.json\n", "└── [4.0K] wandb\n", " ├── [ 52] debug-internal.log -> run-20220109_220240-1g372i3v/logs/debug-internal.log\n", " ├── [ 43] debug.log -> run-20220109_220240-1g372i3v/logs/debug.log\n", " ├── [ 28] latest-run -> run-20220109_220240-1g372i3v\n", " └── [4.0K] run-20220109_220240-1g372i3v\n", " ├── [4.0K] files\n", " │   ├── [8.8K] conda-environment.yaml\n", " │   ├── [140K] config.yaml\n", " │   ├── [4.7M] output.log\n", " │   ├── [5.4K] requirements.txt\n", " │   ├── [2.1K] wandb-metadata.json\n", " │   └── [653K] wandb-summary.json\n", " ├── [4.0K] logs\n", " │   ├── [3.4M] debug-internal.log\n", " │   └── [8.2K] debug.log\n", " └── [113M] run-1g372i3v.wandb\n", "\n", "9 directories, 34 files\n" ] } ] }, { "cell_type": "markdown", "source": [ "As can be seen the *5-gram* LM is quite large - it amounts to more than 4 GB.\n", "To reduce the size of the *n-gram* and make loading faster, `kenLM` allows converting `.arpa` files to binary ones using the `build_binary` executable.\n", "\n", "Let's make use of it here." ], "metadata": { "id": "kIRC6UcOcRMP" } }, { "cell_type": "code", "source": [ "!kenlm/build/bin/build_binary xls-r-300m-sv/language_model/5gram_correct.arpa xls-r-300m-sv/language_model/5gram.bin" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X9qg4FPt2zi8", "outputId": "16608710-30b3-4a0e-d07c-73a8373da7b8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Reading xls-r-300m-sv/language_model/5gram_correct.arpa\n", "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n", "****************************************************************************************************\n", "SUCCESS\n" ] } ] }, { "cell_type": "markdown", "source": [ "Great, it worked! Let's remove the `.arpa` file and check the size of the binary *5-gram* LM." ], "metadata": { "id": "fY2M8k2KdCNM" } }, { "cell_type": "code", "source": [ "!rm xls-r-300m-sv/language_model/5gram_correct.arpa && tree -h xls-r-300m-sv/" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Zn4J-4OZdMPc", "outputId": "e6a0c124-9cab-4f13-82c5-aa00e6a58a84" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "xls-r-300m-sv/\n", "├── [ 23] added_tokens.json\n", "├── [ 401] all_results.json\n", "├── [ 253] alphabet.json\n", "├── [2.0K] config.json\n", "├── [ 304] emissions.csv\n", "├── [ 226] eval_results.json\n", "├── [4.0K] language_model\n", "│   ├── [1.8G] 5gram.bin\n", "│   ├── [ 78] attrs.json\n", "│   └── [4.9M] unigrams.txt\n", "├── [ 240] preprocessor_config.json\n", "├── [1.2G] pytorch_model.bin\n", "├── [3.5K] README.md\n", "├── [4.0K] runs\n", "│   └── [4.0K] Jan09_22-00-50_brutasse\n", "│   ├── [4.0K] 1641765760.8871996\n", "│   │   └── [4.6K] events.out.tfevents.1641765760.brutasse.31164.1\n", "│   ├── [ 42K] events.out.tfevents.1641765760.brutasse.31164.0\n", "│   └── [ 364] events.out.tfevents.1641794162.brutasse.31164.2\n", "├── [1.2K] run.sh\n", "├── [ 30K] run_speech_recognition_ctc.py\n", "├── [ 502] special_tokens_map.json\n", "├── [ 279] tokenizer_config.json\n", "├── [ 29K] trainer_state.json\n", "├── [2.9K] training_args.bin\n", "├── [ 196] train_results.json\n", "├── [ 319] vocab.json\n", "└── [4.0K] wandb\n", " ├── [ 52] debug-internal.log -> run-20220109_220240-1g372i3v/logs/debug-internal.log\n", " ├── [ 43] debug.log -> run-20220109_220240-1g372i3v/logs/debug.log\n", " ├── [ 28] latest-run -> run-20220109_220240-1g372i3v\n", " └── [4.0K] run-20220109_220240-1g372i3v\n", " ├── [4.0K] files\n", " │   ├── [8.8K] conda-environment.yaml\n", " │   ├── [140K] config.yaml\n", " │   ├── [4.7M] output.log\n", " │   ├── [5.4K] requirements.txt\n", " │   ├── [2.1K] wandb-metadata.json\n", " │   └── [653K] wandb-summary.json\n", " ├── [4.0K] logs\n", " │   ├── [3.4M] debug-internal.log\n", " │   └── [8.2K] debug.log\n", " └── [113M] run-1g372i3v.wandb\n", "\n", "9 directories, 34 files\n" ] } ] }, { "cell_type": "markdown", "source": [ "Nice, we reduced the *n-gram* by more than half to less than 2GB now. In the final step, let's upload all files." ], "metadata": { "id": "4g112tF_dgIc" } }, { "cell_type": "code", "source": [ "repo.push_to_hub(commit_message=\"Upload lm-boosted decoder\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WEV1sx6ee3aT", "outputId": "18c021dc-66a3-4736-ee42-609207739abc" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Git LFS: (1 of 1 files) 1.85 GB / 1.85 GB\n", "Counting objects: 9, done.\n", "Delta compression using up to 2 threads.\n", "Compressing objects: 100% (9/9), done.\n", "Writing objects: 100% (9/9), 1.23 MiB | 1.92 MiB/s, done.\n", "Total 9 (delta 3), reused 0 (delta 0)\n", "To https://huggingface.co/hf-test/xls-r-300m-sv\n", " 27d0c57..5a191e2 main -> main\n" ] } ] }, { "cell_type": "markdown", "source": [ "That's it. Now you should be able to use the *5gram* for LM-boosted decoding as shown in Section 1.\n", "\n", "As can be seen on [`xls-r-300m-sv`'s model card](https://huggingface.co/hf-test/xls-r-300m-sv#inference-with-lm) our *5gram* LM-boosted decoder yields a WER of 18.85% on Common Voice's 7 test set which is a relative performance of *ca.* 30% 🔥." ], "metadata": { "id": "gUPAx3_MdyQv" } } ] } ================================================ FILE: Diffusers.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Diffusers.ipynb", "provenance": [], "collapsed_sections": [], "toc_visible": true, "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU", "gpuClass": "standard", "widgets": { "application/vnd.jupyter.widget-state+json": { "e1e117069da44d6a8530871132934e2d": { "model_module": "@jupyter-widgets/controls", 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Because of that, they gained traction in the machine learning community and play an important role for systems like [DALL-E 2](https://openai.com/dall-e-2/) or [Imagen](https://imagen.research.google/) to generate photorealistic images when prompted on text.\n", "\n", "While the most prolific successes of diffusion models have been in the computer vision community, these models have also achieved remarkable results in other domains, such as:\n", "- [video generation](https://video-diffusion.github.io/),\n", "- [audio synthesis](https://diffwave-demo.github.io/),\n", "- [reinforcement learning](https://diffusion-planning.github.io/),\n", "- and more.\n", "\n", "However, most of the recent research on diffusion models, *e.g.* DALL-E 2 and Imagen, is unfortunately not accessible to the broader machine learning community and typically remains behind closed doors.\n", "\n", "Here comes the `diffusers` library with the goals to:\n", "\n", "1. gather recent diffusion models from independent repositories in a single and **long-term maintained** project that is built by and for the **community**,\n", "2. reproduce high impact machine learning systems such as DALLE and Imagen in a manner that is accessible for the public, and\n", "3. create an easy to use API that enables one to train their own models or re-use checkpoints from other repositories for inference. \n", "\n", "This notebook will walk you through the most important features of `diffusers`.\n", "\n", "We assume that the reader has a minimal understanding of how diffusion models function. To refresh some theory as well as terminology, we recommend reading/skimming the following blog posts:\n", "\n", " - Lilian Weng's, OpenAI, [introductory post](https://lilianweng.github.io/posts/2021-07-11-diffusion-models/)\n", " - Yang Song's, Stanford, [introductory post](https://yang-song.github.io/blog/2021/score/)\n", " - The Annotated Diffusion Model [post](https://huggingface.co/blog/annotated-diffusion)\n", "\n", "Or papers:\n", "- The original paper proposing [thermodynamics for unsupervised learning](https://arxiv.org/abs/1503.03585),\n", "- The paper for a popular diffusion model, [Denoising Diffusion Probabilistic Models\n", " DDPM](https://arxiv.org/abs/2006.11239), or\n", "- A recent paper covering [tradeoffs in diffusion models](https://arxiv.org/abs/2206.00364)" ], "metadata": { "id": "PzW5ublpBuUt" } }, { "cell_type": "markdown", "source": [ "### Summary\n", "This post is designed to showcase the core API of `diffusers`, which is divided into three components:\n", "1. **Pipelines**: high-level classes designed to rapidly generate samples from popular trained diffusion models in a user-friendly fashion.\n", "2. **Models**: popular architectures for training new diffusion models, *e.g.* [UNet](https://arxiv.org/abs/1505.04597).\n", "3. **Schedulers**: various techniques for generating images from noise during *inference* as well as to generate noisy images for *training*.\n", "\n", "**Note**: This notebook focus only on **inference**. If you want to get a more hands-on guide on **training** diffusion models, please have a look at the [*Training with Diffusers*](https://colab.research.google.com/gist/anton-l/f3a8206dae4125b93f05b1f5f703191d/diffusers_training_example.ipynb) notebook." ], "metadata": { "id": "aCH4p1dtyaXX" } }, { "cell_type": "markdown", "source": [ "### Install `diffusers`" ], "metadata": { "id": "02TD3O6LyeVx" } }, { "cell_type": "code", "source": [ "!pip install diffusers==0.1.2" ], "metadata": { "id": "e5MYkuhcRGAS", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "729b5b23-c9a6-4237-daad-9aa031f973a5" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Collecting diffusers==0.1.2\n", " Downloading diffusers-0.1.2-py3-none-any.whl (94 kB)\n", "\u001b[K |████████████████████████████████| 94 kB 1.1 MB/s \n", "\u001b[?25hCollecting huggingface-hub\n", " Downloading huggingface_hub-0.8.1-py3-none-any.whl (101 kB)\n", "\u001b[K |████████████████████████████████| 101 kB 6.4 MB/s \n", "\u001b[?25hRequirement already satisfied: torch>=1.4 in /usr/local/lib/python3.7/dist-packages (from diffusers==0.1.2) (1.12.0+cu113)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from diffusers==0.1.2) (2.23.0)\n", "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from diffusers==0.1.2) (7.1.2)\n", "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from diffusers==0.1.2) (2022.6.2)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from diffusers==0.1.2) (3.7.1)\n", "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from diffusers==0.1.2) (4.12.0)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from diffusers==0.1.2) (1.21.6)\n", "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4->diffusers==0.1.2) (4.1.1)\n", "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub->diffusers==0.1.2) (21.3)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface-hub->diffusers==0.1.2) (4.64.0)\n", "Collecting pyyaml>=5.1\n", " Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n", "\u001b[K |████████████████████████████████| 596 kB 61.6 MB/s \n", "\u001b[?25hRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface-hub->diffusers==0.1.2) (3.0.9)\n", "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->diffusers==0.1.2) (3.8.1)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->diffusers==0.1.2) (2022.6.15)\n", "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->diffusers==0.1.2) (1.24.3)\n", "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->diffusers==0.1.2) (3.0.4)\n", "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->diffusers==0.1.2) (2.10)\n", "Installing collected packages: pyyaml, huggingface-hub, diffusers\n", " Attempting uninstall: pyyaml\n", " Found existing installation: PyYAML 3.13\n", " Uninstalling PyYAML-3.13:\n", " Successfully uninstalled PyYAML-3.13\n", "Successfully installed diffusers-0.1.2 huggingface-hub-0.8.1 pyyaml-6.0\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Overview" ], "metadata": { "id": "wW8o1Wp0zRkq" } }, { "cell_type": "markdown", "source": [ "One goal of the diffusers library is to make diffusion models accesible to a wide range of deep learning practioners. \n", "With this in mind, we aimed at building a library that is **easy to use**, **intuitive to understand**, and **easy to contribute to**.\n", "\n", "As a quick recap, diffusion models are machine learning systems that are trained to *denoise* random gaussian noise step by step, to get to a sample of interest, such as an *image*. \n", "\n", "The underlying model, often a neural network, is trained to predict a way to slightly denoise the image in each step. After certain number of steps, a sample is obtained.\n", "\n", "The process is illustrated by the following design:\n", "![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusion-process.png)" ], "metadata": { "id": "xkyOEnzuVbsq" } }, { "cell_type": "markdown", "source": [ "The architecture of the neural network, referred to as **model**, commonly follows the UNet architecture as proposed in [this paper](https://arxiv.org/abs/1505.04597) and improved upon in the Pixel++ paper.\n", "\n", "![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/unet-model.png)\n", "\n", "No worries if you don't understand everything. Some of the highlights of the architecture are:\n", "* this model predicts images of the same size as the input\n", "* the model makes the input image go through several blocks of ResNet layers which halves the image size by 2\n", "* then through the same number of blocks that upsample it again.\n", "* skip connections link features on the downample path to corresponding layers in the upsample path. \n" ], "metadata": { "id": "5zrjyyxpW8px" } }, { "cell_type": "markdown", "source": [ "The diffusion process consists in taking random noise of the size of the desired output and pass it through the model several times. The process ends after a given number of steps, and the output image should represent a sample according to the training data distribution of the model, for instance an image of a butterfly. \n", "\n", "During training we show many samples of a given distribution, such as images of butterfly. After training, the model will be able to process random noise to generate similar butterfly images.\n", "\n", "Without going in too much detail, the model is usually not trained to directly predict a slightly less noisy image, but rather to predict the \"noise residual\" which is the difference between a less noisy image and the input image (for a diffusion model called \"DDPM\") or, similarly, the gradient between the two time steps (like the diffusion model called \"Score VE\").\n", "\n", "To do the denoising proces, a specific noise scheduling algorithm is thus necessary and \"wrap\" the model to define how many diffusion steps are needed for inference as well as how to *compute* a less noisy image from the model's output. Here is where the different **schedulers** of the diffusers library come into play.\n", "\n", "Finally, a **pipeline** groups together a **model** and a **scheduler** and make it easy for an end-user to run a full denoising loop process. We'll start with the pipelines and dive deeper into its implementation before taking a closer look at models and schedulers." ], "metadata": { "id": "9fbI-e2HIbyO" } }, { "cell_type": "markdown", "source": [ "## Core API" ], "metadata": { "id": "TgB8uv2dzXQl" } }, { "cell_type": "markdown", "source": [ "### Pipelines" ], "metadata": { "id": "TI04XgY5DPeE" } }, { "cell_type": "markdown", "source": [ "Let's begin by importing a pipeline. We'll use the `google/ddpm-celebahq-256` model, built in collaboration by Google and U.C.Berkeley. It's a model following the [Denoising Diffusion Probablistic Models (DDPM) algorithm](https://arxiv.org/abs/2006.11239) trained on a dataset of celebrities images. \n", "\n", "We can import the `DDPMPipeline`, which will allow you to do inference with a couple of lines of code:" ], "metadata": { "id": "EWDMHbX6d9Pu" } }, { "cell_type": "code", "source": [ "from diffusers import DDPMPipeline" ], "metadata": { "id": "teX8pwyzd2y6" }, "execution_count": 2, "outputs": [] }, { "cell_type": "markdown", "source": [ "The `from_pretrained()` method allows downloading the model and its configuration from [the Hugging Face Hub](https://huggingface.co/google/ddpm-celebahq-256), a repository of over 60,000 models shared by the community.\n" ], "metadata": { "id": "u8c1vpyjfMUe" } }, { "cell_type": "code", "source": [ "image_pipe = DDPMPipeline.from_pretrained(\"google/ddpm-celebahq-256\")" ], "metadata": { "id": "3xl4aBa3eEcr", "colab": { "base_uri": "https://localhost:8080/", "height": 337, 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\n" }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "markdown", "source": [ "Looks pretty good!\n", "\n", "Now, let's try to understand a bit better what was going on under the hood. Let's see what the pipeline is made of:" ], "metadata": { "id": "saaAH7skgSMQ" } }, { "cell_type": "code", "source": [ "image_pipe" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kdF5b4LOgThN", "outputId": "1fafc082-3f3a-405e-cae6-5cd6387e7a3c" }, "execution_count": 6, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DDPMPipeline {\n", " \"_class_name\": \"DDPMPipeline\",\n", " \"_diffusers_version\": \"0.1.2\",\n", " \"scheduler\": [\n", " \"diffusers\",\n", " \"DDPMScheduler\"\n", " ],\n", " \"unet\": [\n", " \"diffusers\",\n", " \"UNet2DModel\"\n", " ]\n", "}" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "markdown", "source": [ "We can see inside the pipeline a scheduler and a UNet model. Let's have a closer look at them and what this pipeline just did behind the scenes." ], "metadata": { "id": "I24pcMdFgxlH" } }, { "cell_type": "markdown", "source": [ "### Models\n", "\n", "Instances of the model class are neural networks that take a noisy `sample` as well as a `timestep` as inputs to predict a less noisy output `sample`. Let's load a pre-trained model and play around with it to understand the model API!\n", "\n", "We'll load a simple unconditional image generation model of type `UNet2DModel` which was released with the [DDPM Paper](https://arxiv.org/abs/2006.11239) and for instance take a look at another checkpoint trained on church images: [`google/ddpm-church-256`](https://huggingface.co/google/ddpm-church-256).\n", "\n", "Similarly to what we've seen for the pipeline class, we can load the model configuration and weights with one line, using the `from_pretrained()` method that you may be familiar with if you've played with the `transformers` library:" ], "metadata": { "id": "TUDXg6FERHmB" } }, { "cell_type": "code", "source": [ "from diffusers import UNet2DModel\n", "\n", "repo_id = \"google/ddpm-church-256\"\n", "model = UNet2DModel.from_pretrained(repo_id)" ], "metadata": { "id": "9In8sf_CR1dX", "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "7f0a64645ce44296b32186750ab669c1", "e5e65756a7854bfb986faa708330cf97", "1130d89b462e43f6a9782419a3b67d46", "e430b89decac437c8665dc335fe66f2a", "3611a359a7b445019b9184c7b5c14982", "e3bb2e59f6694d3a9628c9ad92cd5035", "b46ecfed3b944cedbd4e9cb5625a260c", "de17b8f80ed54a5eabf3f245e03c4ad5", "1c874217b4d345d69aa884b0459c1ac1", "faf6f83065114224b2b348f0cd838384", "a9603d9f71ca4127b25581e5e4c7dd4b", "c990d58d7e63483785133ce47285443f", "18549513ac3e49a881ec2dbf8eddb196", "212e73678594439d9414471c5096fa5f", "c1a4a85f9a824b838adf571b21fc7551", "bf714687effd4966927b00f3e92cb3ba", "50857e959e38448696d5575b7ecb3790", "0ec02b9695364187b4fbcacc48aa1ef0", "d7e4cb424ddb443189c7a7b47e55e4e9", "338de4d94aab47c0825bb28f4d8c0368", "e24c055ac558415d8177dd1763afafa4", "bfe9dcc9818f4918acc0fd38d26dadbe" ] }, "outputId": "4a33da6e-842b-49cb-b0fd-363878c2e372" }, "execution_count": 7, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading: 0%| | 0.00/790 [00:00 x_t-1\n", " sample = scheduler.step(residual, t, sample)[\"prev_sample\"]\n", "\n", " # 3. optionally look at image\n", " if (i + 1) % 50 == 0:\n", " display_sample(sample, i + 1)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "2D9bO0LPtMcc", "outputId": "a2086660-3b74-4a2f-ee87-4bab6b0e8918" }, "execution_count": 23, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ " 5%|▍ | 47/1000 [00:02<00:43, 21.69it/s]" ] }, { "output_type": "display_data", "data": { "text/plain": [ "'Image at step 50'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "image/png": 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "\r100%|██████████| 1000/1000 [00:46<00:00, 21.57it/s]\n" ] } ] }, { "cell_type": "markdown", "source": [ "You can see that it takes quite some time to see a somewhat meaningful shape - only after *ca.* 800 steps. \n", "\n", "While the quality of the image is actually quite good - you might want to speed up the image generation.\n", "\n", "To do so, you can try replacing the DDPM scheduler with the [DDIM](https://arxiv.org/abs/2010.02502) scheduler which keep high generation quality at significantly sped-up generation time.\n", "\n", "**Exchanging schedulers**: one of the exciting prospects of a diffusion model library is that different scheduling protocols can work with different models, but there is not a one-sized fits all solution! \n", "In this case, DDIM worked as an swap for DDPM, but this not universal (and represents an interesting research problem).\n", "\n" ], "metadata": { "id": "6kQ1TzDn18-m" } }, { "cell_type": "markdown", "source": [ "The DDPM and DDIM scheduler more or less share the same configuration, so you can load a DDIM scheduler from a DDPM scheduler." ], "metadata": { "id": "Qhv4kVqcAHTY" } }, { "cell_type": "code", "source": [ "from diffusers import DDIMScheduler\n", "\n", "scheduler = DDIMScheduler.from_config(repo_id)" ], "metadata": { "id": "r6krWJ-MAL7k", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "3b957505-b653-4258-b878-640b30504e81" }, "execution_count": 24, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "{'timestep_values'} was not found in config. Values will be initialized to default values.\n" ] } ] }, { "cell_type": "markdown", "source": [ "The DDIM scheduler allows the user to define how many denoising steps should be run at inference via the `set_timesteps` method. The DDPM scheduler runs by default 1000 denoising steps. Let's significantly reduce this number to just 50 inference steps for DDIM." ], "metadata": { "id": "eEjV1dS_APq3" } }, { "cell_type": "code", "source": [ "scheduler.set_timesteps(num_inference_steps=50)" ], "metadata": { "id": "x8_ldCENAOhA" }, "execution_count": 25, "outputs": [] }, { "cell_type": "markdown", "source": [ "And you can run the same loop as before - only that you are now making use of the much faster DDIM scheduler." ], "metadata": { "id": "f7hw5gCwAkU-" } }, { "cell_type": "code", "source": [ "import tqdm\n", "\n", "sample = noisy_sample\n", "\n", "for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):\n", " # 1. predict noise residual\n", " with torch.no_grad():\n", " residual = model(sample, t)[\"sample\"]\n", "\n", " # 2. compute previous image and set x_t -> x_t-1\n", " sample = scheduler.step(residual, t, sample)[\"prev_sample\"]\n", "\n", " # 3. optionally look at image\n", " if (i + 1) % 10 == 0:\n", " display_sample(sample, i + 1)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "xdHqA7C0Asp9", "outputId": "91f473a1-36fc-4151-a039-a3a3c325fb69" }, "execution_count": 26, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ " 18%|█▊ | 9/50 [00:00<00:01, 24.32it/s]" ] }, { "output_type": "display_data", "data": { "text/plain": [ "'Image at step 10'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "image/png": 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\n" 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\n" 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aO9WdooXCc36D6z7qIeJ3t3/rEVh4ntc1twJoURrAha+ymSmfDph+p5x3qJGJ292Ud1qzGgHWVKW5V4exYlgs/GzJR0fJzy3wtlOlYqJE7aQUlV/JR9vNOGC6SY/UAYog9Vl0xkJud1yYnxDTcq36hP9LeBtTiWScSHuPvPSwX8TR8pWQ2Zw74eSXx+CaTdEEUYUzbKVqXG535fqwTuLIG8fblEpYlK8vUfS5RM4rIr952HIiWYPgmlV2FNiNMEnJE7uWfJ2p8wxcP+NXJOXGDLeM8QQu4RtraGoHN6VzRgGZPeib/fjakVhKBORtmr2PUnlLSWHdozYICYzEKJMhbz8gDxgN7ShJQ7vfRD8Xalapldnb7wRGhgTwESxbPAWdMsgwsecvimokK7pXdwItKzFxbPsQsBcDRKq3sz625KbvETkk8+/E2/pX3zPwyqhHOFQlI1nvbKWpJZMoPu3YhudSgqWFLNlm5idh8clOZQ+jpRPil5U9kEMk543KlKbQXFg/UslCGV7vQhKoOf1euf+G7f3mts9bf2G02MndOmUjDvczFkTf/Dfz2WZ81P+otVINEXQb9RzgLikFzYZ/qLTGtJn42is5OBu37bvkUrT9EjgoYVeod6Dt51hxu5TYVksREyGrinD5BLOko1d9Bkr+vXmJfRv960rQDmzYPZxJuQN1jxW3mLXbrq+QFdPzuJxNmGT021Wpe7Lqhwi457cCJIg7nY0jcd6ckJZ3pFuKNxcgUcOirMMHXlYMgLmgf2bX0QMwSHm6xOTx3x3eUxEf2NV3/8dQPOqqjR9D2+hP2eAwN7KHycsrTD+S6DpAtaexfyKa0u732hXHHj3HbZCsWp34Cy0+Rsp8/8z6pIXOOfkyhPB46+UaIeuWdUkqSYS13WeotLXr+fT+a+hpaxfE1EM7T3kJS53ORX12a9X7ra5vMOtgRKrlhHiA/n7ZRrW7Ief5qJCsYvam9jbTIaw9bTKaS+cauvgSYF+syBn367KK2QClRFUmRxFgmqE8oBsvkkWPSfodHOrkaB0ngbOgu/p02rioltaTn+S2jSgQR9C761Qf8OtQGhwl/19mXeRf/MKJfkl09yd78AXget+SwmBxk+72Xln3GBUKBTpU0OyiF7CbAOcZceHJVihdjoSAmL7Fsuppj4pOC0B8RohtrOuB5yvGJKz0/3subNsdWmdN3X3zWVfzc1KoYpwNAP0epNU3MaAyE6voD4FxOTlg5S71couu/obgee8c3khvi616pIFRRRb0dPr0NpUtQDvZr8h1xTOX6uwayFqfJlnd8R5muHiBJoH60PEXccl51FwhOgS0bX1Zo26ZXmeH6j8Sw6OAmTSssDmaoXP4GwmwIpMDbj9JqgJc/+oBIUTvTwKVaYHHjx8s7IsGTOQx+BfwYtf9DXpWQ560GfTv+NycqUr1uPbsafC9vmhgCk16mEuDXsyR/GltWZlj5+Lun2we84QshZQJ3ODfrR/Siv6+HNAiRgAtiZ4hoskGHkBbADiPns+cCfgegqe/J+Rw9dh7BWcXQXNzqiZH02/rmGrVkCbYyE6QYc5PZN78eCmmwdzZH9VoHzJHboGPCsewudMrfddHI4NLFo61K/di0F+/8znfSw58haFgjnnDs43JGF5Lc+r01GEa40QVxtphDv6naVwky1JNBdtghJTH5XnrlM/qc6+lbEP9kKGb27AELAwPH3JH/5njveni3oZgw3k8T6driS86v3e6HD+TfDLKOxTUqWEaw9TaHnyVLEbPuEvHRGjVBxRI/cRxxzI89aHKc3aSWMiSG02yeXd2AJkxs8zfGUgJXqFt6ZfyREhJxo3+bBbu7dABmlpb+X5GAU457261I4xUA94rURcF8Ya0bxBhQkNgixlk2jSNhIJXBuewQudHuWCcgAYhOkdk6NOKgHcHFgDoQ+suja436/OQTGGiuIsqe0Mz+NIlUgd36HDZkf0ClQsjNBqkApleE/g+C1nkpue9qGJrSu6UDEmCDp1v8OLY6uQn6ot3ExaETmPgIfe2m++ivGGiT2nhfKA8lXlTWtEoAiIg6lr35RAY4iKhn4DMngYcALpNjmWDlZjngXH8e381jbcOVnIxpylbmlbeQGpo+yNnnc84GUA/4PGtrGFB7AZ4MOQjfZxfma08NRbtZP5TMqG/4r/3r2B7SGljCtwVXyD73YQ0XGswSMsdvJcbFwGxfrK0o8t2C8Q+mHORcb3u/jGYjhh13gCAdoOVMAPDcLIBphPS/MdI44SDh/YQ3OvnNXNQA73XB5ULGgHfWxGbVwLxTkDAGJCVUgiPoA8xI2vhbNfVkjV+erG/+Rv7TXEcJZzHG/0Zuk3HyP7cjr4TpuAWsUz8+35UtCfvBUTj+BYNXBRrEIH/uG82zzhiuDhYhbR58j7K6IX+sBA118UW+T5IsbI/JmWmJgcxWlYWrkAgcR5Kq+IZGzTDpUVpIdRHQFmq0CvjPdzWNNsOnnZX2ofSw2yxamk3d8tiiTbMBdLBmVVSoo/9/z59W0h5w7RKNksNKuI14mh5J9UCHhsx6Ce5WSMqB2qvudvxcEeB5neiO7z0KamAoiMfYUGM1TAwQF2IpsfvocJbhebzaN58Mmb/NvymsJU9/yIUmrIh1ZlabNojvzwJ0vZSDTnz9PCaKxiMRX3RXWI/yuc6Hh76yHdr/+RfGgvOeFtmULom/crUUDNAh4TYDsk0Y4B5/9UE7NGv83zsAGFX7FJWp7OzR6zoMlk/TTb3B+wPOWbaIqk68azbtiZiFtysOyjLkw5l03ZueFaMc/fYhFJdMkesYCsWiMFzITbuGYNPNCZsx62y6Ly/PRnDwkRDnosy8MPabuTk069tl6GB+Dl6lI4Em/uNUcxavJmMTxwn5WbGvafIoSogsZ1+zaPEqfR81qlIIu2WcUvU8olhg0tOgKDwopvVadNaWuyB26fIHc9hAPGAs5gBVrCNKx4rr7UyilZEbBlYkHiDU4DkoZjjLJWkMlkCW3cnvhS4stX+hQR3Ol1uof7GQa8d1BRXfPmX/uWuKK2S+U8VmAw1p2A37Jcx14phrMsfm6pPvOrr0oioKelA1JrZHK0lX+OdXjawLBsX0mw2EpDOLAGZhFDyGu8E+pby4XYQs+z1o3uFWMaIDXtEmo81l211FG70rDqqtzUCvd87a+0yPeuTi5ty6bgRMV6w/l5FO3UNqMbNO7GPBVQFQRFIZtQKTSqwsF0vTYCTJGtXqLr1ts5D5JajuD8j8B2V9gHIqGgEHrw2FuNaw/zpCdRUT7bxCzGRYSzudPDgONOUAaXHqm+/ppO58LrPW5aLjsx2csrLAvNgJHj61SPvbTJNyc1JPG8bXKiEPSwVoPClZdMdLPNK6oQ8ZoIBlJRKTbViHRW+TfQHTG/OOUVnFq24Vi7Pso7nzEsllbSa4S8Y4To6oPLN9eeB7Y1Fi7l/zwPuiPwar7fkpw2hQMZ4sxFmfn9Ua005IVAsnKemuyYMRJwINZiiC5PIPRD+d19kO9dwlytOlVqPKscJn/ZdTjkF410MXPQVtB3ueKwtJB+4ChqdetazmVHIbqSjjkGH1/U1sHfnMxbaaU8YNdg7m9yGysnOiGCyFdOYHG/1p07EkJVvN7ncpAgZizxExmKsdEh3faYisreSqQ64B2qWuWjC9oByrT5EStHlyKKdag5YHs+IZly1YL2vpB3rZDoc3faxzYSVK0pAI/qMN4y6iqGCS2y3K2wwzISqk8KroPBSNJsqqwiUmAXcZyiIaadijHhooKtWX4TepElNL3nk+LrMMN59AEk8x8QcI5gG6qRW7QNALquNq/KIuP8k5Tu2LHwL7SPUoxPoaLqqRz++W6sbn8AQv/NZuyvdPWYjQnN4vYclxANIxEDayMJo3zzHAVUFPg4RNfjA5Zra9bcq+bT9Kp/+Cqsr3drzWvik7vDwE+9RNzIqxlDVRQYXysWoKdqTh6kCSMKM15qJGnBIIh5P13onp5/Rq+7UVnO0vgNqkAhv5KIeXlz1ImIUqhM8YrAJqj/VMnctf79kw7f8aBUc8UpS0n4IFfGmhosjhNj8J879SCSi9SK/x61kuuq+onCrM85PHJSuyre2YoNEpkSpBPG2QIW5hlzSIihxfzHj5x8kU428Q/X199z+kVmazkZosSQBItvjq2/qF4AYCyihc0ZGVaZSBuEXavVNu4jIyttlAP5f76FcjnxrhEgixFDsepg1SXteUMWZiXQL2hTsOiMPz5tjRFn5W9XLmJrZs8ES1DzUIqQYdBFPZcWLTmfb+LW/AWslbwEjuH/Di44lcTiD6TkHPTikt08wk+f0st+u/3QrPenGwyQroxcgVBfZ4bpJdFrd1LdSYPPgwcYXWvVfaGmeeP/Q1xKJWG0eqDpKdhHZcTZbDq3kMwUmCtt63E248jRM9abtwm5a8Ezven7Z2bNfvXPTVn+V/QFrvnZiDE6l+W8mtVOINdXLPoxVfTqGD0qsmmjqWo/H6OkHNB49TemH0WlAYHQRPzqjD5W+46vEeI0Ov2+k8y4g/MoukTfCQy5AI2cXEraIdeucxvIdaegTP+rD+dBGoPjwCo/iM7zgV77VEuYZiAUvt7pHCU2ZCRBExYhxy1lw1o4B1kF+EaWn3i4iQhICLA+RLFJX1L+4m6mblvWC4mEJUSJBacl/D1xndeZUMKwv45YUC8w68z2hihh3GEW5he2m3xavfg4bAr0lCAfmaIaz5TG/T16O2+pDXX0SGLJyegOTSU/MN1XcQifR+XwsVjfMA7mb+Kq1LKWnCh7FeygTCP+Q97D7GAuQdRbhQgHR9qe8O1zWBp7mwlyg69gYRAxqfLW8RxLqJYsSb+XupzZysVo2wujf4QPRn4PhQ+O20uEqgjE5EvsWQ7WFeqNdrDTx0/Pgw8HxjalPTUkTnE2CtwCIyy+kVn7+3MQtBrLTi7Ve40osCbqlTLOankB1JjgSY0IAIhNc6w7wyV516PPCBlf6q5DX1Uvx03PDnNaqdkjzY1DZTiSvGnMjC/p3b7XU4MKvCRB4joXM9jDMbSMe9WLxRspIPqpSeolYF2fxOs/ms7ft+L8gmK1j7MZePjLX2ksaQ9I6udKvrOqfB5/S0d98dTCK2fs5CXmvimutYgWQ6r/kvUHj1CbELeksH0wSUueNYKnRokMTqAyYLpYWLIRvwLBTHclTzqYdnrBBJtAiEFp78idPK/EzUltD75DurQaTG4l8wS2MdwbRgPeMUZTpYWhZTeyI97SFIa9a2fMQaN2RMyraFPvE0iVxf5E6Q8VD2+kdloJT2J1mET4JkrzNVaGbCgT8+ZX8FAbm99x1lFx4PLLw2kX+rkFWQcdIXqqBBJ4BZSvJ64xDyY3vGCI1BMkoRyCWBnJkXoqByjlbZgLhDnMXi05sBnx1tq8rXAQz4Z6NGP9l8vCRCmc0UixqVtI/oZdgF/zX98Ri0/Dl9FFHwwLbULoLEiJQB8KfnGDaZgs6yyoEmWTdO9hv33Kvxhb5AoQiB/RQqa256WlSns2IldyCaHs5l+qiDY4RPhYBAuYz0k51T4jTfJ7GD8k8cDrvtnlkeTt2bM9KiXkUP0W0Ut+ljws1z+xNwRBBttjQ1FBlRl1It4ZyxfFDnDmj6SwTNRnIMnx8QilP76AaQy1Ms2mZEPeT8yKfPZNC9Eca1sPJkMCMu81TyvvsznYobVLWggPxFvjE6vUJkwl0shg/72jN3cjPJiAFmkSPpVar4wwtvojaYPxHgijfxLq4CmmxRUyfTH1SynoNUB6WU3E7LzWuLgcBdaifDC6TpH048orMmDUKWmFC25u/n0U5kJtvsZ6CqtmVVkCKItCvngo48F3/XG7OjWDUVVkZQ5YW4Nhk0VHAGUYx3KMKNOUgP60HQsPjMzYTrM67Vapxfd8gBRu4HTSEK4wxtnPSUm2DzsiY+24WUJPeSDA7SxDw2tbZzaFv29XZhg9zwlQ5cGQ22k4GJOyshr/9u/X/I5GSjtPXxQ+1C07LvxYawbMdyWVZqt7DU6hYwO3F8tDcAQ72Rz3klwEY6n85vM+SOsBF/GSL8nEP5wGecaZc+GlauCtNazbsYQEageMsC9xaN4y1SRyxI3/7h+L6HIPYml2MK669NikxigJAOU6byfl7LS5CEXh/lixxMBx+6gZBhEVoIXk9r/gw6R/mBQ7xIb3+eyjkS376cxCuRmydp4mkT24IhglnOSzA3+enrsfNX/ELb7PhFU5jgkHnFBS0fl7LBM5X3jt1IzFyuKVU/D8yWSgajvcgphCukYvmKqgyUvZcru++/LDDDqsjF/24rO3qNfYgmHRVAmHIx8weICz4/F6Nj0ACCZL46FXcfOUEBmbCmg2ksbTN2eVS5NHSMcLK/2KJLXaUOvBakcEhSukCixyPHU9ox0ivplmEAIZJTZ5zvOh2bqWWgq/uzFRZQMBVOxeKdcgGWI9sXxusYf7eiJ21x/xppdf2JDq48pYzqE+NHHD2xRBuOmaVIM4Vjwu37RirnMABUR9zhuKVojzN9CYQIWm1//fKhDx3NG+bnaLbnQgVO2dW66jYXrBC1H57zDvZkxleWgur8ESJhmLM1wHhZpIo9DOCqnyPUp22+Z7rsakx4+fMAV6jrUpeo2hbO1vKOSoCiPhi6fTnkIlHBYpqD84+ZVhhKcIDWHLiVYjRYXOsvp+ejNezShgZIvAvez8D+ZgF9/mIBdnSQqM2W9ALZbC2fYN1p16abz7voaW9FL5J5nrEkQUiBcsfO62OWs0Q2Qy5Ct7XA7iMl9lN0rVJ3vycK/XGCxYBNDM1R+For6phZhgtL5D0zZKrRxdM2BymSvEUMlJUrUN1vbz204zKva5rfez9GyoHOJikOyYOozTKdrGUHEDs1Vom4HXr0E9P8gHi8d4BUhpShHjKTdyRFrrvBML9C6fv2OEmHJY9o7VLdhD7rECn3WW5KvIRJnXvVmRY6yCUYvraUdlscFkvVmiIjyk2DHBZqtvwVnhHT2MIKr/i4/gOqBcm5sEKkwb/pLUUpf10TwpCzx8cM0zf5zmaq7cB1f/QwM60meGfPoLmTTJG+BDPAdJ7izvM97MTCNj5IsGXuXL0S0+RYu5SzVWCdxvtONqWxTZTh0eGGoGqXFBZ31/ENVN1aDYtMpFGe0zrIJAb26gEzrwKKxZM/Rb6pAvrnvF1mxHz8wUVy/98xyC0/beOgorszRJktS8/+COHUqmWfzrZm8D8ENm2QUWwk2LI2FBGep8TAEHGbUfwC7YjS7OzxWCKKd6MWDBmnRxk3y2dxiOfCWd4Ki6z+MRI0wsXQPQg40mDcxEQzKkyODxod3HEbnVDgiMWZSOVzrfV28Hanj+jGA5CRei2e5bkAWj18+KLaPFDCVW8HRKw4bqX16aESh63CYck3MVrxcT4Qtms+I8U7mzZx86kqswXnbCQJ0PfMp+P28zjLhgoYuhWeimhzqXeycWJRfQBCjBR2QbMzdVfmiKZmIrZcezvEOAkK+df0EoZfkppdDfD7OQeuvsrIZeKVQ0nn/7mEuoJD/pNtmHmJD2KWn8gv1iAMv9Dy3oiB5cLjXmQIWAnlhNAfjJNUe61NqCGtOQpjEVvt77jWspSp1o7/pMTEFrgzNA7lMC2W5jQpYXM7bm9dAOYf6nggZxUvPZGA4J0/FLif4SjLCVcRxyFMghAWS0zaZ7a2eMV2P+e6OPYKIf6vKcL/+8LUBYwTwNsRxf++XI59yq6kNH5f+MKuIMBxkCcLrg0BXA3gJfSHZhZEvpZRYszFFYh6YH2KwqzSeUeTftTmk12TuARhs4y4PywwCX4pYygt0Gs+UPKj3YWd285I+Cpz7MvzqrcaZq/yZZyUFeVzA/aH6ogB26wVy/ymyxx86VVvFomkFLltMQqlwUQjZoXYTkOGe8VDeRsLtxMny8MYCh7EUl8Wje9UBCbW3q29aQTzqUE8UvDMyVurKjPH7UFjp/ABslDks6wPFBwRtja0PDdoZZwCzvNEhZv5dEJOxvJkedQ2NqptEE+q5Wm1AMmn6zvjMeGBtDSsFj25fQDeWLwOthGzojoV3oakRMhPvoC0y8QsJnUkTXFL12O0vx3DFlv4iA5ENCgU9Qc1bfUz0R4WKAlF2AAyoBRqP0c4nZVsIpTO5HuzwdO9rsjXIpuEHcRN4fbKQMGkr6XctDRXh7MhPy0gpDecqHMILLo7fZbgylizRZc0uGx76L3shFKFcUsEnTybHJ4PoxQY7Myxxr9lBF5le9g/PkzVNnrhSMS0ZOvdJMHfiJygEth2A3mR0laIYwql6EZzs3INWcak5oN1iGTsPcR3FNtxcdnJJJD+YZL/RzElgcRJocnCMdTNKTmj90LwpgDuMVznSYVh37tlrh3KKVqOOg1jcRGPQLzVpMoxHOfwrfkJuEi7nzo31tEHlWZlhtwiMscIEWan5st7aJuGZVcdlqINLrdJGT+GrRvYvj0ggpVLPopPCwF5tzLmaID7srkU4hfOJcRWizY5e/mNHQi0IFagqWWw6sAOF5GiDMrmMUMkaFz+Rvz4X5Rh5fWh0MTGyBEwVgA03ha9m0o9mkY0CHqArCvHTmNUPWCVKTQqbC8tHDqUdmTyQCpv5Y0gF95o43ihiOE9pkml3i5geB0wCRMIC5pC1AepDsZKM6iOevThrkYxme7wlOI9vpBUs9b40DvZwwHH90S+TrXo2otekb8RclRL2Eqy1hScIkq3evUFDiD2eUQGq7hE0F49z6d/Fp92ISy0HG4OCn8+SLe22PxssFfSmqJ5dAflia7sIyI90Hsrk5rq5/Z44FibHEFCUcBbqBcJmj/CjSbdQq5gGCdLrQ26vz8JBdHAi1HLsz5Y00Fh/u3lUVhCYTNHrQlqi5Tu7bDlRpoH3VB8oLYCj2LqRyH8XxgU/YRPROQZCj/tHq0OhoBUxhiyeEJaRuHzOO9Yv6OvXJhhTjjht4OZkjD55sVKefGAe26p2Q+bD+yfUck4Gk0/QXlD6yYUQqHfv2/35Y5z0KzOKrNTh3TuiXvUntGJhrsCJXsYkEW4ftThuY033jaPD1F57Tp5DQDCiMNM8IV/zyket/z6gxE3ym1OOOfVedCc9Nb5uxJOlCcnlRr8nmaCi7HVrIltPDde7ugnAF9zgHLk+WrXBGmE9dJiHEYxkvQF52sg0W7fg0bXGbFpeZ87GuJ+hhwDgcRNgVngN4g9TcBryUoXQ2jIxafPVMU+PISnesZaXKY372uMvP/3BljCuSMT16Pqv6cKShSUH5Lul8ARaDZmTAwp8/Fp+w67x+bvHcO0lwBVtBKMUNCMC2NbW4AaaC5tm32JwFdUx8Tj7YpAmn9BtLtge6zR+UV/FjW938mq5MdAu9m8hGz8Mm5S5W5NQT5krJppVa8l65gEgKHVZJpGBkiYMTq9lkjawMC4ezhs5HaHYpy2k1p8CQ5qyKKU6RGwZvjChk5zRmcDg3WKQsR3cWVyDFFkBaIPUMGG2fi8XNh8vjYTYstiRDf+cc0Tx4zzALw4cVGjvSirYq1nc6tMMRE/0+zr+CHmxxBQCXdrfhGmPEzklBnmEixHQvP+QVhHvQLwfFKnqVi6RmcBJFttT9Pa51p62UzGUeCYnkeTbjrAqvDisMCK36gbmxY1tcvZp4H5sTyiDkwxGUUqnQCeY9g6zHC0U286Zskj91vQ3mLXHTWmuQ7MfxAcDzDmyhwZ2W22/7+6KZw/SKc5gvIHKfkfmt+F3K5IXdfge7VwTV4XzX2wpi+J7vO+fUE9pgFKpTbbZPPoSvkPJabFdR5SYheQ+CrA/lcyS4kHO6JRuOD9DYWS/U83II+MyJPbkGbiGmQyeT4I0ZQBIVXIlLgEsm46eX7CC49zdSACZK22j1w3PANxMTMsQwk2ptC8SP3cm9073hnrE+4mfRT43xu48e9MRbYxEr/YOxzp2Bo9xggFIg3E1pIgTynAoB2iGdAK/ohmmJopJC/EhDDT1mREBHBBga75HPUlG30ACeq4TK21hkX20qqhVy7bez+QerSyZUQNo2+Ojz+dxG5bs6t5YK61glsUsJYEqk9liUjEBtvnnj4QqP1kpi4F4tslg1WK6sZOfgYNEAeKjRadJ4a5NBoizuXOjgTx+9WdsOac2TjeMuOTEdGW+3ZsmvHJuW37KgJpuH/v1sZXSV/lb+2/bZLdTFDwdLBO+2ERIgKyPwql1js5nKMZB8KllBZ8qeAl5Svj18lyzIU5AmiIcyRiV96JDOU4vnWDPqoTmnSedNYRQB7lRnTmFKZ/6eL0uo0vhJjeIx4yoz7ROsVfK31zwmkGWmHewKaliCfZ5eNLgXDkQUn+lnw+5p3Gdo3gfzXDHEMuR1LDE6iuzcgHcwgKNPn9fMpOZROf5Nt8atFBB4TyZ++9RCEq7wJFlk5Rgn8dIGQTEYE1tnYIZDU/H40rNsQbvvaYRsASUe6AvHyIxk8ie1TijmAB04OXtRVrtNMkNgVWjD8U9mpSnQsPoYPHOOITMZIIQH7YS1d5rnGZrYCmPCODXJftELN4B7RJz/air1+p1dlcaUMFxevpJGjGiRZVOeTwPsOrmIqU3HD46v2LVt+F77yNqzSHKxNuoePDPAXutTiD9T1txUcPWwjhycmf9OeiMidGxzhpAUc4nKYG5Y0t+OYf0nwRF1MSFYhP11dkKE5kc/xOu45cNeJr+ygQ2Vb3vfmAKPDqZx05/s36NI1tzTVinelKMkdBzgsmukYS7584Zs+1Eh/UQJe3Vs4xJ3Y8wartZDnay/SRycwAFCOUb8GyznT+MoUl7egTb2fTv+j/IM0QjBPis5ktVAq6j9SaDMuSytbIB8gfrcxAYJXAJYQksgXwQg+z3APE/YqRzSkB7ycCg1aSdAEBnsoUSVXNEOBq+SqfNMLBwmVhUISVtc2tVsiOT2kjhJde+jRAGf7N5d4uaP3M9TxVEGiwMYAgLbolDLuRYmO6JAP2WkhhkSO/oZgG4HaJRhjUOw5WsazyqSJgJDNQWlFxA3gC1xTTy9kChT0yYLzkFWtCIGJ/vewQRjxnhV1MqK/XF9EttrXH+ZJYM203WtupSuSk3WRd6GO+3tjiNs6w1X+53y3NCnyFaeHjCRUo3Qsg9Qd/D/Oj1MRLycIwlFMR5vCwlb0fpS8B9aAGFkCUgnxl48VFGv3t+qbjgwFXCUSwynoqnpQbQt+84RJ5nPhELcPckIZo0o8ymSP+ivLK9EjxoAC531lg5RAwdYR5M5RTToNVeXNC3z3feZhlp3+Saou5jh5qbzAw+yxr5gLkgCC7BTwWXvy92GaiKHOlTZ6BrOcSsJHf+vQrkHEzV1D/u9tYmFtlROonif/Eop2SRcxfx0TVZ8K1u7/yLo4H4vxc19Dai9IwpE4Vh5fjQLBdr2O3xnEYQQn0hIYpt2FPQ3BYrzOwHMnv1bVPXB/LBTzqzdreNnQo3sRHMr8OtfSKE4N6cJLbrCcHM/9NAQAfO2Fh+Zk6hkd3DDdA8OEkomJOTxrnynJbG0EhterIjKe8Iy/kVpXk8+6Mxow1Aw3xFoZTPHFf3ZLxcXryOq7csoRrr5voZA1A5B4yw5Yr7JNAR46SGE5R1UTYl9yqBV4z62M+fv2PNB5ObqVRZIJSKsYBzagz0yA/bjCKKrwt9xcq5PGtUhaGOOhwcxc8mwjwzwPfCe/+KLWNygnGCgxvWtPybTDmIMWWRFlkWNku9xvethLavvexF7qY/9cRn6hKqCxergifHBVufhlmz6Okgm72D33j9vcuJZlBfaIKlEbmcgK/Cbv+U5/Gljz7cK+JqM/HzZOxUUPX68Tq1FYtU6u5CqJYz/m4N7bKuXJPoFIXqWLXmTmEx9WSAwFwhi9MnTcHCzyeK/XMlQP35UBdze7RFcyrMMRKpxfuOgoACED5vo3OnQYDqPqWlNn+N85cfMoOkvjhN8aA40dfkLF9L6H38S1gpdLxXBnfHFkVlLSyl3lRi9paQ+MBBcVCH0UMuO6XwLD20yejal+J9CtDO3N9didwxWlKaRvpyc5CRO0xentokOIMHS2oV2wt/83NIO0wt9H1iRiZx5i1heIC/tdkCOov926SJFArhXAwMCndJMNTQTIjR1GWtudKWlP6SgolFMYEhSbwZGRR+n52QqiDrtmndn/zxzJ0hJJIQDRD7biMB/4E4FVHmA6htcC07D5YRHSJgKUl81RpmqVeu0kbYz9jNcClAn0DCPv8rcKLnm7vfD8sSkutDRs/hVxQa2KHWVnI3PjS+zuU8zsXTBnNuy7wGii5smqT5WbC1Sh7M8s3GGf74/TVVT6JTB5uh+x6N24G3v7y8xu8XjCjtQcJi2DEa5OW7zsk8nbFCDj70oPRgEo5eqdFBXo+kEYAgTEEpGjErIl363Sau38/lH6D7ikPw8aTATUz3DMiWA2rVXrsJapcbGmYnBCXQ1N9bAuOVhSfikQEAgDycraGX5EgryYiOtikR9RlwvYQDOBIZ30ucDjpUX8HWSdSW0ACjk8isp7T9qD2WLslPvl7mv5NGFsITrNgP8Y3N/gzxG0/uEdG6Q+qhRRYBA3HzJwM/p4m4fKU4Nucy/yE3v5SkeDX7nMfzAktbxzDbqRQYIUh0aQQ0vzRsI77Jk9cXQoHDZpQ9XkkAih/t5qXacdaue0qnuQaevZ7fDRO1yCNm+wg4RUzPQXWzMjt89C4pG5GmCrmRIBaGgkxONrPkYdXbw8h66TEsOAOsoUjXb3lW30q1p13OPbW6jMp+PEDIiebq6NPyq1fq97fWDdlgP4JgdDolM2eCtObLZ6fbostHaADUVakX2/I72PAh2DTh0vIAd1SeUZotw+1A7IPEL8+iKO8vPVoBmjpf558bkIAdxHb9Md+NMJANIkbzvr84RhBQvVQyhA+T8thq0XFcuvTMwxK1pYlKD8OZQSDZ9fnZSIzi0r3CEhCdIqsJCrODTV+aDHXXzH7aE4mSd8t37b1a4AzskVdsofEPAmGCxDrRlEY44xsrEhbQbMPo2YNZRJSUa6l2OkkXB3Di0GuB2n5FkF/o/hNsCa6q78cYv7MNOPkqQp/xOw/ASFTiTzJWAiqb5XSVa8OzM0RhGDa3vvESjN4GvzGzjqMx19rKo1Xtu2Ad+awgvRHAn7n4sgTcea9mrsR6RBgNKelbj1iIgMxK/pfhms/q3fiPvJi0ZDJ0EhB/VzHrDouo1ZNQjeG0qpTplmKgIHbUF0dPJiarzEuyVs5s6U2cW0XCWBJ2hudutsboNZcwIYJn13WkExEdcDUVDdLxlgJWhAnwyp0tCHO/GKZ/+soEf6h0BkDXvnVemPnGtuuMl76j8IRmPtyYEBLaXdJc1TsUcRCIVyYeGIVCeTi9vHwD9BlbdopAc4PJIalRrjKeGUsnEqrTJeDdEjVzYvPrwOEaVqtUi/GqWEUcCgYNwYH1N7xgsEce8F7JgiGZ5NhCcY/nj9SfySURW8Mdviss6fRyxfnlzinnYbrfQBifdgYixWDxZ9rXEK6iYIVDBcCt/+LMvIjxi6Rww9APhW8tYUB8VXbjk/nvwBVRRTQVX8IZOMgCpnDCLRgAOlLWN/6AmLGFBaT6TP2t4pzbz8foKZfDBGafHL0RvPfq/f+liZJK6USPmoAcdzf6dwoJjHCji2N3PKn6N1AyR3CsNBpfrcM/i3scDj6gjJeL7vhPHE1YSIOY9XciLXXoG3TUVOBs1M/FhThZOSEZJ3QTpMLd4S/S/FJegUZRn3y6zK/UTkTQLHJotqyT6u+ehTCMSPiDg89eu2t3SVrnpzu+aXSF/93iy56TpNN+OjNOxTk9taasUpp+7bUmHd7oLusI5meMGAiJjDzYUV4vS6xMrhzsPlcmEGuaUDQKBu28mP3Msk00c+95pu8nYeD9sTEgwUthBH7HZ6rE0bFyxZlkaPOgXGKO9AEEtixpdu3OscP2XrUN4drRu2IEbJ9z1wOYiaRcT37Ov38W5p32PV8rtyYTzpHHddQjEGYI/r4w5L6OrnWIEDA0gmmBKcnRE/12/SFGKY8WDDNYnbv0w198X2EQZi3Ezi9n0dmvZMUjj4pJVrqbOvbr7VjeeuwcnAo9IQP541VrdoWTkNWRgm07WYANUSNd4DfdUdPmi4NTCKndKzgc7GUETE7Cr5eSkP7tGsHshpFHnbBWNtEB+yvSW36NlHjQEOb0nepkiCI/OnQpgyMkLC5y/hYfPM4zkUhV+LPcrTRPrFlvQU0vqWgXkS+MFSJdwHmOw60w5p4+qnkqq3B4Nl7KCClI3vRx+fP7J4hL9YuD55EcmwQRBpg6WFEqImmLpO1TMCGIrvLU6nz943ylVDr+jNTuU7APGZVq9uLkmxqLnSXjmDYQBSowoMhnu921vb1AVixoepujGHR7r27zP6PK10CZyKkZ2k5z6Y+rje8ED9s8sN0QPtA8wciH5xyBUAN/PAZlw36R1h6P+/W6goi2LYwmBNgK4OlE89Q4EFMkQ86eLfkq7v7PtTrV8J59UjoVeqGANpRQf6C80DA+6+tYhVIw4UzIX5otGMeuxF9YIXtkLT0I/eXwJxTQOOK86XN/eofGFicHmY4kZImmM1ghkGpBX5AyIc7sVuKB1psd6ITCFEN2fYcP4oPPb0VfhbK1+ERloGJCHY1Nb6GtjOdqe5OG56+eS6Zv/f/ZGR2V4TGlb/Jxmi31MUYpB5OqmWqLSXJO2/o/k2LBqSBKtYpRgmvCB/60QpyEyx+MkWcJgES15gxG/GGvb4PguooWEhO5FSgjc4JPuV/sMBdY36bI9rssYXOSdk/QSC+vDXlEN3tw4+ni/2QY3a4KeMUbSQaqYAcZWLRCSvg5sdPFPldcgcRfbGA5EkZ33FZMT3idsaUzNotGMtqxoRIWNC/hSiJL+6XkvzGEySGVo0SH8NSKL2xSMqX8BcYT6WPZ2WBnwmMtL7Rqi0jha2mgq/S99UQG9Gb1iaIj3Ok63Lj0cvgihukUy03OjsghcGBEhaqpeEm92pqApZ8akBLS5IJ3PYrcxhwx8YEAFRSjNuKIhpkA4MaVVaya07bXVta7eZEm1QdJCsMGpz42JZPgKZE+/wc0iFOg2MipfTcsvNqNPmPIFi9uX5rIwUitzJhT/kl5q8/uXA17t30Q1BTPrQfUKVB7Tp2+HPQtIVK5y+ZXlivT9JFsObH7mpZluIARwx/o9NIuVlTeuD3hZS06GT3f+0VyobLUFnEN3TmrLab/1ntlCyFdNOSHTFQYx+1gssA4Xz0UqxXsBq5YUdXOecQbpzMKbejj+iMnavBGpuAfMix8Ea8QtEmmxkUj47xYUwwTwrq4BJ/bfr7egPB61k32OJGiwIg5gLtDR3N4lBm9lce0JcM9q9JFVX2+OPIAhYrE56gXiyS+8A4wHJIWNUmNXulcSItnBhX0+fz+xVdxJNL8VhB30YTGqR925fWL1ZneEcHCjh7e6uZvTpGQ+c2kzKkpSZKPoZCZUyxSD6IDcFmLUKwy9zJ6ZU6nGZNk1HW5fw0h/ZEblPadUB37YlikZzFxlAh7IDFlq1O8yJ5XwnKGbI+nRQSgFLZtyg7iRg6bbM78Y69Qirec4EEZfny/ZkV2hXYKx5dMyHfhoZVh3rZwz+cPfRo2tTSeG5vXqgv1QM82rvVg110B07fz2bfTZvw0Cagtv3OrA5c18+NRK+aakMjOAPk5oScU3ADIsEZB3peczidYACVRdDYeQGERS7yeL1qTsOoN/rv2PRkGb4LFq2r1r6C/puYkzwxqs+qY0vHkyTqHASaoSn2jE9qVCnLejcFP7lVW24gdkYd3LIJQ52EPHKOcwBTAwzJpT2vRrcyO+8TWjVk5wPdXU8s/taQzv0jYQwIvsPXVEH9LRDR+AtxWqEtPRZEARzSKKv5c+pI4Wia3G0G7KkKU708H8PEoCkk2u8GmVTd8csGTgrK24tf3ZxErD+THZPQffMWr90fO+/7m5N9LlIhEtADibUAkXaySNUCX8z8Uw+XcsmyLsz2VkLE/V4NtrZTyAu7ZRZmbShye5cs1InfEZGdIK/mIX42VXs5STgJ4JC/GLIbm74ZAux1jcp0GakvnxgpIAeBynd5WJlCuotO7I7NXL40cy02QOCwG+UsaI/LABJppYcWHn0uNgrA+0IjAFv9u+gvzqkE+oYN0vfMg4QX8758gweI9T9fGSTQ0B8R6IBdPt+HxcWlAH70RWrkqJGPUZ1eeyfGlq8soI4r0BWfNmI6/Y9xqTofcyO4zVixsxAbBEQWKR09Ja/SemHPAhG6o4MrZkuSfoVA6QV0RRwXqCVv+wUqsHnBuHSlXXl7OtCrjTPqlQm6MQnsl9V3VMpUib7ood2IRiM4IuguogHeAxe5FKb1AfSXZxVId9RaMcqFDrvIuAOKneeqQDvJW19IdnPNuUcZE6T1zGo8/uoAuUnCXvdOl51k3Pyovf6tKjpmMDONg9KkJDSLA/3DpnBY5et08EdSA5gjs19/+9Hb7+CD+4WqWHbCe+FD7Zi1DPF0UEyhDeY8JflM7kw6tJiibOe3XiTYruDxK1z5Fy2yb80RLT81jhfDH+a1gO1gnxIfbMdAi/0rn6JMwKueOX8sdOpDUWtZzWgEEsMAgM932WDicssayC7/FM/p0KBwcG7QfxV9z5P9TZ6g14YhoR3nhfBa0oKufXo60mWRaz/tepBIcrScrNb7EHM98GIY4laQb0ANPuMhJbq/xuBzZYLT0PeV2oGwfaTcOyZcecc5UIf6YATcsUy8EniCnzp+7DXsDkLZmTtiF7cBTyZDznu+wXsMF5q7het9BoxIsFPq3RtyQE9omrHGyEh6bAApqW04C8gLfxjcDz3Peu4tTPTGJh8PKrDVgjSgjab27nsxHith2SCsupom7VLegIN7H8Gguh7Fd1ZVhKxNJsIDMPI6lUBrXXiYdWhq+KIGj3DZHLSplAGZt05rRkTCHAu2YBPB7Z4ycqokjtHYgRQyFoDGBduXqvnyt65rzfOm+jnzcUuwU5GVFFKI9ih0y3nePOxfBhdNhjuVb3LW6E/9Q9EAfvm4r9xEvdixsaUPB/s2nM2VsK3z0K1Pgi5kPOId0wpAXhAL2H/7UJSpBroxa3Y/JUc+haMKBQKKfagSeR6Cfp7PgvPHsVDKs3mvvg/wub5+fNCuDQMfjtpy4uXGPlu5lKMrNeWo30HyPr37EvNJLFUAo8xfVvyT3kzxtqlC+mhdL0RxyFKn29v3lTLY0yf3lJbjCWKYQEOlkYNpsCom1I5Plt9KYTkrN7XzUTwfBRNcFTBW6x5CjO7T3mDCwbOKx9OANAMecDWeZMJgV+ORcCZDanpiXWnhfTsW+RSffgyakEDm1yn/IATfWLUieZA2HHB8FTTtNSnh6Iwx40r9Mf4q3HusIFZabrklPS6Y8gAth9YhLs09JMp2NxlCaLfO3fxd1YiDso9hv3eUfwY8Du7zpQ1B9PY46UkOGqdTUIKQht5hmckgMcT569RlfH9QJwmbawkbZJid4fJ6PqjWeL9hb/ReGxojyKTp/CXNGWdAkTQdSd9KKxVniz1u7nAGV7hwOD+Kow5CPOnpHDbdBM2D+L/33RCUHLDqn6ULeMLhicfc8TjfANDj36vrDfAzs8idwoL35Iv3CPMHgFyVBNYL4NLNXLoALqiUfUkhcSNYVIxYfTy4i0eeMU9QUACJUaD9KnudvyPr4BMpGVD8GoAoVNjml33RjuvcCBfAHYH7D74NyBhE5QhK3e3UsmGCK1e3OPdusNrXBzvkRebf9+2kTdxrZPQdaOmtuCd34NvMn41dh242N//jm1kIne+gEg0fE46W/oFTvoM7nskirfMKOgYWavY3vHjBcKQPKS2xdKRf1nU1wkGejfx3vobbLFfMLH2Dr7C61/EDB0RsJ6R0ScA25DCacxQ5z5wIxiumjp3eJCP5kOfMVM5zJorHwM+as8dOvOjebCiqwAluFciySR2xr2Ts6B7i2LmOlcP8o8vobVKRdRoh58wvP0RNfjkyIjdEn2c67BbC8B8tCMTflMlLhxQphraSN8Ka7/+d99OBJMBD36HIumy0cAGqYE2G1UVjnfMF3721ykLJUn4Naw1qHi5oEVFeWiXpQ6alvNSiALUMS2Db97ZeDOjqxhgOKfbsZOb8ImPSeuJ/EVRtT+bdhwp3bwsJagUcn6qaYmW5biP+OLHQDLiz6aVh2CLTSPkUCERDXF2kimDD/sgsQLajnBy5umHNtpEkzO8n8zkJeqs2Ier9YWHO+sRkDzhEMzf4st6SSLde2o8DsDSKU8EeKqz3bpjHIdGb9IrJB4gBN6MAYnr45PqNea1x8oYqAYI/X6I1nz9PEa80pSm7Y11qe+mZOCaM5qAfR3rBdVxVwtLuJgZRfBQF4fl3SP3ibl0PgMWVL4NHJWqPlth/PaYef/jgzDe5DrswrSXw7ChsvE2K68Q8jgjeTB9hO1Oe7+dt4DwzA+R5dMAQ1PmgR3f9nsyNpz907xYXYcf41Tg68IUpGcD7gkscQffWN4j0W4En34iuFpLX7MX8dBWvgASXBM+Cuu++AOxMw4AZ8hI8uQWPho5InuJQLBa0GWTp5tvjhjqcEEzg5qBLu4tBJ9VTTmehhwnABFo7UDU669K6H/5SnsZZQ/KkLcbIVwqyUj0Q/hoenBRkMsEA7kLoDIulqviFV5CqGhIbF/5PSP/SGLnfL8xMiDJzRY/ceeSTSPfHZd1Smw/ctkN+zBZjpYJvFEfrN9qJ1KqPW6fhOT0FcNsYSRaKM5zc9jm+jhHG97lXHmEGssZJEi5gvJBubJl7f9xtpjb8wbH4qsRqw4LUGXqEbEeXXNnBpLhhKHbq9Li8t5c7hsAisuExukVfLmcdXAU9MDkghGgPi6YFPUHNwSAP7sClvhfKn+OhWaIYLSkqHjOASafWcGLW2phEktoB5A/QDEWQXYbF9y61OnUaA+6otZGr/dj0X9fkiqpWRskuyOSW3SYKxEKbxWBjol87nevGc3QlVs31vr3D2+KDqj2fXgfMfstA5v78A4JisuC/O8B4M9jXH4zuDS424mxdlHjnI0+HBYthO3/Z/m1kAAQuGFRDGsuVIOXsbDFYlhLIAsAb7SRORZ0AK74d8CqEq4hPx+/qUKn0nGVsXKQMUtLNiDoYFbUB2Rp/sf4v/GEZkB+1aY/n7sjOMLipt3jl+tIURwiy1tTEjXulii7jZ59qJPmPOIs+4IjxmPHyCUxMOYJF1c276KHSuO1qwus5AudcDsi2LFOqPwjp4FdLV1veXz/3LUClPcebhhE1li7mEIo+IemGHPwEpuvqh8el2sHv/OojunxJGyLGRd7O94GSY1aZcnGWA8VFmPpYNKmiG8RD+yhZnmcKCBKvOs+9BnME06vKbMQK4RYkLbTewaCnHspbGxGlZlfV3FrmInE+G3ldcQ9x8phcfB6Nwslv17TH5za+1H6F8uwn+Mbu23+gxB8zqUWd957sGxjMyUDOdIyx6xy298XRZO0K5So22HNuEQibY3ar59YItP2lK0sT68QDpG1EWlpAK72iqkvMw5LMU5LxXFVBuJWRITB9RGn3of8RvsYSamzFnsZHjusxDIS8mUzlq/P4uK0ORgrWRr68Q2oMR8dK6YqAujLzaoTsvw/LiqWJooNQLBepzNPYrDxDo4qj58J+72ReTLJ1K1iKVDu9uXMAkCekkhxQABJipfDi2iwukTUINoEhBHlrxkHClsDrBhAlaJyPZmYZg0r3DEhb5yuqTpj5tKwURIUtU6ToCGlH0BXDaJQGa7K06rBy6zQbqHDFObrfwSvNzR3woQWLjYyPVfL/7UxWTFtgBQezvCBxUfTnRuuKSzi6VQ6UBoXT7rANP/doeD4BK6MLtZCTYaQEWt+L3iKAtaGuliMXdwId8kG87NIAGc3oO83RdS0J04Q8RfQVeauW1GbCQTksPSYt/p48P9oxQzMSInuLWvojR4SJy1QC+dwEceTdVGcIer+bgvfminw+HpvNiDYZEUikQwjPJp2X64oxhPKXSHY3ZMZwKTi8vlkhjVQ+XSfyMtDE7PafPlYahm4Zq50F0UoPLYcOx7YGEWpbIuMArTU3iUz9uzUnrHzHm0OZI0JvhnTUGzk1HAYPwMkU5FTtjcSdKT/zUKF+G4rowN66zATcL2OjjekWz6VmlTrNQ7Z/KpUIsluCKs4/VsQBewGIOFhf3COvvsQqoe4L3gbFh32mJTEsNlBfSbspbdV6gDOARbech/98l7UysOnA00a4In1m3wY0XAEZ9RW9CGlgAetOBicS+nOGCd/ZLlYzqkzZXoc2wngSvwlZhgrWJ6iruPbNhVyHjpGpO9kRnMCIRULyoF/v/p6c9ilrbxidmqRJcvpJ4XMRFc9lsLtiOmbh9JzwUPf5asST2OQSGWbGu9zmgnezwTiNr4+QDHnPMYQWdCeGgaDBo7coXOt9JmkEvDYCd9+YpYp6PxQOj0YZJ8j0g78EFbH6Nbhiv1qXz5DJOB4NkWNbcvT2SvXtkD7cocj1fUlitUf8VSWhrU+VjskYn6/KTBoYgCW/X3rVZqjngDXt5DzypwP9zXvglHVCrQ4PnU52Rkj5NyfrzVVmweFyrHy4bcrqn3e9Re7vKw/wnpnfABD5oe1MIx8OWFxy+qa4iKrll7D3/JdPjAhZGmVLpvk2BCquhikZr4aWdJZVcZA9W24bnpFooaACFgQe8LiPQJBQ8+/d55Fj4srWdEqMe8VHIdzJMvgVT/ZNWavJsafsHX0xJ/LmsRUB4wsfsWIOZcls4gUXyn2siNagS57F7glbR1wfn5gqhkJE4kgeirwKNLjL34VremMo6lXQFCfpI9DEvNzzKERiFKOO8PNrXq32Vp4shDaTM//UdLKuCK9tFs4RaG/0qG8SePi3YdIDOfupOWnbSDc+fxHTMxcNHIAJrT5qvBwk/zCqhFJNMs3Dr+m2F8ZHMQnYSPEuRPpaIfuxG0F//474x0omlULqZnsKZg2PMXELqki2683u96Mfu2t8sUqCjHHDjGynwvSTOW1BwzDYRx9kgte+vTBZAUtg3nl/+65Vh/VFA7bZBmPgIHT1Rpcv/8Kvb68V99bazcd8BgFCB6MmhlOb9GG6ofX7MKdDuq/FCsVvLsaSfu6LeTPf7kiTw6ulu3s7NuJrIs1vDWv9s5KwFkxdOdqiex+uoobCJX9+3vOTM426bdwFHOUz2ZPtnCT7S7ITbD1R1wUXNM+KF6Ojdlv0Ry8NEV8AvOo+HU3bf852orGahKM6sjIGvMX7CqHprYJFIutTByrHa0I+j/mT5ZADl15VECiGp3NJj1qNdsOw1IVpJAOF9y24Tn/MC2vYjB/2dqKBWWC+LchjyggiCBMh757Lu/7VMjSYLS+8lJd4F0fotnb29k+TNiyR84NkCaJ3eK7HUVO5C9CUG2PvQuS1JN54jRRKYEehtlyPN9r8h8/NhT7YC01Y/UD3TP64RirdPmcHd53G3Z2ZVD4kEYD4RFkSCPlFtGlPPd9iFujkVkJ23fdsGmSwFHUnyb89qscdeGiCwYe+ao3fBzLwyEebf3jerfqd484gEAzVQmoQHTTU/+nmVxbYKrUhRJ/R/xDzOCDejyTYJAlXN3X4Bjs6LZvy3zuEaNpO7yJx93RpomUHpijH+Lk4r4ZSqdT70U23NVzwilYa9b9Jyy2NXCvP0zFUDp4fawDmPBQGZYqpQ/vlbYv1RonoWtDpOM8KkZcnykLIywAgHiV8u0pPw6aGles5w7Y5o8X4wBDC3NGpWTMOWCoGHx98BqhdxuBCQOplDBn8aVCNK5UdjEXTUCCw0VW8n5QrR0OxkgKesLvWdrk5GhFFpMLPyZrzy+Oi4kqRNJCSPdGx9ghUsk+VMKZldoXzeJ/UeuPX0PnaqFBRItDJ9aWeXOLIiYnqcYKeYzMiiqZ6rFOU8DKHNgFl5VInBLM7Hd/4gFti7+51DFj9epE2qgW3wD2dsMTGdgKfcXi+sTDHL/y6l4y7n8SDn1Y2DKvfmlP1AoSt6juy8WQXmrb0vn03/Ht/tSJAQWhgldLgyal+hD+81bIVdZTA+QU/VRarzxuDgYWEvSdYVlD47rXvwkRbZmzmAAGD+kKf2glaNBv7wlGarbfYmR+QN2Tb+5RycEoPb3utzOwrS3DDmfR/y3i3HaII/xhN206W8ln1HTXDAXglpTG/vsh89joR848pdXsO2LP0zZC1VGYBkGumTUfZPNqrWDkgf8Uz4UYzjuD5iqXSc3YSjo/x8YSIp1sTSvt673IXEP9TGwdwqTlOi2jLc10lqfnEIKGNbN2HMecCWj2a2UZ2qCw5IpIIa2R0kTpPY7OlwCDi353Cc5e0bf0FJDGrlvEPboo/kMW50tgx0hawPJM34lLt2xVAkazyIvUCh6DbWuG6uiCsDOQGkDmWodhog0tRxOstY25xyvijtR3jJJjwm387pFChlXX21gEPc0hJepsBKNr7Cvbj1MZOQgL/37osE353F9fNedtarXdwOdOoErYjg0wUH9wR6qbNNoM2SgniN6gcB78sBFbhblOlzEXAAiPiacqiiZKOVwKRixcFSzX2d/ZOu9Weqraxb/TuNAmF8Em8MJN1Sq8isspDINDL1d/YXhFe0zL5P+aVUIdcN7RchlAtcJ6+iDW0otiEbzi9+smP5wxhvqKY6ke1BU5RHYxkTfhbYG28QLHp1hLKOucteZnFfbI+yyd0sxdEQmTHlKFdqRNafg/nfWg8Emp8mfow8iePlRPdrkFBBRvmPnV8BlAKB73WySxLj/svcqF7Mka+FDEQwrnW3J0djzh5rh3UUeXb8QmBuGkD1nYs1bTZI6WrYM6VoE/RNMd2X0Z5/E0bjIQG1rZ/lFKkYg5XcR/ehtjCNdBpFU3bJPdQvRIkQ5AmWGDIP96vwE2qYj4b+ID0AhH7OKjw0/A7YO760aQyod51RG8cC76znnqVdgaprCr3SeyjvNRNinF5ygIuAcfeWEKcSYxnUgJhVKqEEN/gw68sYexWtc+4roZO4ouh3GW81YrOeWmUnRQNgOeCBd2jwEm50XQOTTkydpJUy/pxGw7KBug7i517hweCY2vqTzXT1TjLOYXbMOx8tn1FkTJX4Iq3A8MVveS1ojKxgYDfV4vlPy0zNomb02klVUwhXmyEKajs3y7oB/Z2urRDUcR2K08oxpN/jE6ICnWxg2T/dKToufeC0zb5W/zMcLehDyp1ej4ZeARQyV2NjpSVARypQh0KRfEwPnC+7OwknUovWtwjxI0DBRwR17wnju/teVhPToU5rmwqKXAQzk0xYMhq+lPzptgxwXm/lXFF/8Y+qQlpMCYc5xhjf1xwOMwetZC7ALGEZFY8kIWs5bo/RMOg8Y6v/J+wTqufGI2j+njJU6TOzbCtNqrgSe45WtyPsLU97ONxA2QzfEDV0GpuFP1Eh8XvnC+xGphEZvk5QyPMUx+yaFO5QphCSEYp+uvQzuY/fXHYxwkn/hpEoDXfI3TqpTbwQQ+nKd3wnJOQ3qV8ealaq38jOkIS5ywTsMyqpX1RHciBaHtfztUlA62AiC4cHgdnKXZf+3KRj1vnyrWeP1vh0nqaPR8DLsqRznaa82SKRvLxrybFYSONGtBrygmrRHQYbNbIBijHkIHSs+ezrPTmYnv7LDEOhO5ZrBLFF0N8u+vA+HzxA90UyibFbU4XQVkDiipo2DkFctUZSsOtGgaIECEUl/j/poNVP/IkLesJrE+ek4QxO1eFMwOnf6E+7E0AqxvIqOVzyz+wfdluPElWtTmGrp2ewKkNh4s7IZs9W+0h3wDkRqOs/mh9tck2KdC6+XDO485t4i51T4jNJNuOoAff1vvenDFm5BQknthua5lraJ+hOeRXbu52RWyat6s0FZPTKbByB1coE/rUm7W5PLnQu8ht5kivBheULBakiH8MJfsi6jxxLbfc2EFCbF3uSrrDbCar7PIqUFcxFy9xEUk2ivgEpNuzg/ScccnzUl3tLjTC8kT4T799Y/dVOM0DRjOyk8FpkuIU2vmP3EEVhXxnMwSUavZNGO5iJgqiQ4lZtWonJg0EAITFbZ8lxYrrFV1w5l9BvcSLaF8qNIjx6wJFIyFQaX5mZ2ILtHHKb1G/CTOG1LFAV6EPNie3UrjdOeyqNhVfk9giZvB3c07o6j8gRgfb/0X7qUX8mDWsqWyQd7UztVr7M9KTWZQKimi/FFtVkh2rdKWQzYmNk7IWWdFzs0D6UyTSWn7YzF0JRuuv2t2kp0m30XpOWBkzZR49UvAdaSAlPPYNWV1I1rbSwQuHAN28Rj6GzeSLu4tNrQGXQnX5k6e+UQE0xI0JdhzFWkx4cfnr/b7A7LbFJSeS7vNGFFyhP9qdrIkWi2EEbB0TR2VBR8wRfvSw+U3OSGsm+MGHi4ifMw6UR6OVo1jLSw54dkkEUPEdvd3F1ssb71qgAt5Zx4/ICNMRP30L+CkKCUVqPN0nl8JInJMB1UMhOitWUAJy0uN1mRUOLWZTnkai4dT2ebY7NJdpBsTua+slaL41GLLSLo9CbWOmtak4+dNza9hGqZ92NWmCvD+GGPAVhiLiinzmMHRNO0LAQ1as65gP96yAj9KDVJ8+wbJS7AFFrMOvc4g2NE0GxPhMSJ6EwCJNSxbimLsa42ROPhT/TnWZCQsTYUbLmfJ0Pcw1EtFoK+TrNj8WWovzRCXnXs0kyt0YRG18t6sPYQ8Y5PEJsoP6rQyrdlJ0UqUWAoZC3wbF0K7N+mWe7RHQBpfJ48u9S/LmQLO5lNdrySU4cr0wnfNRsBj/3KpshZ4a4t/2eskL4joyrYB3ltH+wIqDXo8rH3jZfUN1tzhGtqD6Tvb3CgQz0HbLW+Yo31usYlG3tQXckRGp3px3AReGIq906uCXkAsKGU7L0JH1dhRFtzMLz1/rizPXYN2p5agAvyrGGtmrEy+4lZSkS4LfpPqSfE+s2tLHlnlMvLuPKnTse0BzXDcUdKb66kM1cIj/DnVsUfpfJB/CWBG1i3Yh3CKyznAzeyWKCp0rp82YGaGGsv+JN/esevU17vC8VysXL9RrMLGMhuKKcNtVadauZKkfFpWbbDg5KM0RXlxFd14JBLTMvfwnlZjQPBeYNLCCxgGaeuZoUBV9SBJGrnYshGCXd4OzxjASq3BUXJ7qaAcjX5UzKyDHQ97cs0UfwMSQ5iggFkTNggJ8Rjk/20kU/QW9wOMNNSeaxJv3rPGgJ6/6/QNHnPGhYp3VSYolF3BQFuG0TXsT+O1x+21LtvDuzBrxi99tFgOoEu3mG2bq8EGlOYhXFC6TpffS0ALhAkJhaJByvxR2tlaRbTkcoCeACg81vNigJBOHsDH6z4eH9XER03aBoiu9nU+eGLUx4GooYZTvunUZ3srYpXx9uGz8W1mcGjJaxBivB7Znzq3C5hNmuIvnSEAj/hCEJQ1WJFi0WCYskqSAUDrBI26sZMm0BW+OAdYg3UgcH62Hcp62wiBTt9gbEEM1AsoDtgsysfYuOnQ2bYkN6KCEA6ALC6LKmP3rpnNBcofFV95rqZb2NEb35gPdKEdi/nV7GANYdJavKUCcGLipUcp0l9/OKCuJFkJ6feitWgTcWgxX0syQeOB3UotSjiiT9XV/snU2/UneA3V41sOZfEAfqazVndCeDuJY4zxU24K4EKOW21l3kLX13HZLKJTHgZh7HyNmP88vzl43TK1rP4o6pRp4EjCs1qVh9AH+VY5GaoKb7S/6xIzLrh0j48HC+jYbv7bqw/UP0vhbwgdDQVzrvlOtw2nevQ4CZdA4snz9c1Hr4rUFOUAnIYn2SeZuirkGHNp7acNU3WnDGZstPNA0KbgAeSppy/Irdvv6WQDs06IH43XVMAnrzZ72Ja4CiqQ+rzjjqpvo/V8rOYxkykl2xOMI7IAGn4R5Z07uuJDLYnavnFSEQD2sYrSszARKRrsg1to3l0owsiv4NWGXRVxdW+tt7IY5n2bKyt7gAGZ5kffoWOW1+v0YKJ41BWkiWeo++P4bzfbagrt+0h1ufQH+WscHgjXZCZX2gZEU2J08g9glhtVp3+tG0zsanpxAR1Jtijz0HTSel+tKQOtzYGC/naDIuw9nIinmoNVK0D+Bmw+deS7WBWQqO6qxiKtGxy1rqYX1ca49+KMn7tmHtrBraG2+dsBlOCw638MPRWErywQn+rqNHNJoDHRifHBnSalJD0UU+3SjgGd4BcQ82V1t9/dkJ4/2tq7hGw19zOZ/Qm+elV5U3DF+bRkuTZ2pO8n2He06gYc2WWUWuxA5VnYaxkBu+3lAe/k+m+jCvwtyXZKbYR+00KGe3kb4MB4NI/jwwT3FzmlOYy/dN/JzsCGUnoBNMmAifuMdO3+s6jPEMpkeMS6Nl8rkc/dcwIEKhb8prvsZ5WSlNLhF2OKsNE6KyVY9Qt63Tr348fdDwCKbbyYu20bpdYGUkEr/FqNtc6YVjCifbA3V0bisTJabf4Y1UJkSmOu2qMjoZXFXXQeUCW4apuhY/vRx915ySq2MUONYuxAnBIh5PTwlEj+Dc9lBqCp39YTElI8tktEhWGoNsBy+d1fwJsuP0Z4ix5H/ladXeuw/6cWg4l2iQbYkx7g6xvfAyFNXrlSfftejaMk49amW8gSq/Jz9mO6qODeXoNXHTyY+JPROkLWlSDG1n986dKdNyDxmfL9YPV0jovCgYeJcufWX0hMAZCvhczXBggMZ5MQ1QuHPMphmTtPEDEA/3HcF05yBFGT1G+xIFlztof1otDxiY/2/fPGPOjtkgO1tK3vRO5JWZkSJVxf015hVcR9L2b+OaQpxIwIqVXYJ6YRHWtUoWGzLcvkPrD60qYe3Exan9n7RokrKSf/BmwsvjT4YQHWgq8nyeBrjhX7DbbWUlE3suVbA2q4qUruwWPqiYie+kKuJMT8CTSgM52WockfzbP4o/5XYw3/hudTHOZOsvEuD7EfglAY2pGTJs3Qyubfp2h0fPHPCQnsD0NDGlZKQgjS6G8eKhNzSBp4LmQHcq2sS/2+1BfxEjkij6AByaXn5YETMZJdx1IH5/gX1PkuM5PyXKGK1Lb0nmm21MQWruFNC9P/xR/qAoDAs6TSGekcYeGSAi+1wBA/CFgwCi1dYeQp81Aix22qGDOKXs5oSkfKeqRnYwyi+DtqnVJXo5hdGO73hQ5HAcrbIZFpL13jXY5IxyXnJaQLsk8jhbz9KnVyxfi5rQYBEoMGSxEibfiARAgIETtfXZa5qjlo/Fa2UOKgEmadOlqzE7G8t6Sejp0EM11aEPkOpNQGfAL2neefEU+HXIxJxxUMfPVTSXxJ8sthNRUMOnIyu+wqAokxuwUe3djoTzPzgNEbEpmFOn7DDDhWbE2HHvD5+7vkzqPZXQ/P3xcNyPxhRXb57Gp59jGf76RhsOzRzRk95SHEbrG1yPUTMM1m6TUY+/+OKI9ElkbaUqFcK4z3/Yp1NGG26j41OSCaofMDls7KsTscaqe4K85NFB3ZJhSg/u4J/t9INSj6l91EuUJeM26p7GcKxff5Pw8nkIGP2FkRE0IdrlSUiFoOO7xy29SetkY3VYwTPKllazukGAPZmhQqR5RhhV3cL1el5tvRIaEg+qkXqUxbx0aP6psiIiWG0bCG1XKwkUcdpvRjTnWa45QLuVJQuNPE5M/XaB6ncUTKV/cXLh4ZhGJnaP3F3OYN6BEApmm7j7XQ3G0GRY05H8dGCOdjBzo8tF3oe8Prak2asEpJ8G3F/M27IQZzgfiG21tblp1QjnPQjFhSjMQAi4Rkz8pvqPhUu0bRr84lz0NLcrpCasDnUdW8jq+S11UYV7lIof+D5WdO6AhChe59EJ67C8y2dx1aJki/bL152knqsEWLQZH6R5If6W9hpvhtPlbeGnVi1joBfdNLh9OkyMzAfcaVd29hg+nqe4cD1pJDQew36Kif+greW8uyWn+3LfZCxNVNbkk/yve5n/5lhV2jD4nl5p/bvWIONxmNRzvddnhbnA5Z05oZGfXO8P0QeqbS5cejGoHLJyvbefsw9X2dtU0gisGlLkjKkNy9+kWciHK60vDYgHBSEAsluz1TYhuTxopMn7sZz/oMln5VqcrAXrML0zISrBBLjwGwix2ZjZE2C858pCaL5QMsSpsLz1OZepX+HXaFNmgVbRAJ3AyBHGjO5me61+rcg/rw0tAvuM7WNNfetTo3goVgA7xfub7agHW5N8DYCgzDnZU9JSji9iQ4xue12lJlIfY5ZdlZLzHTKhIW8/kJibWbB5uI//qLeXEX6rlyLJzEeCxJom1ASstHs3XInleDQUE1qs3+spLJsVm81lhiztJNX+FF/AG0vs6a3g6DkKU39zwtqW56P2E3rottu8yd8FDM6graVyxLp3Rb3DS2UHLdfh4ku9rYzsUvVv1dqe+Y0WL0G8uljfvoxr3OOUgxt+5YHb1zB+dtK4hYzzKD1N+ZDanu9l6Mbx2/rdl39FiE85dAwQoyMxiI6NrhubxsMrHGSjQNlK920cLW/L4OABKi29v4CgczM/tKmojbS2i0MtDMcCw5tXZJ5phLFGKdowDgh5aF3n2DLmPSS8MjzroWcMG3aynMlhSL7u0o/sfH2o6n7B0X9D72WKtQ0w/EdFXxSBKbKRRtKWhLvk9GetoQQ4Pe0A9ubmZ0szYUpEASCu45uMz/UIlVzGG4ZU4Fx5jZWdFZ9G7nE9aO0EHFaWECTqk4+jOiEDv56lZTgKSmlhvPuVqDY2SARWCjVgtCRYFpDjMKXTVoNVYZLcDN5kwyj4+Q8/3LQR9x5QSdfcd1DWaFe7D/Wn/S8hfjs0Hh9qFLOqOa7V+jU7byz/UREWDNExeQV0oGHQODbbTEGcEYfWeFGD7dMK0yKyKagKDv19ebYVquEcICV/EY9CLk8wEz9fI1mBrqYhJ+eHUyAac4zJ9JsQJwpTyy3SAUK6JDeg95WrAlPlbhNmO6PALq7w1Xox/YSYJls83hOzPFl5uwoT31fnEywm5/OfpbrUBeTrUFKDd7c0HcNwtLzowGEG7zCT8vsdrRriFRLQIgh5WrcL7r4qj4/O8C1caIU4y7E0PuHpaFKLRY3B40MSxR7uBwwZkyRXHjKTbcX6rmNa7NBaJaNCHTJo9AFZ8yI3evy8qau/pfXSmdyYZSfB11kbi1hJf1IbVNQhXRQFDnBnP81x8n+58AxMoaAwT+iQLBcXW/pKCmU8Cq0wUYrCZUEcqSaDLTNZ27AwaW+JasQXojLPQ2bSmXA2zjrpr26Qios5t1NEL4kUAMcK574H8xVtkQIqW73hX9t/MGzkso1gAp27++OruK541HLAJ3Z4cgQdvEhKOK63cQGscQ/EhJyclYSAj8ncQ0XHJGFwuI2Fe/A7aZ/H7xGEQXsBuJJCMaauSC4CJ1nNUqyHIAKVFLJDeAfbZUA0x4faEvSGDHPJPAMWEm232hcbDABCbAPR2fcw2fKb2NRzPPqnsGUFYxRiAcY0SVtKvL3XX03Oidu+oYgKj0+U++pWrRzJdq7iiLFAopFfmczsaDEmnEPQYO7ArD1Jh5So+vZzl8ZS2quA9peQK3zzkN5X9DvoEiNQbLyVDV303X6rSyBQuNTwpAWPaFvANG1b6vr4R+zGHBYVOelp44joubX5n+2SAieuvRyinDXjKNG8LRYQ4Q4PhPbknGCIKPGj9PAlHDNl9F8O95j6Mx2Drh1IQMVCFyZI4C1PLpCUi9d40N8M0aUAGVjj/thxjk+FP6oAy/wLbiyBMHUVfHMPK00tPK9hRJGU6tzUc/XE4OWAbasrHWGpKrUhrJ9wrRgCZ+3c8BIdF8sOMwOWYWs+GXLrF4Ds08bQ41KuXQJbwc88tWQ9w/C+bKUaMxVCM7pbv1F2F/5AmbrEf/2gCAU5mq09jAlRmCa4Oxx8ByS4f0sgPhicnyaRLUPQt0QxNGB77zASJyJzR6XQJOAp2PoHutcSneW5o34p8B698sEhqCm1mbBcal0xY8BtCFdvZp411ZIudJVtxzP4+Rs4lJjq/TAmyKsbzXq5GJiZ8zK5ocZ/Pty9GJ1Ldp/fpoUdqWeOXH65aV4WkqaYGksV466yZqDQLVsGtMdXxlBeYc3WoozKK52VQBxhrfvchHWxYcUBje/FUlYpAsQBcSC9hwRZZYyuGQzLlmvGnNbogx/mcmIKjswNbnybDkuiUmXME3ymXwM68ty7YFhp/FFiFsOS/FFrSnLaAGHr7AZ86bEURhLZ+TvyImMRc3jOAvht561wc48Cbk8vycvHoAzyH3c/pzu3Gg0Yb2jBsa8tnCgyqkAYB/mfcxuRL75U5tMsszLKsxvkATvEeD5LPO0X01G37EDL1lpvPIcrW+ZabuDENWWKD6ArBNiUsSGY+XNxKDpnAVh5UdqJWK89TCMBWA4qrhDPturf/sCbmfivqv4PfoZDMSZhsNbVYrfJglY1luFGs+jdM4axKF8z49W1jSMxhh5pC9h6c+79JMn1IDMhA/+S9aUB4ySaRC9kkCua6ErnpFaEFXLnstGozGSf+OFBrlhof8ZODCeRS995vM7PPC6T2oId6l6WzAwxcQxu/TuCneggvIhtDTDdEQ7SJ9E+vazV5bTqte6aebbUqw+O+eRytFjWmAdc8Sh//7VYWMme2NkC0rX56X4kP/YLnLTmvtDebUKqPWzsRg5seyVbGdN1+h6LEM5aC9jgSFkIXeaYPwXK50N0S2TxvOuuhgrpDA11f+g1leJ+qxa5ezH5KJM0LDsMEHaJLAiGg7LUpvPlhRlte63EY3gNDkJm4z7EZcSf0cOhqDknjov36Sa204VbWE/uvjyVFXf4tgXzJoFta+hGXHzC1HlNgLgvoCV/NmLxMBpjeLHkBmzkvdwuCE1JREaruZMqyb+t2BwVMr/W+qyr036Xc66AFvq+60hBQAltYhTbn3SKZM/5QDiGHl28tyYipxWuA6R2lQqdUnxyA/TN+KSxuE9OgBKGPED0BhYY10iuKmsSZOi7G/y79t3awN1j2NY+ezRlN65L0OYUUeMM+mImAEL5HJkTooEYk06xVaCXxLTPBlG8ny1jHSm+bRyvEYQImjbmMKsgq7ktydxjq4XNPU057AiGcBEtWZ4uJ/aONqEhlE0UlgJQ0sFJdjdAr2XBByOcHgQpdyga+3Hje+wdBB5O6qpNOfdKUK6Y6xQjg8in9XNM+egfeeeK8XNcjcDD304BC+0R1u/uCP9ndCKLVn42KRFeeZ1AbpBQzH/CqHQujvzPOP9nST9dJvL9Xu1HRXnZufzXaHZK1ayMaJf4cwQ+zsNXmQ4Lks68NKbczCPv3C95nWJcnnirpGMIrdlSXPicxcJKcQ6cfQGbG+TEVQH2bx9GP7r+44iX56We7aD1Hj4xLTkgZYy2q9w5qbMTy67Qu3s+DdERTeVwAmI9imfMYWydnFm0DVRqGJvhg6PgjV+NuoRIUMC8mSYmDnK952QAiuSfykOhV9u8pdnNSXl/v1dNL/Yp9cWOgxshz/6lZ3htXrwUKFzrcN6e07MaSQjvjnikictnOciz1eIaRbVmlittv+m8lvwkSYzf1W5B62RIIWLiNZsuP2eZ0YkmaDRKogk87pxAENSfY484I7XbK4yrsLDIIAhZdPzjxUZmZe2a43/Z3dkKiMvK34AKFPE8eB5C7VKxyNA1UPjTv4y941v3daxUTgHJD9Typ5sQ9MGJLFRZjsQPRf2jfhwBm+tWQmhzr7mcHZneC7wc6IDmFeArsnGbPJBYwriPKI40GHPxeAZB8at0ta2i0OTXmJAZc4leDyFlCooVr7UmU54Z+orSVPXQqBkFmRavXqUOHHbdeYqfH489yRID0IFSuKnYDhB+LUbJ++RXaNaKkfBy3A7Yf3dHS3FOWrFCMjmrVKheJTMKF2opiQ1GTggTtYfCVk5eVxBikwJbyYukge7vkiiMLF42K2Mu10TjXQ3swYcm4w4JE9kChFxKGBR51j9kXoSLqIB3EAO+sntFimjYLgBO7oZLPTUOTNmv2pdgH3w0yzSoE0ACFIlrgc0xfmjdoJA347qoB8VfzvawHlFFQfObPo+bWiPNsXSsyuVw6C6c6sl2qa8Pv3eOem/G9CPyNJ6Uhu0h+SFWUsLmcsGuuu/wRCRTEh9qvZ+10UKKv5Pf8omHiUF7f4VbtkCff7Yi6HsSMg6PUJb832QJlN9qU7sYjz6cykufFbaelEUTJ5bYhKotXovEuJqN0wMk6x9Dh8GN/i2lm3ZJcA5N6ubAOIFnDaLo11aIIW5G70eSdkBj0MojWatqXIOxiPaHvwRY7uRUn8KtAkIyie5pYJchEGyugBzApzhNK5qqzlYjZYrMHIagVx2MnVKnbe9k7UHXSrII58MmuJlmNl8yrrevxjYQmZB6TCdSOFlWoHN9ruYAvzRktxfRTwdAjuISvK/EoyEJWEu300a4OqDMSMh/F/ic7ypCxByNWCK/FsQrDtfyPQPc2r2+d3eLJXc6e8R0SaMFEVw3Z204rU227c9vYLPaiF0yIU0Nef3Fj4PP5oHl6uzPn2WPdeyg2lLW0YJA6R6A2ABV+cOUZtYOOnN5/Pvn95jPTWff4JSQn71TwFZhimQwptY5/KCHwLF4r2t5B0oRwLIOD1RRELocVZM7cg0na23UJB3kAB8fKJCxDfTyJE4bkgK1XfQUGZNiAsz9R4Kk85McmTV9nYcAVlzZxlpx7Avf84CczN9esQM24WQ+aGJB06Wdgxyw1MPtuiBCT3t/67sngXlCzfQagkpCPW8VMQyElctulYZSLX+YDI2L522iMPU1lmpC2ZTWTYTwVtXOgPSkt42gfA3r2uiX9fG6ftZLRK6M35RWFPkiCokNUqcgP8i/VOzVLCeibtM8Tr3APuR+tWZTuO5Lc7DngS5vk2X68XlTE90qDUivlslmh1IEgtuXcSf2K6BRRLG3yiDIYydAwjmOybiNo6QxIlLe/fM/DoptZ2dOL5EOGKQo5CUasNMzJ+ku1o6zSMEsywZzCsUoDAsboQmlWiY3cNggLno5I4T7cGJr6Ewqbpwgdc2aK1j3td4VUfjxZlThieexoyPHqGsYoVPQxfIxJb2MWDNDSz59Q3CT2ay7G6deONA15zmBNJWvffW8TodYihp3EQeCa7ykkAZRznGflmnDoqGLt0Dp2pE/TfE4V/zNCLvVSd84abCuog6ZmQypYooEqPTUu93YtSrPS/wzt/HeR2zSP5rsPPb9/jbTPhZ2OXMm+BxZG5yFIDGKoVXFvEIElKTn86fS+QkvESzEd7rEiDyhmp7eC9/Nqb3bU92tgOG7jnKE4myX2h87uZ/9JIA9lNSB3tqgF4BnP1j5GfResUQAIU/cbcGXH98vnSVpb1pT88+82df8dORoSV6kbIQxjqhzhxVliN7Xt+M+Fh6E6umDbfxOg1oq9hkJWXEjhKPX4OwFHn+ncI0RpAN4SNjWkvAMHz9SazaMvmJaEyKdlzvcicmAGBEu4Ajtl9T8IIbBwvTlbHe50ociq+jaFGF4BirmLqyw7cDUCg4gZSDUnAuHR05MEKMEMDgAqoLC9Ltuc0zlLAaml480pCvlIpwsp4CWrCryrnxWSNB+ZdVyQcWO13CjmFKHk3yCGuR98pLfDXdhNxU0Xk+yhJt8V03m9D4OI7pBNvxDzk+OOqiMzKiQopAcxjp/kQDQ5zXBFNs3YOhqnwiNpcvCyuTK9AiZwtq/go4XLt1lq7lhW0+dSSI12l/RXcGYeWquVJgH751RCM4LUzkmG2MaehjV8xNpohKZcfdrT2JF+9sAkeC4OB2MUMNyFwkQcuhjU8BEMIkyIZxEwIipnfBJLZHNw6O4c2p/pecaSbvT0KPHFk1R7yhhTgKDFGGAjw1tbDdhETYKbA6jI2aFagOzN/9nuBFSbDF11woCDD3GM+ShXKSn9S2Gn3MTEsulHA0VWJuNkS/OBi92Qq5gBXx7q6jHkXq2lsTzHNOgwKxqCpt/NS50RGFfb4wBC1rMtK087RMofsKyJKjYCmdH4YOVaYA6SDdyREPKf0O3nsgzvl3+M7a30Coyg+CsltIOGjQr8KKd3V3XEvMlq2lACcXSaqAdQtygpJWPfY2sBHp4XWWqAA94J8OU7sC3vZX73S7jKWGMj3f6nZOAzvWqHS9edvVtjNUByEHnM1hrQMQsKWvKF9+TZtn+HiOW/h+dQVBU0SRli0vbn3MOrGRpiymt5pw1LDCl82cZNQqypXcMn8xkCpdW1rox0sUYH4z7T4X2ddj6S7zESmtz76cysxeszwhJcGOH8z6JRA99f/WXO7Rl+y/uSbQ+/cma32IEZsQz0V7/sMI7ZDr8MkQw5hT9xs/S3hvBUKRu+obhrQUOXfAFr9hqZs9eUPH2yQUj8UcVDJlfpqxtJpIiT8PKG02uCkFpDxi+e4RXZdpRDPd6IPTQ1ECT7XqjRJvgavynO5ZyKN5j7W+t6jnvnOiqEJoUnW0zLkzk6bvmTHiibCO7Ud0ke7OWfHgn7rHC5o1ainhgu3G0bDU37v9zNF/cuXNpscQob+rLI5/IL3OIbX7OhSzWjdrQYURONhEwmhSQraaJfv0xorfDEU0kSDhkVC7BqB3ihXpz8uV2okYt0NRHUVbRDyrXLjFeItBm/FInikGB0X2KMDkbDcjodNwtYavaPTqvtm9emmZWHc9WScjDojEizsIYBeBy2o0JFM4HRKmtsYa8lX5Evjyh46POgRx5R4XO01asmpa1jQAjVpLfM/qbFHdRKujzDIoxTFmAdfAXWXFFma7+Zmak5i0SMyUFa4vncjAwX1V6ACTZRz5jXkzRxE+xI66JUELe2WIM0g32ew31ptY1i4r5p1vJ5MgcmL1y7BfXwN6sNOPw1RQA0gdAeB/MWvEQOocTe+lHDeU3GuiVTvVSfvQEGWHuZbT2flB782w/WoJv3cCeVIBwyCv2JTSPBMBez95W4Hk3593xLCwP8JoU1aeIF63wnW/ejeJtZ2n/wfZhymkGqAcZ+2xTwswuxD6qyJIdVmo6BM+rP9R50lggHlxiArOKzplMcv3N+CnZrd/zWAX6Hg0dfwwK3pY82HAVqEUhYxdr8KVn8mQiPaOZcW0VBbR8ajNz5Plitp2XDKpMCUnHd+YTqksxnCyFzvyEMWqyeTqJt7MSqj+nB0Iec/v6mwao8deLT2wl85o5B3fRDMd2jP8CZqpMZBSkYHckvmaC75cxZ40ayAS2QsL/yingCW3L9DyfneRrdUq4S2sesc9hwNw1YtwMnPxMogmlOiU+GYo7AMvOtgetTdo/x981YlKTb2TMrwqth+dgYDp50WrKI0TAXhP6exHOrMIvGa8O/SXZh45LUL2yG1zwKNSAtboBfvrPyvRV71zUCDpuguph4KsWa5cUZQRoRXMGUF27SFdZ9j/ZQSFzMeOEzV5btIhTkOFt4gVqtae25CVSB+YHI8y4Ip5o2eCSYVDFPI6yRr98y+KIERHfy6uao36k1/eFGg4y0JxBdA6RPRZOCF62muiGx3BUYEPTNa5QgmQ2jwq5ClveYBarWzQ/62zSnjT/7jdOw3xYu2jWS/F3cnNcIPY4j0Lvt1kV4OWE/ZFeUBcyfQbLlAxGFgArhK7J9TNAMZlCZ4nPgWTHfJSZ2OIhW3LtTS6TScZCmVQ1pctjJ2FKAzK8IejQpXjbOLZyi0dK2W3CsQOTMfCWMbM4GJgAR0oeYzt+G3dGO4vvY9wXhVKJL+mwi7Ym783Ed62iAFv01NUeCUgLZqpgzxTjoLawHrpyxhDHCorbuzHaI2GFa9l84Lt/tO2Qw9Cc3OnHqifExRzsZgbhL90GdG61NT2vAjm1d96uqnYAp3msr7MW/By6foNmyDQJ9ZbPkzVtExDfc/1wjVAjdxgScaN83HHGIJtDMgJFcnYld4xzMKMPdkQH2dDoV1ccN/COEnQR3R5Yor/2UNZQ8SmfYYfL4FmMZRJ47GWLmpq24LGFVHiZeewg2qR3fsjm8aLRvJLyqdahDolQnU8OJUvx5AVybx+zdLNDADFCqMb2NS2X/GA3ZCqPLcnBoe0Y+89roTGUibrlwSynbz5hVyauqHssGPl5iJJpY6zy1E4OsfaAKALn3uHWJYR2P04ByE1ovgHoiJAiUCJgdMDeD9bE/nGD0dyxgNvax+VeeuyxxoItqu/ykJaDgef42oZ4IlKp0iOoX1jjotjOdrE8Cz+uzuAZryFob5WrBO+sWutyxUxkkiSlxDzOcu5Qr1oybK2L0hQxr44PjkMhHtTHAVzzUELuY0ghyf5Z2xCKXGXU7n+bRoM5KqnGy/LEmq7PEJxL5qEHYOFmuxVjNBFuVHK/7r7Rc+FUtR/H8y3V4NtAqWPzpN85jV9/zt99tJh6DwQ3r6rKONMfwm0p2CL+UNbmlsTTCodo+Krh19hOc9jMt3Wpxc77QJvN/cV/uqyqoDUC1onVZtsQDJObNrQvcRYeX07hcnKX2F1L+kEwUYqe+lBFhXGvuCsqjwjqX6KG/FCmCtynY0Yfcw0M+Vu2kL+rbyuQc5nySTzENmQc1yRGwwjRVzzz5dVX8bv0jiHxhYbU07zv8FyA2+zIsGOsZf/pMYe+Wo8VKKnEsd0Ou74ArnRrJxJZOaNt0w3sTRbSGqf9Vh9dnLn1VzSu28BogX2YzHP958N9Dm1RAmf1F+yeB+N00OAyJ6cafdufL5+VrhDsJPkuEHxAxLn/XX4ZOXGJHmrxp+Da2isYzIpCXBMGJRwOW8tuoVPWly2n43PlbczD7cnC/gIU/nQrev6ITCSDZJp2jc+CKPZoKu9Hr0nsTGxAuEtrWnvUFqRoqNBy5Ca8fGQkOmJmD+DuD8aXC0Me8aNzPpcq6wRPHkT7QbZ8lSiN2rz3bOdjx5jaJqFJ2UActHzxMxXWQDlCDiN2A5KX0DirQ3v72IF1cWZkwgpS2IZ6s4jOAzc2LjDN+226qEbFtc2uHRnmViGhFHhMc5Ij/JhotZ7BNPR/u7j1/jK9O72x67Ph7VekTa69C4LgUm4mdmHRT4wSdMVTKY93/uF1yfQljbJvdPOiieKRSuii7oACdjFGAwsH7ZKp+JwWlJpmLuVWG4+HxVVwsYdMlB/r4bZDLv2PEbuokzkHhR8aAkHpYBQjK0FLL1XKPMdr7kcbVU5ZNec7UJ0enSvndEDw41BEbv3Wg0aLI2cT9EuWHORtRQhqVBauZfvyrUNsVI8jRw4QeiHAPHItL4Fvpz56n/AxlieCrf9ZNxEu7BMcK7h6ZMtrBGPfLFB9Qozr04Uh9MEdM0rhhuqpX2nirOBJpbgBm8Nc3ryirRps1h3aNNI7K3yOABBGoNkBmBBZVqnTOqxJyW2mj3cMzSyLeziBvONuAIKk+Skshzbg0IzcFXXFfc2Qj/37g70vyPx2pte1DjwTq6kLoZNLwfXn4VLiVoZzljTh/m9WEBablgjtCa4qeM9/mwyqu19znOx1YfEFvUwfmGUn/M8IlgaePVz4mavuLZmhDSNeZu5rVCLvhlCJ83yN/gP8wO2JVpvBz1hIxXNJHBGh14nDxy1+Jwb00CwoffOqIYF07OjgMF6VG2iZMhn6gjT0Zaxv9fFfLIF1C5Lqo2wUTQzSpkGXypX9EqBWzWHGGSruFQnRkXupLcQ/x6nkvR5koPCBy9hX+NVQoKkxeyhwyoBMMJGWSjRSMutCucxkfZPoWZhX7d/+MCFmGntf7zB/t5EwVp6TEdviIj0JUbPAIcHcz+0QTI9k9SDi+BBOGoF98m/OkLc+yXjN8ncPpFyLfpyPODmWMsdzVpLUs0C0dTIdCmwxq7VhK7H1nPEuLluvvHYcdjriIG5TGt35tz8BxH0YNxK5PNFw6SOyxcPbuEITAE5XTF0LBhovYrOb0JbRYUBuqnUj67Oc8h7Cl9B+2GZDvJkVJgSZ+ZJ2NxQUKZo/zQ9kt6+0fveq+CaKfvdW9SxqM0sUiepTMtQumLM+G3qxjsqUvfLzCMgQkxYGLr/u42ZT59H1/sM/aGHqqO4CHpnTC8P276+Kjbq+OMemtow8sZx54GG1gxkpSDEwLHgaW+9dXdJY1y/p8a0DqDJA6orzKZtNsCtoK2eLTrKo5orQvRSDxf/pJflIN76nu560Rpxgv+0loqFR5/s5IwjWqAuutB6rwNRv4sBHnDYdv8unnXaBEWg8kr9Pc8uPrJX5lf0L33dQ09LNQcXhVAZuszbnfA7l9FUAFC78/bhA760WB0TOM9wFqqBkuv/fdqO+bfqkWz7mPFWkTMrBwoGjB+meupAIXp3E9cG1Tzy2U3car8SWxpbBJtqN71zpXPm667/Ba/9WUQq+/OJoDF+W9vURGPIsM8ws+g2u/u6ZGxrT89BtR9YLNcXIMTWu2IJLzgKY0AYxQUBO9AdzK7s9+TeUzh2wTEe8+A2FJFHAGG8OBPIfHVwkA1Ud6JhAY8Ss+7ZQH8PGiHRkSH9H3dtdqnfa+LWISODwjdLs7h3UIMhVR99fbZ/C+oPcx+a+UA+RD8t03SKcsCuQSMXKzlKSIGxiiZ2MPXGWRqXgbCcUJWqvHJbbkg+l32pcHNRN93AtfIAWxCKsW20/lc6+Wus+gkgalvDW5jxh9IO0yqJnEnFuVQKnxseg0Keo56MRQaaMjfxqSyunURji2/D58HwUU4/9lijF7CyrcfT14OLHZX2dNOCplaCSpXD69KPCROwz5UMdXCy/hnncMaqZl/Zq22vaeeO8orfFLEipLIKJwuIiI/khj+TZ13JUXeJqw+B0p36YS2trskQzlnhWn3eQTBA0AYAAAAsGzbtm3btm3b7tm2bdu2bdv2t0x0TnQVbgjABhGpFz8lXk/3jLFjdc4mSMUE8RUO2/YLmsR5m/1mSMpyRwKfczjhBPM03A1bapKFssdNJTGvfr4nP5t2l1Vu8UNQmpKwx/46ufRCESpNLY4p91fJNmXmoXng0HvwHLRA/iHTD8oMTTfOQ/woBVO94DJ5ou0nNFOhOGNxJ4RxHiHrDKHsti55cmNca7eaQbrU2Z4kymASzbCY78LIa+A2tkekLU6/4wNkm5m4yUM5oHUP+le9Vj5hCFnMJyaeSmQIDZdAjweCsvAkLVdZ7O7DYQ8pNJE5QfgJq3C88TYuftKiy+h6r7Fj5+l8s02oQtz2bqzubXZyX2B0otoECazwkocparNETnJ4H9vW2zNVW7dELeY2Oh9wNGNVyQnPr+XlSwe6bADZGG5oEy801vABpP5b31VOxyPuQyfOmjePskd3g/f9RhhxPphj/Dxl9JEBzUZmhHPHrEHoAyv8h+g8SZTD+wm574QQtnRsltc13hDnYCxLu0obFiF+iU1MEAgdZPIPKe55yr/KKdIlMX5gDQX3rOenmatVw57bp58rxn0y+PTd5SRywLYQhtSFXi9Vs9jHe5toq79LismAKgNyNwQ0w3yQBVd5q4Qgz8q3P6nVLrSkHvm+on/bF40j0O/38Lxq6EQtNQOkrP9KnElOYjv9owjSSqH3r9sDk367wtSagClCyd2TmircRuotUw6Wr23gmpetcY6no9dBHPrW5nUuaPyX222tPBSvkxlKbW+SgQJQnG247DwUhgGB+m8F0AxM3iG7FNUkP3txDz/oZRTiq21V8X6wMGEjYx0+eSfAVdbQoAfZRRWadhlAMx7BXodoRh5ypy4VHhAgmUirSeaM2gMas1vHUIM0Xr22uMszCn3XYwit/b9DzS5Pj5DuXPKfx0s+j2HzXL/IvXrcdJ3wyC9uu0yjLzbUAZGspZfJ17CDzk0KIL7EeFrLq5CJDuiAlbwzuTOI5uO8qmnYsBFUhSu14+n/LC2SAdhACAEo0XOPQirGnC8MqLu2jnj7snTIk7iAdMdOQH2tkzjwgnXMgKlt7A2UKPRJc5xKpiuMnfW7JF4sjPIqVjLRJzRf8ix1D+kPMC+dLbDNNIaWoZMVwg7YgVYXAyY3sxQDQpYo1SNiLjfj/7gdR9hHvoEzUIUwrpwGhn+7Gyu/8zrhVvZmeIqykpI2NYvKyzbKTBAWDXZ6tq8Qd0mzjWOHf/lzO4fgTmux+bP6XT3pJfU0OqJIvsAMdcWZSafRG/aBKkHq245c92r3EPvUCXNirzqn1QtMxwGV1S2aG50f6honLUK5DqXACmAex07GPwxeZwVFdT7FuiyHxc63zXOodOzU8MH+vZTnap6nsAng5U6LLP2zgZc+OnFZWgUUd2b9yILAlWA5HlwRjkqptKXPEGiIB8/8hBQAqeSFI9h99R9/4yVTKw4yIkhLbk/dnqRNzgtRx1xj999zt2s8+sKRREKHR79f6OJqc+kukCT6U2W/0Z5GIxLKZsJOMv1inC4dbSba2ZehgkfLFIOTXKehyKjuy1f3sOAno33kdGW3azjFLRzhNlLsFzS7H9KmAnvTi/S3L+sjYkJdT7vzu7IXmnvAiTMuJANcKB72lKLroTvcdNtoDhpI9QdQu2gFK59JmVj1xJkrjXn+mUWAgiDrkXhqMJCdWScKph1SwqqVJZLq9uaO/AGLKuDhnl5d4fuJ4F7L3rSBwL74YnxXXUeNaoXdsD0crIXqFUW+aPh05SV005haRzO363MuPJie44zqnblURB+3VBH9JeM4WOFloAbtr0huLTS1DqDqAcxZuUOR9TTqNc9KTEMBqgbT1nSN3sFDPtLKoPDSpiOoABI/szq7CqZJNG/Mwf3j7ICgJW/jRY6zOuds7oHn/22lNKK4zGpUp6ZsaDuyuaO9/WkZwIzkYzNy0i2v9wDQW95MoLuAjfFDVI4uTnwQ759METmmDg1K49sToJcG6IhXCUqbqSAA/jnsUM9vw83Pyl34F+qB6OPaJlVX9sRwLXIktUqaVWx7/cuzYcQhwgaCwZdcvBxBCXVPym5cDRxXyemefE32nTluBQWJIIKi8+29JenU8GO5jeDHy1jBuNR6K5ZEt6sEyFcvQSIaKYFyniz48aYOJ85bXVzm9iwkxaGs/1ZX+zq9XFmqBCnDgd9l6N/rG94qV1s4c9TJwCbsrpxDAWYzdkSMkilKjaFOW1NM6X9bgKsjfLZ/QAEaPrPIHbjkP2r7fbj+25IIo0Pf1C6abBYmE2e80xzCP3QOC5qTDAwkNxn8L7qRVgEgdq+n/w49Hw5jsGaKkK29dvJ7iPNNBteupwq3Rm/cqw+Mwvor4jDZnDMOHu4u6Y8UIOEovrZUFhCip0jLe+rzIcGcsbHYJiy5tZTp9q2xHGJoKKm1h/QEldQn7aHwKSw+JjGwSX3Jg4TvpAcVFe5nG2iffjQDa8PfWdPT9Y9Oo+dOL+K1E8FryIQuBf2MIGW+AUjHJ//LSY7DRx2lsk6qW6ib4LNZ6JrTl+1L+XWb+PeqfOrskpI1jHfWPkm9ih8KQwLJn8x+cXFyS+h2ug0bZ2duN7nRwJyYNX0TIgMp4alvMJqW02WwZPu1FyOmacdcgoAnej/lGhRV3F2ERgiEDbXeoMVmlIC/sF5on3IjSpDpWgw4N+u9GwZVb4UitXlG1stjSAvAgseaF3wHPmX4HLnn6nnQne+lD6PwbNMLVtEBqYjWsEZ6P4KPyqn7qFs3tW/zqKI0IiDhDwneheMnSI29s1flgZYRqN2MG2FAGCSBroH620w6u8+y7lhjW9McGID0o4ag9lduZbCDqnuN484aaJWZKk8coBvGz0BHCJxQ9DWaO+dyW/+KAu32ha/A/Pl6dFXC/QmOjEaY9Cor3fzWwDDRpQAsbYXqKEs/PA/b9yCojLUq+pk8aWBm0djI0v48XLT+ZivPVGzAtISJQFkfkgvDooyK3+d+8C96dfioSrJH9XSI015xQVG9O8e+7/C14pOl/e0fZ4NX2j+6IBKdXKfRK+aiMP4aq8YXfy77cDFoFuN/c+cY4rMZowCupG7/vPO8mm/VxZVh5EEaXMXyP0l/Vs6ox5peLgnKK38IspJUZGyvgo49vCAsS+iXvB0RrWh5hOuzfXQisFudqcG25vUm9jz9TgoE3bLt0u3V1T20/CuN8IkzuRGTtqPmPtR0bJnMO2aY1e2Ld+sA/56gQvZ5h9kQ4a77Oakr+Pwtdli464RGryAa+BOzi+HGH5f0zYLT4YtBByDIvyGO3Cm9wS7MUVsmCH4Zvsikh/GC1Xw/RuA5aG/UVRmwL4bbPVIszBjwF0nJLUFn7ID4ta5ZOo0SuRUqlOi5zJGXkXOM3T6oxmhmTjoV/JXDtPE6K3S0SQt1y0EEk3AA7Arn27C4N6GPBaHEVwNqKHHEHYmiFNr7Kdc+k3Su0euXFkT872gtosQVSBmVPW38JO11kgsBfy1Jpn3uiO9olRoD2EqrD2mql1M9eTeMHdo7rlFBkNTw2GjjdsObKaum5gCqRusH/bjhnXnAwZrui3JtGnScHTx2bt8DkYo0p5dgKvzFVXd2lRy6XsMYBs3yZKmiTUMKyDllgSfE/uglTPFU4Y30uY22TFKnF6Dm8sj9yIBPqAkro2ht4wrAwBOWJJ0AMZ2jpPKBpzwrVH8h+4EMLSv5CnIWzmhek97wE3JLf7G07OXoWLOHnlzvvHZ2qF+Uc4YPR+S9QTNTeQ92qw7W9/diAbZvLWt0nY1ogh/Ox80xlSkNg6nBkhi0Rys0PIacDzbT4lwpaSMHhVGfOA3LvnrxncNX5ozMgVliZbE8YAk7dSAIwAxxw7UYqPGpaqUxxyk3mW/araQAQjg2hQT5yFSlYD9izz8/SGdSVetoFGP4XcZ8g30rSRiBKmqiVGVgxjRPsK97QI9BslVh0YnYPqjMDnBmJ0/YHE7Q0RetXndL1eaDKNY95oaXVPgZ98/VYxF1AoDfzbAEpKmeeJ5/sylI1qjWUXoPCmdEiCFrhy9W1QIL8OB8qV2m23D3JF8ctL6OVveRWaMce88rv2IK30sOtt31+SZX8F2XDqhe9Qtpso7qiP4co2qHTIFu53c+dHIj1tKVL8uDLywlXnVb/agwA2EtFVZ+MkAtgVb+zhfpsw+73QoxpxA4ozlfFQbZizQJk2IUYlz8fbeRwIAXaMcKoAJx9AblLvRf92v1Zfhp+/6g0nkxcRnvn/TKKe2jKlZNBQaX+Xi6P0+k8Fg51yCegiJv+fB4J2Ll4Z2wNexCJaJvbAIGgcCGTyf1jdbEVrAJTp17sEZVpqciLiTS+6aQ3JhoGlOw2x4ckOkX/8iJ6MOk56kIJnJxG6iz1wsW65d0i8Sx4YzeCnUzdxhzuUwYXg2a+h/YzeO09ICe6qQbG77Wu5svbuWDp9iG2Ii9toyKdPe9ZeS9sDw8308287sbs/LkZ20lkwaqx/xSH+DJ0470XQbAm1ASXpPJ8O8WT+oIAGn85hGgbBIAvX0D7PvfaoOlWr9EHW+KyKueDr0N2DU7ogsC80uDrIwlvipPWRA7kQBASeP/zXO+ggCPBatviX3dQA02Jj9P2jk3VzTGgpniRHuZSWWJK6KayepshM2LPHUASqTeleOfHRqZzlQQx9t30492ma3wkRRYbOtPY4hWT3SMPitC6tcFXrpeaGHj01oy9L+6VrTMFbpu/CKa3LXvquUGRg9a7ulA3rIAao4XldG9ZWTpC669Zh8rNx9529QLZiZ2X7XkCkQKB+fS/8QxPicRsW9uZasp4BB9LH3mGFOl1zOomeo9OFU+TZMlVqP8+hsAQ7x6pdAmLhwaJ0AB+aKtBxoGAdg2mrcPOR0n33mV+WN1RmTOSvy8qkVr8wl75fv7IWfW829bLzmr4GfP8x+fZNarl2eFOOdfkjdXoO/8nafSkBJzG/fW7+8Wpjq3H5LoLb24kkmGS/3d8Q4HDmghNt+2snOvXne1AMp7iXUjXT/yj9+O9BYeByPXOExy/7ImnXiSRiccAD2tCvlaMJ1Kb1zSBrekywnoge/+cLQhCeHNYpLnvbQFX4655cWfKhjGDHOmsscMBAUrSW62wb6L8HWdby+3Ydk/YJ92bbKD1XGSgFPxVJWPXEFBWXgDr6Nk1RCf32lMnhdd8AT8RSlC2Kope7aGy1Nox2Vf9dcmMTr5ZuCp/utSi83rLcikLX+RsFWCbaa7Hb0PH2hvrxVzayniZVKGchFrPwuC2rcQbwtSnTP+VSIXjPg7uGwE28CdwISF35KYqrdIpOQWBR2J1wNRPol/BP3YhfV3Yy5tlLWPCKpyByzWAn4MLfKSiE4KcjgBJAqhr0okLlaEn7g8ent447zbgqolxlAUe3dzdgsJtxSP6gzCzkAA6lBYfIrh6oALbqBDz8D3R5St07EGtRqq0L3lOFhksoQFNyBWkLMvPbhPdljSn1Xv63tLoquGbtf9h/lnTQ0SFkYSkBCFmDQ+JXaD4nFcaImS/i2PLC/iqEVYTwZDo4W/6wGyWWgAD/Y2fpvJv2NXJjVIxUs8hBsYFM9WW4qJXuQKB7ouSDrDWAyeD9UlGwTLnreHEd+OkZ5om6qgVMqBtWQxx7X2j9dqhbrfJ5aKKqE/hSE7iQbR3OZLAnywG3xUy4gVnB9QI4n/erdefx8IMjBeAm4uYhQ33Nt0eUxvseBI84NjTEwTZXUv5XxEzM9aOUQOrV17tsOXVVvLIMYBh1tgBAer8kPeD7xKduO9hb0cfAFx+s/uz81lpnoCReqf1NzuNwE0SprmDG/GGz9W04huHC0Pf12AYTPTjAtAcBPw2Ss5/Pop9ev40iPTwwkW3OCZ3Pc3Pp4dTCXPJWG+kNsj+gnNHq13V7uwKCkg9GxVMh5uZF6VJemF1soEz2NtFnV6c0faOQe5Phh/v3e31apXLRQGIVFKVQodA8eOGY3r91POCe8ZIbSzQUkv3pugYUr5aRGzX/ewXODJ/WW2jvNnM8AY9daOWBoHEEoaZerO5DVNUO3K8h0EpaR26wjJifugv0CMJR3EJH7dxsdaFOm94vAmq1jXs0Re5mrs/Z8QeuySHsougtBKI7cjd95OFyySuVRAWGeHch0Mt5gUnBfxM7tBawvoWSWp62y0+zTlVijYRHLbNQT4jUwFR18Fd5fUL9VQbnI6VF2BBIofuiLrjNcrwlc09k4d9KJ6xKM2H447c6M+a2Zp0a+LugoMxkzJNPuHDdFmUePPRJEkphrZCt0LUO+h4xV/PYU02ivRLky0dJzrcnojse8V7u6BIW7Ukt96t0ETD//bnSQniLQYWEF8c+pKQh/DB4JCD+/4JAKGWwBn9c0h10qsQ+KOCEhQ3icR7NzzU+wIDo29nhSZaHGEcdtyJcczRYFZg2DMTeqFuMgKXIwLu68flVE2pPiapOu3VUN6+tsmtctfpq2hCX/ocrGbxpFs2xmehovt2qpZQE8ZTmAfhtLtgYA0hg+1KTEsMOLnuroQTwyPEFSHdPn0JkMTuYln2/OkAAOVf0VY7ZT1mFATpCIlZS+hbG0H0N41M68M7aPaC9sI5U7mZFSuPuqsXOY+5cV4SF6h6g/OLEHPGCoHlD60VqfYPf8ZIEzD0UrVEcRSFbnDptFFxZwZ22vzYSfFDA0wllHA3ppM0OmfG0M/bbNqZgMBXfQnLp8/CLYWvTqFdQoqzQbPYlyrPlbZlEuWvefs46ADNAKZ7aeBqm0JdXCY+DPGkfDaL4/aHlSR0xqz3Xg8j+UGW/HPtk5ZpnX0DKhAOQkeNnYcENYmpEBTA6oq9V567b9ZVOAixYwj/Cs8XZTR0wDRX1LzXhgfIOI0X6ylzNxGu6pwYp3Sg1wuz5aNW6uhXCz8S8/yubEunmOiEHkUcuxleV4jDGXUinsV5IsiVGvkPgBopUK68IQPakk/S0HxtVHvbFzxh3qPKaxlofcWjZEf1SzwNJCadtCXzGyymbs2Mq2yef2cNZxFVpnGYRe313k5U/YlfF4PnFWBaAxWAJ8l0vBJ339b8r/eclLdIynGm9JghtKg9Qyvp+iIu7Q5DYLxTwyYdHYz3D4/igWUFXRtkDAZzne2XvvIzTZVDZZlIX0pbmcsfDQ1CPHTdnTyu0S3USTHXQPfKuZ/FEEKz1tGqASCtMnquqQvHTpITUj6Djolg4GlKxQnmn2PDihdoEqWqFZwIiy4UuVuUO9pfrhbdARIol2eyexB3PT7CLybcMiNdQZklPwKWGUMQ8JBeRC9CJ7/pG0jz5UdTMioJyIYi+kg5HidO+X4lYOtfmDDKIPxbe0uQ66qb20qLMv8hPPacvFnO40U9+QakCvPiS6QiUy/K1HMbvFiCrOGmz43WNZkLI8gh1mXu4GAi5OpY+a4QHHx0EGie1K3o4//5JSvfNagNrIOYjnHZKw8Ri7c6v8VQufGAC+4kt3IwmUVV6s4s9TAmzwEI4zAafGRimB+gpyVSNJLC1CEKOn+U1l9fXo3xKtQPs2DAjpwqAACndAk/KVMhnXsMRXC9vNpOvwJMVjNTxFpjo9x4Rz+G06OuY/6Qd1YsjTEFX6Y8SkOogGdYwbUEE9Q/xm8WbC4CM16G1rmHitrENnUXKHiTbcKg2aqFDPHv878ER+rAtLSELzHiBsNl+NGbNQHLfLG97fB7jwUBqTS/XCc5samyCB0zDyttBx8j57jQ3ZKF24VEeekbp4F7fHRq98GELJkHOYDv7Oofbgwv2LeFMSqjCgQd5YuTkSY4JwuvLKzKVpNlF2mSK3vB7UnA9y/+yUfXhvQz+WAzd0b/NIkq1fMnfKu+3k36v2mkkijBpYth9+EGi3ky7XOxPTIC4lE6MkhNHhldodgFsSjCyEqrvEbctVcsNklTJMRoA0x0oM1LuYYwWYgBkCCM3yY6DBatpPOAhCuNqlhdayX+fxnE2XlgYgtaF7bc/pkJQgVKDGxevBhfKqopWt/Leo8KTKmeH7m5HoGLfGZksv/9LLeFbxy7ABViVkICPBCWKTMVcMHqbza53rJgDpwRPmWLMBsIT7KoptfCENs/qpVTO3zL5iAS1HxnHu9bQ7JnVxYa2Fa4UTuhSIudkhggZlm+G2YlzFrab3LyvqGID+WZ4cI2JNi5Jn+5OyQ2MMve06LkEo//8v6FF0V5yeYeiQcgRL/BRBY66ihVOAV5mGcGCL1uSwS6dAZ8T3g78wYrWJzRNXVEoq8Zlw8VkQrws71I/W7QfV5qF1xC3MJnzohFvhyFvb0bxme6kxHVBZcy/9KQa7mLcZWl1xLtv1mUgKPrv/erHbX7WCtdKD/jZ+f/mookY5v3CK5r2A8IvLTKz8SMkcXRX5SjPfPLidcpEF3xP4OEC4NWm2lveel55KDql/uIcWtJKBjfXH75YUdm7Zlm/H2nm/XfFbQid7yn7MTHs2z37Pf6OPHGPeVI+y028J8jSsKO0zy7EIP2HgDKxrVX1ue2rQRPVCUy0bFXQYzM5GH5khruvakNmwg+fb15gsOi3/OQ61fpdIVbDujirDmxrM8zutfepKmjaMI13IOsxmn8tVHBr1V0Up4999HEk3uNmRAEQdWY6lwNe/D+KJRRM5Qq3lGLbn9KBYHEFXCLZocpeVoIP97dnVsAH4PrSk58vCT8/GFFFk9LJHFshetwSi16OhbU9AMx7j41Keo2gYUluUsDQFULgfxKU8U8x2LtO8bDUOTLHCbnc2+ujPB0FRx89UxL+fcea4UzjMYNVmQCY7VUUXZBmQCmbHxtS5DElGjPPQO+RrLKly0eO7xgQTmV5+yjdpQaAXckEcQvpce7zdZ37QQi4ZdIbEUONeL7SZixvIoA4YOciycz1ztZa7SFTzpxcMoIpRIoFUMVQuyZUd/yNQ2NSilMhqvP3QJ9KAjeyXhAqEM23jle3LYJVVjEwtK30iJEspUQZM86ljzGoZQvzcjOu+qfTmw1LGRCCCgEqpp6C9iVXFRgLtnrYw5BBHUbxcutVKOwEd5u5mXlXCqt/yh3VHr3LGvYzNV2pewmhCU90s29Bh44qFRMDfxMHla9rdmbSLrOV6UNwOEECY2JGcOLq3Ibfy6DLrLNXav4s5dLA7Fc5C9I5mTLSb2wg36Ochqlpnzox91vxlc+k+F4boayER5ENEdVo/6OkWkp/1hHATRiBLGChSQARY5LsSWkrCP0FDLq4+xuEYIEN8M6urJZAjQCkM7h8zoGQ7AqNtOrx3qAyl3a4TuIPUs7umeBwfT3tSeYX06g9CFlMKFG/aTxVv2Jy9W36AZTExAm8JFttinxC1VlrShD157POYufayllN2rwzi5p9qai8M4RIaMBkSXe2FOwnks7nJb9EUlSfSS0IirpW5QZwD7ZDc6p4fa9VhRByl9zgc9sjgi++dWFQ4c8Ar4aTjFsd1RmfIJAt7v0Bv82D+qE+o1v6AAdPkyIUbM1Ex+WLRICb/DLZWW/GHB8aBwVFBTDWO/CYmUzstwMou4B1tkFlR36cKYsqafr8mxCUC50u5yb2vpLHYw3s1hf5KAYlJvV5vEj8ng3eVKmtpFQYYU1trDFesZXDDYrsqEHoXnC7+lzDmmSrwv2nayohTA0DBD5u09dKRy5ghx+emmU3IU/OLT4zrIf98HFQoKKAALJDJNSp4M2702MjQ3iBreLg9FIDHmJ0VLC76jtRdf0Mr5GS1XD1sgc5/yXOn+ELo89Q9DNESJJitFIvp7rx75Rchmy7tYMVO/JHvwLzu6WtfzAJoMpLrqudWzytrfxrK6T2tf02YC1oUbVkDeyFdtQnQkeiunJqUi4YXRgpsrWt/OMJ7KoWLO5tj+x635GC/EaCHhTOQdEgtEOLopkX8ooKSb8ivAN2SpCu0Z1zXU70qWF0TlpY5+HkFXSNZVF/GyGX0Jq241zdlodawQHV9T6YIysxN+jSX/cj9bQ9bxONAOvc2obcngunhUYyvyMIIIDasNDh6ntm3KqUzb9pE6l9mGHS8/lYcD40u6Yo4WOh7OC7mtpWIV2WNQcqoZUVqgcH03SBzhx2tA0EHPB3dcEGi3WhRd1Zn/UMJ2f2YQ5jiOqxnMOvDN9v9Yjc+r33UG+DH7nUO/ISgf5VqnVMczOTiAKj93c3CZOeAGLdGpawtx8RJOyljKyu668EZnja5xruubIgtX6mQMiHiEhE32zXf8Ubp1Dv2h9P0bct2GAbSbei8D7VVyfO1FYkhT7cT0vEU568SfOl5s6oeiKRMvowk/ja1UL2A0LoJ2GR80ZvqerYHKJmOqu7scgpMzBNvMSp+Iw3F3uYIQm8d/B+nCdqakWtJzWx12YZB2JUAxCRK6B2tuawt3Di+/TT+Iegy0nBBdpHwFabnpejvTd3jdB/POVc/Oh2t2KLn8HnXGKBVU9giggrfCHtv5tqpzT78QShP/FNJfbjxsYhKXyk03aBkeHcBse9x5IPvxu+XdxCQNkO2Kt2HdN6loRaRXjY8kA6T9bnNXAO5onuVUMBLt9QHyrP622YXdL966k9ZCD6CArfIuqI5ZlOW8VIfAVspK1+97A2k+UrV9xkIABLJJBvj2UPrlWvtnMkAYmodgOd0VcDwHwmqPHujjCeu6h2JYCfKFri/nArYf6acRydBR9FwA6qKnz37KtCGvjT3G2ewL0WQkmIAiOMd/frfGH4iBUntp4iGgJPtXh1odN/PDj96stPduk3EBe7Ff9xV45yDNVJvbWtCxLe5pUaBplDnaIvmkmcPXHmPPtd6fpzfGh3JSHwYFMq48AwpffN6mphNzstz9W7MM+5vct/V1M7CAkkBTlKIoCWDxWRkhkPiaTMsqOOf2sS4q0YsU8gkKlnNxVOIYg3dErmJPdpVlBgnjTfvJQciPfvOsTy8O/w17Ee1MkFo184a8JEKisqoWiWUB6wEpskY1eJinP7IGSE82xG8m32hbXunHeZwzIzJU5KToN2y79LYQgVN2Zb4YfZS5xijMvGYKLjztqU53skZvI1yD3Nw9l03rtpEI/qqYDu55QW7qyISEUvLg6E+zJ/fU4Qm4yMoxf/8ah+AOnz91YTSL+kZIw02YVBuGm2NvwXRu28/2ZzphBBd/IlPOyuAOHYxsKG+cOk16y247A9iwvWIPUzuCIneUkl32VLR8VnlYY8txH586/y6itov2hlSPsfBXHW6h1Nxzji9S8rlqszuWLU2sURdXl603FZIqWfIXNmA63cscl5t/JdkGmxOHiFwzDSbmuoSyZoMdLok8cIDS5B3cwps87SJS+45F/TbuWbpDJhUptmAZP2ggxi4lDw7S7NU5nOSNd3d3SQnjK8HZSma/ShonZQdc8IoEQmk/TUYIJeZe0N2NI3dWAptLzKTLxLWG1rkSfoKdEs3cu3kqfljQ6UuIgdriXhLXUwfuBVS+8axafHiBAqJD4QISqaE/xfGjvvj3ikesB42I1NThmP89JfNPooZLQ/O29S7G8kh2cVEoahpWAqbkw60XxOxIFg91xoQzjA/TOV/3q5rImNm+F1VTNUMSMIrcE9o4qNfVUliOWtHWq3LKjbbKvZcEa/EriPOlgWIiXhRqWHNEKyNTR4ogYcwgSTXGp36LAonTXndA9okO1pKcv1eyxWTMv/1rKd2kvMsTSNExZrMkLTh2tbMqAVU/VZz4sM/WHdOOR2bGsqAMV5IkFfNBQuaKsL6DzztX+MRe6RcDAioQLQM5vyDyMGSo5XPdpkWbZSz3wLk6bJfQjCUNYStth0yLs5xlLo0Ldu31y/reEvgoT9V4cqTN7ZIzy+4z0I+JMSEcYEq+XsjhF8rWwdjgrgCCP3WDDzHIRMeL6YIw/zWc5yCIgH+r90fRtXUZ4eQtATdTSVzlYvmQqMcIvIvc+yDxf/zcdNTnrONnrigQyyt56QTW0gu+1fnIrxW/sLjrGeQ1oxqE3QGYmzNexpm4w6xZc36DIH6IdNhu3FzmYYiAH2w2UY/CzgxPN7bZkxIO4SwcwCDNuF89H7K6mu8Ygmn6eX12U+j2ifs4/lezTcxXAtCFQdZ2NB462qNlTTk/1itv9Imy9u7qpmXKpfgCSzfoMH9Vhb6mxmHsbhy65UFrScHuOGUJXrQzJrWz3Pce81KFhcocRTBJU1LcP63QDvj5mvJI5ZndCpkvCJWGZyO4XSEtvJchkVo3J5I6aSsa8he6pIGVhMQQzwmXRqUD4gF1xRQCyFP1kIECNjCET4kJGhy5hpooYvgUAeI/lz4d3BVvHI7dUsz4ctK1v2y2fUlHXX5i0my7ktUiDSq5UIWibLsfU1Ut95TspmHGzycXHIVyjKyOiN4wzgdXwtN46gWt94zsHqi/+4NaoBHGaRnwZF4wqwuqZvIc3E2giWt7fWdIRI08Odmyu3UFECjD1wd6h6lScXkyaqS3lAv6+r1HULL+hNhJxPC3coUx82Aizwq6BMv4E6QBz/Y+V/FgzH0lm3e1sYTbS/8nZSRqCWNE8ud0v97Zxe/KsfXlfipBtWrkbskOKDNrNMsQr5NCzN1y5dwqvc0UiefYQ53mVVir8QDRebwmjgbevFTtYiqx27x87FLYhD2ehTUAaSlloboTosRIkO/nTTds5Sh6efj0ueO6EfWw8gj+VMMspug2Z7WztGs3dIzRgROOTd2+ccsnRDrDlm+HxZQiCJhPzShVO3VA581XgabOlQ2u/VCYiQL/uEEXoZVnbm/EgbmgRRyXgDJIegPUnctXSHN9rgH0Iuq5B7IMOqEblw7Yj4L1E0Y8dH4kpzAACNOelMMwJDLZApbfyMFpjHSQQ6nT1eQg5FyOg7YAx5SNx8ZhDSPqq+YMgK1e5brWUq6Wa3VDO927maFI1FUZFZes5mCKH7cWNDwUJpP4VJ8REUPPrDXj84EKXg3wiDexNSCCqMPVRkeaRsokh4kbGts13WXFt7natZdnlVcQQWdaOmp0ZFgfMel3Bt8p8IZwpRPa4kTlch8+l+3mK59v1frwGpyvRiDMCoBhMYbibRMhrvK9Wrr7CBlT2RWZmprA/TYSj1y9f61fjhrhUIuMguvKAqMqmoGBbNx2uUSxJuz1WuEk/o9cbgwlkQfI/GUzj3K++b4CtDc5ZXap0ubraqtqLOM+Xu6YkmcUdTuzrn4//WdNCE0+y+/0SEJmUzm88+ldO0MSiQdbqNFW9lB3/qMslFELo3i/kPvI1E/aI/5Kvg2bjdnPBw8yVPAXRjmGPC9HAfOaqUJtLru4g4IGEGnfgwUEMLUurksTSziZbL2S61+SLj7ERLDRT8YULJeINUVG0gty0QIHRFTFMCyhnbTCcfvvqZv7QLq810Q1iMf2AO7BcU6Ezsf7QxxDGf2q4978IPdpOhAfnbif1+4Y2iBS5ufnD2z1+CHKry4oJcYZ8TIrOUrxx2L2/eM453n/fAJpNbA2i79SdCLGfkkTzQSKIMouS28TmKV6h7Jlhq/4R+LzdVWcO1yvflpPx6Per816wry9bD+aIlEV/jeOYTHMmWNEggb7m84Ic9WCd0qH6InAEZ57T0uXHdgmDcYd+X6/8vJe1JzG7i4j9/ksMU377Zz6BLtOsXw4qGfD4k7EABGNfjUtGa5nZaJ1tX9R9kNm9w3WVERe3NGgNWqJeqaHU43qLoN/TQOYFRzcNPVlVyGoxutXe/hGzQpczWc6U7siPR77vu6QahtCByiSGv/8a/XP68f1WcgzLDwBkNtPq0BZvlF1lNdBqYEwBO3WOb6pTyj0oRc48ZBzrihchPtUWP54Y9szYC8rto/Nk7ARDrxuumCxcX0ylVRwZ61rz0P5D2prIFW4HFTIRfNmtQNy1vBWswHPKzhiZZeVdc+AYsYBOg3exzMsaOwDehw8blQKxLOyQcsDBXbWwFnXocko5N1BQX+cy/DZqewC/6niHxLIOsuHwymd5R57kbzled5PUzqgmgTULKK+/dmkRG296XNbh6/X7wSd9+RQ2En9ZY18zHcDqFTpP5ENegFT2wzxbRp6WPpxOzxFMsSVR0ZEFDKWt9DDPC6hZrenVZR3CNieqniQYWgv0pIJoQgrnBdGCYbuv790H6kgLF9kaz8Fb25g5lOyWRAkrTf8Jn+Xp07CDJQ+9akJFL+JhzuWVCjSzJEvVwWsgeg5PRAIh1trTCj1UItC/KjF29mrFaHwkepbaDF2Ml7sNYE4BOuBjT9itTqEyovZ4yH6+jD5L0MwkARW0s7RGygX/2MA6riVV0f481NSEEvUiGAKgxHqNjPeJr+a0tFC6588i0dcHq1j81zzp5FaDNs54/CM5533lH3sENRv8PAEWcao3luqBhj5zWbmrDBjwd3Ji/kj18XjrFGrbTGycJrAScjE0v3wNeaWvu1AiOi5S1EeMTpr3EB+lbr69VQ51C00lDFaFg0aMF2xbpAiPa9Kjj9lOCrnt7zSz1o8hB7Nh/Ffxj/XIoMvUvdiWjcxl3CQwOQ1qb5rSz2sSDXRKvSxNxEDZx3UL34oUC8TTTh2xktmHyw1aWzmGEZEZGELDaTfQQRIopFHffAipCrgQlogTjW4jDHP7pE97V/7SwUJllFZok9pZgdzWtnTIB5CftmS8Rh8XgZjewmldRcmtocIJtPzOsnfLdgbkDRxQJ2r8pqAcBPVGxovbaaEkSIGKEccTECYFelWXelUOWb+FVp8wUY1bLIrXqFI9AK+pqZIdfrdoA6yPJgLsh7+C1AqipbKBW+ViKA0zqRDP2nwIpFB3wyrpngxbRynAk2PqR7VsiWjHQDlEODYEnBYG77HbF1uIs/LJV8WD/tRtfMRaiDpxDfKnUwSF00Ogd0iN2FZlnImyL54qDVey2Sw0S0+wJIFJOn0OfrEgIf5f2Dv/0yENfAPa6/Sp+8sGt+foFwXW5ZieCcGxvFmBLXHlP2lmYYoi0kmAa/xfnW3tAxy8HfN5hNa/Rbhs90fappUV6KQzYpIwOm2dgRIKzGipCoIjprPfHvsukdm1exWvUbl18iVCivQJqbNZ7syQGL9BY8iUV81J9e7O+feuN4e0FPG9b14Ng/txLOGXM8fCR0wlD7ZcW5U3ARRaInOQz+1jhwP5OAKKjLUilDtQW4hX6whVg21m2scX9EDdSLZBuFM20c5hFohoIHnTyWm3fjDDfm576XkgCZl7GqBap7uYt9deTBkphqh2x8WCRIscQQq40Nu0pvpeRYRtO79LQge1ZmJ7F5mIPuzPkhv8G/eU3JbJ/UVedVrAllUQOBVuBpLCIaMoxrqhpIkigPbMYV0VrjBLC06K3/fVBx2IEA8tPpc/4gm4yKMULDindRKCm4mCO2QkMOoM7Ej4NPzxTuTzQaoJ5LU1JnbdaI4OD94V00BnoKxGzhkDkqX2V3F00v8uNL9acxufYkSre/kKn2lFUZQNWfNzJefJ2IkZCgbnMSYqZBSOrEsmbYyHCkIpeXc5B34lWESEjGy6hccnlhPURdT06fRwBC1b1IbMTFnAT0hFNsztPxDQwozLu0/X3qBqo2ICBg4nOoufKpX6InvwTu5MhBh8O3TWIS0etFawOd0lgZoqOC2LqZ6gZNCD1acygnseEwr7hrLDvjHIOT30ELl5qlA7EhXFgUq3xqJmJXTIkraShUiZerCMbn6bAfvjOngPlmZp7OSsu9GfRQD30OK7Foip1NsJR52Hs+J+pTLtvDHc6LuA8CQ7BAzQRFQODeg/YbBocKnfp62eSyy7FWsqzeZgstZKBxZRTiKN3abxum5sC+jKD9RRefqxUB5z95dH47/9nPfkjfC3DDAIdYhqWC+frBYsesGWAcEkeBuh1BX5/sN2MkeChdAJQT6zeMBpzGPfo6CbOm19Nv81im9HvOnezPTDOXztxbSCoF6I9MSPzwByGdwyCrzvbkQJiiSBSjle4MuFeCoEV1E9h7cSGSgt2UL+jDsjpuuRheyUMNR0tH6CK9ExpC2lF4iGvbi5mCUm44z2x7Z9I0YLqtFNGN8LLReJYRANQzJBkUQKdW1PqJ3GKqTe8R7poS4TaTjFzkketVU+SAtkECryfXGdvc2qFnqQjS42DacFb1qSZ7FIfWVmDSdhKQ8IEEtFcZv/Nlx9ozq7+cX83JTTdUb9x9IuUQr9ChZvW6utbAjVoKGKowXoulhF18XTXL4YewlhO8pDKddXaTphEGQ4O4kuozWyHO/m4c5gX1h8kF/hS1EHHVLwQchShN2pPz/XkblW+ofcMPblZ0RKxD+qFdC1DTcioBd4ZojZjPyakJekOvj6HwahxI4KnSIATUZKxuWmV0XWJnujbK77Tf/jBiJUL3d+IFccbDObTN3yEFFEcjh+jfzDZDapWxr+LUsoK+XCA3mRad3+HAnB2NqsyW6p4Bhaa0/IumLwzIXuruDlugos6BiyE8GnJp96Aw6cNG1/12F9ct+6akULi5DJUeji0cIJ3Ru/HlL4nJO7wXZJr5fu2PSTQNyYGSoateqvFUQ+GZBOqH7p0yFpo+1g4X+cxLLeuSgNCr06CC0ne38hlaSPE9J5qGIngDAm3SYwdiN0C4yRDxB+3eiMmwqxm4m3LcGjHzYdE2qi5pAuNF0XR0DAxqIesJX+BYnLU3gLCU+uV6WO2kAyBjF3bcWCykV+j0v8uDR5NQvWJ4zvQ4Yf3QbLB+ANnbamh/Ao9eGeakYBxQa6cmIwz8GvurQchibe7btLFjnlCY+hrjqygk4b4wLcEK9xyDh/y0nBlhri7QPfMjxoYiII2LdoxaMtGvMCitua6O0C115mhRJXgfg09j+D1xo6oZ6t7VQ8aXm5IK15hvFRmtNi02hsSFtDxufD9t0Lg8VYHcn3FElQon8WpHOSU53ZwqBWzL7gBCjiGzoErAtvc9S4622y33mhWUVGtJcfwlFNxsr2dZXpkyJ7sqB3bAK3igxmo/uLRQUA5pAehDVZJU9V36Tsc6EpAo/+H6trNn5wy+bx6WwbPxOhXjizqovcv6RJXBjx1sm4b3sfUO2Xf5cq8Gncy8DOF0UW04QWuFSWC2lMhM/nXH274UUPX51r82LYozzu5WIDvYvyjZZa9La2xEFWZsn5/ouP3naEfC3z9dQ3iSNjBzjSo2beJVXWWDzBUtzOcwY8qicvNZmzKykaPMEqls0HDBGzWjsZs6v2JybwyQIo0i8gCAPlabntrkeUgUttMiy2mPxN1OeACiSkeO4u+cThGuwnFfEXOhu2I4alHlu2fXlBjY+G+HsYVjPagfuT2i7UQ4zs6SJugvWnFTh3McdnuIcnKQr0ifi2BI/Rh3MiCdQOUedGNlFqVyx+kHo7s0n4gEef2BwtoL6GXzMEivPDdNaHhSbjLdnIbfchoq8hnVlDSb2AQU/m8i4c64w6k/4g8fKAJed9M4ju5L8aovtJoZEzhxsMQ2+1YxRAqSxIwbIj9+1Z6SacBeH6Vm05rq+zyxv/Yau6n6iNSXku9+gyGguc0f6morqSiBLnLoVxcgldJCZ2ctImO64qIQkQPMIz7h98WOF3MPSURM7YyRxQRlfbP0uwjKZMrS9BEr+7qmbwKzBHifhcvZBeO8Ol+fwt5nTmX9RAfJduORwVxwGnO4MlUJNCBko8wj/O33Wh1oB2+aTr3aUd3q3g81V1dly0Knk0tHJwG/f2nSYmL0EtIWR/nz75LHIp6nhMdaHY8BfmtfJSVVVn6l2Q15inOqpGg7kL36JCfL9fJoPWW4DTYfQ/iwO7iLjqNApafps70qC6QzHdf4RIw5hN99RK+EcIZ7y3ZMu7c5vPJAlhHd3hDIgvq1IFKS2isqubm6nGCTulwBUYSLckbKe82oJPMi+/zUAVYDwWK5gwQzwVQe8Yic60SAJwmc5wkSmnOKyrZxbG+a7RPiYmtWaAJgvcmy17UVkfQ7I2cs+TR0HciNjxliET2u+Doddp8TE3+HHN+SRfEZ9euQrVL76pi8iW3taXMCCZjPYf8vPE5nhQooChbddASY7vZAal6hYHuG8htnNChXvVFKLOKT+ZFLSdAIiLj3c7AUYgp4+oTPuTQuESSTPsjKEM7KgCfFgydupswY3oJXTuHAW+5EHCfer6O6vyzSFrYnU8BTWsJ/E28laarI9YO244P0LKhHih4tyHRHTDbN87yNGF/gzsKM7jBNj9TAs9w7kxLACZwA0RebCITjQZlWnoVzv1s3za4QVMA7HuvLtejqIj3dmr1vxnBjDGri/cynvczKtZVsOE4tRrr9ecY0cjTLMqn7sX97XH9d7o+7wGIaZqsXAZCAH5jKsR7FQkesFOcoLZ0E/ve2/4Ot/HXTM70/d5YbZfH6yWNMOy56WJl2nYUjlie9T+xxp9Qv/FKZSNv2SF4DgSJRvMvn2oRNYY0yGqH8JNactsgGOQs96/kj5GjHUyXu/uhFGwUv7OI08Y3aQVM87Oqqedlt+ICev9a1IM8XVBQuPWYIvgOVr2uDWWyN23WjxvJUkMqnhUH7MYHpKuPmZvEO2YnnQYLNAgkT1bxuSn97BD2psKR3nNPSQ+I6wfx8Kyqjan7If/mFoTNj1Iik864ele+xDHleri8mRsXh7LFilCz/XQS4uVKRyhcGpWujSE7fBL2C3d4/zo+vv8GEtJapR+wNMvnOeNX33lnGHpfiZW/hV/3qh4OwrA6Rxn57kjlqsP9pYwtnxNYPHRETmyWL4oBfVxR/42uH+Q31kfknWIxRHfd7X9X4pnEAWBAbeUCn4KZJeqSkEFh9gCtInuaAp4bUhpTpVIgb28cuG+9cqtcRwo6sIkl9iW5ylyobQoUK6K6ygJ6DU36cLgU00Q1/lJgIsllXfFkTYjJ/P76qAdULNNBAyQ9qhB9zL8MC1G3/2VGNMli9NhDYIeH6ABd6GTi+zHK/m1FA22vX1c7w24FDtFFQHsW05Kt/vaEnJ8JY6soF04YlxCdTEB65Mwibaj1+LQ7bnc25cR5rlzMpCX5BzOaiwgvNO7Th3E9+edR0CGKNOR+0+2tGIk5ISWwHOXCAq5+aeS9WRNFcjq0yvji5/FH4ZMhiw2wwMQhMli4ddZ3Vx/Tw1I3432aQgUODKtayKDhHiOKP4obhxnSoP8ZWWkobxUUu0BfwHF7QoPxG6I4QLaTXKmOh8hx4XNeHkx15QL+HDcAdiVieJdVamunLSadDRkecjLs76Rqk/TLH29zHuVH8Nkp+GCM7Ie/w1s/YIM8MFgeZQQz8SoVcqRDU9bKMeOrvDaxuiPGekPIDair4VuFqL/4bVrrlW2goLzOWLQeb4DORjidpBPAbOGrdVO5V0sWLJLhWVHG1HKlzOf0nTZBWwzzYXJhSACuiw4mfsM7kljVKBIR0ZALPF9Ec9hudkutw3VedkbXH6pHwzcqL1wMw2uWBwglXsPbNh0oeVOMWjl7nZLa9uXH7cAat162ICGRe+zOXF8kgnRKZdvcg/7KO9P+YW/uRFMjp59ERAkCjZ7IVslQSWvQ1HyQ9EIClbgW+YddtGQkm/inTGKC7rBe/kjqqeRMnjZNykqQkRxzT7+E0CyZAYEZcA7TnU5Ro0CRwAj8u3ZAvHd/anFKK1V0/cjzqAkAnFcQ9t+LKH4lzgJ2kzKZy4R4M/jbBLn3+gEwsiLm+bLO8V8vgPeKp+cGxDaddyCH9FQH9lyx4s4kHzKpSfXfn0Nar7uyMWk9/R8h3n3PdY8y9Zh9Bb94BTwoiTVJ6qwwipKjFoqoQQHCfFxhVc8wS/t/NsEF19wGWggdYNzZphFidewVs88vjdehKIvdbcdbAAntEmlJrNzkVH5Eb+CzBtAxWoaaLVRIEJfulxUoSWj6Xha0Ai9cUrioavFRxrXxUB/HFFXe0wFGdph3qgJK0b03jUUaiYCfPDctfft+dPiI1IPOUITt1a0JlnvTjsuoJc0PrEX4VRgn44qGoZ7icGvgjRzgjpntS3Z4ZI6kB5goHeEiEIA8Jz5vRZjhXI7E8PuZU4lIscqzxNNkQHJJi5nuM20+agz3PrH6gtH/xDsOXv4/Ro8u8zlrKdDuyyxFcQIeelf/VfIH+rdh02HamrWBjUJTZo6w6Mt46n9EBhxmHDjCjzrK0Zi9+QMoLUrgo8vs+94e2CxsA8i9q2i7xBDpo6XJsa8jUchaMH0HMRzFgCSHl449Q3SMJOOsZh5qTJLH7hBuyTjJIKiyDPDp2DYK8cQQgF6NJY2ieNk8ASZg8PJShNkzuwx86PIg4DhGtuPGuJwkr7T9vxUfOEovev/6VNcMQyEj8qpe2pmdND33ikjABjE81VBlMtvZn4DtRJthHmSRZeGQF7TxEB4kdRuTv2IuQi2gVdd651WvbpQbhqxJ3SI7ozpD+Q0gg0wPbWsOF9MW8nRHkMflnR2apNk5ibTCOMpX27AqNNbjiUYn69MnwMhk5xdTm1TfNTo7de8lKqdZQIrJvwUCF/N3LaiRWHIfNRd4X168y55vYUbJ9YEVMGJ2I00kgmYeFJIbuDKZsnCifnMsGwlNAVy2TWCCLJMaEBe4AVcOuSzkcwPjOmoKSUguAk+bfwEFaVt/Isc1Tj37luAVqgvw7jUDTitbNNhik0CB1MFBFfWA7USjeZLtFLX99B8HFZk9gqASHveoY37/lXFY9fsOfwCcRRfOY3f+zN2+rayz4q9Ios4eGiY5gKtWd+/IW+swn9l5IL3zJ/NoM3n9AoVEsUwMLWExx1GZARLrLiZutTpRPfV5ef366XxUUl5slzhlw94dFxdwSckx29jobTISxYkA1CbvtTMv+cUHhVGCRwy0bW6f7W4LXewITmhG8HviNew66UkXMPdLAaXU+6r/G1aJbQEDXNOcos5IJC8rMNjbmFWJLV90sN/u233Xz2piRpMhifKSw7sejy5jDfOzXVIEtTbzhGCdTr3NdJSsnKJ2tVGfbUrcgw5mUII3zkqXAtHBL744oBCwbgIgmjQofgY0QtSvAGscNe0S1mOAgpthp8ni8WLUo7OwU7N+KFaE490Dfp0pTZAoTaRENXzZCt1DxJAip+niNVjxzCQbKWThWINsqEtTvhz5rpAhT4JN6Gi28+y+L1EqqTjazKffDVQOJjahC769RvF6dSlyCV+uhqJ74lAb31vbGCi2LyVPTPTK6jxla25S6up9eIQu8QQBtAs5yfvi776UWwJn9qAGFNCm9DZwrQad2IzNITvUd6zQuKxsrx6Y4h0HECu7gpb8d9RimNUKVCHhWy5KgDiawpav6K7yDNJJq49Mh0cBejEZOwtMede6zKNCCECK/JOgCbzzXkPCM5VvIN0AXDtU5Ed1Cfg7H0rxmrMQGRady2vLWztGkn3TXVRrwBjotRPlr4K4GbR3CE/cKEyLy60grm9hG1OSqrBR2vjC/bm+WMDfod1SxGv2+NSHI5omyt1Sj+uYNTRKBB8mG6GmwVwZhPewHJpTCyNsPnRxb/NOEH1OEJhJ2f9W9ZdR5RFBvVQ6dzv574KItkLZD95zDF3PAaYdupC6LtwSzFooIpfBeMJUF4KQtv+eXFjgDMGs/rUwdHmaXp7FmuRVN0InsBXaLxe9hH5HohZeGqsrXPT0L7XigJYJmhvDy+oQPnd6OFbySvqMrqT/yg+ZgF8pnxVlblkQcc7poVOs6ID/WE2TLwI7pFJMSymItUmQWq4N/5pSE8lPGj6V3j7rLN0ZAsIOulaYpFS5S9KcgBN9kOytCXe/N3RCac8Hdn7z/U+SkP+lwHNH0Qa1dLemjTCzkP9PchMAQmFhKa854yiCcPN+JqLeKtEiZWA0C571aZ61RvRNUn1DYV7/K4h4gPVsma+7pPDqjimfVEbbvIZW7VXLT2dHQrAKZKf6avklT0DNO61MuplnDrRGYgnFsGyGbDK6lH6UnxEJwhMjvcefcnsuDTu7YmRedt0B/4brZVsNQNobdVIEP37xAWtghz01NrlyOoxcQSAExP+XJ1USNGW72ipjfh8zFlLR59+hLGjzX/gpYwoMm3CvI7/nfJ4GBzeAXR9xacOw7QtxhHQU68tkeMURD04A2t1PkxCRmW1IeGQ/JCBoI/I1U9Wa+NQ8xf2Wki5wyGEx4hNL8iaKudPgEKPsavJoM+Zsogd0aI7/BmApSzUHI/hQsTaIkBrIlmxpowdzGrQ9FFdNRJ4JHUE/G0+1xn3mUkJ7VNcDdQFm0YBfiF57Jb5Lt0xujfL8wdqjWJvCAw1DGT9o7pKJhoOMiM3QU6ltB83y46/5+14LQqy/CyS5TpoWEzgP1/yzxMuBPQYsfN1ZrzwWTUoPLzvPJIR4oZFPqCMNqjScfVamwMu34P3Fk1w9c+N/2UfN4A4mX5H/4kSpHL0YKw9SBTU7wpAB6xR28e0x/0e474oAeN3rWwZ/N4ftZjnmht5t61QBZESTptxc4oWW6ULvoW2d0MJWE/lS2gPvcv0a9Gj9sDC8JqqqFv7GHXfDz8tUEudYHmlrEmzLnEb/vHtu6oYKzvXbXAinDCZR+GmdPNpSM2kn+T1pIVEEQ1btIYOxGkETH4JDBfZO+1lGvrEBj4FLr2I/DKk2AMOdSnQsVIV29q0WdsGvCVY5VQwuRs+7UTli3FZZCzR8WoP6ZVXeiz62Prhwn7+Zcus3GK3zlfRg+sek2S8x5TxLcOnXZvdXdHlfuW+O/BgqGirdAY9l0y+23szXeCuQDlH8YtwF/TFa/fIE0gAAYVsR/ZAa51YI80g9ErA/ii+eyRFB5yUgXQiPjeISLxNZveiaEJmbPnS0u2zBa+tc1pqCp+kvfSup3s9c6o7iTJcyImtTShXsey4d6n7ERw58LoSB0pKhuWiUQoy23c8JG2RHWQHjOsieZAwhVgoOOGwuDThxJw5bRY4iMRyCE/t6c2Ppv7fZtsWDAsNseyfObK8PUKFIi7wrGEYKWSPYs+cMe7e29+Rkz5R2zH77BuZj87gYHV3q0bPdr7tqRIPEGp2PxxlUwGka+MPTHnRgAWJ/zmjnqCoDzqzr1g7ECLks5lYhnyjRgomt8kA278fsZKURWdlFdBvMJLxqPt+s9P75EahAmCEAdbLALCwQQAHJh31xVfyts5F4rAYgAWPdaKY1N79fS3U/oULIqiP2WisPc1Ak1qhNqzlmhbjuw/s00fRArqBeakz6sbTt+AT9z2w2xQLuAN9CcgkOcwloG2KcalQX3SqAVxtGlHsCFGhMs6QdBQSf++MgnbcQpjMTdtSxi0OD4w0owdLhITdmWBxlWhdQC/2AgMqNBth/RqiQibrKCpLcGqhn0WhGj7FOgpSPOAVtyOueY5ZFeNTDuNCefAZKJYPgRS5OJ0847iR6k8rcf4q6PAx95CeKwjUBJ9wMzRIYkrmzUmwgD1O9YfpqlnKlwXsNwsWFEJ5epBRtktnx1Q6jtsvhH1g4hnKOMatu1IbQR/ASkauqdcKZudRzV42uPup2spsXHT3lWVhfKXvKHbcb4XkSfCEWGyUwV9ajN2r3fFSe9IppTkXFrlhIuiBUcQph9tGK/iEsRmDKFdpLTZZ2pysjSOEa/KORYXaojLlPvKMsfWzSi+8f+kZvEJAGR22xbGHYTOTrQPtBM3aupP6LuyxHJC7Ys2WqE9Nw0DyWW5ztX4XeH8aykgGkWiM5Q3W/tZqAD6vZ2P3vU7uNtCJxHnSRjzSOGbmlicba5TL0/gbSH/6de8ODeoAOZ5HmVads5mLTpChoJpmFUBz31BAdRsAZTi+D3CmOyFlKZn8KPCUAIPitFjVgr5OfyFOnSZUBMwcv0suPHdqccxkcMY5/A/xHhZ02v98Q2zxfBU66Emk3Qa8YiR3OZ5oL4cYapn1gbm4J4851MzJD4dPxzTwO0p5z/4+NxoY0/35xB0lTqDEV3WwwGQL9HRuoGdEJzvyB9KfS7rsCQVKvqADMu5Qrfs8htvuZ7E0oiFEhqBVO0XUIw2EM6caJhXlxQMPY78pfppsvpTM7rKTi2yr6kCsQ6GcHytCjL9LctJ4rzBD8d90VA0VzaNuApzaA0aX0a0cKQczX714IoPDZSym+BFJjgRlKx0jZKu6MAOsMlU4qC8Rp+31bPL4LnH9Z/Sv5k1p26TNmMsF1R2A2roWosah111AdztZQpU9aQHZTa2GniHfOlF4QQv1OidGUljHHTseY3yY05RKYqnRpepLqsvnCm5szr3QwFTc9P9KSKqsBHQczWtstiy13u63aNHPHqqfmi97uylnKJpDqOinKqg8ekk7XG2ARyeDqiSqlXsf+48w6hX5PiLJraZ20lmp7Z2/6DggsQag1PgV05pWMchMvlKrOePWljy4RpiXpuUYtJGD26fdDqYtRQVAFURM+1/C3+AmN4iTPChV8LUuVp2s9lKQEY8jFa3CWzeAI6qnn5+yGpu82AhVV3eN95bYg3RoKmBgsWlZ4V7YsKcITVjn3aJ3V/bZvnL0cTbsob2HkvgKqv/AYegWHb0SZDzv0yuZ5r3sNhyuyEt9MPRvTb0vSR//1Hsk6uqmKGLjoJSF7pxGq2WbLBbe9tlVyvK9zO7niIknPRtLR9ybfIg3xwEUtRftvQ8DAJW/CG3HlFRfJIKEduITJngIaY27m6Il+5GdmC5lCoKMn5iHhVtvqgWg5vyx+OtcbD2ScUCsER8y6jlH6w2+uu2HpbKWGyVcP9Z4KcJiosW0q8TLfxs3v85gXgW96lJ6meWK7mZApaKenIZI+XEh5H5HFgPQuRKm15s36aauG8H8Q22KuWuBxXlrqER2b9GiacE9Fg0RWVnO4qFbKGFM2mQC94iLa2JFzbXnUaF7tQiEmalZcp6vdpWMDqEH/8oCIS45jrIrqFBrc+FwwjMsj0v3Xw7sN7HFmLOpaGal9558eFz06a6FBWRNT9tC20o206l61IQlJsrheYwzldFhWW1JA/hnAavgUIw96ou+1Eot3MjQVHCjMYLKmFYu1c6lMz1QAB3g7qTsVlzOWB2cbIVRLEEE4JLN1DqS8D6vhdqxTQCGXpZwL6qClJgtXLX/l4zgi0xdFY/BLtXZoO8nPdxtdwJHKqDG49YFrCKBlactuGIVYkKbodzf+abX8vgIi86/j7ra/+tvlVxeITP1d9UkomcCcOP2DtLv2v/mZ9F4cRDzAPujvDuVznMfHL1kr5ScaHDilwQufH3qCnkrJsynxqu64R6mO8Fw/xKzx+R6h4lax2weJshhmJ3jxP2CgFm5U5UVIct3J5d2sVqV9Q3VIsJJoEVx1aVaaj1MjrLFR+NBgGJJP8zdyIa4CNhZt4SOHnc0UZKzqJoci9hsvubBuTzYIAjJxFwr1KbDvBWIJl+6rqPWQN5r4QSJOrCdsTI055aKcRfjV7F4HDDAWMfFdDENjLPftHQzmWrFDw6ZUBLmEhWR+Two853vN4IuK6YlyhHILO4u6J4QsaHnPGKH3ymvvqD+iB0bxexK2iWzxJAwO3RmydoIolsgCSo65AEDi1VxCNEkDlKsV7Hh80GxtWLH7biNllNC4tgtN6QLvcRHC4B6e/YR/8ltg+fk4t51YSPjSuIixhM1iKKHmEK2UDylYYqrHAQlvFeC0IxsnM/ZjZI4xEMSNlEEZbGr8B8SDyP9p+MsNM0EwlG7VmKhQU4/8EHq36OUtg+tj48yPWdSLf/OKh79UHX4NC3FFLVUSD7zG+XL3KjN/9BrAHfBA8xoeYgbPx3tSfDMAec+FkwOjODQqOWNH6XkOFG7kZMAlMEvnlQvNtCUHc5FdBc5t3/yHiJe8WFpVC1B7quBazYwfq+T/unVrhS5/YqZtQVnCM3MrRug3KAAb+Y3L35OrJjPLWObjxiz6quZsHqweiOYgKFuSLsX+qYw6+xJwcmgJ+sDbny9jDvKuFHi1GzsMSrpsq8o07Wc755zXAnxrc1djun5elTjYOk/dB0od2FQ8Ir32sXOBlU+9X/D5TlDujHQMNR4JfvCa9WIaEN5MN54uJwYXgMyY7QYIIy0EWVB7MgV7Ufg7YrP74cuannVWIvlNn4IGryyfxcWM2MxURJeLyRRKD4OsIaaB2eIJD3uq/UBLXFDL7X471DPBkq4GbU5rYyXJnwAhZguTpSw5AhoIxWuNzo5VV3/9t43kN3WX/hEpnYLtvihH5sFudhqRvj2mqHy0aDGp3cPRRVwV5z8zQViM+KKinwAldMIeTuzYW9WxVDlTi+yHTztQFtxQB46uZrJwS3YBrIn+HRmJLl/uQSpQCVrgffKbbdKiksz+LrjG4jh08mtwrLIleemB2bxnGWjioAF8brA6LsoiykZQXs46baUKhEA5dotTBUOxV4sk02GzutZHsNo9IMm28uwnwYYJjDP0LX8AJniBL1zgQRR0br5W9tkSvt7oRPYNpjTnWg0hKHPJLmY63dIWK3pLlgpi887X3BgiWpPGmJyianMp5g7J0LtW3jp00qRSLm79xOdiNnW5L6yVR06mTbiyEpHSAYrwqH237/2BpdT+oPnsGFlWbWHiEdbQetTG8coYFW2814rgk2PYu5ZB7IxIfNyeH9RnzCg0rkoVsvUQEADavcrEn1r49p7YujXua23TihW/wNK8k+Z+0K40BpSgbL+iak8qb7cPsK15IpTujOvTo6ZCJfdHqnw2aCw2SD1mfRHJZE+b3ZPWjsO9unpu6XFlgXLhuSstT6NU9B89HWjGTHGBY/anl1tUrxenEjswSx7Nnxl+zPiD3sbgdobT0E7yO6duKo6mPGzoRJ60ov/7nqftLitUh8M7JstNL5z6awT+PY6fBqBwYpalFhRzV8X+1IrxKXEjvZt0M7EBkC5bZ9Vc5nr+6ZWxezw0JbS03K1DuS5badfk5mqW/VEG/zEga0OI57UIGHa7qryziM8+kfQBkI4qx7BVqze8URQGfj1k4DyqIfroguWykBKomjdCasRjjlktapIy5fSUJK2PpIGKs46HTTtIipmix7cSat2Uu1NI7VBJ88Q6vlYsAzhtgkYvcGlCPPnoDPdmzDe5GbxZZAZHp8JhWahsgv4HvoXXGeS8+xASAKx5GKUBolHfg+kVODfEoNc2DabHcEo1qAq/A/CjoqvK896c2rjat9jGm+qGzW1RKAq7wWgKVHxx22DNB57UU4P7Ow7rZTPkk8DoJEBqs//71wihKEv6GobQC7Ee7Uft4+j2x5cg2kuXeKTEX2KkhzgNUZ/75yQKC9fo3uOgXWpSYl6tTxVCHxJ0uHXX1SKW4bsrl4IhV5Q8GWPdxifyv4enGdlWYCET4lF2Cw1RTH21F2wtnmgCbHs+HESoDa3LxJqkpibumqIACPfEYOKTrcnUGHkBwYK0kDYJG2vThfUH6YrS/DMWhmtp/opYZcKfN2fNqUq/fjZRGmURqbM37/ZtmfXPN6scysh+TCenDEyGNaFSR185ZjNqQe721K7Slj3UcNUMo9Lub063QP/hzPOoEoSpaj13zbb2/yCbbMjPpUCvIo5TfTXXUALRotC6TuS5jBX1ZyxetDJAi0lcci8iIFk+5FbW2yzko3IW5kavm5yWHhfXoOYVbxNZSWt2QnLOqP/cEpEjlEg1g5T8jiKF3Rfd1fdqug09eHdP8Wb6TFs0NoR1kcW0OvgC/wvwVnAIz+JKPT+sEEIzmLI+4IFNY8Kyc91+HXmiw5n5qurO0jO+gJWb3Cao3rEgCEsDHh3b6UL0ssF6r9ixeXwzc0wUUvkIB2hIPB4jxZ5Tyl2bZabeG69bNq0q80f+41NB8JdHlkTnA2FbTeTSiVM+P4eGxUQxAg3Pmwo9shLV9KWU5LTI0Oq5C1uqjXKLXPm1oQp9HECLALtdz8l+lTm4BymNHp6R+szX2uYHDSY+ZoRhIWES8eOCtoLBz0KGyjySJbaCiTToEDlL8u8+KNUnhmJa1YOLibR/6LjDy7DOS+1nZJF6xU1gGJKnxr05COJy5qoa2R+9qoTrThuxK48kjZa8QmgMPKbRN7f3no+edp9NMkduehJvwZxxUZTgZpg7XcWWG0yEJbOtBf4s5H5z1qsr58eDjDpZKzgg6QMWaplk73qTGcLV5yh7BiIU6sfF+Rdfi3UkHGnZ7z3relXXxhrfMKqsoTGiDmllIQXTzvBri5Sc12heu/7kF6R/6zYexTWR0lLtKcfw1v9MKjckscCnJPkPbFuhM10Exnlw2JZCHLx5wmCKkW5030encE78bjINOyklzuc6i2GT6ezvO/BAmkuUA8mBiAiT2wNKxuDq9Rlwj5xfjFc/qAZ4bC3M3rY/R1fPROmVCBrE8ZJMFrGmqX/nOUZGJNEx/itDVL10P4jizGEWwd0kuBgEKq4Cdm2PBvuop28gEIuS5Q9kqgz/2l62kSStmVdhCrQAY0AQ0QeyIeniRdKOguFEZWvrcGfc9W19Zrked0d2vikRySeDMkGuu86/moQfctcQAlygZPdS/Qd2t58pjDAJcSxpsgIE+7fF0GZ3ijsxu2qE3oureGCFTQcxJ7ImeHFHHjcAXwLLheSoWqx4kjekiG2/4KsfAlGCa+MnoWTpfjkkF/t2kEFdGut7+yeSsCZOJWw/EztjqjbP9I90mpAr8OUpgs+tFTYxjApJmTVzJg72QD3zYKjXldMHmqR46VyOlxiU5hrG+eVMpEdw9oIYI9q35fKPXK0LBUUiXEeHVcPBE6G80+zYZoYiUeveSDfEX8Il2gjHirdS2ZLIgMGufy5iSbqk7vrh8f89H1upM9PcRl08jDd17g7Me2hFFnUvRKR+v14lPac2e9p/JbfTrDWTlcGwQjFHAa5hTf2SEkgD4DhvC2nsnsOOk04ZgB5/kdgktJ5gRz5P8GPZnvyVL0sid7sQq3BJQw2ijh4L4nl4RZjMdoorqTtZhCeRNKP4u9DRwW8uCQ0ULyRYg3KQ3NXDUJE2cR8ite901rQdHFiLZim68N9CcPKr5Jld2LuykDaTyCwDOgVoq1YW1Kv0xLiYYVW8zksT4OErfE3HQA8yf9re7F+XVSySijTdCxnk7A1+R9GU6w605cgKpcHJ/TTcoiE/XulxKWVbGCJ7a8hf7wkVBpSRPS36MZU+xTicD9GJrXwkoojahXVa/7uyiSpM1/O6v4f701SjA1c+m0NY4imcgr8+8zRPLzmzzVlcdlXYtLOG4WzgJo5NDehWR3pRirP8amPE6GhrSaIby69H+ENsFb5eS//mEy76zWcyDxoVa3wR597RBojitslqAhxWAtOJr4vJF0yFXYOjpE9B9h4pXvGPDg1mXQAcjDtaEFYPwrTPZanWdg7DQHU8g/AkF3xsQFcVNQOEkawSaOe4AmVJ4LA0pyppeFtR98CyjYrNkwIpRr5rkSLD6ls1npAWrF4BB7rd9DrXuVJhFX0J/A5+kQLBEPaPln8ITtRTyA+z73LtkjgyVk985Mqrnmnh28aPt1xkROOAETVugtq6FgoFuN3VPY9dzhpYLia7KDqB10G3JXrBv7+34h802lgL1rjLGCJuRQbbx1TLQdwbahX7Sa1knRyjvDgjCV0UCpEuGVhHULjWKQuJg5E5HywBpGRGdnaFBKmsKg11x5DnGVn6SCdf/vXmot3iIk6GDh81x6o6pnwJALyDFD6srEMBoDq3rq1phPox65nCROSF8xro8DxSMkkWgnGFQHPiRwig+DMngiuKfxtPZaqGo6qLKtxc9BZKsp50UOZKssrc5PDTRabvJlS1sobOlusaniNZJgoyniaHORe4gc5Km/lV50pHt2FbNT74AO6/DZe4TC+ndBbraMdi6mAl2Rwm8rqItva8X6XBLX6MM64RLinGu92o/6bTwN+8GyZaGqybtA2qGr7FtJwQ+/9pr6QCCab9xxn9++OpqN4n7n38+YyXY4qIPFywNyP935WoTonLnT8SU5okeYBVTsN+at01RsWvSITS36SZm8ZAaY2QLbTeqDFO7fddtVgyzn3EG8hFBUkRVkRIHRqkiVqvoJz3hvrizLlPEVL2GC0Ab/e+8lSkSMjaYqKA6r4FKmLXbqquv+LdyXhWJjyh91EUnzzmcCTQvVA1CyI2B0V0ldRuXN8uLbvO3ELKRl9xMeWOKXo/DlR44X6Kdmxc7P6DTkLNW+Sf1h3xnTWsVtu/QDqUeqz0W/VadPdJvQ9BfERYpcmtYoKD6YG4QO6E/ktk3mwPMHXBnprGC2foEwhISBxS7QU3lR7D9KRoWOhMoCdqMTXTE86SQY8gIcTED8BhoANL1xd0snZtJizB5/GxL5ioxiv2bL/rkYrFolGDTdX3vQZ2OXGI2515jwu826tTagobQdZdDeZwmL+faxLr68MRy9jO0TmCy8Kdp8V2PWlKljN71IMsrVuLcICLmiDjuU3Fs/fmZUK1T7TRJ3qPM66g76nOA9RTo8v7VrLf7TOf844fi6hcl+zhQG3Ku8tVnZQ10hFjtyGQvoScm2mu1qtMrwbLm2jQPMpvpB810noXMXr6y7nfhfrpAWYID8RlVpEazh2u70127x8ZYr3wYvWV0d7Cujtt/MwRyiyKelwOyFe4VLPIYHRQNE6vJtiabtUS5+loPCuOnSVT1iUIVA2kyJWek4nyRohMrPEHVgxtLHfsBWFlsy01NK4Pd16JXB6eMGN/5ETMQDjTzwqS6rGvKGKvRr4fDOkWq9okvhLVsfPVhRyQc8ySM3bbBY331MHEIGSD9BTtVoES6FI30wn4Fwa3U8aDhNhasz6QGC+v77g0YZp2Gnrw4oS2q1sN3UKauCGWPOf8lz6/B2ULGzdObO1Jq04OM0QIfwF8EMSeawIMND2n/Lt5hK/5A49n6QJJ+xcgGUXxtfJWj15JXIoivJktiszahq54LBvam4+A8DFRUoPmtgwaFHYPwc5ZCBv2EAoo97B2DJtlzXQkzLNjeT99UvdyK/Fyk6W8YIrHOGHDWb9/EhTpawOhhqrmcUeK8W7tcMu2CYyn2HRlVIYatQnw1FBXH7MvEep1gvplNJrNRIUezT9hKmcNfLTcZj3zDA208wVBy1JVBlhkLc6RnjsQxWn6GTafcuRcEElH/uxFFIYC2/IuPtpY/mzguKSb4fYK8kJeAp7pb6hT8QqkN240NX384CTZIeg12zNwYkCer4a6RrznRnNxvbx6PQ08uoomeeeSoJI2dkO3ERFiR4vXHObPOCrlv5FR2HFV3AEDFircvaqylryOmAEx3yDqk67hR3RmSbR432NUkmc1QHEeaKiXhRn8Io9UvU75VI3XORgOq0rfm1Og1oGPAy4PX2Ik/cwMkJBQPeOdg26F1N8PxF//LfHNg2aeWj7QuXvXoPt6qW2jx0gylUNMnOQUD9qRJh2cP8hDbdW9DdspyOf09cPMLA/nCB8DkLA9cvvLU4V4cG7n2Wx5iHrOfIUCUMIPRdAOOniYnWgu/bXGBxZx2+ww+h7KCJ2k67/JM/EtbBxLbKdYhhvqaepgFn4LMv/nIJZplr4lH+VL0/qQ5jeV9avPiEbvq9HRxaVyPn4bRXdugzhHDdxulmjKcLYwFzss87Jsr/5jDM2ReTnfzxgTxzJlWGR49E5inB3SXv3jkMUI1ouAchJfe22UZX0qz9ovEjNs+PCleXnaglR8zEDG1EyUbZ0kXVZ3QmWOITjUVQNoHvxWlDJLLq8K1rlayoKssEpFkvgdikxMNy6gNKqYYRsy6fdvOC+dmGP9tOSn8TKwMTLBXvpl90jTXd9mCfmU3vyt46KnA52Nx4UKkYgqmgSkFIzSoe0qZoKSZJ4NFv2WNL4UYaI+YYzKdeMZRQUjp5FUR3p+LHTtsn+O5qJljFzXQgilB4/1ENHSbxkoTAhZLH052wJVLPLfBlqqlltT9yCsRwcX5SKkyzgwtPahls9yG12WkQ6RThnJbHV0qSFyvL9S3di4dP4M7J6p8RICF8izG6K01RwD8x8/HXPRhnWjAXVRcRj4WvTk9kXY0l2Tob+AQX5kYeBs2SM15cn7qqqvdqsuTeVKEPSmhjrRLpsqaAlO/mBM76LqUJmc3xJ3+cFFe/wm0+1UqHDdmAMVj7rn6O23TraUYP+7VCNkVb02v7GxuKGssglqBe4cGhMFG0M7QhxkzhnnhCcbe6fxEKjxePLmew2mhlLssF90e9FL8GpY9+Oi4PgynZ7OV0Zn4dpTWHxK6NLrN7NqOiU/PcFGdJMXGhs1iXzZgh6MCRCpeUvs7flY4LN6zWjj8wZLXmUrbHqeCtkcN2KIPOMO59mT8mcwSgDOZ9Mapj3jGW094COirp0XbjPS65juq9wMiks2EXpluJE2zpGLBZqBAHtY1nZmsMOEWwVpTe56pes69UAOIV3KxfeOZRdDc334t/FbeXUyNbjZsW5ADpRd8KyXcIxb0dPbKE5/DSUmim0C8ShTAEMmDvZ2QL+5QHUeEU2LneQHyS0WjvEkEHj41B3TsS4l4KoKhCIfTCqI3E+72P2Ek5/spNUgJwpUXUWLILTkaVMfuWfmNEcOR+wqGQRHy0R+0B1e3DufQxtnRJT06H7jiwXdQEQxirpIzdA3fnVnQSNcFa4JShGx/yUjLyTpXwl65ZAjIEJSVIPJxyElYTzWSiG408Vz/OBnBLGQCAad8oFsc188QB/vj9mgtFGGW7wn7awZqJAIUKqcdF86n+cVQKZ8C5wDsDXqKcaJfZ9GXpJYn0AlfxT4LrHld+3+4NqO5zjlky6HEY8CGl0wAVUinMbXk6ygqhbPdX7Z4hIvYjysHXwUCSNbwz6Ah72dCejY4vfqxO6JYXkvPmUTFyt0WThBInghrpfRYDudYaGGYMD4UU7arSvbTm3jFu1gQGPDT0sT9pMP2Y8hwoQ06tT0V7cyovJWxsDkyEsELdJvOCLlOrWnYzRAXiq44PSaKyYZymYHWIaHZCk5dXp/1EaoMPJp/HD8MDXEskcXyLNL0Fo8x0qdogIZre3qXSRbKS2xvMZypJIHSWiJCq4B/nm8+QnTsiWIhlM9OM9vY5Y4phTYWVAvNz2t8ex4PjtfL31AjGVDjAESj0uwL2lRYXLg993gwB1H80504qRfnyy93+dAKHf7R41tkwZ409Q7DpFlSB3mRsIjA/SJBy+urlPfB0Te9Tp06ujYAqYVjVqd8yE3nZ7oV2PO1xGHiwrJUNNtMVYTGjevP3ukvthzkiUOJqox6Ud6mqTt6Dk7yRqRcLKKPaUd06YoXFCQAAqwBjzusa0u1z9fpZzmGWtOLWR/gQEZBeBW8WF1TClrElMQAM/EzXLawFVDtI0yqdSPuB0YmjuezoAxM0dTa2LMXZWhzrgAzkQnEpx6WGNPdZvSPTL1FBJHN16oayUOl8x+ysjE4dQmgmBFLYvM77cGqseBpiPVGNk5pHNuB90zLYK96eLgofTGsZscQD2ALJAb7amPKhDd4yxrRCjkQVhZsF0EIjo8ZT7JiUHT3uhTzRLBBz8qlFz+9S5Tg2iU4NuJmMBULwFmK7vmmea5P3awhlrLhB3+ns9oabA4EXC6mJ88MS0hCSi++wKT5zOcz86oj2eoGpyDOlYi9wS4Rm20jZ9poS2Tg5UQspWNg0FfO6G6MHUHffFIyGfm1WDhnf1X8DdAfAh2wGc97UdGaV6euMkvZld2dfTAE8YQalUlG90Rbd0sUyyToT4qxoUkYaPMSU9FRSfYLmdhKPgKBtk2chdNs/SEPbuK81hQoceRwEZQvA/1C/CTEgIr+laHOWGkt5C6ol2BQNEG818oVBusdfesdTKchtDRjxytbHiQMK4l6BJHnySBYICSoY5o3Ob0yaw2MjbYQroHLjgtYF8l/Lg45aQ2IuhDmgXVMZZWCXQ9TSW27b22CqsP2tCUckKqUFf6NgNec4mnFdyTklExeeCBu8nNdwSSu1DINr+rQOVQypd0VkEMqT8jX1zFBs/aVO6fdTtKcwXj9k6KlVcGsqkWZyBTIlYWZy0DpYXghtk4OMWmmbXlhWaW9n+vNzV/LCv0K3Ct/keeyKbm9WoVPuQbbhN+rUnIIiFqODrJ26w8zp7f9gdHbtcseUm7NSLIQtZf+8uK34UantP+f5ARnc5F3komFPzjdOQtPEJh4cIEbqwbktStISdCeJhPk+izKq5znCANCbcf7F5wi5K+7rVi5DvR7lM2YdRCoLRMDXIvyUco6JgVfbCAoS71O2Xa2U6WyhF65fnURT6GJkpofPcxdAHDTccsQysCUQC+XmzKMMRuaEuzJibGlDITo5L64ew8kk1P+YdfBCjNX1QqBiK3Z7YrS/Hu6QYfmLIWMH1cbEnFhvLv8VwqPbyBb3+7apdiF16ahkAzpkV5uR5XXE3d2A/jLv7IkLw31H1TGCEzQOfxqRFrcBuXtNnt1fyLgnt9gGLQUmliGG/0uSZQptjjQHRsJpAfDRsNPOimI8yNdA5ndokVw8BhABAFR3nEqIfcMuEYgrOyAHMrd3fbcspAQ1jLaf6PUJnXTxURs+FiLOsXvwKwEXE1xHk3z7RzxhGDNwe7p2bbT4Dhbs88QQClW1adw0NdEhfIl1sXk2kRcb0Y89r7oQ/GqFkXQdowML+V1LDV7bRuP2/GFPWMoohhzoy5OHQAEZg3NUlIHhKJUpsrGeJJdeLf/gJvtp6ilhQ2VfwGneAmQRXdJOzczi9QRIO6KzrHrYQULlMYozhG6PMKx+ndwexzYlThH1wjm1vZ+cYnwkfc+e3oU/qNxyNCVvifBkzX3RvTE+/8O4AuA8Bt5fHYUtWt2TXnP0MMaMQ/C2YbkzEhARyUEG9tio6fXGQNv68EAUSKjOVhtLI3/IRj/x7989IQX4DLwc6FzHgmF/vxMaVZ4iKU3OuiCaeSVH8QybdWrUDxaAjKFrqbxmMqgOlpi/vbiJOG6bSebZUe49kJrZve04zHQo9Tx87Vp+hVzBdpoRUP8eDaAWu/B0eMoyajRBPjUkmCkuQW9jBDBFV73pwyw9bqkughQrgm3Dh/Tf6fUi4KzbNrF5r4IcLvEq0qi8AGUhujmO6dwFHOcPDPX+pbWfQJ7FSyJ0iE04CH5lGMSLj5VgBTxeNmyy2lM6QDWWUFLyUPJOYm9ug0cD2y8UzR3jVuIs+pZqfL8HkFuQyVieIXqVS7DhEurZFpsqMvlNDiBba6iguhBMjdnp4Jq59qbX6v7iAncJsaPoLKanmNXaHfjZ8hlTz50GWgXPQ5w2NSwTviXkTKcbU9jH051yFcxDVmM/PYbFH0843Jdprywy+w6XyvdIax1DF3Hb4icLl7f5lxcO1dLlaUvx3mohooPmmOmOF2RjMwqodZXUKj3WNzJy/7GJ3AwYbIwtryvtF5dYMj3CJhlnElSKxDxJM+wYnsiHagyuqqdZI9X5TXmnyn6uUpeVJWSh3R5SOIb8EsoK/oINpj6dDuZxaSsrwMs1nNdMuoTpb9u71MeX39bH39Wr3XJZZ2r+j2PbMriGZncdgjzINJbpdo4wJEUv8CaqPkckSPU9Rso9lp2QRT+G8tZZiYCetEe+QwX1pGI4LUJWszDdVUDbfbw/LTtFHSxiWluOmgFd3ANH/w3syzkC6lekD8Z1m1wXo0GQNjUGcbUNyqanBTnH2OPQ9f5syiu0qai44j1ota/g1JBVuBNirevnfJNzriowJTX8GjpzNoTIpgO2TfUbZsc+gdeFA5z0kASTl5ykJ0no9HRXOxSfrBHJ5tYli1P6O3LuTE3dCdUBiV8rl29oRFHpEWsNVw3y1y1ekFXLwHnzRzgPy9jotnjQDrmIDmrDVwuBPtujTu8rerr9iGA/IJtpfTfgsc0yzCqarzNmn0r1NVguqdv7V0jF2Tkdl1DJwheqKQq80rI/WkMzyWgpIiodnMoljj0NxFVCwuSXKuPQUXvsKwigWwAKZLrqMkfn7ynHQ82aKCFgx3cAxS2u04qxFhz7gaYGw8Hb1Xf7SnFY2/KwpQYaJUSeiMCZxhLFYjXCQMTn52b+37cscJU/ExIedSY52fM/pcGk9Y/LTWNNUhjCDCaQ29KrHGJIgsj8ph/Nf1sy8nUoCw8ZueRTrUZ707BMoPFpkxNemiHnpheLASZxiaqg0Fe/qvSo43/XK3l84U9wf7Bv0+ep1pxurB+633kFsoXGNGVNsRLL0Abuhw5ezqSqJHhJhYGjcEW6mbWXOsrvMcYMNywvsJvvUXVqgIjMcuSkF34cHrbQSvgtG1wDdNq0B5pENe86StJjLSug0TmEKLMiqALaAVzDxcikeKo6EOH3ySqWvADOUYqHnZnLR1rURqOVu5GkgBOjokdGcidcSxNd3f9A7Vm4pASCjJiXfAXAkKs/kJRvVG2YP5VgLA5GEe/dSsZ6429dlv822VajTajRFD85wpx4Mahao19WEKh3Ij/FspeDeoD6kRdOkYOsyZZotsMMhkdgFAweA4O4xebYuGUhTs/TgkwYS3yHEOj6Mgi5t5lM8gTOjJ+zqj0g8XSANKi03bat1Gu06nZDTSvLrXGcAD6hglbpF41pxIXxHxCxMumirD9wKO+8SK3reSUFw7fJWPVMyTm8kJV5CzIZsXkjcA2cjF3YXfIs6p2NaBdSuDCVJlToiHKwQJjIcLoM13JWpK+7s4qnAShCY81Sitjk4t7I0AM5UfLme4toXr93DuuKdZ3RacjPXDyqYbtF6d1syrd/ERW1xPgWoAHQeixwdggxRgYXmJdrUeUNdeZJPw/isbKEI+nZVsLlJbJU/gTM4t6HsQ8rrGNNrGYTX+EhAG5PAqrjuwKaSmqAxiPqbm9YTaQ80zbz/SPY1/Hd5zu0QogVQPW8EtVmLpOl2Fb8QZQx8EveysRxxTIj5CKu2cywvt5VwY3nM1gcQJm7/NsvSbPnEHakVS1ij5ORFuslIkXmpj/NBADkcKK3247a2CN3ssFooceA5EoL48f7kyfhPwusXDDrDaHxvsNLBvdAxgmKdGBjIhD7aBg/fhD0ljRee1C6O7m/Yc8GJ4nxug+k9J8sxAQMmIqLW22gDp89ifdb5jyyPoMLFWdJwDmJ1J/8ckuW2nlQQEhcIaGOCk2TSyz4YPEHQq6BJr6T7lC0gzJmw1Y8C1ogtAAAkjOou6EeBUXGqUbRfeGQsws1HRAewMK7KD9aGjTBzISGI0RJ9ej/mZhSpe3DjCGYJoKeCVWsQoTTppdASdEo23Dp9MDZZaGMV1J9lFqL9Z5WlhEMhy8/xnlA96E3XOzJPLEkINjtjjIUgt1ERdrsEnUmGbm+HK9FeliuyH9tFVqvd6hd+FihnlqjSQ08BjPdqmbUlSZlI0sFIPpW/WoeJSnUJQUl1d45W0cfDK9suXS6do+db2WUQxsJG+++6HNNHe8VN+1f/4TBraZALeqQwEuVZEegLS51NqpxzZviELAbYnhsnU2DUS/gAhk+il2fOc++PNSckKiNmRwpDlxDjawHftl161Okbl5qoS8d6hn2TKKM/y5LZi+J15DaTOyXYneghrcATkQ5vQlkNWY6rJBBd3XVZW4eu/Zu9vp/+tF1aNT9oAORYzlY3DTYvsOI39e0bU+xo2TvXDXTCOmNUK9kfujrAKnbHO82DoNBqTlhO6nv5mObJjBnOF+lrBUbOpkvfXuG6HE1cQVOG2DUr8LEPUEO+Uk0xbRSL9duVd3EWGyCl9EGJ4DhVRwguY2/1nWFQWpulDeFy4btcw6lfCMr/Jq9hYaahdCZCwv/OMtmedoq+0dgY1MiCXBbZMzS9Z0igvBASv5w1RRwhECvMoKZxWwhp3b6Tnc+NC0lyeP4eXiG0pEk3l52intJ+77lBDdft8ie1KjCBhnCof/NYCYef2dvB5lI3aPelFmBuTgXPoJ+kgQYd8/QLxYWu27KH9H7q2dArL7hPIJx3wB2rGwjwf5IGaEEk6lZNFxm7CDdqB1+6PP8636hmRrl99EH2aetsIR+UuYVKH7KWGqR8C28MEuZGWvniRfY1q83K+uccCnRxnYBPrgZBCL6dEjmCjDWsyDAQQTk/yRWguptbZvlL2jg1kqYMxXYkGbSKAv1hhUZM5KU3BeYjP7t3qwGQVlEZEcIrbMYmNz5uhU+x+m3wJnGCyMwRkgLYQJ/ZKzJ9iVhkuU5wcSwSbLt++rMI9uDuz8njzZBkNst+7SbvfBB7JXDocJoyHxhnd0GaXWPkKCte/Xgs8BoH8rxD8rkLJHuab4Ia9FhmQnDqj61zBpsBC7VTLi4MG1v8D2XVjWocXsvCvg8VqQBiYDAZNXojx3UEkSy0UIWo7BG8kDCvWkQpGoMSSDPbBIbgswfptKwjdUckWzuEP9YWnV+nX5x47DB/QehSRaXnoOErTyupfhJW5tmH/+5KXiBFhDogOBsxPHdUOYZL4kL7SJu0a8b/B6Bf+XaeI38I7QAKF56dsXD2JpRRJmvCFU5inYeJQuK9XX6K5QGX3r3PihTDZxbCI7S+dvABzxuDFU7fNioOC1yxc4MySE6bVp5SZcRaUUngOCu2zHmuCaKCXFGtXNLNq4zALRCwWiBX5ywN7QcX7cNRF0dfqKOw3pbgL4dMV6H+k6s6OUP1H78giBVg+WozfI41DfJgiYs/pSX4MbNEw7kmKiEZ7BeqOgpz/yMD95GgdGVRgmNh9hf7XMD7kaFCELDB4uFWZ6Lrg2lSSfhU4kuSqH4D5FzUQkFl5y2qu5FdMaGBk1Jr5ZLM+LbEygiTFhvAwPHUiEIJQ0/GrTW3ZWz4m4KnGExRF0Ajt00GtmfdWgfNwdVRo4WDN5/3NKYSXR5TbENjgv8r4K9+5LG1lrN2foJ68sYYnm3GjJB1PrW3Lj2QKMYCLaprc5R4YvAbG7v4fQ0ARlBf02CfnP9JQoKVkeQsyzde1Xah7oFqdGcoVXR99Y6rzTdoWWQvM90BzO5d16/7cgoqtKwBw0sp8RZGVF08x8PWPV97+uMTYThd9lelxKuPKn9L+UMEyj01hirNtfLFIX9FR89dmT/oydXU/VZXqrRRpipHrdwCCX3SDaPVRSEwGtJnOBaXXz2gAYRBymziRU3/dXq+43hQynvXtVT0m/YSQ3xQxOCpWAySs910v04F6g0+ivubLAHrHTN5sUF/48gGRIlACqZx+Z47x/QfnClFhiDd7u7xEN2+y+dXnDfPsiuBdJQNbVCVkTsM1mzM4QDZhONOnDhU0zWzkRe4aS30ne1l8TfqakN/VHRt4EKYc9lhwIPKnb5CTczfC7nndAfYJejm+OlpCzw8bI6SfLE9lcFFr8HM2tKb/FQvLlEritXRKQ4Cuqx3GyJwq8Mglan/GmKi+tWD3KiH0M8e48iN8W87maLPKp3p1Qo4OZmMZ/Fo8ZB3qkYbQkWNQHevndNagFpwszkfjqdBfPLguFd8NYtXTUuT9GyEHSwjwISfOSJwZAY2fwjSHchzWx/P2BQ/fEam3jOFLUeZV7JOe6fg0rRenuehxkVilVnNH4NWHsmtSldbtkpDrc3e6p86zhyAKHPzbcblCGOPvCFM8Vp4xEDCpJ8Dc23oN2T06cNfgi99c1zLOckwgkiTJxt+Epg0F47kOybKOiViIMkK2o9MFdawyUEMB6kylW0SndnF8hrBf177scZZ6B6lkKEOJOAhsOkSe85uzCE2+12g/SdpZ5F92CFaX9CTZwyhbYwRjvodi0gpjwRqBCLKGOJk57Ha9/YjeHHfb7fEwTxI+Qth/lODBdu7kQzAAk/ARo+j+RBr9t9j+xfr41EbORhDOYatd4lc4Fi5xI7sjt2CPpv0/aXa+EpbhPuqy+T4g+qBX7l7wl1FGAMjSHnAjVSmBeIsTMVB/pS6F2tA8CaiN8uul3Z74Ta5yqzVATHBKR1/hjqTKiUBkGpGauWC7yoHnbeM7iVFMbbM2vWMtM1bfc6uCPh0yScOm0W7wG71jc41C0ItgwafahJbCTpzo50AI1FZJ7vVzk9WUxQsjt+Z+PxUSuP9VhGy7LDPhQ9lXDVMfJQqr2ZCFQY8ig8dwS6rTsbOw+exJagFUE/SBsB7ilFH0O0SOY3MXspaDyIa1lQhED8+lCpI1s1QWIqCamvleW7+k9Iw0fRT843pX7ubGQ4cNrwW0LQ6AwPlkArydS78bRXm8ZOctayBae8/U+iE5VLLY6dFDljhdITiSBantud8ZyI3oKVlrZpOG9KHUuPAxLg5Wttd0k7hu2LVRQFaFVQ9pWD4YZC7MlST7inMPBHG8H5/FsLGj1myO/7zdnA9KDD2Sf2c1rWaUT6utFj6nPxpfcU7LmvCBnp09qs9swquXKHfKiw0pir/t3LwB6F5ufXv/vwZUY+NbwQ28F+dCJ4Pfd92DjTBKGqOgz+2XALDI6z44mcBv8Yc1BTNfAetHoKvhurMYgVTK3htsGskmCaJ/074QH79mmwXEjxtE5uwlBPhHsf72YLSvE6KRLnUqyxqQgQT6UDP7ZK+vGmF3aQOeevrEEqjzJk7DO4Ia3kLgVfqyIYedP4g0lOsrM+wYXWZJ15fLYiu1Rm5ehNDJngaTjrl/v5vB0sT1zcFXzCN6iVF2rLIVRB3ezAkk0SshpT9DroS9JrUJbceJYpuvU73anqMrGzvXaLWn1oZSMK1D7O4L+nF8IVXNWH8hMDFTnAhvt257wTbaC+HU70cq2e/Btj3jW21Bdy/jzzVoOrsZISH//Anvz0xTx+U7Dlsjgt7T+DHxZxZhiv9lETGRddOtKnL9U1tPAdhz36ksy6RLFyKDYHgT+d5PEhJysyT/rfhYDbES/9ERhkVewaGUDXTuIxQ80lzL0X2GZ4DsgAYtbpJlpRqLF2I6QZkjzyrO1sn9qXOvxQd3Sf5Bnh2mntVEbMXWpGxTSrj1lmmwH6g7NXEjvvdkTpwpgX2gOU2lu7W/hDfx3116f5OvyhU0YpDMU36FjuZaQCRgsZ7ACl/vNWDfu8e3jzkM/nNRhaNHpJarDvJ0LbBzAVFnN0SYrqzfPa2G/5IXvxD0YhPvZ5PvnCsT/mvCTE0ngAtaQx/H7N91V5Zfj+g5oKwFw6S1ujCXf3g67rK+yinkZR7iXlpXQD4GOIn3Q//us8VESUWHWaTNry5rg8+mgbBqkqOIPLXijl6IxXscmSHN4h23klFZtGz1tl3IB6KZcrkGoOMRhBjsre1bf3+YEQu/Echpbm6/Eso5prfvqgmb41UMzGEX5LIrkCeIlSpkqR5SlSQTFyYaA7EfhqTvac9S2XqAh6+vO3hUGjvXGi2ldlMW1dauzgtMHr7S4YTntVONGkoF6Te3koe9X+w/6DjKkOUx3WolqLwsKhCYcsdWSgpLOwZuUjiSnFaDftHov80tkTetlSeLeVglahLG+Vi7zhZWeepgGFmJ8zXQkMOD/SNiUVlSSN+YNsf7QvoMjTjT0Mts41TNUfzES+0dm+X/IUUBPe2V9rBRz+XtMqgUoc8X4Et0AN/zggycPSCeSNr998uGcFSreHu+bFMoITrNajTatRoQK6iwwWVbDfB7FbgmwE9zUjCfW/W0uIhprygVR2qasV2QgvvEwH1TvKq5RCszXpF94cfJJKiGe01RGNGFwLiMhbr4AnCpt1FH1LfMR3EIO6jO1cIEUseneKhruoYClbaSi79tbDGq6UI7KSFRnNfQYgkd+kyl+2rK0oUmTC+wZfQHId45atgtUsWK4qyr5/rs4lNNpVpQwEMK0zG5vgAzKLSZnYZNYCl+duit172TvqE9yuGfcAYZ7FQ92eXeAW9WYvlO9ERjCOMsaxIOyqBpABu11zc1+qXm/j/ObcmyR13eqGf17qQwYVtaQZQ6j+4u3+ro94BR9ibGWLAnH7dVAG7RLc+BZrdNgGGpMLq7HMSMRlFILVVKnCoopiWUUNklhQe7MagMVsaVOdq4+7ulBDf1dXi1f6a2F2wRR7k5pMSuifk4+8J9XwLxF7tb4vlSswOhpPOloSrdTaTaJA9wYOUeEo0GA+biv0oG2mTBS/fYXdEJoA4sxW6/8oNUzPkgxTea244y5DT1Z6oVYNHzEnQ56fuyT21ZHgGD6OeslH0hGkT756ingwXAjI+EQeQUmYBs9J6Yc1wN/6pW4S87RP+ZsBGJP20E5ofyZtcX/6Ih7W5W2aV2iPl0FGuZjVvgwZjsKe3xOlGP/BC9FVfrQJmJf5lCCpeeMnXGqWJCdu5M87MGa4+mjxQeCtj+S9eD0IZOSlFdM6ohAc2mOAu/LII1S9ZNIy0gL/o1VQsX/dAoV2b1XsUe0sI/blrd4C28ZIho0G0BrLq09J8nxrfHfSbGfa3k5iFvenjn0e/0FuZeVFyDjPhe3D8xcWB75hC/ID8iZB8PJs2dLZqr24WU+C0M2h/0ck3LXZ1oXOGjm+bxaXrp/9jo4V6fiidkFBDHi1vokD6aiaoHdCVBHE/pw+DRQAN4bfYmPWDvgEXaNJNWjSDxwWUBHqf0XIk8oh2Sz/5xndP3zadFIvhtWhpm1YDJvHzhUZ9JjH6uTSglk2KJZGnaUNzkLhk0qWwpBokZLC5tMXli0BPKLNmSv1e17wHV2UY+W93AU3ctIgtp4QNx/jinxTvDIFvlrzzSzkJkN53Xpffht4xm2+gMomYgjVf3nqvo4EquT/WQpSTcZL/3Zr4N3697igpPkPo9qeItTKrhcZ/awFZjiXO5QoWYp20R+BMm5a2LNlHHO1e/q2ghLQa9ZLiEDVPdQzaQKZ/bi64Z2EI7dT/zP4F5WtotTOBERJjsZdTrWmmE/lIe7+blpGdw6ltATgG2CQEu1rvq07TSX8EwM+vkDUnRurrWg9WjNqwVSN5Ou+NfpAHopy8Rw/OkH/E5f0AejNyNtdQl4aHnWfdWfBXo7HzvnQzIWwbqF1bENlWzBBXW6C6QUSRegdZAX19lJ2e3O4BfuSPM+Cha8wuCof5CwaS44Sa90iSyoQyelEJgWGQ9jZQYfxeN7VThDx4QdlOgNyNffTq2yIqhRyJwLNlfm9uzz3hJpJl/WuvUDAA99fieSXw5Ug/4uAXJf+PvAqyz6lQbpcRjIEG2sSEJhdc6APT2Jviy76ucT4p8QtpxBznsaLSYY2+NBSp0hi1FKNFyIuLxIdrjEVf0gr5jG5FXXsOKocVRZ1Mvgnf5AFZol0q9yHH0haByYkKtKtO788XvrN4OAmKCK2u12f609oEj+FM92sqLQr9RId8n4tg24rE18CegTkldycvDI41Iwzm7g+L9lhlIiLaSGhBeYWFz5LAX7Ko0NryqlrGeifdKMCtMAkWB5R9a5OOjK++8L6J1EpwBT2JSFbo7awxoxgFkUWMVM+s7E+k4e4WzXsU9w/doiQJ9v94SCxA+zW88Ve6srzB98dQ66bhg0BctBpZAtqUMttpQP/Y9oVsictSMpoetWuA373QmJil1G8K10mPEr57w9a14pn2QURQAzUoXM7oVfRUrmxqv5qLyFWw8EFMsR7TwSm+B9RRUa3z7v1qSPxlZenQW/DXhoBIfjMjhhs+WebIuODICUkMJzGcHQkma5SwLzUjZk1Il9/TnCXtXppsPC3vPrxmbu+ugFp0Q8EIxG7rSCfhJHXl81SGD/FL4AtCH+BilGwSmdYXtHgDPVKr+xPyN7tzT8xXSJPSwidm+VApMBgehx0m4slKmt1gCwkbB4ITDU5OTRFGZh8G+ZsfT3Ez/ivzcn5q6E8wURiWvSr93G0CIvylY13NZaaAPqZuHSnex0OQPbZhPvEy+NZzKdMG2AjTRQgSYoK7OusJrHFWri2ldpKH6BPJrZjU5NUaCxu5An+VEaFd9cPYJQzNkKDpPd3OigM9u2B8wTslkV/edl8sCjuA8QTd5PMlHCP0tLbbIP1hDfGngF9rDxuePQxKbeIqY/N1LBRJHlLFQIPLjd0FuNt+QABAABJREFU3DCpcmsSTaaghnsEBkgpZ+nzJdyAwkyCmT5Em/rgnieCYPI65VYVV6wK5v8EwQNAGAAAALBs27Zt27Zt27Zt23bPtm3b9jeqPsdsoBqvXdyS5PqCQ9yJ16e2mXsddfN95TgTzD6K792CAn/h9hOXKRPVdm184FnM7w2FANv1rj/czd3vRSa5gO4howDVg2iD5FpizdcoGiIlMt2JlOPDy6xMVwwRwPnF7WvHpqs+M4inBGT0gao8/HUnvvL9yLUg9Eb3crmGWZt9ugbdV6vDZZHtsvQPLetyZ69ATroqU6WZ5hOrMHp5icmkUKmOZbnNHn3Amn3NV0/jXMgh43s9MssIjTJjrxTyF9rDeOSJrPPusjPO4z+nWB4p9cy4yFW8aRezRB/AkrAeSOadUzsvTY6kq7LnocVg5JlJjJgRCFhnsJrejxdHB+PuRHSZ6tcGtLtJAxHFArF6FQ1vfETQTgko9r8rnlorGuF2vw+4rz25RqWrzfGZlHhWUPjpYJV2EpnophqwPSB/QGP4T5Wa+kUbVEOTrYN/MziWm7U300ogPaw+C80cXr9X15u5viKnFuXIS9asVdBZdL1fEz1S4mUSoFzCurcb9Q8TuEZA/0DtdQ8qFd41fUNhuFhsxadkQEm7oTauOInp8iflbLFd3ksF/c9ACqPWziglW2q2fPo4mvg66Wq/IVWQ1AsHGyK2Cd949AsFrPtmPCBCqM+kyQw6416p7n7MAzSTYuhNgdpRobrgs2VN9VxXgI2hpahBgVrB2d29TKsYp3o02NpizkFwSJCsqKeqWwPsvAb1W4HFTO6ucT7l5f9RCQkxYPAZOGGkxPzyqiO/1Fqb3usLsyRFmsWsbUfQthHd9bXQO3QWXZc35dZ3GFoVPYSAckSA0BrjHTmMz9jpD/m4bFgi5XXVDM2qGtaoWZ7VGKeva5Zz1xOieg/a8i5X4c80oKTqZaQUxfrjT/G9zfXxIhVM8619yB68Sb+FNUIZ4SUf0E1Xa6Tt5xpq1p2oh4Zf2SfdWpCzyjDSgzcWzEZMJ9sKnMPpHjVRvBnMfoBkG/1Fw4wHldGa7HMdZxxNfwLNGlU+iVCgIbOu2TNNNhWsVR8nPRavLsETtfwW7D2tu+7Bd5gpemEkWeRUBkanfvn9NizGU99G0YM690YVmRB7lXKgQ2lvVkOJkmAH623+q5H2pB+S1AJwz2lCtb6O87gGJGV/Y2+llMp8CXGk6oIIv9Gn4xu6mOdr+rRLt18DTE+islQ+HKlNtk7j/EBL6bVhfURfPBOO6rZ3ZAIDpHda2rub6okfqw4F25x83A62phgsIITHLKb0eKTq/SFcav3o+lv4+6gdJHb/zlzk3TxyRA9BRW0nAFWumYq+TPOuSYHic+MDxb0ML19QA9ZCuYnmBGhURZwHfSwREjpEOwOl6zdpduyVD/4pvGOe1Hv0m1ky7uDyu0zGf35/NdRtSSo7j3adb7+xoAhv0dOz3J9SDn5KGkqYSaE24eDbJsvcXrzqlOeysEPRE0UybgW/hCUoETFyXBY4/6c9xQXvPBVCcVlGz5eqpYDuLTdK0179K0pA4v4zFAz8+gqV0YfOiqs6aKmRys35azNQiskGI6eO3rUNqymqPJ7glvQMB3aB7UqmdEdrwdL1Kkf7p1LeDv38FSYMBPAPiUtkS2V91jM0FQSAupnqyuf98XkGIecCNENde9yJXCMPwZtoADnLmRNs830iqDCp1YIwx9DTen1uy2i5TgPjtupXgw25GlgoReNo3twNFD8GoXZi1a83tJX2ZX1VOflRJ7PY139DJR0XYqSQ9u09ntX56fCeEoICKFODjK8mTvcxhXhx/m37pZoJ8EeOQ6/ENvWskVLj9/a2IrdhJnvVBlJ28PEKxuxHwGkvDR2V0Hi71iHJhPvf8pWi1i9/N9Bf1z+WnRKcvzYpcIQkTlnpBajVYZEZfZHMgXREQE3izSGbZccecVS3+DWKZRe/dJ00ceDHPz8ZSRX+itYWk5fELYSY025h0CCUs2+A51QRfIx0T5C7jZKMbu3Y1P3xLBXISFgPzEW0YEJqJwCWJVG5NkvIPe7f+GzDZfXmtKt06lMK9JkiMaiuUHxX+Pb8CXdXMISq9ZIYGoMzKwcTQTYAeM4OMUMY06i4f/IhnxPpRLMs+JZG9yH2eopl9wQN5EbM1PIqq9I7mq9C2N8eLoH4H4q8yeRxL+J9EJHGSQTRg/OTdSjeCQPsx1ltALMx7wljxlN0QFBMLIrrtACvZtDdmxxEoqHL/Aqfqc+su1CjYRdLc+SiWZTDfucFr95GGXJcVg9cuPlm8meudky1hCyFv7MRmZy/AQlPq7smu5w2LLdaig5uOKM1ZWcKZ3tUWVq6SBIPaUOuXLilAl9ttzxnxNDAGiP2qm/9jYt5nE7oIzoKS9C/6RqbMt2HjgCvxL+/4B2xuekZu4ha59M+tCzY9WBQdGFk+pGC925D/0iWONDXFwAlIGBE9LMeNShZUuUED4Q6GNYizRTSxp207VeBmlrkO/MyqW0PFvKvDi0rSv7p0qgFMxqkHdZCkWZo52lAPLzFSbdbfZ8QRIUyqKJZBgOmuyNn60sYMAN6JUEJZmWI74oqNTHzBYyiB/q3VC7fIElLFc6S7L3jc0TA3+1UpOvWWxzYE+dTlMhWuPuOZfAg4HX8ir7aGOgzNiYttlWxWKlljClYACJEz4n6XA7URrWKEWYvHkuAwg7qUXzfRE1udn9Su2+D9xGygVXuXfBtr7bYLrlVBm3mcd6NQ7ODFz5Oxsm2c9aE8rukudvwu3ZAiIgIfcZXv3/wntYl3GL/5dLGAbx7KZ5Elj3zUENUQAIUlPuUNuW+oHyrvQvYt7r+9fTXkTRNTXEhLz3DskLrtNFH4+rMqjZtTj/YRTfE4SLy6hfa1WS5WoRYv28vQh4lf7cgRPGRgWgMBBAXztIBOYrtzOleyO7GLYLIMXEZZ92MUBLk8MM1TxMmVxphOdr1ni/IJ66RTgLZcAZXhINDwHTqpainKIz0c3NVKjrq9+TvXBbuUGkOogTfpMu6D4KZgoJMHj8FJHldNVPXzVaM8zYKaVcT078dbYcZIa7aPnsr+elr45yEJ7nA2xjlskrM7HO1HB86C9lAdoJLFCQpFIEupqSCkp12Go2Ce78pknb3KWs+wd4vy6Ht1wzPOwHNwxNZL3/4OKmjnMHFa+mgx+By1zQJdeNDwkWgseh9hmPmcOlnbpmhXcoYI5GAxzBoKDOEW3yHsKTXwXgbs60IkhGj/5qFczgc5ylHuiHkM8hdqj6a81TCIhfgBuuyc8vk3SNJ+so6w67GpVTtx1ZVr0+chw92u4VF8A8CsyDwaSDT7Nvu1wkw7DC0pk9wpzvEv/Ff4CxMjSgSqIKzm6qHkTO/kr5JSu+yhkPOvREyPZXwqkz2mLgTy00EvegKGX8gd2BQMbuDYk5dfv+p2PrWhm38WauOVdWQwIkjYEd1Z/pb20+EUXCD43Bq1dtgSAnVqnECvEYQRMxXi5pugD1ar1WMqJBoVa3kvIZLJ340NDNAKiYgC46Lb4uV0/isKKWb7aoNiGHL0q4bLw0Vl2aRIm1GP/WvTu6LrdhNVZGjj2fJ5wGgmjJVzzbgfd100ewDHFTUQkrVjpRa9mSBJ5tNHlJWp40CLfdBIo9jTB3yjnrGTkxgMtfRjFzQ5MEewIbP9WZxge+SRB1T6k+Lrinot8wtAb9HBB/zOmFxx9cI9uilXIcxXv00Fo1UaKV0cQANmQSAWY8B80COIlWjAdVxM1GVnb6wXC8l4iVoMafQUl7nt1LAkOBxxHlhKeBtn50WNI9tf79jSuyxvlC6S0e8RDBylW1NQitPONH8ruLwSvc+ESWOxlrMYVmBHldYH+2diI1GuN+UXK3NxJB7/FoDvrtTZvgzlL5i7Re6BfCdZ+85hwr+ALVoaTvaPqx9xWU4DvwiyGx2feQ+Us5wWu1uAUxqa7Q7An/+FkFcC+vwDRVBnErNwsQgL+30zfVbHPrygsEG8FAjD3zMUeDIlFcPCB/OtZtCq6hgzSyoM0ui/JCPh2avTucEHaT+NX9E0c/Qbm9Wx2mZargS80VSSALbnDSv84eqkyfwtZCyejOcXDfHNU0ZgRv/4gvuCDOMyi8wf5z33UEGUFegkwj+KNghviTAjQGeD8w6A0xTpF2aJZf5gH4T474h1XCZyclyVzhUuGADB5TXUltFGMPYBb0B5zjdMkBsqetL03bnRbNXC0mnmLVSXRZUdAZzLZ0Gd87tCmjoL9fVRR1rWO7A9Us6DcinmnW7OdN+2MwdKrGRbMKwpEvVtvOhN3gyEz6ntwy4psO+9YnIcdwMu1OTKQsIkHnW0TxTCIC8xyGIh8obGoqMQlvcUKm/qcMKkmf/rDxFQg3SUaBRh90levQ39K3h0hVkXeWzyaj72NPOBI424mHbQIAtlSDf/lC699oQ6jTdX5eLdJ/Grf9atDmp88wjovydPNKL1EnP74DYiijR37aTAlmHBnd5H268ciIzlVirPfGEoXBVj1qZE3nNuOcbcNPYtUAvvDj7v8Fi7wm0JuPmcfxAWEHtpk6MNFnV9uB4aHvBEN/Mn2Gf2qEYHOuA+pSPRhs4N4fUZux4zb8JbGTs5X+U6sZxQVs9rHo5LfDbAawq6Nhq35Bta4GS1L+D21NzQlxNSnGYgoZu05z4ITOKQe2WS1RbsWtcqb0J32VNCUqGwFS0+Y7ay7VvgrFzdevrpB/EKlMzfaoM6NMfABrS3SdoZjnm8BTOdRf3dXVNzth2MSHtgc80GOucz8u+FdfF5hfWszAO9ZqkwXb4gC8utZya59CS/mBB5FZDgpsKko4sGOJuvQ+D6IqSvDpQB0YJiw7ySHyIQqs3ThogOBYjVHv6r6Gl9VTNVsXVITXoJ7kBKkguYVXiB8MNLRuaQwUETj3Qj5/dvXb3INgzvBRCpxQiMa8bIGTh5b0tZJIMHAC4ED8ehK7AUKzPKrLKEgPwEbEfYJTjxLGTckN7GxnlpeWb8GNdakBOULo8wb1eSY2CztZIvwNcecIMOk4xrbbK3BdfN/AYGmDamqvVcfJzqHWNGkOOUOvNbeD/FvaI3EyT/vRa3iJoc9Zvzt29l+SH/os0CJfn+qacgPRZRkvFQUPjflEj7TLEOhMBYPNOESJCD7ea0ST0ZGsld5tOectbM8DMhBOm22nMnuKy4oR9X4ixfKuTsywFe24+CO7FOHgEs3L2iGAJArzYZiFAkT3/hPouM9cKT80kqgDuXU90NksDijEulXhJWhlrTWTa2qkmJwHV6nYzNA+6bb6KncGGwM8zzvCd6DpKtOUQayLLwkhqdvWHdYPQGox+0BXwX2p4lagw0xNv+14n64foUSaGnPDKRQlXTW4J29Pl3X6+Tn97ojC+mohmGsbu0D+5PnI0/x3YLTa9PPlsbHMxshjf7HjFmD2C/30DoUqQfShdA9QPF5b65E3e2IuPhIZqE7UFPaqykJioRdRbCeiXOnZ26dlPSsOGIAGNwqVzLHEgsZRO37RWSFrjOXtWErgyYMD2wo4gXL2ZomOY5BeYLxq2dqaLaowHhcR85TTkFhSTIvosZVuwVIRgy+nFTNze3ym8slHLseX07TL9bgoxhv1OICrBl6qYBi2MG0nfudzdQn9rRzMp0ykNX0X97opXGjP0SxAQTkZNWneaOhHHwphLEgLT6m3dff65XkqsJ1gYqeul+CrSbAOu+32evsF7l4NMklCWkj7WRrqxQQW5QcAZF2Oh9dgv87TmDqXN4XlmqWVndTAIORIIQyyvBGhQATx67PvQSZs7e716h9cH1HsIcWS8xavMslPG1HVDRgTCIPqDXgur30V2TgLA4etdM/CRHYj0cLe4rkb22lgFQTTujwHPSTwwSOODG/LD4G7mH/8b3ZcSwdHRGjQ6GLv7J6ogOxlAj2mUbWM45kF647OwHp7CaBaQa8TwRi1nehZHrIM3tZcWp46eGzG0H55d5JJkZ+UqReMcguS5QOhm46MGiJxCDj3gVb7it7yfUegjwF/sjDlOoVC7Kb9KXHV5ZgFft35mdvK4PydLwZ3DNgnZ0h30qAJZtsXCxg8DMc1WmxrU/2o8l3Wn+oNn9cpLTWzxXVMrRSdeXfHKUeOu8G/dd6q0dbD0c/0877Klo3SAXIteb9ahGMAhqi5Hr8r/Mtf30M/+jAuFhUHiSCADy6zyzzbppCvIkWAeBFQf+3NOZBiwLjFqPZyq7pUgZXh8mDgEaJoBNF6qImqY6LCDqdhXkL2RHG+KLd5XcfJ9xJ2F2b42tC4xaEiRors6eS139HF+b20vFgnZ3/SKZ65+NLM9JGRaTlop6jRRUE7vN+HkVSIpYtKGfDVvU04Hx30WZ8PZFzjyVptzdO1KFzh8u/gpmYIaykChdpYlgruMqpPcdCLmMokPmPk0CFn5lpYLHJn7YTq+b9FpyXPHB6L8xRdPsu0JpH+T2awzpfNJfugAQEIcT8LCVXMa80gcCfr1vpRn76Ank7ntYfADGmx+7U/GMCpcrg5O2gKylBZYbA7JDZuEprJRGXCpQ2aULDT5cUv/+ewDvM+qxbPkTUJZWiMAaoAYeZ312jYYMDqzpBCZrR8AAxx6JG2AOypHbyW4/jHhobVUvrVTRfRJCb7C15y8hnNqqbo3BcuhuvAbV3NqhjNXARd+hK9wSkt6w30iytspS3YsgHxJI8pZ03CkIL00TpNrpE6aB/d1rwCPBF7SN1W3ZZRiX42ceHXB7+DjNAorjlZGuoLoc2p8ruttwgPe79xgwIL4wkOt/6fFRj1QIyk/k8XjyYszKppLyUhVNemfg4nplOXf6my+Cv9bywsCeT+IAlwkeoqs6gIMI15pMPE5r8Vku+/YV4LfOrKzWZHmw8S78Q3bFs+seIbMb1XwjYE04Y4rQ4cb0WT2RYqn5EDqy2Ce4hnaZdWZdzbvfcXeu8UGJ6OyaXghUahH8g5WAdZnn6Re5waeBELl7XfqQ7Nbm9BfSkGhaNh3zuRcU27XhXxlg1v0Qb8GrtCoB8X+EIAX0wPYpqIggLF9MP3u66ajgqIyOGBlCAdJepBLMxhaCuc4xT2JQGSvH7TlHW97/AXAN4Lki7q8Rj/0ROrT7hBryG3lNfjnzFMtS55YMncoqzcUirpxr3PbFanH09SWc5CO7tEXUUx8mx6f+OmhtMEbmJHl19MIcIPFXGBgIVWzohOFNnJRDj6Hum2lohwBR97eUSt5SvGrQCVk0NrnWnqJHWIUXSvmsWC2X8zs0C5LUS35LLYjiEMYuS5YZZL7FyH2KCzTLUpnryU1Jj5Zx6Uk0sB91yaEnXk5xrtCxhZL8XjfNN1DKdB499jV611B3knaJzyxK60e7OIVCu7vY0cb/azI+5R1y7+P9IbcM0WT52WdLV5cLpfoxFa/+1003xSMz65s7YPQj4fPUE70rkaX3ABt0xyB+r2/PhwgitGtT1kNU6kyab0SyraS7x/0JiZzYXRDVCEh+F1EXSlGgkYm7dTz5SFjn9mDCWYSvxq88nX7Ud2q9xg17/FJGvUAH1HdpJx0sLXbX49xzRevQve5K6ULDIm2Cag1RRiiNH5KK5Oh/WO6Xe02sas4muiW3TYdqfzqV1QUlsIAkDGRIaZld6XpOK64xDIO8sypXlq+8vxMoeiOQ0IsVWMb+Qjg5xa2VLzpgK/7SrQzshBOiAAIwPjP5Ptz9YVLyPUP1xUr+R6gOHDlFm2YFEXCc4HaP8JaBEzocBJbFxnAMkvtYlPc0iIPuz4KerOgWL0+e+8zxdAWqY3aru5yPSR0JweJCFv39h5fkN7JkNbyksgrM9If4LQIu8abU+DcmzmKHDA8EOSJSZyv4ncQgNco1AwX09+pwuI7Tv33OF+qjsBC5RRVA89/YfHMS2yAQojnvWoPMFgqWDL0PhBy1V2leVNKeL8IHfjwkz3btHF7fqtvwUMB8U5qT7b9jBjxFH9KW15sroiarCs9Bt/yCXjbhxWVvA4DIHxQwX6nY9rXvfiLDR92XheFlshHew4RxWNJ7Na4mlxLG7MDW+7jAlA7DzGoYdSAXFo7QRD5nqKVpmJDY7n93YJQ7e828cSCwVSpgeUmqpyhupWXTIkq/xpW7DbWxAaFM+xU/sbelYtYjIFL2cg0M0NDqtgd+nUA69bPOXbqY+CfyfBusNWln41pupv+UWFodhwFyuPxyy1jGMCB1LZaOXwMCOUf3hT3hGtmLBKtXtb+o7g4Po6ErCBIMy3S1n5BlRJVHngBpFgq+VuoMfUJksSlvbUH0SppIkNVBTDCdhyKD1qGEGp3iQLzDnYSlJi/bfll/pkpywYdTzJlHy+e4EQXwOgI0REsTxqK6LteAMvxtNCfgRhb6VmGPrdCoq3n1C7FHNq62axT5sjVISdtzTHoXVvPgtxIKLY8nrjhs0zlGqcBuzAd7aSgccnX2HqAnCpHnv0TE0ahzb5gGtxNLYZZld8Ztm2+cgwGJ/RFLiI7vCXRHt8S0kt5+ZX0N2/HRjuMLyXH59afkyMAAEP5VL7thPglLvWfnjX5kzzPkZhtqKd4PV+aPUofxSNFZeRBhLUFSMi/zNq1HQjV2i1OhCb/yv64rOosSnp6VHvKabyHbxzrXliGNmLdW64iFpGLIH+ceVaKk4WYyLQC+hmn2SDreZPMPmZqCUT0fheJdbXI5EXL7t6W2rrCaT/yvRhuzZL2blfRXGxVPMigFZeaYecowc8SIHIsepV0xAQr2dwFc/12DE2+uHqP9sB1yq+8arx21deg+xi4TFuFTK0p1JlVoyyQvwLnoabcWlcrrUb9WzCab03eQ96Eo6ft3Xd1GUTdZimXHyzzW/+eiZvA5i2OZq0kfdsw9SkGXY6UT/CNppiO4pUTONeQu0SYS5T7cGtV7Z+kHyrGZW3dN4BJtS1TFJRgEcWo/i3l57FwZRcEwnYE/j1qruOiKtxogcoiKiIusyK+F4TA1RxN/SVfhxyu4wKnjHdBas1G1HvybFmSzb54SnzmIm2reYviCnMXwcQVTxntjLVHiAFNBWHaVq0qYimVWIxHJl2IIL/iNQP4pfqzVB2YF4tNsJ+Z7BMrBLEzzDyeD6BWuoANCviKvssYNhjB72AIqFvEK7ul6xdC3/D2c0Q4/UsVjEP+oTZ8N+jl6KI7nIVcL0RIxkHmnYK/E3uPdt/p7jrkIqXPxARX24VlfniGIwSjKRIW4KOPOs33IarolsFq/yNu5rjfXjrMNO3oyudldcPH79hPXeIbd7PnYhwY0H2fCSjktGhQnaww41uQl6s6FQGOBVwU9T+9ZJB4kLmozMJ+fJ32v/krX7OUYer6yUsm0ND6BJqYEb+0bV4/qMJalRsvOagt/WECRuS/tbT0TUcCUc+xm4fabs1bPOrl9dsEGXmUwW6hrZPE0bTHYUC9SrIHPLjriHt0/0JLM4VFRB6DTSqeOuiY9kSVvOx1wiTtLuHMflmRl9E6jhBCFEGibF/vchzwrvnxJGnLlccqKav5QLY4Z1HaEnL7HmfBtGFKBGO2C788L3rWnv0NTsDxqBLV5QkgnY+13vCMvyqOPv0iUfmegQFmKdhnWO7kG6NJJLhIQB9dCyUZ+3WUUIucT0Vsjbd3ts0cDeYj/LvuRm1t1zSnJZy7Epdv8XsNWc1rJaUDalnEeJf8qVJFF1eMS6vD7GvW9CnzCvGzrdDT0b3CnRB5OBYFR1jisYqtqtoyvoDFLY63KHni+d5+zh+XgNihSSir95fb3XV9vBP12jyPFnDk6QfnTcQZwOqJAeYfZxv7ekmJ4gf0dP8mt0NdOBJF/sxbPBvvphmCBVLqT1JxQrtFEK5mghuupkNUiU/knF0I3AVZbUsAWOgj3ZOM9SBPhbWN9T/ZiZIGNfNsFf9nbSol6hed+NW8heSqGY2jK1EGHhm6vzkKreB8bZ02XcB/Fjq5AMLpzKTP2E1hqh6zdg4cXSzVNDY7tFsEeZR360686vQmcTJ1QOM/0YAR3QHOze/mYgH0io3ecplnuPQYmKAzyvOvYXQc7JE2dKroUQVYcpOnywLJSQ6TZZd47+GQf42vEys7lG7pX2hS0uaXtNRCpe8Mm+AWMfOJcIFO71T9S1syhSoQ6JM1UBURUn8urQDplO/1hiZ9gJb4vBT/veoR3q8i3j14n+cyfUv9RR+laIFbuQfI8y73t720eJcSZ2BuuNwvcGO2vt1w2PlywPxh9UZ4V6nIveyW08Gq9gm2A4uvUuX9jvhsMtR4LT22FIxfl+nl5OqeE723L0OvXq66/T5yUxu7ecso8h2yypW76QljHfZtDXJ2OJ8VcFACkwJGHEdAu0fgU8ktcOhjI9io1cskTGux367YcN6wn6bCoPCmKIw1FCFq978l+60JI/KBdNIrDsPyo8ocbFk2+M/Yx53jY1xjsV96VHHg1Bs4x8VVd4prvSJza8lR8b5TMCSjgwuzwqly1esMs5R0wlwpwUOBoI4lKGVn91H2iaLxFj8wF595lIywKy87LQa6R/OVYnpAVz8WwAGrSo+B4PaL9DDsWquMTWiwiXmcqxyIvqskpWxtzd/H6FOX66LqfLaOOor8VrrREcNfvrXsDzdljSksDzoG9PsLM3e8XAqSjZGiXx+KVpR3oHwz2KyJ8s06lDvMvAg9/xbfIe6ACPTY6UHIvNIwq9v5hLanf+TAWU75EH6nwRN9aPXgREiGjot6Ku9ShIsFPcqhySydf/oBSAwKXem/uvk3wXBSO6E30U0xQ+DYI4SCocwynAfRvmUPzEXD3DWLjZ8IEtyeqJTgBcBNGGVIub2OU5wOsmhArOdmXtMQtYrylLZ5KZjZ/zpa47fDd9KKgPhbBlKaUOwgAFoGfzJ0nClvMMGepvtM+XobjyRYlf+wyqetqZPHHVmfWih7K2mixuaID2ZyQRb5bpVGysdvKE6/PE26iOFzkwfhDz21o5PFeDIAW6IIiuDhZXCEwU7ejyNLbq0mBt2jlXJUlS+MX6CEimqruMArztjADidD4jOPQ5yXUgnci2CD70P3ovqMlk19HXyHJYvAaT9fphr2m5Fqeg21okwtGVDK72V3YioXtS7WldjiJQNKhAx6wKOi60D3GsPogNa0qnDk+Rlmgs+MhRhoqu4Cx8HRoMFUAXsqwMh3hmKas6tJXESA8O/721WTIjVQVIKj+NZIOpvlEBOCaE6y0pMxjbL3yXs+m/kxPfxkH9z3nF7EGXbKuucAQLiXkvTuDACm8DRin+8bFF6KwAE6jbl1HhHGj3Gj2+HSZ9bIVY6//fHsGXBiUmszzhXXcrHd6yT4HLK/6heMCzBTVbQNhj2cmTcWKAFMLfb7A3mC9Yu/fw+5K8aTiHkFHorpxNIKXXRzNoqzFuA1B18gDGPPyMccjB06IFGmjB50/KgHzZfiOCHMdPIXQ3o5y3wDUDZrtSAO1eflb8/0rSR1OZimfR4zdMnn+RQKFwfg80inAJ5he2W03uRRh6C3mnqoYQbD2IfnwfxPJ8wun+5mjfhJAWiIn83AjwEuw8a80vSsgJPbzPUIKeiRcTlhKPGjBKaFyppQ4c5s8j5oz+fkwjV97/Y6qnjOl3W8dDyxjM+usjOCTTYlaAwbxu+IEMRl5RTpBEfrGsprg89p6ry9RNlkRAWaEw4ajsWL2Q2X4BkihU6jTbyk/jEiG37rK3wp2IJzHW1nFvLDuZ9v9N4shlb9c0TfAOo7TWFrTLfXc/7naqTMneItiBU63WfB/5gI2wLHyRjz591bSCb94KvZeoX1rSYHfzAxcdfvK0ZAx2OnzvLSZeDU97xBGJlDGPEmgqZG2xVDvESMHod5FFKUPeOnfN9C2pJD96zM4vGIDkG+MRQFm4Zht31Uo7bYuvESYH20SbX1Jn1Ifkeh+xL/nSxGu9eH1RAbhhR+WsC+5+8GOoq9SNNldKLcL0TNsHNkzUDx48/1+8vNRBNpUAPuLp+q0jO8RgpVCqTS+iiAx3DVY6iUhbD2Jq0HeR02s6MS/9aOi01/LE9tPvNFLa+1Sfu4ydfwrrmW/zcYSEuk0IVCv5AWy3X2rNXBtwuPZkDr6EcPBRaUKCl1x6WYdpBNc+RzVVE1QOeAgD2eTPk63j+Uhco8bZ6NggttZ6o3AKqCKkVM2N0daxxymCy7fCRN8I0e6Oj65yxfx2isDgFkiWvttNWFjddU3HbmRkPr/JQzfqoUckKkF4MTt1Ho40XdvEtJDuROu/eu6YgEkDxhMf3uAEfOLw0yblyJdradxJ31ZpFYcSiMXkyrPUrLJTfB7sLxZkBSoB+Fqh0ZSGhopQS9GpPMKZVy//Knv8AYBwuYbccaRffc8dNLZLlvF4T8PaqhDHmes4EECD66RI91y2Eb9iyJkskbzHy2biUjvf/+hFsOxKtiykux3iNxSE+aqiq43oqYQRA62DLNCrR/Howvmhf2BQqiYDcU28Jx3D/ReDtR6JmigbbVc26K0+n3CjAGcv/DcKEQQdnhBR/rfQAWpN1qMvQVJI8KpNtUasVhM5R1iamD9TjMkVac0iLKTSUPavaU/oxs00/hbMSaU5MzzY2CQe0MQDeSFSLxSo8QyVTYwTwRp+tL7PpdzniDpP27SytF86j9lQnogHkIiYgIeIBH67q0WTDKonH12C+QPfFLxVS0Dd8/aDNDDsJpc1ICGonOJ5oGsm4OdUNBe3j7gP6I4D9dqTYbK7OrgIutKsfM2MFPlFQGfX2nmaVAphx2jWUX2n7SBi05XGVipV6WtXDm99J9WnfUNAa88pGMAR0yoxY8jR+2zcArxeq5I64jNI0wHOIrwDsKIEomP6YN3q8ruwzjJ4IPnsb6D9sD1gF5ckv8rLv8r+HBE/T8SP4SBiQdGMbt8hOlNvuUOQdVi17p2EKS42mHI0Up8GVFZPmmls6RB6Mu477y2NA4oej9OfnlHoxXBirNCYVR6AnJYPAnm0sMuzlFP+LiKLf4YfLBowT+TW082hMTAG8H4nty/hQkdVxJejkcrBDvoQynACtDzdIcpR7eef2nfJeONibEOHSbQtIAoEEnfuFzY+IIjskwMOF3NpdiV1We+wbp0a99cBw1BjcJgFRj1xUIrm/zO2TbNiq3EZleMhnGK2n/ddpgxbtrNRYWotmf7CBxSI6TN/xOdj+ek3UX2Iwj4fzgQpEqgK0WxaFH1ASDG2zXBtN84KFxxNlMy6oDUHenSJIV+/QH2nmi8DuYp+a5SJauFoPLqw1DUjpwZMgZWq82mjGQXvuvxec2sduGGoi77ThEGyfFlIhVYRx4s4dhbG9uYjQtKUj3Qs6z8YXa/LeRImnsw1oHUjPZvIXYvc2q9S6tQTW0X95EnjboZ9GjGW6M4XYWKeqvEvTr+Gv0uD0mjCdC1HvQMLwQRPuMME7BeqLHpgPifTAYK+bt37bYs7ijp2EKpvEXBvyEIL5i6N9Es9p75Q6SO3zEa2nc0OjZl9h4RZYL6sNMOAVhQZhmZIhSfT/2rBesDGTbF062CQcKSM3EKd9ZyPAAR3OfYc8wP74rgdl4pc0LH/MKnexxCKm1XFZdRQYJAnijQcnQEXwdXItVCp3MRxQowN06Q9jH/OnzP58ULE3+EHsnaudpH4ljoendXZtYd5FFKFD8sue95NIDyQ7d2tBKYM7MTvAnpIYUcSXEwOXY+0UOINgY6zK9WNvBMSBg9i7VE6bj9MFbgDDyFgfKSND7D6AuM1yzduTrTtcqWE/SWqKWluXEq47lwEqa5JHqYX7vXCTiVW1xJNjXqhpLPHYbLhJQzV1uRhoTrIkbA6B7WtQDrXaXKb1gSolT4SB0NuEL93FRLiPMaDKn7XdRYQn2wVFWsp4bzSjl9u6fyrWIkog9RUhAYIVjz4HqrlkadYr+iep/g8zSeTMrXognDmbaE3EIV6Qd+tb8+8zUUpNv2ubccKoe5ndmeR6MVSCQRlhjjJ6CzZBSWRM8/WwYP4XezH3qLQEf7plYXBbtDU3Tevdjl6Jv95/yTeN+L23pM8fb4iFMJ3J5YrUxy/V0Y34uPP66V5LuTaaJWKLD/qVUJYKUy9dLBwuHUHuBpXu+ZOxxI5Himjat/A7AKzQ5ZFyRSziRRaz7MGHIHIuHgLqwNps92OX9lF3eJLg59DF1kJLbDFQMERb3uYV/PlOHL1OlKEUqmGVCa3bMa/oAbbOn73N4u4cpPobvqyoh5+PzFGGTnpfC4bdGrU4z7RgcxZ0ZL3KxFkCQPIBmXrW7wOaCk4mN5wI8qh8kP9Jx+MA5luE8czh7f0gjnP5WvFV2fLWmghPb07493g+SoSBpOTNoCHg3Zic9igY1Ll0OFJhF8EYjuXN5J/beZ5h/+aIZHIPPU9vwAaXdYHB3jgLjEBGrUuCPRSh1gUqNe9MQMQulMC6kFKWsaOZnDlgiLXNkFatr11pPu9KGFXvZZ94eXrHQQLHwKlMvpVKfVwQn8+JiroqnmKbUvP6tDsqUq8bYt7jhV+qQEa/uuJCPfc9UoLqn+ed4Mb7NvbvyqedBTs9uV7cWjK+uWykSfyRXgCRXg3rIF/bqr2mxn90vbnwuEqxK/OkfyDCwaZ/xPoFwei/fxPWF/sh1vwhyhC2nxRu4oWON+JvlG1X7pyO854t14o5aFptTzvXLVN9EIDFn6zB4lmeJSrn4VaxWu5YktrUVRiE+byaSksC+PKREbnx5Qj8IbsD+ND6z0jtb1nm8igtOohRVGb83L1eHSDwkt5OlGjT0cnC40zx3cVTNPdf6BIZK/U5i1gqhr5OMAORMmfoA1bfRfA/JBwLxWvewKvf3+GX2f/vbRd8iLnl1B8CHHAhUdzwiHJAfeHMx76reyByp2eUtwnnynCQrXptb55YmdBds80buDU15aCGZ7/CzSLcOUYTSrlMA95r0yq7NMo9MSnO2C7ABld+AYAJHZpzZgTRIDAxjeN2RoQgrOdt+E9lRtXGhg2mhaAWeYfpBsCr+yJg5aPDzAZZ3ZyBd426pKzcop57wE1cpJOw2zmJ01AYQD+BWfoaStP4CBULfW8WHmja1ZsR+YkqpDZi+rGIzmzyDqykc8RZ29izMGfa2dtYvIGobhWR1IhuZ1+PzhujHEYplqxbj3YDcs0ygzFpIbPAGf5kTRD4FLBtsxB+nzIlbZ/KsEfVsT7j3AWSEBvMi104OyrNzuPa96avVvfkAH/5CCG7Fyk8aC49fl+5yUYL1kXh5hucytxM88UYQIB/QMLkCpWDGdt4+VqbAaKfY/8gCgmV7dfZBSr+8bxANb/aeYlMjLj5DX6JiJYrUuw/bwhTcEmg8lO94qKpMWE9PmrXPClrrZxq2M+aSPQBMw/a4/v3DVW1nDeJF2PEZRNrEG7x5YcAgb/Jgq36IzAsaJJMq2abIOeEArAy6uW0l0exDd7oVtVYUagPBW47L4f4m1ma2QnKE+VwSnLHW8S0Gm4JoJP+7bIxfpQSJVA6el/dzPux8Mf7YUgmbhZSAD2jaQPaab7Jcflicib1AcyN8JaW5i4WjrHxLRnoKmzwcaR7eVFhUleJV0dsY3DQDUxA4hjoo80Nkg/MEQ3pIFP0n/lTCQnog7EXXjiC86H7XaZn0DAEys+XVOBr0MtOAxh4bBqTmLJBXmakARIAWmiurE0OkSteCqsc5d7HuR6J16cnlp1HYxsALyTExHFLn6eqk4eYGAlqEsURmlV55k0zZc9ljmfC7LQR+DRPKSOLIz5A7CGTEeaAOsfgBFsOTN4MJB6i++0YzNEIrZil4AJ2NsyNhEZU9E49EDhorLx3cK5sErbDCbuV96xu4Lh3CJ+U/uLxfKCu4UCsiX347ANuHDlbicue3ronKn4UCz64cNE+vBSvNpcOrOWF7OFwDBA/LqxJEYVuvU3bVawL+qV3V7x5Q6sZ8xOnZJC3AqMVuLSlhB1+lalznumtCIS72jkmgEXQAKkzgV2nClU1lh6W42qQT5rwE03nuWJ2A5Hcl3BPJ/obzg9c4VXEfFY96qpvysn9f85hXvrUN/DDUfTXQqLwTwV7PDCEpOdOXvrm7pzp6LLzP9gR+ZMhfhK8jJlSN4+6k4DFh8Pv2e9y/GeuI6sQCKaLuCoXvVbZgiqzWX/ngB3UOJhaR29uUwgP09jmdKYQ6qGhDDprbmFXVociwy6Lp9sFdP2G0JNxe6Ndx3Hr4M717my2ReDNkBcQOefa9wRoIT+Rgdn9Eldj0UURr2Nr+HAM9LQsWHFVK47x3Nzob384EJ8UctwwWQIzOqqAAtvDqS9Uy4rLwdHYUKHtj/wW2M9Z+orFlAXKU9DhVU1Db5qgKv+V+YurD/+B4HLwl/ECK+vLyh/dIy3AfF0NfHoJ5LYYi59PenpgHlz9iYtTuLFcrto0Ro8rkQr+SPmPmxpVzZt9yGXTtvURxk7q0RAZCihH6ZB8g59LZD7SJDHUMveY+joVe8IDq+o9tJD1bt8rbz0i06Hs0qxS5Qy0TIhZVk8k6LrSuOcrjhHCFNbJczoxBghlYrvWSBKir7h6uV5RfQmC+l0fGU4YlSO0onPJ+coadRhYnNZW3+kjFpbCyffddnmG+hO3MXYHAgtqHoV8XcsjWnCHveV64nnlOSv/GtF8Y6bHDSI535svCwBWY4Q/Oge9i0x7ntfzqFDWgKRkNwl89qZWn22SkW4Xohytl0cJY4Y/cfEOBi8azxPzpM9/Fdhy8z79iFUfmExPMrvO6zjE0ay8clz6+ikBCTi+39d/Z15u0tBJrcTUMIgLdekgOZhBvziuQDzjcUrVLNn2HhwttXREdVv7BntTKP0FXGzu67aQ1fuGr+U8DQMVd0LD8899j7uFCtU7DQoB6AtUUQwtjoEk7KlII9kOA2e0aDoWlRNcAYH7/qEkz1QThP2cOXB5X99Vz8c1I05iEldX0wBEg5r8rJZnV7x5NuF0OZSgJZzHYgrlw9JXiMTaLMoe/b4Jpc69O7LaCCfdBWy++manCWcPJhgk/EDe5rvh+ubJ3Vg0JG7APm/AlOkS8B1iTPIyl11OQSV5abXkqZWC1l8expEHh6E9G9GrOTPAkHwuaG+683+6RPcUBtWi6gvXF/J0w4rnHjuF6f6YgfA7WvQ/Gq7DFPrIv4eIsQ+pg4z3TX8K4TQyCSWn7K03ptlmqNmYnyfEk9G1sUgxUthPdz4U4dQQVhXBGNa2qH41kkPSQ6wD4UHizEmIiuD+av2ZmP9oavxb2VcEnbTMAddsytmYooPn74qdIdvq7lnou01XawQ81nh+nN1ipesHZYHIO49dH1/dUhu6/PsfSm3Y346NNcKzivpcfaQTRUOtILghBfXpFRWigyHd16BDImc2939lsBosDaSL7f8vkLVuLKzbQNK+fnwSqGEs8VwN4DB1vjnWxOeHjYIGnvmOYA3YOUP05LnsH3vCS/76pOh1kunymcTLVpJtTuA68H4NckHg8p07V0S8A08BTDOyxY+WSIfvyfbK1/HaOz1iviY+fanU3gGfZUM3UebJzm5INr+PurBWkB4s0AHlW6bDLVhM7nj2hahyCCoGXYSLkWVxZoVOAxk9RFYwlzHRMD3uaG36fAIloLD+bQxpLHIpG9CK7ZEbFluRguqPFDQdrRQgzN/9F/bntcKrr8KtfTHVni5ZgM3hYZ3CrkRZpvl13+AWeEz/wWnId4YC9p+yOcJW1TpluG5uZ/HrR20ofRAisqz6m+zOa+9srYGzVHYrPvTzoBUpNOrvxoE4WjRmjAq3ROtOkZysAOcXqXPRI0ZvjijSLgg0T57N8toTRbNEjjRLR6ufqoW3NxeUPbMJ062USxSVSnSKjTriDJBGnDYcRtU71qAcv0p9+A5CQS/HyfZXUZII+76k9bK20zrWEqfOp1QutWyqpKCuf2nbsxcgB4yNHVkgdFBUH+LKbR5K1EHlmLu7j3IfdGSPahp6+ZwLpqLMxsOJWxEpJ5rM4G96wL837TfHlAb4rRx6Tp6BtmHw+zJVrct+vIT/yBXmyFdUwkHH4sAXHJMs2bfgfQw2meC9+ggLCqm5Mq4e3OF+x5QzPEsR6S+AxLsFkgJsLDZK5xFnvu/LsqvPVnY8nwnPJEq7mT9lq77BNIxfou+OF0ituGqzHB1KWqO/6ZxedXX7ENm4XXy/JU/92C6GWoW/IMxAnHvpsLPr1xKiShx8podRmxdViic4BnrCVns9fCjeReuMbyOWwqM+o0TULPx/RRGqLqyt4DF+hhwz5H2gbriE3hbJ0ng+L/SCTKiosa+m3Kiesd/+Zvd3sXFCOg1uRbYEgrjTOUwwRXL32bx0xOmhF90ezVCsgFQpPn/wiwVP0YCS6qxrk5s+zFhUeRgt0pRI8dSgLyI+0AQWAI0AC9HHh7rLYoxZ66N4MFj4qUjCy/Y3FlxvrITDLdKxgQrY2SemwU18rbLR4jQ3mZ7NyGfriZdjQ7Fg7IDp+l1zqNswNYsaw5ZC+pHwZ3GZ93BghZ4z610T4ox81nsxDUcRg/4489C4feRVcAkTnjwYAeOmTVnCpfciATQy6cydPNkNRNZKcUjl+CLJ7LhfRNqrvHTNwjd2TDqb3urWeyLQ1qVrsfCGtKSQ/tpjzPRjiu4c+q1H4DW50+SJG8N8wAYu+VbIvjFR01kh7364Gb7hotxW63UBQovtT+Hx8fvXMkPAejijeJ1DrKuMTqhNEv6kChgnp//DQJwEQLPc7mTd/SBqvTnRj1a2YwKNqsZ/jvJ7CdXpQLZPBHsRc16Uc+T9yjWHHnVJUL9S/sy79rmDwEjuRnu8dWdmmoHdtynXMzPIp1ZPJyTHFydddCXhQIxzJKHWntGE2+bSq2EXT0DidgXHOIMfU2XxWiQaNQGBAWq4KsT30xyC5o1trSCm2iCMMexKa/BhRisF7KAd6rA3DOAcj8VX7dkmwx1LEn8Kh6ws0a66lonk+q6MRkC+xBoW8EuNRYt/c/pg2neC3yI8i3DKTplKrv1lS11az63SFRGwYkqyYXEt+63ZTVxs4+43h5Lhyi6RfnL9aXJ9kKRREHhktLnCdC0RdHL5/u8IVnly44KwrogBfWuZO3y6RusN5M4+2ZhO1EAwVplxJ4Sk8udiTOyTIuLSHi8tU0qmOMK23PEkDWHtcBJJsyb0IpJhh1qqux9hxN0cj4a+s6Fp5joqBNFBFaLJyJi4OudBPbEKlUnVSGB5cPJIyQIgJlOQc+aprZWCUQ5Hxc5x1DqFLLiBlPEp4J71wtoy/d7M+dOO1XgWaLNSHyfSjHpMQVK3qAwTfexRavtx4jXVcx6RXIltY2e1w4P+TYLeJEZsY34JGqiqKjdiThKk9QjGmB3Url/bP/Dy0bwajH7n3JJ71lReXM64PYl3OW7Q05gRI5HWKuRPiZgggcgEvvgIEsB4Vo7mUZSnOebsGcRpH5HrwreljoFBwfluvqSaPYBBVJ/GJJ5aT9Dw3OU2zd4l1Gq52Snq6VTDpdF1EMclwbSNESEDlccp+ee7MP/tVomCep+jP2KNQv8MYk3NHJChveDdgJDytTAik7Z7QI7EbDDgAJFuXMFs4LCvYmg/+TgKrpVYFWOGWVpraKdxaIdT0r1mHBiTywOyVEhCJB4NwDjHzapha9Tmg0uDLsaftTPiZ274kf934A11zWu6mqitMqwI3IJ5glVoCRTvUK//imITVU4eZMLsA73+0t5WHpBHfd7LGrgWS6qz6apb61WgJ5Utx35swLvcwkzO15tijD1+slh3/kuBz/2xtEw54IKELS9FVhu04eCb4DqgCentAmyj1bmwn3tolSO9PBdNQsCRmIWHEJ0c4OCzU6wNdkczdCshsyC++LviZSi+jpfCZdmsnN8THpPkRbZ/7VjHMEa0Ju45irz/ggYSQPhaMCHFipgLYni1QGAWaxOldhvoFlipMHTxqYkQXsZngQe4KItSbnb1Wao81ZRJrIi5fHJBfjz/GO+fyaUQYU/hKJOMj9ZMd6+RmawZwpQg7quL4SCsgf442DQDP4oQKm4Ew4fF5n1kb4F1eFhsdt5hRJ03PTykHHkFSzGJorl0CsbCsbl4znI7PkwOagyYJjIwWfCxHLdwjb0Bwf6Rz9hzhGokkYnXfy5/yRJd3MDhwxaU7Niom/4mmvryIFvaUbQeM3V43YY1VygaJ3+qfDsqWuVpu1tBPTRa5pHO5NXN8odew9Ama2EtTcoT5UyxUzGelITr1t4OYGFsAW5mwQrB6fhz2cCdreFljfUOcgswBl4H5zkWEE2XTZRUl6cxSRh76k9uXN7uaxnta8p3v0ykbFWfsw3ygTKR0qwAVh8Of3pJMfTug5NE14dMnfrIGZoJSYrdzqXzWgQsGAiE2t3OTVXF1UrfR5jn5c3Hsf1PrsnGEMqz/2Pn5CdIHzyVtyFCTm2cBKFUdnq2V+2q3WdR4kDwHAD1zDqDI3gieIUgqVq+wKMthFnCwkXkfW57IFYatpeYtlNx8ey/Dr4FYXGDgnJW8ZosqGKhSkbuS65jdugHM8PoFyb/acxntAlN4eRo41T51NEAgMgGh03P7bT5YgyjbzfS9tP6AOZ47Dy0y2zuhGExhGJTVsLu+RTKyZQYyyGi4r6HU7i0j4MVil5t4TU2eM4TageiJlwOJNYe2ILbuQ8E65PsipbGo22Bs6qLytgz8+7Kc9jRmmigO14AhAamffTcqZSs7CwCLPxNaREUJ9Ass3qevJYwS3lpnvXKebdzy9BfyBAVWs47wsCwzNXPaGA8mPfosd174KN1NKbK7doL/zVpL5OqGnMvp9mJXQGkjbNfpIgqN5FSD5iVN5zt/PcZW/BPBpzOXFJPlJyw+rUm3u0D+vt4KINbou/b4h7YIAlSZB7IeZMQw7KOb8mhwR0x2jO75stMW2zzvw5lbXW5s69hUiUWINhT2ZD2pA0/fzC+n/M/PR26YK5xNiWOCWJGk6wPjDfjnPeujvi7kzpB47wdogqBq7/38bZDB1jMVrjnte/+eJd09A07OZB1K49nkhg/eN7Z1ZulARFvdXx2ugWjb097S3phqgFkO2oN/p1TQMe5T5NWmlI6fRaleYRXFbMwTznUxsIMHI0hY/gnXNKRA+VsymeaHYKznAxZAPe66FA2BkuKKMjSFW6bkUEQhiWSB71BpVqdRnPiiRtnJLKRaAjz0Xy1y+t0sx0yzem5H1iVcl+Mt1VJHp86rNvAyVUPEHFJV1F2P+SjTBvpgELMn6q3+aGWBO6HlVRJkM3SPrA7gfqOro43m+5ue6+v7EB8Nlr8uwOGZ1nR5CZf7HwxZUtHz47oPu9Rd1nRZCCCN9x5ebuU5gJslhFe1PZaqJh83EzkjoOo9X8cr1fwEMknI/pL4pR2FWbEVsImZffsC38cLitHr4Z28+F3nY4d4G552VUdN8yrTsjtO3hrErhBFmjPFN69xsXaub+qm9WgUk261O35ug14M8YCJGCAH8VE+PVhJQdqTmPBQ55Cz3IXcTvttqw0KWMQeG48f5kcZHy4TJAQqdfL+ogP2fhSPAI/ZYHVnkxSeJ6Fggg1ktNkMhTbPIpOpfQhyuph+rf0rhS40t8zWMjBnRsEeuCdYGEAafOK5wFiD/SevbRMcU9TdzXWIsXIepnfuU8cg0W4nTdHO0q4uBo77C0amrtSxZtTBOnlAy2ylu+Fi2L29RDysAVeNQ16nxfKtteQr7ey2D8+5M5w8VumvMgNKrE7Hy+1iQsAYOMqze5QQlwhDlll6DufedT7e4VcuBoWneVCi1Kl/d9w6vhyh8pGzxPkfegYdOATA/dDI+JuL51rTBQCl9DHE6kvHlDW9iCbjQ0QQM2DikPZIaS6ICB6aaz0FT2fxBp2gTfMxOd/vQMu3NSfOVVqx8XIK/leydQhCeRHeHA9aQs1d1MZX22smbKKpGjZiqQTeneqLtJJo+73zeC8fZ/mX+lIBv/EFEpPbRckfOB7UWlZfLhMu6p7GSmt8kFXXTen2MH76gSU65nruUSfoalhYSHeAxEjttPEWX1jGODcmMG9jpnN/YvDSTQMvmIlftkVAm8ctqWHWPBtMXS79qSvuL9JXc47e/YfEvQSZW8s94mPGBudRwgg0ZcQuTTDee4q7Pu9bLKSyjt+Gt4JyfOdQ1MBSpvKNgeOcTfZwNss/hjnCm9NDEkQQwE3MGVUFtvJZDnbQJ39eaEah7VYb4HHI6Ut0VsSfl1Wb4RySkJHaIltvlfhcB5tFWrS8QJiTrtyc/kBg0Jr1147c68MsV/b7vz66nvkzD32/UTFbBgRQmnEiHDN1eGH0MZWWFXzWq7N0V7O+3tJQ6CGkucH4paSeYo2hUc7CN1sebNGxg138+uphd7QFDVRUfZPS1qCuxOw4eCjpmwtu/tqQ4/IZpHuX1bCCOQp1AY7pF1llKz7uxYsIn3uIkk+GLAsmkwdCeiD+EChqBMNOeJwwbs2WdwlShdgdajMpFdFS/bwLZBZEgBx71K9dfhTVuRgQfdIYJMBX8bAQ9WdFyf75kuW6RmQs6LODRbiTzeZBmjFViMIQGdo99BWU2Q2Buzu3HGcV6oVnKcCWj2oL8HDes9XkF6V62nsxgowFYcvIvPprMIljb0p31UKu5E8SXU7k3Zi01hgqIJbzLBKl+/Eq8lgfOBFrPpEOaaKqndZNh79xz7z1u7VgxnHEsAXksJVaTHuIfG1Dw9+51px2AyWLCBq3tM7Ll0C1HvXPt0nwNGT/hRriPKy/594Ock/2XzQxAKGVv9sBNTdLFPuljxxHbSUbB22T4mXv+pSyHVH7lx0BNY306rIURvxoYxpH6DBdlHu0OxHsNDne1M4z/8gN5r01zbyPnsGJPuxHjJImopxC82oAIQ0gVLlraVYEKnyrM8fZQJ/R0twd4VvBP3veV2tpPknkGEYS21ABsNwILKpPGsC/cuT1fJ6RZFcbDz3e1eDwRFi8dT62YxeL7PlvJnKdTKgP8QMu1U21hSs25Vm8+z32B7YvalxKcDkSdP8TrHh9b9Ap7d1Bqe86Js4liB7vn8Rk8WTYG4BhqcbHMcEaNdUNRGl/5VHYfvUKFWQq855+cVdx2KcMYa/9cEf6nZOPw2Er2vIu3nUSwwUOfZt11/jNegtqDI4qT/XGPPs3m4v0STMpnIarvyaMrSNufOxvRcfCLOCj/urLSWjTU+R5leQiKgzkuUbq6ALiegMrcHuQm+sHpQuQ5ECUgoZcxX6eH0r4X8gEOC4y1+fOOdBE7k3FBRaCwXoISOapCGaAmFdrwbTlpEkSpyZ0YmWjZwl/0sZG5e7+s/yafZjFZAvqn+gXg6rCxxPbkRCXLDu09eNxA2B2PkieYgcbsWWTwC5ikA1QJGUE0Vw9m7z+H9bu2/AT0hdfkNWZ8jzQRo/8Lfhwy2Bx3Hce6LR/oXR7qCN2oF5FxGFSZBuC7x1OOJ3j3zhrf75XiLvqtedaY9F8FrPh+JfHTYNmxVyd0KuDzEMLhTD46Gto+nGiiD+qRxbmSEmMAAC8LCXOhKXqMZeAgCFqgtHVuluMpRX7csU8wRyqaLJWw8tGkrP4rlBbVApqHmOJD20fZ67SP+xpqGbIWbCyoSsz0SP7db5dzwmcr7pScdaHjP9YlKBMshXLKqaYlvFGJ9KqBzwYD6D/LbOe/Lb3YlnqW2zB9oHJuQuat2P2QcqyFivYSoM4H7CAX2IbYZxyJmOvGBJBRHp0qAmq137OirpyrBJIXAFLHBvf3vIgXUbihPmgLEKtkWd15ckUbIzB0tVjEOzj77Vkg/POJcBAdxnXOIB5/ShQZ8NQAhEsmEiESCFOBUhKMnCgihEXiV03gIW3p3PKUED/WJmUKvDRjbaCDxXZ/YCYQdxxqlTqBacPxoSlTRj7r8mdm0v7FSA2mK+sgkCoZj9Saad9VsX+A6fdXVAAHvb2023cR9kAF7CFBwG0TSiTeOILSuuq4Nu7Aam0PRUhUeTMyOlDze2hhZjV1cOiWBqTjgdHrB80Zj9I0+ZnCukCtnTXJB9OKx+Tu2aaBEMRPt9VSSjbB5j/w0wYAObkZyz5gcTTDpQOrL/J5ANpKYLFddKAwCoudeJNLwXaE8OoTHCBEZnyStqYVncCC+kwE8RvZrFGy1db00aJlDpt/gNDVbFRlH0t1i4Nc6KdiY/DHzlrAveN0K+SV+9ALiQFU3cwgNHwsFXAhqdbhFXrHnyifwV/0jAN1M9Rc2+xTQ38sG0VouLe0a7gdU+DdO5qyu365/sAVa6FdkxdRaRh6nm9TSMExF/5kyAS/Q6DgtHaUoo4eofsehHJEt8bxzasPg5Ca3s+RCZfUmFLcsPs+25EcKy5uIpkEtSVyufhBE7rtKhLlcFSIYTz9GHFNqWwRr8PSZLyiKiueo4ibFIKNUdE/7cP7ZGqnsTPN1vmB1IOdyPhHCsTH0WHIzzC+IrquKc3bEoDThsl8Y6V4PCsExPICNU2aDy+PA0WDzMjcbb7A93v9wL6u3ta0/GbDm+V+s8Tdq7AGfMfYUGu64V7KKSpnVifnpOxkFH375ECAWQV0bU6dIMHRSsIZ/jXI46qFmtyVFFL333Rx03h70oVfxIHll6YZZpR9KTb4MCRbhUL1t59qGI14D8WatULuiHLjr0JVzY/md1qufbjcEzE0NiXECSrxdSjGe89GLlBuafcbA+fjYkq2QuZBxNvvkbUuynJ7kks8+7ibxP18ndcMHVAs9ES9R3tYnFSuKEEvOG5707Ta1IPoRyA+Xw8Gqen3/L1DQRA3wOvmrylkB8NdoonDuhEcAH+9M/bb3GjknTs0Gr4e85MJ61T28e1pqQkiDcmXV+Zn5ez5vdz9Bt8CEEhp89WQ7z2vstMSsPAY11GkAuz7mr4KNXcG0sCIRESChecYAAQq7o4eXImrpTJUfZSXz1K/JqEp5QkFHAPMAcBHuA3bdWiEuUyIqhfnYX5Ifnoio+7HyhZAnLJoVLc1CcvclcA2pEUygziR4R7lECj+wJRPTH5QJjOme4ebNTkjWxSi64V9QartenIFJgpi5A1OMvTMPsF+Kc/os1yIknCixq8qulgw8SHK11VFXIwDxZ/wVDWm2ipOfAU8FwKhWl57UNNRZ3i3VBHXX8bMJJlgVr6CumCEgcG3KCf4luwJ56YLG9GQnHmVx33hA6SoO1IbEaAs7d1l3s0uWLI3fajDkhr2c/RWOPXzFvaKmz9UXa8EddLdsXVs3rnW/ZWEN+Aj/ABTfCYmoAWcW5PzFXB44tvxZkS4Psv0xaon8teyvCrVeODilZkNVVqRzWlKmHxTNaJk6iy2Jc+hrTvN9U4nxoO/ldSU/RWSnKoGkgyz+Kj4+0w7BmG0ZRcINsB5sHIk9k4IcXVa7dxUDmC183WUOFk1enZo7+JryTGsEGInx8LWXPDWUni4dxr91jhdW82/Sv6MefUeXEa1d6fiEx+G8ceno0kdBuC+92g7bWCYeTba0O3npmIhIA8NQb0b/pLziKcK1lNcL+OtLuDxbI/aYFzkaUKBDJUuNE4ytqfKa940cFlPE/GLkPKaDFx81KiTf6veUehqSPIlL2VbnkIAtLlayld2BNiwHySCm6iqtzGMXFw2xACCup3RQwwXrmlmdx7yHqwMG8mMh65mu+v5vLT8xsnuWschGEvx2IXoU7a71vHRybbLjkaXKYwDvi02t9jWqHxETvRzVmDpHzuEIprSA3anr+1+ZiSCPIJC4CPawAg/iFoZS3kX3RdI3yzhK++lOwYEZP9jG2/7oWA3V78n3Q9ZQuuhqd/LCaA21Cxg+bmZfD43mnviRj4k27dNFiNhztIeuxe6+EXckD9y6Ed13rLzasifV58nLYB5StEXW8ftm94w0qJ2Vz9fiMslkFsWV9YZCl2TUHkb7cfwM3p5w+/nWc+cRXAUDRl+EtRJ/GK0Rojub7UTtswEbZH+aWOlWg+qscqrrNaBETPKSG/c1dbtLaaoBHsXEzXtXNLgmMk6j/5DoKA4/UqUjlAKkaqtbpEu13x7pcVOnbinTYVxGZg6WvO/NpoNYazRLPo+UuMovcNUPGkDxpFL841Ma8HWH02DCWRsL8pRVuoyZC7Lr2Swe9JKebBjdm5v8MqRtjcXdLyhRDljKkpa4tZNE9Q2FibJ4xHKZSznNYYe+HFH6qJ5od7be5oaLoLHKIAjP3pFOrDxMg/8m2Nemvu/WZzzAsQFBvRll1rF/5vhvkyIn3JeYzpn/um121tgOY8rn44MNAxymUNTUNbYYjfPhTo8xaU/1Yax2pspqxtG3zNLzxEkIq2Etg3SnQewzp9Pq4Gq4iJR8kHw5DxjmZJBGe9ADfPqLPsGHXa7sEOhBiZEt0lbOhuHXNWiuoCFF14gr4PEAHhH5KgAh7ISujfAy869Qp1LPqBzV8s/h52MIs7aeG1WgHgHn7kfVYpaf3mMdd6uZ7evoCO10DT7FLHelxaJ3WCeF8pEkXBf/PnVfY9eEbU8ZF9VecVZR2w9MIVKwa8PEXSveNQijzH1FGsXgIyG7/doZThTC1NVjktn+61TJCPqKkcLSYHCAZan2Tm22iCJTeONio6f1jmrvTNmr+laxplMjw7w4AXqrrwBjP6IybQQB7zPUC++u3cTO4n1SREyiPHubypF6Y4CPMOwGJmymEax5MD8pxLmRg+19AKGAJVSkKMixMkqHaYk49X2BUHvjAhsiVvIerBufwVEGwTssFGa5brrMGgSjc5YAEq5CcBA/Y7LwBLYfMI+Q2EFkXiSfGonFATgCZix3EffhHmbleWV8ObuG+cms7I2LQ86HF1yW8KnM3g+fimsVKe2SiQTz01KVAzK8iKifSK8CXiVmmRWTShABvWF2VOBAgH2Gf4BGgOoo5RBhkvJBhP8D6DL8WOEtgbJ/ZBV328s/Q1R3VqWR6VrlW9Bl2fKV53kC5r6oDJc6BKqIV6cNMWq5FMm6wc7DG8b8HpGoKCmSCIF2nXeoShrTVaqFotUVjOJhVLmPianjIBGgcMH5SWkSvM/rl9mc1EQ0313/nOrgRTUfmAe19dSzbW4XLaBT/trzE5SxyPZTVW5xqGwj+lw5SR5i8xwY2svI6a4BS3A9ioFYRCmbzbstrxMa5q5CXMkyKCAAPQ/3g2sKjyFztgjCijzNZ8UpWDLcwevCKc2textDs8rbAVyjImc33JZO6MPUr3XrTv0C4+miBGUCBe/DFR4WI54T1hRt+rednqdNVA2yNWjEAPXWhU2RM0+zKol4zRjFJnKCH5Bu1MrsrZMD3O3bgZZ94CTuJamRyjPNpBTEvVX59eVBWGWJH/EBQLNH9FJ6YJ8K1Yuyk3GKmhnCn41wkI4eKPbUQYcP5G9Gl9O4iNy6O/9TYx162/o9x42sGAZpXJYr1nJwyli2n+XNBuysRhbSb+vHUSvUQNjuQFK+t6xQSkSunv7eSspI/lm5cA0hWVJoL3fFAs6SReVN+gwzD/OiNO7vmkpFIiD674gmuXv63dnzVVVEzgZrR8eaYAlFW/8O4fLzIux7hll0/7sDT9nJAXeolZ76djRk0sUS/6v65J0PsopRjD3Wp9AuQEl6BMnN7ja6LSEQ5qOs5XkMfaHQJrGCN4DHtX6JgZfEavJjG6RclWuBVFETDbd8XnQxpp+zUBVKq/OJiJRT1+N2cVWTdufQrj6DIhJb62Sd68zIRxbAWprLRcqU9I5kUNuTf5JVhBr6/8ppfctDYKhGPK4kOsijqRvfv7Yow/TIaYdTRhtXRSzQaJMs/NfU7Vcopm8LsrGiGMI9WRSyY64J3BWaYfmfkFb6UQLzO1/barNpzHVHHBa3e4GmZ6epW7yr6f818sXZJrB8V0VJrYwwodTe5wUVMyj/q1CCmxDVSlxh0VA1CCNNzPuIfbWWRZgkClFWfV4JNjfZKSWNa+Hk5g++vFEpQDhAFqCtk9Dnewoa9ngBSSLu02DnPXIn2OtIzHACZL4c4teGyiY1w6LIkGF/RBDPaHq3sfbM7A61W153zkigrRzXXwvUPtlO4KzuDbOf2ZiYE46Iyo9E7Uy5GoyRuv3aeVto5yNivjelAYDP6DjcnbdI+pLixLmHOh+r2AuSedgUVK7ccjT6702btGKNQH7XoNHGPJU2Dkj3saWGumzRf/yeh35XzePh5phAb8bDrJWC1cMH0xu7WWGO1FHSekuv0dPRTwmyWVU0AM/rtkJ89lGdje1ZPz9gCOED+t7NFWyHo5o3yGx+0u8TIpydjyUA4iG4XUf7fdepheqQjWDyxELRE+xiTmJicZZ7AKrosijN0B1e8eGlifLRqKMnB7WXaj4IU6LEeb5qOM8GFyLnFHbKuI0JGKZ87m1AAwJ1CG6YG9fKHcr/TIBlW74hePh8PwOa7RTgFAFwe3osmzHeWyplZhfkQ1iTZspGebm9pJ8v8xXHypxrzV2iQ8ZN+4Sg5ewbcUo2uENpIrSHpb2xaMspBx6PF/5Hy1tKR+NBAq/hls2zVDkpVvVH8shrsAT5b9Rx/kQ7xDvERd3rHCHKn8cjsRbZvOlcrLHe211QsQdH5bLs7UWC4C9Lu6uMVF8PEIl5RgHXXyyMhJQYktBVF1Iywz46osqKXD6LOTPTPQvit/8wajo1bkNymqH9L5gaVQ39ciL1SzTy0rtQHICDEGAApggI5jDdJ3Mqr74ID0BUgofw+Xq7gNIC33020ouVLRJLTLv42kjtF+YtdAPdOvri89ZQEIXMa03Npi60+pBEH2JAVo4100KNFoFa9crez2sHus2qCQWknof5ZSLjNl4w4XdHiY+e9BGY/1C46DOCDSUkPh+Rzk7RncOXGOaCn2KvFSa8JKBSa6qRPy+dqstU+Ry9AhGJVkpR69cyVJM5MT9PUl6srWMNDhNbP5eufgvMfqyJzsT/i51A7+5zkokl5y+R6hqEHMUiPXpMhZIOz10ZZRGblB4hd8MjHrjd43biilpDT2eX4k8712war/hvyf6GurMIWqyVbh6Y2RBOSWFgpM1Mkl8UUimq68UqzLklWAn5gCb7cstATEM/1LmrALWgyn9QCOBI4uKXh1T4yWmHQRB3PY+NZ0LxBWwu6iSKhn4s8/Wf785XNSrZWt0+5tQwzqK5V6hS4yfoLRn0AT3cqC9yMCiO3eiVmNFmpRuW0gyea14GtBp64oNhWeR0Kof0IV3UQ3iMI5YmR9SQB/uAwfbTZ6VpvzGA3+PdIaTylL3zZhDPqdG/uz/K95dhj8NvJIHg8vJcHqhGejtXPxwFWN2Qqr9MCAPxIbPKMuvkp9sGeHNOXUDjP2Zgbs1lY6lKhCKQz3ARj2rdKU63BCXAGOs+G0Jpsa8eDEKP2/V157lU94tgPr8SjqQnwJi5pO5LgwyG7Ct/JFzq3XQgWWLgFazJk0sAAxJ8yMjpipS3irBtv73eZt2Ai+LVHJWc1gl0SjTjX8M7Z8BLbjCfw6HsFQ0EjZDS4wfnu87OeWgew1Uk1/DL0xEQfezm/Doa2YrHmgoW3qpwIvTev/5D6ocVvNKCed3wbB61gBVt8uENiuqBWNYFL4xnl6SPRUEvAx/TsCsk0fXFulCB8LqK8IBRNKR0jde+35RC2EBmxZ2ntJjl3C+Y+0/E4IchaTBIibqTVgrNOIKSg2BwNUy/dxxP2mawu6N53P/RbigTC1BxGRsJyx9l3b3b/O1S9L0TJfIp6x6jkwFnjr8lhaKnCBUC1QKQfGBVEwxaJ2Pr9cH7tbqK9Tze3p3VQPW6wWjHeP2BiIZnFvvbtngexIaG61zCeV1vCHPHhZqHDoJWSUwENmPfeDWwe4sfPPenMNqvBkIeD5h5h7xVDU4QzmbrDLNaANzJoA/tpniD9c581mKb1fgarL1t4Parw43xS95pFkhKFz76h3VyTs++MI3B7uH0oR5e+f7W8rmGTWKrzFqutmK7O7W7PEo7o0U7SxLx4IYSjCrc12BZ1X2NXNBtITwXzrqTlSXO69zq8nBkrJqV3ZxVKnXRxVSJxmSTHIpAHC/zYF4h29BbY9ppUsaC3envkjV9NoT7LURPb4F7gmSACXnVfzIg9KzM13YnLjuY83ETukd5pxFSWMedq6Yf9p2fqHV/9Oft6O6W0qUho4zSqOIl/nhVmPv1d7iLG8i1B9OC4KqHQ0/hTJNgB05foOeeqoDLIebvhytPNUJkIuOgVIk9dD/0NsO/UHHhH+jAo9gJ0ZxTgoKHZiK8kjajCKRZ5LOkCrJtdPpZi5xfR4OKDbZa3EdiNeLg4QFJ+MVzgxegv39ImaqxPRRAXp5M8tLSioIQJuWwRLgs8g7VkZMHiQrNlFPkbWBGNsiIBEdcgXxkY+1Bzq2dMMTeyKqsHHIqSJ/qEYgvSVGzJUoaSBkvhMXgK8HUL0DJyxZJ3JTpmSEYr0zNFb30S2UPZXDQMtERaoErQ+e/oN+gQosZWEKWGsd+bYi/Bv5m3hbmYjBrP5CVqkK6iJdHL9I92vJDrNKh/t/SHSw2fJSm+m58ZzqFbh4QN5C80l3iqefTOXczsCF4GzhYIwWTEBkMFfinCyI98ba8ARNFs3SHojTiWFIsJkDO18dB2Fb7QXGmB5FcIEP3h1dz842WRdDjUaG2NXyKV46cW7OcPI2bAjSnqdk/Y+nOjF9Xz4heKgRCSG/OsRE/vhndjheRgmPIZSJj1QNVjYpg2fJ/8F2UnBN4Oc9PoY1rqCdyIKEb0tso5BmFqofk9dJ4QGOheFG/v6l7lCCNiPt0JF4PZ5eWd2kUFTskUWob/GYmvZ6KLBRK7Ouu/KrPfykGcEk6VK7LoZf3LEpxsrsfa4gedi330q//lj/JFm2MOYeM/dDVOAU0f1HtMWxrTha1xz5wd56sYKRpecbA7WETK1OlieGYhLf3ATuppkuWVWLbJtO8Rd52sOtkTesWGaSKueH138YWZ/PztEgR30mKJJHnl5l7cOowoQDeJmOM+ZVH2Ezy/t9Auh/zFNonV3QNRsEeGND84wUJHOVhT7nRIr4zS2ftaxAF4dTK50bHI/6m8AVUZ4mAotXicCQ/VyCKQevNQC0E0r2bnGfZKz5eQEnXjZCprrdf+w58srPaYGleeVoUrZDA9usu52kwwiT5G7nzro+kB9W8a1ead7hdx0bcjrCjA4bfQk6eqJtGbZG+hUbZDQGXC5pAn9OCujKx2JLuMVTBQDKQSE1jqDVqlthLymreuvSaAtyf5miDqnbXhL/gDKb2jptgMmKwVIj2s+8h6Ec1/MNgheQXg/ZWjZycPjOit+4fwnkoY/8hliTJY8JJFCi8pbE+TJ0payTvaqbq+duudhYJ28N9tyFm17CWvKt1qTE6PdlauQnVPzGDa5LYcSmVkd8n034iDHK48jZ+HOcKoLC1CAfLji8WA2ADbriM+xAVSvKgFotGTmJFFs2Lvo+IVcGozRfFukwbNmDBA/DjiyFyOALFR6GMJb9ZFrUMXa/CDVpC+edq3QhMn4IrNXBeGQxQR6pt+HU72r2nLRfRJnQs1c2//M1hSGw6diWuSoylXHtGNxB848fH5yo1ESmKSOg1FIbck5UIGIWSYtRJEIEl+I2ira30EbKqMwezbAzP2ikBKgQTdjeDw4qyYI9h1X9C8FAmm4qkW89DDMHA8UdIPrF3Jgf5UrVcpe/GAQCnH+w8rcDl2bzO/6lEQIBXuq3gxXBtBGX3Mo56QlrBEM6ZU9D8i8B228ULKNPYp7GnrF2Kw9k5UPvrMrzkQRCY50ovEBfdpz5fdWuZnTp7zWV9+XoAwAIFT5X4KkIgeNGsS2zD7DZQQfwrRLxClAcrRkEMAf803Q2+oytTI+iFRM7NesbIN39ygm1SWvXYO7JWf7k4iaNLi28X4xCrux9lbevmhMymutSjn8GC0sf2Hj3fvCwHC6XI6iYKITen10UFvMx462AJ2V0sXwUGboI7YTJOPnzjtm6QAQuaTzSGoYJ3Drg1Ldkw/VPyl8FynVjuY3jo5QXRxRreCnufUVsegjgnzOG8MOghsSmgO4ujGbDDW9/D19Of9fe9IDHOAZJynrk4pwcZpT84vSdnL7wjgZYzvUGEEGhBO5tM+nKIXc8s8QfbJjzMTz0LeVhmt3df5AV0lUTeZMkhjADzZrL/eaTpx/W/EiADyVKAVcqXIA6LLjEYo8T1BM089K6sBCbtJEOlP5HSWAsrxe/xC5pTP6S6pvXtMovwsCb346WPcEzFCR8kOOxhBxo/jdjVZORyyYj15gyyfwUS8MkG8kPFDszFKqemFcpd5r6hIw133oOJsuCOicOlTWlErttra5euBvTZ6MguSgDVcm3ZzZeqDYdx0NnE3WbSb17d/wyIw2z1TCh5vyHJC8VjsSEGZnbuxfu3E/ZZu7fkWS630D52HQ5T5hZEKtE1fbmnC5N+Xjy3nOL7kOVkpEALTe9EDEuCkrqqZmwgr1Pqww2kH24gON4KhpFeF3SmZ927SjdtpJxJkSHN7m5WfvooS6xlbIWN4zfMdWnVwv6Awix75nfX/xR+ggwLD7nTIEHnc4QkuTd6L1pIDvdmzvmNDp5VFC8EWR35gzRhChas2aP6lPfSLHG1B2fctipr/9OfkOXgmARqGoXvtTCVe4QPeX7xkAlksOdELg2E9C6jQukb32q/SQPk9K/jYCXcd0Mwm043ysve8wYR7QuEmPenz4X1EQEyBaHE52ScSLGtbajHN4ied7RgjWGVTjfmGgNnTGkItYg9VE4dJKgndpds56ifNbm+gWqjNSR83LCT50vZfoWZjrHjz4uMu9u0lpZ4GZTBkxc9+54AXRgF6hA55L8QczIMOy6+LJiSoIoSN+AlxQWNGEFYjPzRzM/41MS7YchgmUt8Kvb8flcu2ZIFrCqSinM6xJRFnOowxa1NyeY1jjpmJ4jyFcRRN8qWuICnRdCP/+0TwXfLMTXpVivQXKYarSh67ffoW7E/j+PuyEIKvUu2TrdMyfyNc5+44kcG4QBu38Sfck4i16xf7rK4t5h7P2ws9WtNVbef+ZLYxhzPYmgJ77SBxHpa5nMsgO+clMyLPIXvxlFNHX5G2f8VoxWE8QkHGoQm2+l4uGKkodn+4nwPyCrQCW7aoytTK68JSIbw2As8JQfI9SBbYwFqwj+uO/mdzV4zH+rkzlq5disdUJq9iDrAw93oWYJ6YMl0uA96GkBwbfh54BEzJf1Fu1AvqxDVTp8zS8eOX1cmT9M0FPPkt8YltCr8oDhhCdsEGoktfhOxf5+vrCoEU52Fr5yPWMY09cfr0JIfjgMx2O1dfkdTDom6nGCyBz1RB7E+WDUFwUrg8eS/gkMdMHZ+QQfBZbVEPuoBV9V10ZNUgqfDdCvasX0vJABoBuPXJv5EeKDPkQavcOdad9qL6d+SJfJsC3ZzRSvmNATmK0R8Iov0js+daJ2dConX8FWjsO9wuTBRAWC+G4IrAFSa5jdpTy6fqRQ1WUvHLjvBjqlacgDOQKBk4KmWjJZC8sCE07TnUYwbtn66e68+3NTpcWduUb1ybmsw8E1C9qZZ8M8GWvivPgnn0YIsj1e6IrAqFD63RymWGnqT/bTF3KZOIOEzYbWnvmEXpJeAhiWUzFnFOf29K5MtHmrhln9JYXhrsbF+jIuWLwASvAiB8KtVT2VKqe3vqaMTgT5a9EHjby/mf5+WOYvgLJuC2ttGmckTy40EhmdfsypDzpf+u3fxS7LbtPtsmrpNSxlcWDnQKRiiQV9K1308t6h3cY+XNX17SyxO5RQreBQkINGxX/c0F87T2GZHIDUza2iDW7PHqpnuXUzorquqGQ6ZSn8zHhSruWpppoYV57Il9vZvd5fAyIZHXeumkCyITN9JEDUOFx1wmH2t7HR/4c9h6kbudTTg/PCm4DysWD5enACCN/DUl6SrG5lgUhNIe7A1dfXSMEr+GYeE3SenTY9Txpfznj82EvtpnV3UFhJi+eYNfaDgAYUAVmGFtHAXLt0m0yZ7oq8m+u/O5tsvaoEUPDm4sp3AV1Z5OW/OaLYQrgr3ie++Hd8maZGOGhX2xukvy28unohKps4x6irBgbUGcbjx59Dw5vfsXTZQrvhpdtZ4ZupELCNbCzMaM7QLPoSuxeZeFCnK2zMDlX5GfyyV7BYvyCkGv4n8/130SlNzUnCOb7EuozTSxXXQ7dDCHdPOdeqGQeBQD9jVPz6PfkpPFqfieNea1Z8g8BHMwYar6lYHAD7LL7gHJtkTnjJnGHso2DsJeaTRlAyQBY9lNIEtjn4AsMwHPb7jxYfmJJ1AHD1/zdrubCCoW/jRZ2os8dGgvILTy4C9VD4vqJdl75yLKwnvILEEwZORdLg8160cO4RlIQ3vPVhV6R60DLiKQhCumPTVhb7GcnhZkqcMjXezAkUAW4w8BgkE8NhA5hMKD4EZry95IuG9Ygdy9vrAYvnYWhFYjAZ4qYVdEgb/WSchC+nGestluYA7hFjByWe+QJQ7O1hsoOqSBVUq7rRiTU9mJ+Wq9kR6vBG+gL+0D61zE4bJYPT7m6s4s7npbP8AbkRLi6yienpTVafH24RWQT6NSzwh9dvc3HOudHp6/91zFj26Se/cTM4vuSOlRPmTELOX8JsKKh/4+VzPYiViY8NGCPr/Jk6sgX+i/k1NqY6oALdkEe4PwqKmGMs0g23BUGwpbkOhhCEw60G14GFzhw7dOsCgMaCQaD/LSqFU3S27BFYOo2HMFgGPCKBFuB25TvwDWEZRDmCscmcdUYGYre6VZh80UpCfg/XuxtAwwyE3nSNGG+3N4mH4hPYrcG7VgoarERVilFV49uTHiuiL92dmdaByqOUfdco1Gvdw9qWBJnasUbTHa1e1EoaHo/Tcx5HeWCy+9qoD2hEkY+16JBi0O3sx7AFg7aq1zcjmiKU7NdmthlXs1LXikTqtYVXrTvjXGudlNcTgtvUQGvDGIT6ZOY+AVjHskPzJSzAsxm5VzgFpQkkaiAbphjipk+WuwltPyHkvFZR/e0OQ31bLQ2rSobQak7dIAeN3zbSnkLlII4GUuyhin4lq8PZlwT9l6A39j0gA0ZD5KsGAYCTzST4NlLYgly4JNfsvUEj3hbp5gDm23W7EIS3lyWyzvO25oW5EyXDAsGjqtB1FrjtrkF9nws6MS/UhwnRLjVGB7iJdaOfMGliuzDrxwKkpAxuS3/Sh8/ONDch8i1UyWrKmBzQGbpbdndJB6ouv5x1d540hVpa1vopzrBcNszFSVHUn88q6wL5q7fv7nhStg4A5M9jhHI0B/lv2hvSZ5XEEx6qfD1jNVh58qJbIl+u8TnbwYznWH1z5HW6X/REcHZPXzBIkAj+PHfxXreYZjT0Xe2VNxVjQN9S+s0KAeIePDvJvYfSPyW82iCdo3qx2BjQKSIxZZpwpy5jsY1fybbwo6Y2zNrbIJwchNdKX5gExYBCaU22VuyqXCX4FzdOFFaTeygC69E36S4ttwA1nlE6GanmbqVp7+RpBX7kBGt1D78MTvsQ1Ugre7GlJzzgEOQQ7nx+7z3RPwU4owJgIFEVp0VkNRSamImvXVcBAZD5ERQstREpzQeQCBM7cthLSGjfcWqvGkBrbQfLhVSyADtuZEj47ERWOTm61pLhnDGjNsj27k3t0cd4xZgn2uuvva2QNXrsyZepDURoLAfu1NF4j+FWs1+bE7cgwafq2uExw/QjxRkFCs6FO0eLs+ZLZ/80EvirfIcVELzSQ1bjiXn2eh+aEDWGUSCqft+5yrWQaWVQ9C2KtAmKXfzsVS6IKMoLKI4vwyqDr/0XvfwkSCsJNq8CUkc9wTMxodWk78fT8s/W/EDoKkF2VtETQuGwwmGNK6Y+HCV4JF3azftFibJN504x4IqTXyqFIEN06X90lyrBgLchG5gK8En4CDqTNIWLVCg5lRbWYY4PEHVeyJQ+BrJaJW44sluRd6MdMxGsVHMHbz8to88g001FAZU/+ogi+2tcT0XFVAZbZxmJUZL9wtQeKDRsVB+7V0CdtRLNHLbOKASYK+DxsCP13qtHsr7MUELLgmhQ8JTz/E9wfNluT+5BSQj/ybZhw/5Yt6wNUhjwKcoAv4FaBmWuXKqshkMMF4MqZWH/u2cf0zkoUeUEzQNF16clxBT1+UWep7lr3lKiAP0N5VeaUm+E3dIMUlbJX5uncr0fzy8LlylCFhT5EMtlfMRWJlJteAxYDVd4kaLqb2GLgZn/oCQbSDMnb5KkgbjMuMonmZTmOA0rqi6KQAuUP6FDIy3ON++EZHHhKJSvTIt3BMvJ+sye5aT2ikoS8zvL4SaQlYlDOnBpoXvcwHCqwp9lkw2OfDK7LXXBT+MiwxN+cZj91UB+MiLM26qoRCWr6cGlTixeBox99qki5kX+5atiE0y9nMkL48J51oNoNZiCwwGKTHiBjWd1Jsm6hB9yWN2JMoB+4hzLo1YyX6BCUPNa35Jjb3iXQ2+uqiBzl9q/vtaeMIIVSs/K+Ye96nhAvwKwjtWieaqrZH5aXyyprjKcHfDyAMLYkC+kmlmGkIjUDd0vutBC14H6xeulgx5cy7BoIExmePLZ+DOtvOKJkPpogtWqpCCxLyoQvvUJgaEMX9LE6S0J6PS2jrPZADGxW9t0cBeuO/I4YhRRlAx5+o01Ot+U3FyX1xNj7kW5av7dtPYGCCkjISDnDIG5pptXbelzJWcEAmlttXHF2ZkyQDSX2zzAr001ck7uKAYPa4TDNAB3bcHalPxKINY5b/6qpGI474x30riaFghYD8AAAQ4MW5eJR2+GSH1IB2v7RWVrMLi+RJNs8Sg8w+54iqtfljh09JipEMUd1zqyZHr14zGK5sUq+SaCS7DLpVoLCDlOwNAACXV3Lx0+kSFPqPQy+NU68RlS9mJOr+nXMJfjEArG7C+5TpnQFxtL1XYANCEMsNm4extT/8Eeht57l6tV48nxe0BsmwGKqKu7QFssmF9phk7lDKhKHyr50S8ZRIWseWnCBiu4Joaf6tuIe3Cf9cHLhrn2ITDWoacQhM4uVSAXQw3XiTQAPKAk735sh1QiYDbO292QVyvkNrIx52afLi43SUYjijqsroeoPnowUNskhNQY8MdAsKxYTYdHrQamTqlp9IUxqtwNa5fYljOr1seCLMyhHP7iciwWyjkvikVvJ35qOhNGCP4NxaR+c3xiQP8EvSPipkcBgD33yDGTj72uD9k/ylmEwqJyjI1dg/C2WRWQI9nEYtyMmcHWGVOl+SxOXHPMJw3uUpMxHPZblGcasODZllLb3GCEGAWuzOuyQ5H0xA0ABdENfP+wIYjTbAXzeSK2WptBEzzr5ZZ9b/9wEzX9CVd7arkyPGFHVjnD84pIx8aFIuMMryJ9IlKCvqv8/AEGnwe1x/8T2UVUpoZ+JZUPAffEqt+qASJmowdHn7lQ6TXIlnhcxFnqCa0ti45qSDjlx0LR5i3GJj9TOAGbauyHOONSLWWP2LgrDS87FDiRx9nGZ10tpsm86A0663NiaEMaELjnvWoploKehehNXAGvm5OAM9qPRjWGtq/vXL9z+2O5681dbRLjjZv4zjOtl0uHFu9XtvZ+mPNkztDGnl14BsZYq9nJRyCp364vhv4r4ze+H6AFZaKYWqORjYSpzC0MApDbi+WbchlvhZ1Nkkw7ERctyx1Mvf93NozqHyvFcbJl9nFq9eML4faGDW1/n2kPZOJvCTBqPmW597PSEn4yupFWcWoE3cyc18u2TEBCN8TcuL2RRNcp/h7FIanAx8kZ4MdZG/B/rcUjTZZzH+daz+8gLVI1Rqca26dXVW1dx7EjIkX9fZHlypc9ouC9hurU5c+DaAf9pXo9iw03TaxKyNsngacE+JcwgGXvGXnPshuUEje9Yk4fJCTqo9vSsHLZAMnstciWj9EtWgvzMA30CzF+9D7wRKvOiPFlB4b8cUdLDpzHVytL/e/euKrO66wZropiECKAZfPkGCNB98JUuLkb9rKB/Vrz3WxOVz8/MrhcERZ9WJ8ynvrfCh3UQZ5BDJUQpyulQoXm0b/glyEKMNnqeGBcESt+kA2ttvh7Z21nK++sN9bSIzZUjsj1okBCVekuasfy+sLzm/h9VijAxgKwH1juA9HbbgoI9JhTZj9XhdAfZ8QfnbLME1sxubTOCvb2w97YtMBXPj3CqsZ6XVoTetexGHsnRGjRd9ztyiO9j03LYhazPWTuvmXFm/2qyOjyR9fFrgd3icIVC3d4thmV3MkUaDHuRKcdVsTGatBUNSMO9gjB798s6EcutA9Wt5f4h5FqOMtiuGLQBh9O/qfIonF8y+y+sdPBUTTZtmfI3Cb5Dt9IxWjQES+n5thRdoL6AlcXrb0FN9q5PcVzAu3AfATkPCHMmCA4uHLv/JQ9eip5wMk9dUd3mua5hBueK2XC991MLT8iaDJHKazcUYgQAxzgOtcAwl2+G/yU4cBjNvyDpoXWYFbxnJ6O9jYFWZblPmhpWLBahew7ya8EDTiyZPHEOYHZ8luQ9kj/84FV5ylr7kpciPXh0DAdh6jOPpraqnYcxxrr28m6QzPUneBkAe3LbOKcZ9t4oE4cXiRbVgmIAtwDXPojY8JnOwwlt+yPIOydfc6s4ykO1IFRvCPKH4ajVTHGqsCR3TTwx5Ca0O1K3sesmGZyxRB3d6ByzYHH7/VMg8OjzUOHwcthriAsIkXJhdl0dGAyQOiG0Qg5nNRR1qV3coeOAYnwov7LcdgfI9IwME/kl20IFAK2jPRXUncbXKSP5FsGhgHvTKF8/c/QuP1Eu03u9XD6O8AleY+IIh2quVctfv7g45pyQGJgUrsXGWe5dUYgotQZruRYarFZ3vUNi2+Hi0MW1bM/PyWt4TG6M2iSyMvjWCi9LEX+oFO9S/Ffdqr6Cp4Ov23ApyHtsT+lt3pkMbJmBhRW42EPKmsKGKUBSSdvovZtBW5MQnC8UbqEJOf31D+zOM682KL9Rfj8G2rLmfGteVTHBBYWpJABpZXZDZB6jNaZ8xrIwYjS/UwfnyfAjUPTH6UuY1lWkwQqMJtnHBvUjJkSxytOgX5Y7E7lVSj1wTB4XTakznoJvsBBbltC3UPrGPsGruT+u7rgrVEbSMSeSc7VNBlMuj3zg8DMmIwzvfepHhdzizv52VJEpU+dL0f/wi0fLavI2kDQs0LQt3W82nad5snu1BSENYRToEsGgcuXY5BruhH7ZsoSz2UX+BLyAnb5c9D7A02GTQwPDn3AMKcpiJDG71fd355rQUEfVTghrTMvakf+bBDqm8mBw6N26FLqc8zKyLeKSOnzjn3HCmsAYhbxR8A2UUIv+XOtNx7iq4Wz1jmPdnYeozEpqnXIztctXL6rBd/1LGQ95iuSEwn6tBc6cq8uWggZ0RY8vQp5SVl6xGe0pb0EL8r2xtwJvOjMuGHloVi9r87GAt8ZjuN4Q6tS6RYv/WqslloAUP1+VkilsfmnlHeuV5ZzTreUxzPw7hgowKDlEWa1tfhsuU/83H3kM+OAxkCSUMg3lxDFHkfmD8NMF+FOT0OQAPLsF6ytb93UcWZTOZFafsnwdUn0G94xyhycQ9vQBghuCPMbMBtZfbbE8rJmXZPDUuttJcLJjXB+Rq432fvMvcCu4kmWubVbNq+9B2Pw18RFU3XAIIsCeeFBr037Eyi/UcbFEB9zrheujrflsG3jtCdb68jB2Opx2ku5eOCTHYsGgTuDT0Nar+NWPcGBXk1FLK7TNCQdnxwU29BJOc2k7lRZOjmT9eeyzp5CaNYHiM5eM6S4qNZFqbw7H/Om0QE/mBbLxkOMvuXQ0heo8ivRh6lwv5kebzyTFWy+WoQN9G6EvZ72IHa8yl7+xg0PGi+yotXdHzcefLPfPOHRIvqTCAgBv5QqO8/+seVVFZL9Y5MLbSIMINVo3dV2O9Do/VZtR33H/4tGmPMfhJXYSAaOuD00nLXNYCC9Cn4Jkp8L+rt2NcRNO1NcnPeV35pz2DfKbyRaSDcvDpwYyaG5W5ZiRrSORKhXq+Rtz7LgEDwtzfdk5FFRSqfb0Q/OQfKVYSXTb1cqlf7S2ZcpWznU8PvyVAR05MDz6KyBDbOORl29BUfQzp2ybSGVxXJL2fuv85spK+N3Zk2+NDGVh9a4xE/eF/LeJwzy22gm0J0SWXzhNAJvVRUw+yln7mYZYLfHEByiOVaYU/eaNfJ2Q3jSSsZwG6GSH3cAAJLJSxu7p3nlZrhqvxJ+fUF7WGETUbN0pQCDtYFoeaPL0AK5wHRGD1nYA+xUzP1K9fylXEoY7H89uA571V5I5s5ZNHncIs5wH7nKlZ893AQSSEHdNrkVjbIaPtZNEwUmXax5Uf5Fcqz+Z4r9hOARjHhYPzIn3939TvkauzVvjevzmivBbMnMtUdpLyKKCrjA69BFug1J3UwQZzW8retXXEgr0ViRmZ9PbUF87Lw6km4YSrjHCAWSS7C8GOFc8Dmf7HvLx2SbJxFibcegBLJ7zJ/VazCB38AXi7kSJk/+PEnEao5TrDzEs7AHdLm/o4XB1rs4PTldSm7pg/5ZO7oaf4bssgDOBuNL9zcJdRD8MSFK+s65QQej5qbFVBgYyy6eba7BAeoQyfWg7yNAkytAsAnx//lRsUlrDTZmkreH6QXfQnP7dcPeGYY9xLSxxc6ZngZgd7SagrkW9yluA7imNcQh9RLcHWTpnMT9oKmmcjVBjj+qxjAN8xWvb044Jxod+yjkVhNPbkMfqzqvxPisk/KzSTpdyIROKCju/kK1hLJJPXJXKfPOo2O3Xfp4e1M2Tc6dxDqPnOaYtihW7oR6DkWRfdHrBe3VwJoCUbGxMX5KojJZ0MuSQbnM+oGw9EKKrhl+QKYdnRYZx9IjQ+6+vCN0O3O2pSMNK9RELLpZ2OSKuj4cgbLskkxQ9LSJkTNnTl/jspiFBB9HHVOQxfcShWZI97XDDkqA4Jg3hpDLcIUpP2HGuvKj3hFavw9Vufl4pOa995XWmDl+Qf9bm0wB75SxG0ReAjNn/KFFb0l05Nlynpj+J4ZBKDfeE/sZa3CTLCqY/tF1gnC+AGGpiX5Q8wznDnf65OAGMFCZzJexUApCPG75JmocdIkgYvXvgAcMvBa+Vz2GTcbcsWTe5v8nPC+OuW9SHOiH4nS+8XMmUAFjhvup4f0i/UQ6CF+0AReE505bVJBVPKi/UI+nqxx/7k4wJ4qa3jL0D0RWZqEWAllq+6NnWDKeCTg/7kPygYpW4q2sPnBNk0kfZyfbikeXfUDnnx8fmV971B6bGrouHj+7MLOw+Swn84njX2RJJMrktsXroTOhwosuXRCt//whSvo4LkHCjhtGtw2+cF8zCI+xvdCvQBkKJfukswvJrp6bxug56fKXFH4LF4U+K94HtXyFp0N0uoG9CY69sMlm/ST4tKYP1yJQ28WJ2YdylrF2ZUsWy07/u+6wjAhEdVCzKsLrvMdzERrEt4Ry2mK8DLBY5AzSljXkLxbO8RgEbOUOKSVkRO7OayVGVnmZq0I5GHJzR5xuInGSSP3FS94mOcPqk7s+icL5eu2CnWfwCi+4xpGMgdj4rbRtrN7efzO/3xdsBD9EELcSTYF9nq1YrFrviE4A3y7twkNA8/Ktp7xLNiHI8va3XCTdE402tHz8Q6X1ZDeCiFK8oFQfQl6BENFC36Na7tE1XXVt3KPD1/lp5o5rWaSk5P4BAkuPMWV6cN8AV38mA3Bo0gC+xLSpHtNd4ih8wYo3oSgUhb2I+BG92rYJBazOTbyaRQTnqt5HanJljoJdgg8hjLWZd4QlvPafVDhZyyNatu7LGmWdbUI0+8n28G8DYSuqMvLYRuS8Jr9U/l1JvsuJaJ0FCDWAS20j4nbiJf5OSF16GnHNwCDbyfZvId2ha1axGBVagFYARUeqwoAXpLMozqHleB5No8RYJspsvmlGsLLzQvIBAKarl8dXMiIIE4Un6lH9jEJFpQlTw2W1dlOzo0bh7bc+4LaZ8aafaD6Un9Nb7V9WrMnGLeradnBS2WO1uCY0Lgd2ZXA9RqkPnGIYCvKXheq1vJi7sU2sGHAU8By5ojLD0p8no1Kxuoe7ssQSNdqcdCdJHPiB+jeG4W8tjyC7cW0oX5twvClNuIi6avDbjsRg2no2YBKMz83fEg776UXtbehcdubfUiT7H0vNOjREWN4wvA46ZNUCrWLo2unYI2OVrByhqpisYSdGkbLCPgqu42Ez1DVEiqvNQlx57WerbWVGLT98q2DcVJBet0InAr+SgWVt8rEZm7OpNWlteNoVw90ToqkTkBd8eO1XnAi1s6xokgI8d/PTy5g/vpy9cCmWfzwuaDHvIS/SIrnqg0S0x/NQhHPOhWKRvk7ihUxeobJGEO6siCoibzDVDDAEOt9AQLwP9GFCbk5LS9iE5bptjitRjBbmJq6gJmMK1Gujl8J+oF332UkHUGBgE3BQKYt2Utbm0De3y6RzKLoLHmu7JFwSuQ22vDSqx3nqb4AcV7KfRX7MpF9FDU2Qcd1aMd2GpkbRymK1JPvnd5Ve6JC1RyNfORQwEPYoRFXHuK/Y+9+qXVR6tlITgcLGMRXToUP6n++W6cwVdcZghGHx/p/MyWlBwVWmLryNyUQxdgDHRaXKkamyJj75n93kICe98qIEiF9FiniAjh923I5f/Gpm7TW0n3hgJCl+mQp+uTgfrfoCkzVG1gSwCPXS3wV8n6/gTn5SyHZDpnqFlVVW4QlwtpQw3Il7QDTkc2Wz+zkGvtRrFvnsDuru1G1vEDm3HFgq3CnwmTXyPcXurc0eySBP4kqpWLEO8ByU2oKLwAqF78izM93nAK2RN1SjZx48xLEyDINXaAy9HN21Bkc4PSwUCn4hrR5uObR3OmSyQHaCoZpo9i4pfX6mWf5uPFZXz8L8nsXTGO1otLuRSPaQDC/QHh6s0NODm+ipqKKWy86zZJqNHC2OdCmzgd8d7E3e65um0S2aOALQNUBd0MQD4ptbyVWTr50m8OZ2pCxTqJ5B3lZhp1tOuRPVPivdFbHP1+Na0JX7E3GhPjwOh+SOYxI5btDAiq1iHraa0c5+C261fwINPq+NUMh+GMmuag7GxX8cGRJeOt757aplaSjMEBCvhhfHefqE7pWrLTCJ0jRuFIFgTkGUNdkCqyF3p3DSBaR/VT6j8GblNW9tdG/AZykto+DyCt8aoE1+fL0++R+ZhOEtuKBK6kkD1K619BAUsmsRuaTJv/0DrfTFulmkv+OOXygb7TCwnFs9cwWzWqpjN7kZh+KakPeqYgxduwlfXfjkPuW6UjV2l8N5kSmtuw0E6Xc84iCTKA9AoXyAK+IOkeZ8dtdh7LBCbN4GxYnAJGIovbX/llp28D48EhCI+lnEywPdOyH6HEY/QUBFSC1lusdOqKAbhUY0B3mNt8Le49/gSQemwkZyKMlVC7aFmqWhT9FDfPh8n4IMZ6DISSM8R3dmprwWjr4DGRROc0q33gywn3rnEJuH1+DCBwDkGnjPhrBzshQxCevzkX5qNWZ1GkxE4AbRs/sptZ/rQwIaUz2dzgb/wSsjMtEcHBAFGe41QSdykmwzJj4N67nMpzAPNmROH5AenawnT5zjwrX5Jma9OhQm6yRED3jF4uK09qksA6cWDJZRnBMoMJ/vD+5WeU+CIM7QyilYnlmUgrVlmmghiDl0yPUjHfg8ZGc3fykbzzJCnklnB3vKXmc9le0gGgjHeoUQPprPfXJEyqe5Vgzgs3aqlu92i08YtIcapvFoTNv1zGWVb1UerNxYbDi9NHOcyVEMouXGTGnAIRoZQ7+wKqZaSJF4DA852HgdIriQpwt8g4QlCvcc2BR8D6mXM4jqDA9EsCaELx2vnkXn21S3oPeamKCZZ1bWN0aBYisPUhx0LVkquKhbKs+YmaaSOy4ZwLSqYX0N3EGJkVCMZEXyQl2j4g8iBDtlwjkXaMNH5dxgwyvMOMq6mYi/ycIHgDCAAAAgGXbtm3btm3btm0827Zt27ZtfQNsKXbbZ6MScaNNHmycYwjPzFX9QSHel+fV+RLox4j3guL0V5JNiwge8dO7s+tDUrprcGNt8xBC2IayvNuRJhdQ+QEU0Z429SABKXiBLoIe+ARNf4Ff7gNheMPu180cIO4+WHb2JA2H1YbEDiiEcpezXD5uRqFTkOA0TlmqiDMyJ+sNA9onI9IeDycT88+F3IVrIraTrVvSQKKZAS70+8YFGmjDy1ESkZoVQjhUOZcCpORiWIg25EhEyOQyxM9ZkZLYtTdLnKPtImcn4Lj+83yx9T4LM+0UIbHLlT+4qgPPt41xhzJkh3Ic66aPHkn0G8WdeDSbLz+286/FYHvUNKTvUMO6Gj3pX3+gAd5NbJ969/dhk2b/CocUAN57DMgQ103FfkYHGg/FzPYSwkmptfilUHriJvfxjMjUlMBUjZym0Un4lTDvYQIjsMWdkNS1D7SqSlv+ZQuWlMaz8lKZF6sS5d/+QpPYW0Jxt53U/GBNNjHWXSeotRXSA6EdgX/VuwutGnZOnECnyzli2cKdJr2T9y8E2LgShSul/mzFhs7Nh1hiku3RaP0CmdaBlEJXsjMnSQQI/nN5sF8Ng3Lp/g1tA+Vatoxmdm7hb3c7K8r2b9vz+HKuhufF62BQs572sggPkDaCHaMhXCoD09P5SJPsJjL//iC9raASD582Xgg/c1T9tl7YUPToKr/hOSksMB85dHfEeaShqO3NFb22tgfE8yTW45qKB7FFfDhjciTobEaK4YNz+QGEeR6OmQyw4ldPNVPWCN3HhGZ6/4hVFDUCoEbRROMHDQ0tMW9SRIa5pz2ff4NW3c6y+Rjzr3vvRf57dXMzTxVS6AoxrioKStV9gEWHnav94YWqHlTIeufFl+ST0GepcRpICK4KjYCnbLIrxyYSBx0lNnrhhLpDdmmie+xjdiYyOTjWyaDkdg4ww+Q73R2rN35ORSlMbtux1hPTgioewPZKrUhx9kFVstXZufJGA9GNlRaWcwKXsi5s4W6Tc9pP4OPhnjZnF5Sw+Ujtx0HIn52qGghyR8cGG8UQE/WceMSIyRqW4kslIiw//yjWXlO2FbrPekfzjGkIaiQytYLoWLTihQX1+2xtxmigHQR2wdDkQsUOBErvgvCKekiBozqYwDvX2hV8+vA4fB1vXFjY0uAe7Y81eoYTHpxDUayOh5jd6DggW9sYYMR1615yugFiu4WPJ3QlRBTvTzkJgHjY0I1n5oTRGg3BQX/ePwDe1xWbcRNtPpY1pRUC5EkQKH79AYln4t+tGTCUJMCejTUuXsCYZmyCiXfvxhdR+ucTDmu3080U9cviXag7sM6BPkGMVSMeOk1pij/a4PJvQ0haJuzhhPwLUZb+I4HUBK8VU8g0XBD0FC84Sff5G6waQFGSeqx4y0r4UlmS0xVvM5L7SgQFztPkgqr2Hk+QgNwO7fDKsSaIVm+w48DGQeEeBJOFjrFockU49selC7zR5fFCONKA5B7EjutKZCx/6p74a95rW+a3XUF0JNA0s579TjWNosYvBn0fwfgcKw/ZbS+wI2MnBxUy8wGBxEXGIFTtHyEudTBk6elyLMzT/rRXXQOIOecxsnNRdwd6X8Kb0uakgGjBUH0BgrNSJzYgSsKLkz3Z53Q0HQAp/QOWChBBLRlncz0epHifixyiHbvngLEQ2i92dx2601qm6C63/3mXvtf1FD+wwhgUSLNK9P33j+sCZnAQLXCZ0FDTdLx7sEmcFArB0ca1zKgIsq1xUigmC5mKZ62i6x6RGy0em2+y6zxl7/e6BYF0R9l4jGq6NEv3NtCZsW9O+w5meaUvAGx4XOaTQV67ZYNVkYzHWIA3nuPjjwmTe76AZ3HZhz2TwFf/0T4Aaiidl3fo5iMbcXk4070KuDvCV2vE3nrn8r/Ntmp1k/1sQrwg9uM8A5FJaxb6/tw/8SHiWR6igqbC0Q/zWkyYlp62F17tpkbknaTcG0lwNCQbh2Zknjt5KfMjnakIERuH1cZpT+8jPJig439xXUOv2U1ngzU6WpCA306ERoOM7JiZBgjS3RzsbpfCHvYSn5mYUlv+qmSrcKPD1b+RHgFUH92Hknl9/HZuCJU8IxtoxPI1xpmyP1xwTdinjrurGqpfQqBP80lGxtuV99FqN6kDctuFSSyw97N/50OTyCYhnB4yYp1TfPwokkU1HD31OCgpmvtca4Fn2OIfAjPFII8UMA649jBWh4h/2CDQQHEYQEhOBDDB+JYiDz7LL1F85NEjDWl37kb8VGArpKtn5M3CWtcczImG7v0CuITDdrd/lj1AUuhrjPO9kEhc3lw2YYancMGphyZA6IZq6WrCT9HIYoxt+fj6MLVv85X6lBt1Ev8u71dUZZkoBjQ/rblvcKxZkCDxOgG6Uf6CsrE3u3fXj1V6IaYwt6NtjJzITxTCHLwArrU+plxaXCYf3VVOVxxMHlC9kGmee3vBnTyAzkQcTCdWB4SEE/cZllXeGARh7qxr3cgclTApUaDTB5RUeFwMTAfsZPQiFvAKEPFC/upefQGgn2jIaueExkCDdmyE9T8ZwxACce/P69w4yf5txFNOcsDcGtMOkubF4YNORD/OI3BcKAAgyLFVtE1ZmVI6YYidO3fVToKn/UYSOuCjufgpqE+kcg/PD1MDG/yM0VKZDNEXPsjhd+fK6JyOWJ2kfeCizsjeRLrCqy7M0JCJrjTvCHwt8IAksTqyzn0XJRRkBlBSGa0f6fLBQnuExcDVdOw4wJsNwCOqGUEY8sWHp3AJU2ldUQhF/Fnjgukjs8kgekIsl5YgthxzjJMtN487RoXIDA83aX+SHcpMI6Y0GkCdsFhq4x+ixNhuFDmTv2hc31rHMl7XOn77CkJB+gRFI3qa//X9pkpo517dUUPsiV5w7V4MxAh+X9AwxLPJzxyy9Qvntaz2ag5aU4HL6wccDzxFz2IXthSa4EcTsDFLCNv1OpZhgKEt79TlmoVM27HpmsepF6j9kMtUrQ8E8xIA0isWAp/83bb9/J1Q2pxNUDiPRpuZU7hWNQ0ffJ1UXPcIHNEY8HiNV9Z4poGw7Z3hCLiuwKRnRiZ1+fqkLvsRgUrUr6fz7L2oC/LRVsT2aD1NbkbLUQpMN5GrgJGxBFCw2LBuczyKEnUlV51FySNw3BVzPnE2imHcR7ZHr6nqkK9cMMmrBAhDIf1wkg081hblg7bw9Umxf0bDM5tHILCC7WEqBWB8lckeAoWRimbkMT/a/X3VbRwVF0Ls7NtG0a+44oD0/ECVH52UdRuonn/siZCb/CttGqhGUBNME6tR+wp/eiWKclqutGozQYXzYuN0exldRF+3sfnit+6jnVAqag0y6AW4kZiEzluWUAY6IiW1Bj3DMj0OMVGWtE99TQzPJpxe+PIbrwkH3AxtoWvZq1Rum8YwmLAVrYygDh9bNqq5aAvU2jAWrV1rYf18qaJ1rN2RPhesmcU6XFaxJxuUEYKz+VvrmWbvgZuUbXjbu92F50yGliBPNpA4iaSDF0CkT8E+n+THIDKy9EHZUqSCWRUs6S5X4kya+ZlH25szFhDGeSfYTKfCPDOf/Y2luwt/2b8gQkoYFKZcTQydJwMrcpig4O3SJOkuZSKYdoKonfLv310D4eWh1LpjRorU/KrESTM1kW3Fp4G4G8eKtX/xPbb7WgT6RKPGPivWX/cjAFlUV2hMl092dytvnPnXoo1SbERJ/K/3qWMR9/q25/xVEe6YkPOnwMDz2qLfnbvFTO4MXNzazi7+/iaKIMmq5CjvtCkzonaxMl8qv0t5K1fI1r3vy/pYhC/M+3nvyWIuMdQgXKORIXpfhiT5qMEk8Axihc7hFFfcTZKe9ME10GNR9GPYMr2vWgZMkT20cIXS4dW47xWpTMcM4sy+UUs4VKNOl6FYyfqzUj89Ovvc00z74Wxg2pKz4Yc7C6A3qz/BgjPpiqGATbieqIz02xC7nIe7u/ev7GX/7tHwoRF1tRPa736c7u/rqaVk+6Ys12EUYsUTsqMwMBilygbsmOZmp/Qnzfcf/lmZ8tajf+XbskzDCVbhEi6jef25TWm6wqhNalQAbLj0L22+PBsBuREysSr5BuBWc0VA/ARVc7aM016+M7RPUCP7J+x8u127/+6s7hTXqtIi42gkcQCG9otYU2Olw0I+lodrfxaXAkkZIUhyDcvc5bJ05Vpx6xM1RhuEAP6eH35pGZBBIh+WGPCxM+DHfYw9gvHlEJVm9wmboHkGoFbJ/AYNL2ZWnrNUtx5C5+doiRtgA+LOIDpubc45RzekfIsqUEsd63tDGQpKF6DQ58BZIaJvIOy2oSDh77aO7ehcEEMS7rRf1Un+GKXenSmRPz9cnX5Grj0bJmSBVqTpueVf+5z8QMtiGcaJyyTvjM9e9k3lIhq+uQEW64DQvJqMOu7V3yF64zXnMUI9UdvPFJW7y9gpsPgatgNr4kNJp/2AwfGKDvBr5fmnoIVNifefFm514PXu4ED6uHpKZ5Td3YUaMtL6s/ziglxKoa6HJh2+ih+kDa39Ytb0E4WUcb1Jh6BbWpkY4vjZrftlIqsz1iLx8kXHK4z0I9M51GU6qLaXYne0vSf/VXiXnHC6wqfqvt/MVKPXlVAbwQ25lTD9qkM1pYDe+JebXtNJYbr63FLI3xbqABEHab8qUrUgI++l0hkb9b2wV3L6SpGmxUZJSrtXGS/iKeFy4PDcKMHKfuudd5/S6p5VR5wy4149hjKwk7D+duOhI7PtNtn9y26OWyaBya0nUZL/Cz4spy9/Eb3C/X1p+Dq9E2KqkTopymWOoucjdxYbSus5Wd4uqWru7/rCeH2rg5V6iz5srOIEsAX8rYPZXdi9XprvjXf5VlrCYRBNZuFaOgilPQCNnU9sDysZQtT1TnsFmg4KXd/sazICC5GbU2bmTnN9HcIxbTXpEZou+0MJbJPkvFZV7/AG2t5nRAkU6hXipT0R/GIhLWuN8vV2UMZvkmZpbTHN5HrTv9tXk3AbBEe0Fa22JFJQqqcO2xrfYYN57SL+pdI/g+cy2XP/sREPyU/tjRaRTlarYEeRaFfrHWAOlPcmRHePiSnL2BrW0A6osb7uMy8arRKd0mbT1wzPW6lL+1sFpndZ6+w3fl0odRJ8FZAoeFZHiX6GH2yda9r93zfCSvR+4pXkAu1icKSqDzCfBoUN4AfjGwlMBwf+lKeyQUmhpKpv8D/fkZqvvoaRuCYdAdBh+0yQObjW7TuUkoTkT08PcJZ1Oc9ZUoCeUfClcYXK2uhDhEbvbOASww55YN1qMNg87smOtBUu9YekwzG77noHh4CrQ0iaRxBb46BWpAsgJVanWrESg4HQIHeegFnznjHDb1DDdU1FOebzovHqfAzVJgpYZMr4ZtpdqZHsz0CTReG8YbO2FkOWPquwjdqzTp8Z3y5cTS08Hay97V4Iaqqkxb4bbksz6EFZm3Y2AQHvSvAJ2JzuLeCht5KKJ1nSOXbFlWvxeffcYKK9dzZUmg1RwfuWTVTc2rVvgfCbt5r2Uh9POMrf9qXXDJSVIgmQGITfW59izEtzTEY5kNMhwWwEEL/QDM4Bge2EgJg6QmBXNWDbr0LXdTNLo/fmQhoIgKmrQKQEp9H5taXfHhqeEpFkSmvnd94Sh71zSV/nj+tCiDG+j04g55zRasUk0cNwXW9fwTKKdQGOBMa0smHxpXQfPYaYaKFl9pUl5kD6g9Z/GESyt5gjCK5FZKuq4yvHQUTk3LlSlP39hhylxAU0v2w5DI4W++kYS0InmJVGIs4g+m9AYtMVo43DdzKkg+Y6uzRvtkfnAJPwqRUsyKjo41tHWp/mpPpOSdoOVj7KNFCqO9f9V7fzpVzKLyeRqG99Ps9tNxXkv4OUa5UUZZhcR8dkZnNH4xRSn+bvWYQ9tCQjEX5ZnuAe22cQU2LgkkF5K31fUQePbatrZ2E9DH2rlpQyvqIGd5AJHXy/q+IlG/KVW/Zft3NEp3QMeMwB19kgAyD2SxiVRuBUtgqMQ6cUMzh9u4FfUq7rKKyLxHa5QvJdim3wGXFwBy4hxnh6pmZUi4ptZBifd3eJN8XdeeWbrPAW9In9+Ipzx0H9/HTz/AXtQEVZiXmhbKKYVrL2HiYmQ6gkQOleFPmCO4995zubXzPAWxysHeHJNVOefNnVf7GiRLEr6RqA5rSrMiCz/UfTZIP9pL3UqFEuIZbrzUTh4wxt78NkwXlq/hvax01cRAxKVoP6l/ewsbTPM0zdg2ALlkUxzCNxxAZvlI+4R+LprKUvgzTaBj+dHC+qdlrTdHfRPNbFV1JU+rPwlIiStzUL7fKd3qZNyCvLusUgtMbxUfLc2nhR9sS1zS+AZOpZ34ppqi0QgX+UIvtjoo/Vs9F6WNYiBScSyyd2jOHOOAuH8Wgp3nNMiiEZ3ootQu94VG0qkStZa0sTmMz/jh9yfIhDPdYV0WF7QvaxsWqiv0JFOf3UMl0l7tA4VK5eoi11i9TId/9dBGPGP0FCPJ9uenjIAoAyNPiEF6hVer6A97JMrLDQS3aqWL26DlewvKhkWNYdwds++j7YIf8DgsvMPbLvByah0bOyzhYZmNrBbz55DBrsMrwFNZ68j0H62XIov8De02gQrVrxRzO4ix8Z9GychkOVTXvZ79+bTm9cB25NnWNkWRR3lvJsO6tG9vm0vBnjfMkortYhcghNSYAtPAscLBp5x3VhbqNwC4fkEv9udzS1+XsxuCszZSMj1qk2V7w4Bq0BDoWfWWCNoO/W3uolxPkHKfU+NSWBh6Wlp7U8XRCGhAcl1q8J76Ztuz4uzkIYAzlDbsX6DVCANnzK/npNaO7zYyyK5K+BoZdHBJoxTL0v6sIXWp5t/EgJKAD2dx6Ok1CABQ3uBw30wxtVL04WCx5a+bbMs/nze15a0rHSIpHE+GZUSakDTZASfeSYzbsetZgwFQvoudQwJQiy1tOOUHYOBh+9j3k8qbOq9rp37Bg6h4H3fWZubTronSWUFBhXznZcB/Z3flNdIwKw5IGbLMJH1ZGwYRDdJ1QgtP9iuS+krX1k3HdAxw+VLT8GIe5a53wGvbpWL1OFgBffEhFDLpanSK/kRE8iCXsM7j7X/ZhFjSYl1JXku2XqJIiCeHDAzbrXTBOoYEpQ9ALVFHlkvSc8FpaZF0r5XEQmGAo9i8e+XotQuFkbNYcnExPYslG7s6jEhPmGDHyYMBuP2houO+uaD41TfUg6TVRGKTLHRCjj8FkWpgOFpEOC/L6nshYUhe4gQ7SSGZuWw7ejpM6e3iKnbKl03mY5YPWRKaELqEsBerosg0D328ELjKkBiW8Zo0D+WFDlOF5hFYboJ3wJ3YsU9pZMYIS7Ct/anz06DsilBlhxAPke+0kcA+Av9mqeLUURvu/IkMgBOD4fTkxJ5C93U14VV+8Q8xDvOI2zRhZLcjs7ifv+NK7X15XNjIW87qpchldoJrLWrxfDkPJ2wOt9B9g2IYtzoGtW/yIXMsLWBH6KS8xTS9FSlvDGnqix98Tne8TVXkXndpTABRL1FrIkewcE9KRlml+xqKgl7wTN9xu/BxhMgh6Ldpbhg0yDseZwnK2rNN+lZeGH9GFLOh+OyHK23m8KoZ3buPxlTbEIjQFsojDijG2hnyL386izxU7n2MM2G3Ue1NgZ7JLlxsSYAB1TX94EUkkPJ0/03rkW6cuveYZin+n4xyUh58YgiEnT8qtRbunmpvytSB/SQf93x7lnL14nYvcRjd6GpkFQlXFBm8kV9CQ9E6Vv5E7cwroG+6P8SXCLV5HEX41m+JBThGDLweQNDatGVVyhbsgj2NMAb5wrHZ5Qrl+DW/2vvq077Va/NiO6ZDqlJqgAyrBb03NVLn/HgBhJxYZLY4JgX9T8FE1+OxANx8zNeJGKnx5smxgIIz58vO9FHn3lsk/PNxBT6wHvM2jcn54r7vbzz9YAzxeE3e/IR1/bo9ZP+0q7siZg0obCtm22sLeCIr8Pg3wZtOzQY++JKB03e8CllW1zG3QcQJCv6u4O45QsEWi1bmQlN89qh2Ovsgykk6BECCcmsDm2iUdwj0WNsaiIPFvOTAPbDQmOpEZaoUO48wPd+PzA2hQZtsMEAZFJUTTAIR4jcC4QQ30PXDjS3Pw+wwutrH5Z1ZfDC//+Q8pa50ZCvC2K0bYFENu1TTRbLY1QK7SU6i9NpAcO0KEhDYW4MUh0ZJiLkqR71uYaiTvOPJEI/yQv5cTgJBiOVmOInu+AeUJHMo+88WiQYewc1zhLBlp1T9/d9fnurA1Tmy7bcAB8S5r3Q6Bu76eevg4+FR8AgUaLtrhWPMmf7bXtlILRJxmGCRfYWpNqNNLfF5xuLSL+kp76/vOCW9gN47buRFzqN9bgehMYH2x2Ct8k3GOnh1gIAvfSMZiZogHwwweyc6VYtGY3KGhOjX60hNZ4Ys/AhTkGS7vKP1XW/6nwTi03+wzf5Q/IKE3mKbrZUB+zxPDtl4jj84CAflLJOZRoVT7unKk0o5bBEkr85BytPvbOknGjNRfQnMm248ZomM4oJRuTUOqlWW+U70JMX8mshUEO9kmA3jp45DnqnAnaSiM4FTIAVS9pNmgFT2Lg+WswBIMD3kaub5X7wKs7em01QOOzU3vfUfekhgrv2wmoI613xOKGXSuRBUm25ihpkIIbbKcTKmUnhRUWmsckduqGcf5P5Y86WjMmWLghS3vPfnZqffXfWFIf2hhbETuT0ZGOIORncC35YjvClSrGUWsyWgFST+8eJ5htu5GWGBlP+CxtWt8MGj8GPjXaV0Ih51dFu1DLCw/vSQiIY3cASW5fdlUX4l0Nxt7gn0LyR61+avqW1Phtn9SxAv4Klh3MCmrHh86+lkATRglz7EuLNeYkHLKjUdOUAA1iEiSa8vniYH2Vnuc9VfcH5Enym5CWMheSSeY/8eWBBJBJSNCVszzXc78GYAPipheF6wWEcwT54c6QCO87aJHgT+huR0Gk/MJC1CvZ53bhmyanlrVPsucxQdbBKmLxNko/NXlyLt6O5u4Tsa7ZX1jjv+oKFnZT8LAqNWOFKlib6G76J8hkQ/7l32joIsKafMJLtPcsw3d4E35eBAya9J2CZMstSqsWKaZsn+R70fxib6sAgQdkQmO4hIQqQhS16aBzreyaN4CfXebSBwpuxBHB/W7QhnX+PgvHb22NE15q1OwoZaHBM7DyOrMLrwf0ku/SQnG5TyR7l5jZkCrRC7YT9Es2l3zPuje+VPdsBBdYhdZshveTLRY6TXV1N0YhgaWgN3wOIV4JGRHR7O5I3IRT0odn6akdkzruhwIor2WfoVKJszILJgyQ1+GLVVJ0IoXu3rszwSE5bP027gsF2p5n9O7cWSQ/b4D+DoxEkqxMpDnTOBqaiSP1dwaCoqZVWqyuRcfAgaJcvTnwFCjM6t5fv+kvVOtNQAUR7z6W1dCvAyudKfSrczNczzsa13S0DAp4Mo/RZ+BSMNxutitzsQbpKaCPLX14tvv8TNSM+xMn1LFcXj88mXy0On5vVK7Ji87X2zuDAe5BLhjhPn9u/QKIPCBCTJOS4EzG6Ugqiyf5fWmHqSBKEM69ABl1F5+ci3rhOAFsGWyg/ZgQWO2s4hZS+AwqZm2DEVtXJuAdtgUEhjbRvIeLajF3s2NOJv3qwAJ+AdI2AFVW6b4z0R9ckp2sML231WjuGS6otKX7P8KrxqsluZoW3XYrRC35De5epRmmxZSGKAXhIU35m52aylYMHRcdevFIlETKDvK06wXkrEJNNGgorIFHkDrhoRPIyB97xeBdfUgnvdDr2ROlyhwdIoQkHsuOlRp6tsI+5LwN4noOaO3Pm5CqGCoMaKyT6eQGTS4v3W1UDX6ZxiyR4yJ3kq+dcuXyUpCXuPOW03na7wIPn4VpaOEQOEydEbiTrpIxuA4R3RfbJr7BV/7b9uBRin9rxIdFx1XjBb2r5UR+is1hv4thSfgL9OshTcTgHInI+9P0F1Ns9dr0GVPOrr50OXG+xOU3ggEh9agXymB57MqW6Ccw58XC2Rz7xnbnCBdXZO9luAWvEvVRtoiq3CSR77J7Z8kj4oVs6+P5fWs3YdvLnI0g7uXmfyjjvmBkrncH6HOnvMFHYdjEAot/i7eQNLO3iTaYyiUdxXG+FGu62FB911f3kH/cXshX5SU62CT+iaMT98qpIAoruRpsghPgElRrLTn//myhOvVJJ906m73QFzx6Pxxs134hekysri0X5+LJMBpvR/kaV/FDuDBtWEYpM+UWD1mH4je01gqM66w1/xpOXdNBAIbQgpmIMmielFX+wSGjd6RhnY1Li9ELBg5ApX+CMwifWJnUrKOJ6tZirw0nFn0b6MH4BFmqab67ErZBPdt6M45kbCc/ZatH4n735lpThUF3BtdrKsgi2EPiyK0qk5CgN4aUtu3PgZKHb31hRd9RWkfLAPUn2oTtr1EzohAlNQARqZecv4JVFy0NBKY/TpHI/pAh03YWmqeJQ/cJPkGs4bjH2E4tcXY5uoKr3ltuScLHDFeLKQNBcli86r01yU8DS/WNTWVoktbX0z0Bh2q/ThQJEmviawlEqS6TB950xQC0vdth2eoS8TdEBoQz0zH7Vfhd6PIHAAsbrG+182mv0D3WGXOUrkmDlm5GTFI/Z6mqYwE0Yg3j9F1AE1Ebh37ZHy/2N1mwLNJBiuJxMGb+ucOdDiBSmbMIIuAfC7RGsAYBxW/51JZvMNkuX5qxUBFVebV2bUYbar/seBnxhhMbbyUGOpQC87b1fEc08eUGipMX9l81Nza5iRFgznJvwq2KwP5Rm+HMqFeoFIzt3AI4vh9NolzVWdn6aAB/aUo1PvjN3YahOjAbZp8o0zRf4IfaDNWrnFDqZIZB9vx5A/6OZHtIyy5S2vurzFG6qDlub/YdNz7Rd2eUUgwy4xSqAOwA+dIjkzcvH1pLdGKR27bYlg9swJ0GEW0OIdejgbb+e20bxHf/O31caeavrL7jQ+O+LOxjU0qVCfuyVgU8yhhdENEAwPO7BkGFEnXKa6OzxP8pJP0gWHpQjtjaN8PSirx5V81a4mKVndOczo/2IVf4GP29MG6EaXbxB+vfa/LyOjKSOkDOaMx+EQ3IbncrZJDC8TqtpYcxuy5UK8SjZnRavdHgEPlh7N3ncGYXtVe8f9SCEFdIFN1ENHqrL3s1agx4NXGKjUiuIJlymqOJ5FxafDYUNCBxIQWM7dp7FBELPxRsvURVuCsk13eY4FgEfpbqbQJ5sZchdam/v9qzGpBCMtVsRcLrmcJpRCTXGrhe3w0YuZaE9wuTtcr43pCmBnmDRWcLS5o6I9+Z5QgWQR41rR+dD0Buk+vCtwTtJttTLIPIkOU7ZmYNVKtDmLnb/9riRSFYrIDdfE+4aQ/48SZRy5433uDXGh9Amw94S0UJqeeBUbhyp/mTGbjNlm+QluqbUO4HfgHFkIFiGhJu8Kueb8Xj6B5pFbDLDfQ8m/8e4JkASbAc4zl+57hK6A1d87wkW3GNhNZ9UcRfF4Z2X4Ea1RoWEVa2Y7DWmjRbstLF131C/wGbseesu+ssf2gyNXxa8PDkENxaOFyhwPXhnhrqQbTzaGjT0eiKXpN7w3mqWvraJJFPEEeOATakyBL89GFT1P4pYJTkewBhLGAu7MYnviB88gP5sIwFDwG3E0r26S/sxaD94LMufaE1fd1t4lmUtrZxNXIn+zPajhxOA7FQ4r/WCXnI3WefiN48/LFEnKDSADI65f/SgmPuVBn5daslToYPrUYdAO5I0TPTtKVgn8xJxeS96tQQvSdeOxbdZ/SHNqoMmY8CllIudRit1Xg67Gt791RUxi2a0upINzv0k3Wk4HUSW3Z4IJWBnsmfp3IOE4/70D5kTAj1ar8KokBBsJZYO9+ehY7KEr5lHHdlrZ/huMQN/UZ9z03fPFi3OCnMnAxMORaKgG9v1vzKUK+HXKO6xL7hW9+PeiswyBFLn4hBfamNAAK7tOEFanm/XiDTrP6S9chIpoGYZccL5xRVkEbrnnJ81XnHQ3RNLL011/rtZzcZ4gnAHLA15E1rF/DZ2YtpHtMSk7yPbp2tT1ucOa1qDHCauQhSTfhxOaEQlIzBfyZSAlPQ47SCf7RY2Lrx1LsRjzvlJ2IjwiWvTIpN1x3z3jTdW6yu9OuKUXhk8IzFyfeiSAv/qZpgYI0xCh7INQqu8bvFlsxeOHzWc/qLPC/szLu7vsZDNmh5A45jYbjnqaqTyjlk2JFNSUF6GkqI9dfrKlQc5iBc8Crkuyi/Q+uAKJd7e93u5n9SLImA3kYPi4/XUqWZoLI5htV6Aetb1ZSAoHt6bGUX1Ecuuv7XyUuDtypPt3rb8F31Ra0Dmo9z9jAxP23JgKwhSiUgeYBghZhKnp1S0hAYe8P+h7JpQ0msQrzmZ5eDPW1pZrxe8CkOz9WKlVx+W6JUp7GLLsidJoro9MyIVmnMJGBTHifUV/UPFQDgpDOh61zPsmT1DUNZxEW/+g4tJ/meRdKT0V4uAWhNpWn/+G3KzOEwDznhFhljjFqMI0khz73JBVPdACoYT+QAvWjZTS3nL7colEAajLK7YrP6u5zMA3uj7NBBf+fW1m+9cf0fsyzl3UB9JOCgyL9atXkBbdHSGLbDvoEL2aS3pek6qsbeogJSCAawpg28xTHccJYmR9si68PGgX0AGRAsI7M5GxqCNvGn9MFgzP0odsdYapna8VFikgTJLeBdVywi8K5QVlnUc1asXoSn7OQq/EyxogzokMBAPozzRcw8rErCINyJNd5OTx4XwHZSanFAGHsBm8FnRo9nFJw19m5y9wNOnXU/bjXYxtIukdfirr+53VivWxhpeUHVBX5T5O4DoIERKQz2xa8LCO6djJuatsXLipLf96BJM/c0W6FJb1frVbtImwYOXmNTDgylC+Gi+hssN6LEu9IeSsAVVcNkMvx8Hj1UQg3haK3EPI5j+CFoD5ZDzPPuv/ydeX08icOmixnBHfAW6yOvVkx+u123wKLlyNBg04hObA6sefKJHtKo+zfnDm4P+tiQXsoV65lNKlYbOt0XHIqmIUw7B/FHCX6hjlDS2c9MGUKGtWJV9MMEVRcgXYCgpQsjO6xJ9QrQ/eVdQ35vPRjCtQDKI/knWO98wG18Ug7KRajXzW9dbPsPm6fdN6P6lbvw4YhFTZ/kV4xV1gHmzU0qs31A3fCBrRN6mqG0AYoSydfRQZf0wQNXRGyWJ7WZm+ZyH6V596z9w0Y/Ucbg0/yWV2VJFNfr+svYJlFX4PIqRV5qfSINVqVlZH2OOzSJRGlRQLgeyyNNS8nkgdPBYYL4iqtr5KncYd3vr89VYVh78P3zOtc6/21uF8dlWdByS2kN5bx1xv4lLHa0IxTEdMcAw0T6xuvRfRFPfthKh7PJk/LYpFZCwQ8tZymd74GbYyw9EIoqvvh8LHp/lrBUez2EUL8nAOgNMooGPOUqkutLTgqPBtQw4UpY1wstqIjMqUl2fSDOCiOQFyt3nj7D+XpsDrTwpHijpVPP6pZW71qKQulNs3YgP2qbamMOh0ZMlyYTIMCSYoBwC1R8X1yJaM5+xLXtdcXhlcpXMi320910hktLiLCLe6aun6ugf2bLIsXwTy8hHuJhv6rXJ8KqVCZQKv4UcygXgsZ1lulB6Vv3fgSWdEB4UnVZKqXPGazco+dSg2vAtc6LWyP8QN9ptV45g8HHBkJtoRH8GpoJnXBiazn6uMvnf+ptFqsFe+j+BrLRJnAiPJYKT88OgaCk1nqQeDcKS+9K9zjAGiJNas2kuLutUEwMhetvBXnSQJtJZ3+OOcwS5IjKFpKf/ABvHEVK0QgH9vWvaggps3NE/Bg3QqFli813H5HVIWKLG71Y8YcLrTNs0/lzjIaCl3EmZgk1v14Vrfpq9lHyW+dpaUA7SMFNOSBvnv3BZQbioIvWRFG0ZwHwkZLKHDKzZk/gkVLxo9PZELd5oExRkL6zfiXZM6wfGY0zx89+Vl4cZDSLMqRASxk41PSP21rdK0D3wxBOP0I+3zp+9OIhJXOQS5feChwfVF3V7A8tQ6ifSjppmoTtKDKTahyKExyYGG9H68+WkC83mqAOFvFoZcXdm/omvFUY2xbWqYODjhg5PmSZwVKpKVB0MG1AJ9WjboapxA+rAFWw13/zNPw4pJBxnJaNkTL4ciPwjyKMnx5ntnP950wG0FZ9YwGQOFLlTF+zwPcc4s6x7yB4vLnrXB5qc9DmfaXQXl3y1qoQ20RsgFPRN99wj/YplVD0KYN9cSs7UafsoE2Mox7aQx+Ow7CVQJbMYkQ8d7eunHC5roPJ/PwUmT8tlbHm/QtZ+JWc2iLVBJkpmz1qYgYCFp8fFqjoqb6h9mJo+P0NsnNaWoi98Qhp8ob5Bk4kcBM8NEZ6ksEOtiGpL1P8aLKaNduBbqNn1/scqlVhK1lRzPdw8Ft/6dIOUcC4dJHiepblllEH2A+/r+VM64YRKoSIiV8ErkIoS7jgEmn8UQrEOpNN0piONLYyebIsZ9II0yMTNKX5l5wiUSRunQo9s7Jmu6F8mv7tsucXCGBXP7mPgFoTbDMnxJqtYKIFYuOu4LuFOLEWm/I71Z43m1b30fglckcwftlhDntGo1glmA1RoFNxKeTSGjFYjg+O5vukqoRIqYJZGx0vd7o79KsP5oMId+GbW7Egm7lVMERmS3KvVfHz/g/m1/2PmCkVrgBpV5WJncxc0GJRwdAovzhHvKkPHstvqUa5eA1G287aLE2SdG5hzscrJGcwAJwTpfgJ37Nn4HhLGXnvFQ2QTnFbtytvMxYeTjSJhR9T1w2C53AFJud6LILuNfU3on5pbXUnUpZ+EG3bV0I9fnmJXr6fJZIJkiuL2MXdlurUwwiDle85x47TMB/tt45R32EeYOvrEjKGk8tvfqaNEQ5QW88nVuGHyjE8cmzHd0KJY29reUt1GR9NDCD3LhPPWZGzXz6MAjYdl8W86nHvs2Z/gdudQJ1zlWCLr9HXn3zrLBdmcyXzT2qnfmf+xaEQDAJ5aS4dmbGqfSBqIGdGAf+yQpPpMq0Y2kqDZi8wLAOb6UjYiDcB8qgKngiGI6CnkbtzF5jmn/lNgqq3T2fiC/Slm60Q8ETDvyfp2t/0gH2fzfs0QccbqOjYjIu8BaSMf7hoss6hCnd58xhsZ824Xx6pu2r6Cj+ZT2aHyLXlyN67AflRJp4/Gf4Qdj0b2grg9SLJQScmhNEfl8126qKO3bMsZN3GikZgjchybakXXPW6+KwvsNse6xmRC6FfH2qvEsYQexUVAU3BbZ4lNLCWldM+VS07Hrm362dsYTVknoQGnaec1EGxh0EGMA7juobgi2DsA8D7kGjYG+0htLEDjk7XtkivLdmeKpNlg1iZblJOvt+x1lfWIzhLmWIKfl67Uybb7IQ9BlPAXMGGmcw+qQ32SBzvNZ0JSu29QdQ5GdjaIj2SBzgZDZHS8zjPewcPqBXlhHUWdybwv4QiDnuO6nxujSaidQjHGl3dADQHny46jV2fSZ7LaXdA7heBYlg4eMORwdTicZAlUNv9NXiUBAWjSjM4PC0zdW+yz9KTVe3zyYGRFwgmgQCJuOzgKUPfVmPhpV48sSO/d8m+oRzYOQt8t/QFMHDRDVnLNXwoDbkA4YnJIaw9MJPIBjD33ETVeeLHVRWCbTD23WhGkofF7RbjZW0O2rxRQKMJHTcoK+lA9JFhDdWfJhWLoVss39jyschILf6QylC1Tt+pcgSI8IBy3pd2lIcGJWvTCfIGLU50DqvsD22Xp3qByt+8PZc2SCSbgnvTJ7Dak7cvF0rK/ghZ4sjpu5kTIP74PI3u4aNxSzm8RkFmn7jvPLa/JjuI8qoUgk3H+3t4Ddx24hFD5gbp2DJf6QhBk72jytOzAMJeSocatdyYVPGVUgNSFELjX5ZtxS+WZsU2+AhKsaEhWlDh3inm7FQpQ0FzAbJc45+xVRPinE5jxYuuhC1DYCBShA0iRD9zeYfXokKJBOvMAbz/ZNMNalPaQIHvWhq2AfF1xeAld87UPTdwb3jqmb0vSCfAnb7trfpmGfqN23JwiBUdwbBkp3O3T6m2BxYt9qay0JgIE1AXnga1LK2PE23ye0aQvtPVWe2LQelTIzx7AwQkxppyFzsVixGK0ybmA693nL478GlNHLfGbTJ//Q058vsE03V2weNrYB9qPc4emmGePhO7Tn5vBMAIl/Ikzrpja1F1GytJrP8ZNtD/jR6JGXrplbTmLf2AlqA6wKTGo2nIjBFe36rhfR5sNkBuLK9aAyan6fpHfekCRYCtR0PuElvUlT3wnVCOhFebcEm2e4yT67miG9uVafDsNRcHv725NIqzGijIszr5+F8KVRvxTap1FF8mJzWt1yT2GVsPXU/PrtgkXCt6WquzWH8EG7Kf13i/oEXAwCnDpSZZgJEuO6udXrZ1G43X6diwvjecCXOeTdfN4pU0iBbF6oZu5J9Qiz+70i/EGF51jBP9oGfFY8EuQPaxYXGVZU5fkzwhTiPn/TXSjOM2Gmfo/Gb+DJn91rlDu+Hf7BpXsVF8Eeq2cqkKGF4YLMLTaomQiLrY5U2AS6k69WGW0SIadM9e+/ZKZE/8hMdottgB5rkYiksKvDfUu317wb7bb7js3TS94SM4c7LgQTiwe5rMUWdt9I4y/PxVkd+BEKTAErKPrnlWt5W4OrfytvUjexn8XNoe/LXb7Sp6NogKL3FEe75QGxi1GUWJnquhmB5oYxbNRzdj5GZWTt/3f35mKhyWH1eTiqi/C5apPhuKdtPOOFUxxTCtXMa04U7KrsXRAKvwgDTB+RgG0wnfwe+eyzfGzr9bpCYnof6ccacL0G2mSQw43y06XvokpElmdFek1Mg2fXwjcG9/Y/+80uCndG2skJyu5NpY+zLGRWjAqCe62/tEU8cUgVBRQkKd1SDfHWEUcN3yEw3/WZ5gKv1DSVduE2I1sOMN80XsoNyRUNHGnBfY2B8lzsJm+mOVLhfzHFe/lEh52xzpXRFzy84H4KLie3nYwKBSQKvPu9yaeWZpQdmWa1nsOvi9vAPTlDJelD//9JHbg+1yC4e3AGPMg5T9edDIxV6y6YCkIfaYlWvRP5lcE7TPqeZJ1mFdP7QJPzucesqkoMCY+cuCU6tN6NFewBhYbKOs5UCmeEJdKUgGRTMkUZLYhziX3Lbv0rIr7Dfbs61/SyCYm165iRXdxluCZPyl9DQa99AY3Mq5cjEiWvxKvHE6CrzrtJD8R9bIKkvs/ZfQbMBfutAt2mW1ffIxP7R+fhCqC7FNqs53Hsz3PIwikMoOrOda7cHUhgubDWgJ/O+SnxVLc+vUQpBwnc3cBEXB2vPszEmz0efqvBzIUPQP9/pSc5Q09El/X0J6BcoTzuvqtAHwZLUUopdZSfOM0Ef/Xiv6HozihxnNlD2Ssn1iXgJND6wd8Ar97ktz3kgLOasX2hn1JxBJUZjcB0OuRh7MbAoX0LgOKgFhQD0ou4upnogeBACRtbKiN4pFRf8BtcbdgD/vOWyds+pVEl8e2ZpHxHxFaPHVOzv2819HPO5Wbr0cHIdT+pCld8NhcrRGZITlJS1tVK3WCsI/QL8a1J2hMcwQ37DEGY76hK+HitbVjsKXOuyom0fARoq3KyC7HM1sweQGoLcl2J8P8/GKmk8+ZRNUHZ+jAhdsfnfEwaff9WJpJ2BpIVjZyAMaiHnLceZFvDSTQ6dvSFtbUFxTmVV3CqjuYSQtLq/AFEAU1rAnwLk/QmDeVcS5k7ANaUdOOpa3aviVstPy2SswC78e3xKbXkpp5nwsCRLobJg7F3lAppc3L+HyGEKq+pvLr/PMhnCa3bxOKkoZwJFVr3uRGo6lfZTyPPYr1jLqxKFBHIxZEShMyGNYz2XLdaG7shUKH/zCq5raojMv13JkoPgcu+QqIlYJdb1UGIcIKrSc6wPm6eDLTRztiANO8RTqayuTfPwt6dxS2GrGVaIprQwTtpwowYHJDFi2PM1UcCylwKwZ/At+VwV4ljKZoQnUpkgx3oFHJmjq+WZ50rNegO3dwkbUaw6+EZ7F5+ACQmLMVdh6QXN/J4P96jrqHyLHnutoRGQKleSFQyWrHMLTT58DE2FLa1IVCBslWeSbnyLF+ap7MMRL8Ul+sKMP3QpMM9tZQ648KJ+Y3/BI7wq9Z6v70hubGhzyhch2n+bPMnEug6BzdZ9j341ld+Re7FRTpmBa2Pu8nASTs1opQ/3WuBNBPckVkOrRpGvacHdh5GMRmtTXHyG0J86UyJr+BrZUEqlwJfZXfSH36na++N/HUmy2JqBZb1xYQ08FFa9ymICzIb/dPFH3pWa70YmvQ6bk+mqGuYVsBnCpFgLMbGL0GzU9d/Usorkiql0xBcurg7SoPUdAEnYEIl2zFLAE99AngYyETgXgom4vdDlWZAxvkm5Nt/w2WSOBQs06dGBEIENP/oE8YsRSpBI/6BLt58y9LNqiiV8qKO28zo/sl9SZWH9lCwYcdo6UsKTB4/iJQrCB0dFlUp7SkchGCUHFJ71WxOjQQAUPQ0bb1zwUhBgwIVArrwZwak+wzjvMwG+WiVJ8U1bKAQR57lYGxaDsc4g5HFvHnfViVIzi6tyHFtpNB0/hhwFJRYF/Bwhjcc19QGjukMBtISaDBUmWuLeKGDGavKhAjYdJVW88aekPiR+zBJTGIhuS8ctSamYV3uw2ZAvXRfvhbrTrUK9IarRnox324ArVPF3jNcabgIlfnWohiAL/gIePRKqwUWIel/AP9ACECxGhAz/o61ZePo+pYH19TdQe43NRx/dbjIVThXAYMiB+TXxuIrl7Cz3PslbACSxFViFqo5Z/0rGdgWJRY8gKy/Ywris1AYzjYVBB7FezMneNNPxQhF36dbsR1kaZS0C8+atXqNbn3bENMVU8DXEpYLxGWEJUUhEMQPNKLw7ETkopAgx/I8dYI8QwZYydciUjbLU61SGqW3aiPYKH3pZU/kAW+3Ldl7AW1ZpGsrCfIHQKkwWymGE8t82lwgfI0J/48OD9tkDVr6DaOyT32Ck1l/62IBbynmcsgfgtdO3hAVr0Ulm2f5bD834Mcu8GYxozIG648MGOcvK37AUF2e3ogcw+C0Pd53Yh/aJc3B8VWMml4j7+Oq9Ls1Czzc121sjHG4cOLwzEcPcgWdx3bCRuGqioabDmjWSAZU29dNq1pe5b3C2/DnkTNYUSFDu66zkCFwosy0dLhhlts1NdBEmRO654xg35JKhxZ0M//Lb5XV8K5eAgkj9UMoesscrTaO5bP1vspcaRlsq2kcCrJt3nV2s3ZPK5jmzRZIJ6ZrOV++M2JH1vZWNO0yw/JuAj/zCwsRU4d1cIufO+bbN5AWIFyY0gKYctCs4Oy85LFf5cWp6NZsYqRsvlmZxF/zFohNPhT8HLEBu4X3UJP2MtcfIQ23+ICoEXzhUKhFPCeXIRNkbOqMA8Axp/UVfGF6X/akyQfZsyGa1tmYhsjU/jlTqxazxFotJgCAQM5F0s1wyrJC0foCzeZevBtsFSFnavcEjw9qRnZ4LdfkE03FIUOBxKrhfsKNTaaGDOpg86hUIG0Pu1ibVYGUzpuYpcjz2DW3I6YJyPvtfvE5MFsNPuHG39ePw0Jw6MOyxNDAK14/DgmwnBQdiknk88cd8peq3ChA3+jLxVpZjB0w9TWV0i+WqOj/HIAzGeop0riJqkKV8ilXDcOAvoFPJg2sdIrbd4h/PdPTS8fHmMi1tUi1UwaUsgTJ86+K7hFtdVEN6xeo+bXtd/dZHcTDSLUC/T8dQfw7KsVZVmthJ/1AsFCE9PLMgdTMe5IEIb+SuqOH/oX1nAMx/d7ycqtt9HC1U8YoDDEY9iuw+sasN960yP5ZDMpRLFIvgzAUPDf9IvO8bBxfE4JVtsGbgOk9/ki44beZgizPmKBwZMGh5bc+TYG7HVr7jMs1O8K0jh8Xiw/oh2xzaE/dgMaEJpQDsw/x1lAqR3ZF0Hr221H+gXsj62KvQdcxFf+90CdLqXoVafDR9bj2o8kHnKeVfDwyca/gpVdRRQgSgGrQANUWar6ZjxLf9R68FXSgn2suVZGWuDygTUIgREa3wir1ZeYN4WMe4UT/ptRxTf0U7WhuILcuQyTWGP62i9dFyySg/Bn0MrDH/LQAd6zDJHpJR0j2kCu0bCxlNl5tHFMBI7wxDI0Mvw0XncMmDMLwouqpRA818Vno5eX4qPmu7ifaN4PtQce+J1aJGABDjOcs088on9daadhDf4WznRK/s/1Pqa9NXvInMCwfxLR6CouY86zw0GoV15jfGlq09TOAZPbU7Y1J1/5vKe1SIhBcOon9ycElzkUJYZqSkGkojpHRuTyMHwL0bfeS8ROjR1/WxjV2Y03H7xNgJ/UEIYlviqMJZF0uZDXbxjn3Zu4mJJEXmtyMsi9HG6yTZd3eAAuQBiY78vF2gGC5+8HoQozSoEm0PcG2ldpkV/atTL8wKIxMC6bYdwRF83briPVCEf1IjJIr1XURE4bqBs21dyQ2NCc6fTS+xrs0VdAZv1OkZ5BgaGTc+svny8NfI4l32bu0G4odNZ78D7iuAjNSkZNQ4yKtMuF3EnmqreQiMWmgoscRmQaGniQkEbFQ62lqdb/3sgb3khqF99iIPiVI/WFhN4UNOkx1MAYK66FFbpJjs7DBuGPgAFr0dmjP7AemSkkvfe1qn3cV6pFM43JbDZzy/BuQFR11z7J6WBeNmyPb4mpSPiJiq3wyjSuNR8a9pISnLeVYoKahJ256sFyKdIyBA2UpK5HsT1aTdaojheJ7/TNSRG6o0stg2eeaIan2rKSEnry8qFjsIq9L2jJInCbasJ3SllYK6et4IhaeSOf/j1gJ3NQCyZXhExpihG2t1rzRG/lU05xPkWq+VwlZqzTp0mfCVj4Kf1QaF+LUYM1Lj/rQGbH4SOM4Rq97HpOYL+ORfB0gUsSdmj2B3OhJH14g42MlaSyJPwRWrRW0QH3QyrcFZ8mM4fSrnpbsV5+QNirzxd6K6QlYzkAYp2w5C1ECFfcQbdOxXJV0xOPG9HcW43Vmws1HHs3sE9Z+gRlUU0Fd0sIfqCrxwN3bQy8ERSTtXzg8V0a30gMmESn0ZHyr5uMZ/6LrhqbXjJjFmywsCMg59pucH185YJp+UczZlNGjEOyMDfF0ZqljhaSc0xj3p/XINYM34lSj2HD2wDYS2zDOWsK27EgZNtL/S8jwVVeWzklJpnEdoaoBpl1gyRtib+6Zs8/WWodSSTHz4evgKM31GYrwLKcWXcMxiWD1J4DTU3REnCZtb9qZJy4VIGQy8K4/8e+F1fRxgxDt159jRKKcPvvSbt65lLPJ26U5h4pvbm/2bolQYdMWsL7aw5MpVs7OQg/rF4N/emZLTL34+J60dBx2KVctC9njZeoA0eLe6ne+TjsyYLOT+dTisdkBrwwuziV9DtwnTcGlftgTpJi+tJbkkDILF/+bmt7R7yx5wMmkx9AyNMhAPCmST+LqGlxXJ/WgRX/wYPLQ4wbjpOg6Wamp3k5W+GL0tExOKprmzXm7+PkXv5B05iyzd4DIu3imH9qAO8s16MS0CMiiSC8ahUSmLGEIMSt8aEXIGyh+1A8IWia+IyBK5uQj3o4vGPE2QyzI8Na22SExCy6iYUOEtHDXQ1MrSJhVXy4Xxs8AqnCSBgb/nhJzBi1hwxzI9tDmVRD3H8TJXmyMPp+UGaKRFp1dfA6FGRD6/8p+6fIVfimjkDkN54MdzikAyG80CiFzbNPp6b2raXvkxP4/ptLia6ifGaNfl0wg7aNDuCKj5N9QqoRkE+xG/N6NAkohChqDIMoXQf37rWc/zYNLfgtA2KF1GZiWFUFfaF8hCB2OtiKSEegfo8miGdPmIDjoe/nbshJzQ3EPW1Szksazq0fir7VJaT2uf67mJLiJ7B10BcGL1Efx9E2PL5SHKLeklxS117NyT2nllsZow/amd4trIjw4h6MejMMYLI7T1A2yORSsYPMsfu85boFzY6v8MaML228kv+JLi1tuc+mBDx73PfJ5dRLiRT+xmo+1HvGloR7gbQvuC7WKffJAgxz5FL3RuyF4rQS+/haO3i4HtXbYMIHeYwaTbBVPiyNx1Z5uMEnJJUH/XJcChjOKUr4uIOcEtzqMUaolMhgZAGbckeQPGnEdARpG+4IIrPLTRk2PZo8z/39Qd+6th7PYeqTgx/ll8OH5u4X9Q71z+D8bFTjBJ2OV616nmv9On8f5nDf09nCTwAhul1LOAliZwSJ556sW5TuhZk4ytPE5+LLYBFxd0isPYBiw3mOqHsmO63iUppzxX5vuk7DzQ41/X42pUHg+hL8vW46YxprnbtEHDeFrl8yZ+gXpBl+MsMtyCILFH5jnpo/ID309q8Vha2GwrYQFoFgwipLqs6LTOaDqO5a/yD6vKGKzTf37usDiDYrkx9EewmoH1Tzku1EexgxqwSt1Q0sjFxHIz33T8OPk2E1I6wJEN6sWxtcYHNUB0mIN6yXT0esTcesCRkfXYnIWHCysZCDfSCDathJlzJNWtdVpnwZjr6RDl/85hcgs2tEfRwffTar6J+zLBcJcfLIqH0et4huHRnZfUt30nMv7/3bHPYPlw7SX2m+vvhiSpSXyNJzGg6NQU5Sgps9srUmC4XiXHLklUvkKkZ3aZtUG4z94yJ2Fndvam9RPeSz7eAmjpZygWwF+8anYqC0fwAwTr5gM7t0M/gJ6AAAPPrWqX9Xg/2hM3s6GF50xwIeAMgT30KaVO7ECBM/cZLMnz2vtsrhQ0D498gsqHdbiDqoyzFfOAjleXD1JcWlIlMS3/X4SftF8YAeKOdKORBXwRpPT96E6ASU/NC9NVDz3c22/5kE1GKteb1ydMRsBRHT1KkZUCuePGJb4xfbdE5nqWplvTr7ouSmVw11qD/BKQfZMJ7wK59BnKv90st26BUlFl40FtBu7Yp0/FnIMi6P/czMFPfFc+a+JloB6ous2XjngfTRNHzkHgh1t4HLJU2gux2DbTeUjPMsQ4BmH0Wb0xHOk0bHIfjutGP9p0kx3Qgq/BPOfCFMbdA2S99fm/Xyx1h59C5wkYcTCE+eRFN8NT/WTyW0g05ZOW/lD0TleHDlKQGFQqSlpHAwBje6FPKqK7rGU7q2VZ4MXZkyojSS7Tjo/shb4Cff2S9XrWB3nx8w2thn3I16a5Au/mvtzElh8e+lO829BWnE5inChS7aquKXdnEHcF8q18TWEwVkNfLhSgITlMBK06SQCR7VnDONIAAy3ohAVvesc9fk2MFETpdXIEUJ108cvZ/dd5dXC7D9ogNvU86mJZiiJfDnkX235InRn9Sn3cxh5GzY7x3hjur9jTXtFIWo5HOWIEAUaLH1SD7pEuDptEXIAdJKI6EQvT45qnvp5TdiujemslT9y1TK4NnV0bcGm9xFg5IP+OZHkuZfgM08o5mkmBcaOr9CpYU8B5UkG8tLasnPDga2zHLdV6VktKlhuMMHesjJ8SVRopIjv587XkhcZvpbB7rXPrQdqvevYgUwdqz+iS2CSmMBqRwV/RKgfu2dcM9GiRlwypUJw+3IyJLRJI6JKom0rf1g7l1Q6t6T605AuD/VrkUHdK80X3HA7g22baBGE3KQXXCQV9FFP+rnXVDsGCiuxVu3+mu8JyLfGg0klqhofjtaG5Cw3MXlqJGQZPuRA41aOyX+bdI3Rr022BkmI+t0tXbjzlfUvUrighOg5FTiVpuhVtq9+LCgGKB/SaMioqNrUqD+ilVOHRait6uQBaJXnUqGJqEHf1pDm4BQn/LU90efpT4M+V1sOdE0+clZM88eQ08Mc5JsSpvMYl19z4246W6ygVVxjxU1fU7ym2yXYT1rjyE5aAqWyMl7X/pxJrhWa7bBfxhD7qeLhAaGMgDiMjmeAZlX+k2y8AVJADSXNhP10J9lL1qfMgrCtyG2x3tiAKNvV7UL0bVrE7+LXME79GMNpZXzETKuUrWcOCo6Mh4ogtNilwOPKF3Uxgs6VIwQunQRg9LQqm/lXf4UrYUi3UocC28gXnBXlAfazLYUwPdSxKo6zoAJDklxxEwyL+MCBlemJ5iicb2xRR6B2TqcnaL7opD0S2x7RqS4+m4F4zT34tT3kKmZz9SB+s4E/d236CLzxSbDiyNlNqF/cUHH+CJ2tQMKFSfchA9AyCcOBFhifvLyn2G/7xUc5ENYcs71Xf8D7lr2QbZaxMSqjAHJ6CEjyYVmrk14Wyj0Ws13Gx0EGbSrXaxcjKdq0xI+7PeysgKwp8Xpe3ucWnrgPYgn84g1Xm/LylgHk5BdffciwsocMdaIaQPgDEmeDMWyCvrHVRkDhDpKQGSbgSOUDkT9rRPpWB3gl8HAB52vKawti8lce98rNiijtvFJDt2sjYh14dXYZ/UDqbFFv/5LD2/hSWRhtH4uvqz4hLwVqnzrlGq9dYDCyWQ9DG8dcWOQaqswtu6/sma3iJz668wM13Pfn5zHbdkEEilOF1w+/iaBX4juXtbpcmrhbiHdOiwHstzYyJA/RwGnTS8mZfk6+WlmxfFPgx4lg8A8sxlchryuY1pAlI7j7NJodzf/xhg/d3tjhanLjeDm29Wr4ko9XNKmMndPs8ZHbPhFZhKEsh5NSjjPVeDnvrWWnRUVKWLcCnk+EhFcRt4l3N5Ks9aZkSTeurtALIQYylaCEVLv/uSNpTa1oLoFxWad3jnFlmcHthZoBH/3+us8FQSFLkFIKtCk+jD55GmdesuSBphgfOC43ZKC6VOQEmW2TS/tyGym4G59d4mh2FTZK87L2Y9eGeCzYj5q9Nad/P8vtSIB2BS23ygM7voJ2n63q+ueFMVlHIb3ec472cP5ShTkxwMMsg7MyfOGbpyIShVN+4cIoNGprHm0PXOMQv1vlHhG7iNCMZmpzLBPZBezsf2tgOkvHY32PYiZalWvubSG+dJ2AQzRe5wXQrZdmTZFNx1PGAlCYJ8bjkAs10KWNv1F/tduOFySinzDhmW21EnAQMfkqrhfLhkR3YGnf1nlGeDq0Otj5W6lNi3M+rkmiaSzV96AkBUmjX/soUvuw927LfrDCEnbOqG7ulYg9GXZb7pK1kJMkWH/BWLBrZhQj71O/btR5uRKqEVr4fGsebIB6tcIihosVO32C34aCGIugL9Uh72vEaLchUA0M2Yb/oyzMWKU0NZhPWNs8CbMYWQwwgGZNa0qRU3eAb3hy7O+0fN/EWn++CyKHElGMpOC8Q2o9eLmsimI/Q5mj9s4QHYSzEB/EbDwdc117AFLkk2ce/E9U52iyz1t6tR4CJriW9tABFZes4Py1bYHm9jOkLa8EX24+VX2aALcFaT1hVlEd8p2MTtGcu7C1SSXU8JYcurnSCDAmyT4XG/s/eMerLqQZaSwQK/mx4rl1urzXMruw0+9R8Q27/VLw3TkQOz9VVr2qTBum5+5aNkEl47LA2lBCxvrHs6WXjuPEyqnOYOnYEh2D9aTWTFbUWUNlfp/pHxZUpR95ipkosVxyCjy2kFsYRSD0EtyCJr1I/vlE5/BxUTKW+zwj33O5W8I2Me3qoIxxfuifsgAk5PhJCy3XgPLAhV765dFkg22zH/WTOd00sZOOKfob1S0ii+8CavPhhbeSh0raFI6MgeIqs4ttMa+4e9LmXijHUfuXfWFwVey5qT4+PYFGvmiJUE5IsQIq4I3yNEewYAhtWvBGsBwK6xBXpXWpsClakw90e+nDh2/urfMEtfmqgijRcRVbWAZHXNgXyotqfnXH0BZqaRH9g73pmoJ3HE2qam1wgeNl+OrW/8MAw97GJJAfgHy/2+cujZCIkTtWQUvHeABjR5B6sH1dPbymug3Y7Mq5cJBkK1ziNSDX4dtYiK7bzohb4dxYzECh3w6oYP7xkRS7cZXLiRp1K8+AMHlV9pYRUbsVm4Kqbk15uDZ7F131Yob7cdHarcXNrejIlkZxqvGQgRUn8Ap25ut82+ftEobWHuRW48LQt4s9Eh3g3GfENmY3qMxGqCppbdMZ66PGyaT75rAXphfzuwqsJAlAgzaXoCjVCQfRzfaGlPuLDAjwW6ASEuBFGNCRqgSOer0vW4sVjXyZtIG04rHOlRxiP0jpr8cOLCvOn0or8rj+55nHjYn1PvCQ1wwFt6hhiiSjufsoXJFpx7rGB2pp5cz4zm3FYBV+WNnEW9Agi6xKwV8Itg+j9roXYxRCovJ8lW7pxvdiLlbJa669GJ9whd4PsrapF7RclejFoHaRkwRDi3JIP09HLfM7St6krqtFhS4ZJT3ZtA9FLGYkzbhBmX8N53rzAsDlN7itQUzRw4+UwCx/pRa0n5e9lZfXH310cMeASke9fGn4cSrqiLhBWgBT3I3NZo6doRaM/DN78kWaaF1xwn8Pjk2rWBaOwTZBzWvFL8H4FamG+2Z2wEOsotaOYu/92zAgBAJl8oEO9PvndWpVdaeRm0qSOdc5lNpT9TQYb2yXalhueLauulZHKnEnU2kB5giAj33Iq/QGT+oc33Qrd96PkNSJMgPp7qPeGd+igNfTvwarR3Rgz2zqj1etz4tTxRufQ9vUb09wixbJltCHSVsi7zxLoWO66JpEk5rG+QH1ydr3CADTuWxC3XnMW5eM3FZbJkCb4SSWeXH0WD2W0pkxczmQQjkb4YIXbyUixUtCyViDn7ha8E3DNUDNIbnRUKevZBmgnKEsJGoz8vn6N6Ar39XKZ1Vouk9RK9i2bK1hA+VcHicF+eOSUEJuJIwgjfO/qPm1ochfxP5kc0ADQH/EtuCpdilMobHC85VPMQkaDBz7Wdk/cd0OfICBGJVLSCeaO5Pfx4depuJSC8dFdMGon74hnlFkS+cTAHrh0TiUN8xDeYMYFJI8ocoB4er7dhGYZbSyYWNUJgeQurD0CS3h4n5HKnn1GPCi7CZx+kbof7xvTCbpxr4abnY58WJYrZO+ysecaOdSIw+RPSNETkl4QvTnDFovS6O7HtkTF+XUwV5l5mBpxsYX+xyOwgYaW63F2kNDoj5QRydUVR+PWOUo4PBMEnNEzSOMbLBluXbeHnbRhbs+Pmy4RT78Ulf8H5PrnKSTrIwkP0RlQzFNnYFaJ1qAlFnxLeoDmC3KKUMEWP0VyIMR9gUW9ybJY35Ol0+pHaXAG9r8XDXDbW25trQztHeKj+H0VyoG2qV6EAHiHTawFF2d2eFmcKxBd2nCCuQSuTHH2AGaS8y9rFGPU8W5xnQw7aBSNHtGvpOOCdVzFdcsHfDgyZOu8fSDusb1YyRzjRKHqDrheMd+mt1+BIO9w9297egJQchRxUuHQuD20cbnU/swLhn5pJ+eR9bFzzGVuAvv5ieeKk7YN30lQIHW1t8p933ESIrfOVAshFx2L2WgGu7GLbKlj3XgTn0E27H2ONbggt8sxmwe7UH0CAY5ekz6oluZ4LDpOuyz3DgBb6qbD3sV0vfljlnBxCLyKrPo9OvZX19nfcNZ6eQ4jEJmGDHcdpu65tmQrgQojfHF9M1xiiwdbo28plu+EAfpromIf2BmXfLhrFxO4TiD+e/dop573W7emMrGFOWksOCt7D6/Y2efkufnj/cDxK6A7cpmVKmtKFKL1XiQ6hc5g596RuccOuv7ZYb6I2Cet7l+IBI13hGm8KDMy5ShfpZIi+UjLKxz2l8RGzMEkGOKq5/HLQUINbM5CpXKSPeItU6QI4fBF/RqxEGL0h0K7FBYxmtjXO2FPIZhk9gKXRZpPetiICJ+IAKwkxCZ2EMfJOW1Ki1uhvV67JsHlvZ71Nh+zHz10qlrjDN1vzcrutdYC8QJ0zDwXT/CW0PV4ReebIgvLxQH/yBA3sTB8gDhI9uqOmMZKPs13QBJJ05kVm0V2Kt69yd877UzbhFKbyC4srBr2CIY4rbADtZCCafACe/umgAywqREYdNfSZADRDD3+pHMxy+GIG3M/BxCkss94kvgACUTg5F3oFasETTJNWSqxx7QWtftPg1ELoyxoS9mppfp/8hk+Wins0GUqZOKael6obUJWCcmI5ABRj5OeUrrOQVEIf/HdzPx5GUo+2b/FGZwwc7KQjcaRzf7UJ8qx7AvdkJehf+9NwqGGuFpbUvRcRuXdMTvW562RV/XEJ+GynPt/3wq3eYuUrlncrrjPZFaO0RSvKA71Cw7bZa8rkSQxxEXnTte2AIj7kS9fpoQ6ylpyh5E1nVUdAnhHbNlzXaDyj+A9tRWnW7EbkimKtkMVrTpXXHvOspRIybamMwH25OY21/ItYNakMurpyqxuo0l/or6GvC9AZAIqa4wB2EeOQntioVA7vozaNPFBO8z523tMgssd8XtdrVLFAdHCU1SUIEMuYeujec0Ct4tyv8lCtNWDRAfNCwEeiza1QuKL9+hVFUSy11YXQZXJdFbdLHZSDtUUAQXvv3CcgROF/ckzJ8D4mVUJVmpnrtzazjaeRHp3fuklFa9V9oHTmpoy5f40CBCo7DsC5Mmlou2sLFnnYf1Vu3KM+yDn/Pr792svBS3QkfX4lWicLUBXQPa7t/a+eDgC2B4lBv6EX1i0Q+xkuzQQYZBFJ6Bgp+URgx/c8Y62jaAhYI7hBQiYWuFutO5akCuAnSIk4M1TJegeZ4T6OHhQegu2ESwK53CJSHV/V9epoZN2H/3K8IERuqOvdpS21xcFyhQgopMCO+3WwwoYgeRw0Zn0cOnrJo2vjoO4b1IoGC+50mdkHPsmOZbi5D85tgNWYxIhHRgmSkgWsDokOnXnMnqZI+Gb/TGQe6fxsONKGSnh34gp9IQqtrMSKgSRqMZ1T375ifqVMgydW6yRImO6uKZzQBcIb/wLCNLWs+u1VcMt605zp4zBVFf1foHMFaker9RHZbOgBujqOucf8a3CDEGWStsT5lGjrJpZ5oroxXxTCVaoi6lhdfA9UcTkNPqrfvVGrkQrPjCYATq7MSOB2bnufSQvKNa9jOR4k+SFsgf6FTNpHFrbKA16+pj6YTnvWrT29UWKCqoqYIytYnQPAQVnC6pZQJvncP035e0xyiwt29ucimw4hc7wm5Wruh3pUPmjnHLWpuJiCWznu0hxSHUNcDcSlI3q6kvtqipfNAixO6H+Pk39O49hxiNLOp7xcT1MpPPykG1+qUxVQtyhBig6BSfye+fNe7FQ+Bjkqi+X9G0ldZXKY+cnzt5cfLkG1cz02/UFTvGTOtll89jgvAc23WINJ2Xzh+LS4SeoKTVcUQlonyLS5S3Ra4RrVacVVTjLaBSFEYPQKDRzxv3JmJ0RB6KlnmEGf+53/7XYcPT8oJ6qFcyXOGLlNKg3yrMC2aMnDoofuB2GNLibhD1EXAmBWEbqctLNEatzv8sgEd/Jw34Byd1C8oI/4FzjFPVoplEP4Nt9MBGRW2L8SKvat+b2G9IckAKDnQCcBOd9X8edik+MbwgE9tNUlWY/vEz2Wl2LHBGcC+2+9m34hKu/FsEPzp6gU67Ws9Mje7BH2XGt3UdH7G2XqlPFdvtYby35C80k3R0mHQYAM3yv3bYyPgsl0twqLDAuSmplOmP72OBoPgzNtGCDgKP2EEftV76o0usLjlbdYxg9J+NKJysqK42qFmwUSgH+ivf7wpK1OOOB8M684yvkG23I5/J+xfSDeTrYzeMciAc7a+bn8RE4oG5ImE8ijnAC4NSJpvffttOj49IFbFVFq3qNe0DjF1UvcZKNnOXNJjM+HPqJQTp+awaPyegCK2R9dXqPDrWCJttPf+f4w68U4w4Frs8/IIjSTjx66zV6++I/uzizxZ4IHWs+qISH50HpAzvFbkWyyr6jPxH5nJGrcmsrXv8nSky8QugAGkhHz3oyJkQhgIjcEDi3mD9maEhR8u+bIY8+maBH5knMgf3b75JUdCbhUbubDsHs0ef2hkFT0dOVMz39JVQ7z5EU2BdR/A9Y3Iloe6shoizr29QCu2+IuTUmS3CDDEbC80Zo6DNrCBSAzs4uynnhxudcyBtEDs4Sg9h9i8cYOFaX5E0GHMkWl1Vdd9LCIzNj9N+6FxgnXErznGKtksuiOrd44HZnG+RU+t8ZmlZxACWvQYzeOqp/y2D0NhuitnsXH8EaOdpqrzNmfRTuGYBbACekKIc1EXjP89IbMkq3O0w0OMQQaVq7vCeB7xiVoqwlTOpr0OfZq40cZm/V7AQ+1ccvJ8kPq5u1ZtBby26e29eDrvGTqM7dHiKmKv99Z2PIl7+TlVKI/dPbEXNIwbfvRcA4T4xQ95THqLXNxeA+4cDhVbNMz5Hp/9aUaL/RvA2ZfhSSO+VW1sfTZnH2YnyozuefUZrgpLizeHUoJEK9JSVnx/Z+k1B1wZ0mJo1mMlFbofuTQBP/GOR/RauBwePMVFqYjxh+yCpZ7er8VgfDQyxcErUNiKLOujOlIji4phUj0kWtfAwaZG34nUb5RwrcYSBFLVxhEjVI/xMcsBTjgS5u0b394FnUYft7aYrGunGfwoTxNFa7MRt6y9KcifDk4yFhEGIr+xsH3khfP/srgyUUl1BvtlOC+YPay2Kc4cXQnTz2XZme4hnM9XHbReH9DRZgXAwStV0AYYjU2IqbNqB0cgZykIuV3sBlGo8/bY5cL+tbpGDsVMknKjgOTCZFL33sfmKChvtYSAcLuF9kF5ahtANTxHla29I2jA3+5XDvqIc2eGzsGHax+66ygzR6OVprKZqur4J78Vdj0Zt3dejNRL9yraPZ/Ck8QeioyGX3flTv9d5pI6q0YK1iWg7uOx4ozFQpuEwBGL7/Motl5kbM+Og2wgOl0fQVuWAgoqHMLPXE1A0HM8ZLG2eCRVEcAX3ts4Djd7HClzAn7BI7hjlbKWL3VNeQ0Ut/J2+nhvFwqTZa6LN+upDqyCGj2qHicMtu8n133B2GJxQsfY2FSVCPUbIfJqu2Zr8GF+5oqgiwY2RH22XLUmxR8i1I6M4kBwq2n+7PX0J06W3WwkK98UFw/n6m/SbpK4K/qmp1MVuq65ofAZ/4m8n5AWGOcXsxeDeAporHTgmqlESl1daWsqz/m36W0jnkMVwzR1jP/GHK4g5MtnztQUpWFnqPVWQgPGZ+xGseZ2sFN0Evrb+oprss7WB9cacdT8ko/K4GtrDMs06HR+j44ytvcZdmgTfYKfJIZzahlrIZAxjCMBPsbITImd0rqHCjrTjnDicvSOb74OLGnDcD9T69I8ofxcVJphgsf4GDtnY3c3gX5bqjHXynCu1yfFltCeUtc7K7qDlu2yEiZR6MR3A/8isLTQLXUwS7QRFf4J6ewt9jRHKc+LBEiJSKu7sB6oaOp9SoI6l3XV8FD9QoRL2GsaAZi1bpw40wy9KnefGRJlWBYpGwxq3o4Rycd50MHWvN3uIxMWwPyk6I0KYQrk6/H0ZR071bGUe59xDup/Lg/egTTU7K10dtzgi2GC/DZswk9Y7FamvMKKf/4SWVFqR9hfKsFxopAFhaAZYtOUCgZfkmiu3uqOfHB3VjK+ZNOKUYXeejyFthcx5K/hDhKubEayZT2L57K4/iEOBJ9lWgVp0aVvw7+sT1UiBODyQH4q9t3ElNg2LaMmETT+MzU+z98j9E5fgLR5bqUdM74Dxu9HFxN0L4atS+qvvAz+2/V5T/zjKDfAa1Lc5R+jD90veaNeY/p2WORyVOzrltGxlCEQnKhpUSgmb668I6sym6fqrVmN2RZI3UK/9RgVi8ai7ws+1w4bxDS3CTXb8BNep97BP/Vxxu3T+szJGBFIggfkKJOXJgrixRvZPlVve6uLOdLerU9Xv0krSuPPobi92zDMR8AE04yxzQ9K4pKXoaVPhrH1k/UMpCYY94NUE1IT7ZKBUudpSrpDS9Tvh23gh1bI18CDd+Wm/YKM+qsVhgQfVJWDdvBECpi8GJlG3p4vivB4xQT4Sabd04xYunYxNGEjSwZumvX9nkL6Ukm3VvGVLLbuTal+T/2JHUKNdY+OufksRmlegQsKNUdo304pbOORzTz45qzdoBjC+BLKJk+Bb1UjKbobfOhAQDcaPSu71VqApJVILdQt9PdCn2kAt52JM+IyKsXM7tnF5CX83+EgR9bkjG0A5W7YLnOnLI858mLgMZc/RRuu+vHfjMSzivgBYQZUa+lszL/ClcX1iLEvOyy9Q2gne9eZteUHrtbesnK6pYEH1SmQVge6+1J6pZr1Armz1biXiOOUppXnhJdynLK1BeeD2Sk7hfXCfg92jpXo63ua+S0v3ubVzepk//WZ29ADkgj57nzW/GhO9TMFsDoT6fEAw/VA+UnuaMu2uYXRTPeNGKI/f1ejDy3w6UfKDPNNrLRGYrZd8E1Zv5LXF1LsmWiq+YuvrwkgqnVxMJL8EYF3EdcBqDqc2sRcmiP9lnHaxgcwkJljO+g3/8raUYIko6Ke0M/K84pND8wUDYlL8Aqd0O4jgFlZ6hwiKIdz1FyfPtiZcw1eSz1qcTRp5+W6J6NJ9ikKnP+wngonBAGWo0QZY+qwGqfuuHOaw/49g635PxriM6SBvP65cwAhWirYXJmTSjd5yfBF9kR+/gHGYAZ0rl2C1MdUE7VjQTCs2maXCkJZaac4CWfUdVwp1jKGdw535nYd7pAC0VsiSdQgn6qyjO8UhlWk0x3YkYKCkwmgPS1J54lfDI/yGdQ8TJU80yflr1Z59109vHsmeuQy/+HjA1q0W5jqegWg8I2vESDJ/h6cKtfCvoHj6t/BYmxfvVJbfN7JvFaYMGCxrG/v/DWkAcEFHN8yLeDf3w0408SQbEUvSKJgcU35xn8JHk7xgfSEJXeOvYX8WZfVaiPklhJIl9ESRhXB+Q9Pgx4qNMybgvbLCh4tSvdP2qEjUrMB/Ixq8vTLCiE0ThIvS5KCc34bUv7BHWqoVEaq1rGfVhzXXK1zqn47fLi4FiVXYksSRtITX0GBjurTPpgwK1RT4g9OT5csLK3XDoe12ldomHv8WfBoOQFA5/JG2jrSW5A687KrMxLh7VF2jim8riVux4028cKvFPyYp7v/K8BfIiSDkGLwJBzCSMmhjwXa9yNsxz7uWYG0qwlkX8EC73pAwzocT5qUE1HkhVNTFt6EtoSh58T7E2Q/4qoR8jpzckV0oywaFB60buIlffZ+rw6SKD7ZQgs7EgjTUg35AQiuv8fkg3eQcz8pd1pJTc3XgqMch3byfhaossn4+TMRVurgLqGX5Z8g4UzdLCfLoMfAxCQHhk5Wu384r2q50+zhHkbTdcihHwnrvxFXiHlQKu32xb+r6p2zPNy/Eees5jt8pL2fNb1eXfF4LNrsTUQZJsim6/KLphcSXtJ8JmkY4SPRJnwCt5OOSm9td3IvB+TuhStcrBGUkYLTWdNanpfAh4juAqRIM6JivPs9jPJ/j9BIbh6jZj83xv1HoT9BU+VBH7W6mkQ04wLoTX6l1geqOg46tJhwkJl1ZPM2bBvhsegkU2cjhD9sDyDZCO/t709PJ7J1eL2+VkQXbIaGkE3lMihRrFwSPrpaesUoPDTagaanHan4ag5c7REYo2zU5i+ibjazSnZ2jp7iiDFohAk5Mo/8M+U2BT+izRVO4Ojj1FqyMeGlSKy2dk3R6Yuog6HYEMmW9PPGGBIG9PVcwiIyc2qt8A3n+oeEI7aBgycRGFjqAjIne/DdxyJdxOxo0OPagOJLiLGrfqmVp/g0dt+0Vll5rQDuNFOsBQi96HJzoWILJb0IXvgn3r4cgpRDXG4qyGR1q4UHdOif8DEcuNayMQe0mB06BvY4TN4bkA6wOp+lggjDUu75Qb5nnP3aZhSsVZ8gF94yyXY1KEPaPxhagh4OlexRlUCX55L2IJanseF0r+fjCfXpruVQPV3/NZ+lmkNNN2mOZMbK0xDq7t4w060T4D7j3Hy7YRxs8DosJRK31Dp93SqzzBUMfKwz9vTwrzn5X15NOswPJTdio/sUMb3wSSKV10htjPJboQfF3ih6CPyujzk3I4QddRdyNAO4HpRuQGlSzdcDeyCQs8KvAqpSvpEPiRbaN+jujtSAxWADrPNbFowjFJQzwmh6yR5CFRZGAvO9gYcxfZ+ejpa+cI5gHPojbpLw4vQDoRB1TtmrDDfgHY5Dv0DW8nTx3FJoe+9Vk9VJ8AsYi149AdYMlVYDbWlfaQ5L3Jek6ihUEWWldXVvDMedn7Vfu5AkNfamm7hh8Hi8xblcQ/NoAtSddwp4GxfiDJPHv4joEBARHBC+HJiX7JCZ4CEtnJ3sfrONumHJZ4DbM5khorZWKFsWcPO01guCBHtPXYFS2ci11CD5dN057U9QhyU0hBDMmbiWr39mohsWUYzoMlEtK0JOb/Zhi+4nG2A0/eLxGTSG7KzQDJI2FDJ5WlwjGYNrBrYQWF4JecXmjnwYr9ukuvFQbZIVYYrcp+mVF8o65RBXrfsHLLksgDeHQG+zWNDfgiWfWx1SilDv57p9nLX4eORHMddk9x/w2b6ziDcvOLsthZqmclRaZxjJKWojeBlu5DnR9QG/uE+oyXw4L4Ue6HzqBZJsfAX+7tdnQFYwoes0TPAOkCdX4hgWMLaXXAns30z9Vn/vs645nnMZ2G7jSeflVCzM6GbF+A/6EPJHfSuwnBWxdvgWrEPC4NeYmhuXEGoVnR4Tmc3TP7Kio3i+ixuzGLIgYI2Wm2/77nMi5GWtO2xZIHoLbVqG7amd3mf3s0ciXvmwFDngs+r/JAKupvlqIyEcYCRSKzH4S2hCyjmFo6eq2ngUQ9e0fWUJbwE8l6oz5Qqz4fI4XlTly750poDm5mOJi4MpUG9Xc0H++XC5tNDOLWFpVPVkwruE4QsScIUIQdtFmxA68CtgHm9rBb2QeAJYJQXEvQCUsn95tXE6xr/Nny+tTV0AFbu/6kG7lmAhho2UsmCRTHMvux0WicEvfme9YG02CPEnHpFSpRxzXOXSOYa2nSJzHKMXMxLXVvw47cF3t2cHydNtHfOOPsoqNLQiNZ4dCvf8dLCYu1Bxm4fy3eg1VTXrdqVz+BwNSxp1/aeyvGRW8xE2yTa6NzmrifmJ1BfGdelwDI2iqWNfakWYn8y30IN6aMH/H6eGTaaS03TuaCH8J4XuszRA67F/Kuig9337u4l7pePG1QsdlWxl0+92wTsJkPWeMw3+LpANz94JwQEWnl3m7MsXIxowIPgjG+KmdvLXbKb86RKUyoJ/+K2uskthl4PEfAJ6+JusdlJHbr+7IN3ZeYe5MyN4CoL8LmXrCaLc6tq84kcnLN4fDA3PXQZPPRIWqaCH2i+wn6CNJI1+GegpwPvDOB7nskh1yrZvUriAzJ+f1Qtj7LC8Emu+5MQkm82bmCYgelvDSZGg7xMyFG2DZOGF4I3HTZ7qJaDMadgtfIgbv9kfccirXN6G6wO1EVOsHOZ4+ri7w8QAVABJk0jQyx8P6eBNk+PagPDTzUmjDWbh5NUQvfdATOix8VAiztny/lukxYHAZwI3AnwEgmoOHgAPJCZhDN0leMMBLZWTosiyuTFTWU+ASH48TNYp+79Z9XHCdt+2qndqJHQj5X1CmMTGIIfkKoHiuLjOK3dFkk+4zIfQ8wgQOvW34v07xP9vSm0Zfdx9ZpV+11v+7Nu5QQAEQlvQ2CRURzcpHV5ghWCQW7CXo97hB8umc+l4UvDNNuw0vpoePFjpMgO0HAH3PAMTipOJeYBb5YVchZJqzItp1v4WIOWbVXrSIw4Oui2+/0mVWyQjunv+JYGgP20400yMu3EZWUFknRxl/qBzwBHV53v8dHD3qTS9UNH0jYdFmt0wZM22jbMEdpov24j0EDcfpvcdPJOqkIaaCUYrUHjZmKKPbxXFG/LfH61lvinmOboT0+7moNOAM3eunjvIqAKd9GASZK0f99iOmyaVk0+JFwgw+MorJ8GvA3xYMAwxaNSjd33ypvMNWEIx9ZHvecuCalR1zCXBErCa+i66VCgY5ueB1p4cATvt/EE0qN9X8lQQrm5DzO+Z1Y1S5gaUthOF5ujHwt+5P1KSAynoaYlrzMcnA3NMvYIQIZoeisyKb8djMeOFGF20QP1TNjq8HBu6zWq4inaJ7XvtO8J4V9uJsnfJlRQ+KY0vH79ygTNagPdXQ9xGTFmh3YCdC3IeW9+LJp2/Yc62UkJdaQWFtoVRyg/2MLFTZzVihYBEJAbNA4trHtJMBlEO1DiK3CR3PiTf7DHzxTHB1h4yCZCJvHrMwsXP3klmxKiyL3iVMpkSntOUXGOboHBJqmM4LMOk+ZZvTNieF/IZwhFZnIV4xOPp9NE0cYhiXYrLOINQoTWD2U+2hptU6VKpOEDrPvnzTLikELdLkBvzfBuApFDkyA8SpLCtU6w5f68v0QBysAVEhGKrYZtYLR6Rc/f76vEmE+XhfAUUGwMMb2nfVac6kIvoLhJLsw9sdR7F/pCkxQ6ROiFqsIqvSAnKJtfPzR+AAZ96eqpPf0Wd3/MhTfdWqXDoxG3/9dtu993z/lSymE08+ZpP+w2uBN9/cEQ4sNPwtyUjKqwSEgK+LIHAEGqd8D0YYy+pZTSIO+/nekixPxwVr0QFRyQ7CZy9NIyFao1jY8rzUGyzTYqtSIwbclr2OOmJTYDB3Vj1qHTMUlM4rDDp5/twKGMpihtih2jUGkd8rblzQ9wnwP3RIMrZkghdRpa6dKK0NY3HCjB2OIATaPh4+BGjX8VGLeWncYxFe6vjtd89Rq3wMSof5oEwVatSlR8iWxRxxZ9qbPpAEyMwgcGde9UONVMQasUxsB56w7Wu/NAHzmFm0GdV2yut/32K2Hi58K+SU9Qk3rW9xpORKzo7HwXek1khA00RcJ8j6pannGcVrDdjL12+cYRoHiHEA3pIBIEId/oG7k7nQKIE6ZAEDfhqQZ/dIpt3IXOp6s5aOFCHCDerm/3jaTl5YPrBvk4eIbbZN/0hst+lcPlXXe05v6xXYMqvc77w64FEK8L20wSodILyMvoQl6yACVbt5DYDugc2Xmc69s1yfxJRU0fLUFmdqUREHm0D9pBgoqTxTr3cmOI/Nm1fKyAzXfcTCCkeBWjMbzm470lcV5MXCcq3iF/UMyZEMD+X74bAE592Bq+emhxrMc8kW54rzzonpW/JxY0GFGJ25kdXPnvU+h8IpdysntkAwbft7zqPa7pMG/g1rxUdtM7S+iVOnJlh/Hx/UKA+zDUbWoxMH34V0DBkj6DogEwNL+b4EQz3z9iOCwBoKxeUHZOYJ+ykFl6jvw8V8TQdXFaFo6u6y9ZRuOr4zduGSmMBWJao+/kBNgPoBO0j7Z3PU5+WjhCHc1sEnJnACDBg5Cd3b2pYKqc+8Q3qEc/eqbIZ3vr2FXR9//Sst/qFy+u+FHNxQH/aZy4b0OYrRg0T/P3m8ntqKB+E5GzG4EIsCzrEdSNLsP9xZIuh2xxKt2SmZTR6INFszfXv5YQb0ONBiebb7D7tTgPczZdpoi1YGgDSOOciu7cKmXImoazaAaPtZ3KH1HO5HDgcB0obPi+IPw5FWt/ft0r6WTT8zeTGjApv6DY+fYesPxb6NBhunqTuCKDT7PSsQNYPiwaLKv5BGMU6Jhsh/ati76QguoytMfcRAFhCFmSUh22UTeDd+jW/85MXEXGcZPE8ATxRqhjoCf8Hb6HjRSbeXLOmRbqdfhagaaBBH0uObfU3cICIq1YEdMVohrbMAT8bLwuzs8yKqKCvb/DnxG0XEzK+8aRSQVjvo2ZF38jAMrldqq+mf0bSCRbsW8CS2xFVCDE2XN8dlp74YVq8/U/8Mn58Z6OYLBSOqa6Xtux7g87fmL2hV6NMWB2x88/leRgkVJEyXQEkPvCX3oDhjfa6ZD9hDnIps/B6/qVFCtTMSd+eRTDV32otPSUhQ2p3z98cZBR4xE0m2mgTjPNitQvtjwM5Jzm5M5fHGv6masLdOMFVMAT84sSoKXp8EDd/MmjZebb2ttzy3s1g2tPBkJIKX4b/esaWyKCt9qNOrfe9Npii/Qju1uwxIq4I/vUO2NqCADHMYI5btn2XaUgBuZJwMuFjqTwGvabne5SbNvLUtRqMUZGqiyPa//F7Frqamnj6yASH05C/DvaZK7f60jJpx6Z5AI0gc1Nb+oKDWjE08Sb6iY8Bt+mjzfUCd5ksOVs1Wz4RempP+LpwXnlLNEVg1NG2Mt6GnfeTrdse0v2HeTTS4v8LGiVgtj6YQh6v7T+6vvSERcw7XR2P9CNOjHpM80E4MwrS85ntmDL7+KbtQePFrn4TVad92e8i32iGPaJPiI53BG89KMHxp3a5QSgy2Ry+MZQCMnhtGdN+9A9UVv2IWyZA0nbXLqUICMRYpA7NqOatUk8vnlX2VjbPh+1bhCbIQ37aAElhq6EpboCPwm3H9umbJsc2XWHfdDkyPqBV8b2MirpHQ+5emAd6aAfkdg2lKd5nyq6aov0wPCgsYOL6JTMb2Ko4a6sxlT1PCt4J/ezPkkf0qGETyNgJfKDJnqPuQb2A2xK/LAZXY94bP1ZljrU/mOKer/v/+YkmG6ZPycRAnm4xSiKQBQXg7DeY+EV7fcf2AYdstQQIAwWM8vd2XWhLnT9bf4ohV4e3E0NLP5CYdrDv8P9oZGLFGXkuQj0ghjjA7ByaytzfLGSect1gUNYOboPb58ZqV+5J4J6c6nVsKJ/A6Iiatvh/VWkjtwNHr13aDcKwcZEDBoinpDK8XBNC4bpAFalxXYncQxgAkvC21zlaNJR3n5cd5QsUbSG2gwIqbaxbk2heRPrEAWpws6Z/T3egoMRGe+Imow3jxegdzoC4HufAUCrssYt3nzgraBblVUo1UUi77PikZEH8d+G9emjp0EydrR9u2/1XTZ6hqmJw2Dxk0an1baxoVzqxXOM5jVAop13HoxKuoTnkbWQBBJP549UniYCVi28euJ2LiZ9K1ShXFwXDIy7LqnqRdobdtgqFnFb3pKZz5ephV67+PbzETcD+/lM9A7blSROGP+ED42yT4umq7vS/unzSsmMPkeuabifbz6wQ+p7S+msvAQz02L/qJKGuL/zwkrBf7MaUagCveQUXcmdPMvClnm1rpGdxkBilBYHfPn3k7cGDK8FBV//rvDL88NbztYRD+rPZGYRer/9j8iea5N8pS1cPFniqF2+os8HNB8DJkSS5AnRl4D3eNpNpmnB3dGR+in9X1JTXDi0kbi5nT5c1DQ4omsZZnIl3gVbkpEkonr2RNpXlO9U0KhJT5ZhLrmadxbyekoD8eIyrmDqjGq+DKDzVSo8o+E5DNolIrroteAxVCFkylUVnQnwz5996+6fiKqTPqDb0Z3I28a3BtTWVnhDaXJtVK8EUO0iA5s88AgBNPh6VP3No5hhq3QJG64s/mJpPDwpAIlZ2frV3CxyStIX4uT5+Aw287zNBCuCGDFeEZ2KO6XB2XqyDKdyYKIB3NGZfMtxR0wOHBXSrmuKYJgXW8mvkIHCAbnNylp2edG2Q42mpxwPgGxCzaQq2fQ1TbYyneuQKRGgYKwplo7fepNxsvx4EnZJEgs2+lgLLpp0qlsCD7RvJ9eu/S9aiXtx6U0GyVDKMEfjpNeV9HOsvVwSqChXBFPwiDlK+tUvgtSBk6kQFc6GnyC3yzuNKy54nYBwqKBIhysMYSnliBUtpUg7/+1ULlMvIo+ud4AhEhHIYd4tmfAyLnewy2eSo1U/1Xx+0bC8vTjBckJXjhi4QwOa0T0TmIlEH4EpvhYCNwhZz6rB6HwpoMzG4fcnIHYiS68nO91AlTiEP0wH/64t20zTPSv35pTjJGGL7EaQJrhMqOGskUeQ/u8hIQ5u+PnKDVVczZ5TSXiVxCi2PNQ/fP1LZ6EarfnXdLs+7nC1mr13a/oht9gAaCXyvHRi3fHuYqt2IpbqpJMX0JF+Pt/yjOkcpjYZJQtc3oMottmj9dv5tsOJb0ixzZHna6yGDhitkfrzEomz+/qIVqq7OKKFw3km2H3r1QsuN/JWtIz0LyY3vYeRwzOELlxHodfch6AqYTxogqWbdR9KVjvdvJjWQU2Xfzpk7viJu6wYeMoBVoSiIuDJGS7eXEcfvinbdCheHmbuwLQwYydVWdXz4o6jzI+aieKhSe2pVDSa6AVEnWJBw1/uKK5+wTFcPsfQQcDqmzlpT2DU5QQbdtzIYcZ4OsH4aPpxGV0DwCh+sW/NrEwXZYo5dFRwTOJdAa+L/Q3HlnvNN4aGZbHKQL/CZvqPULJAjwhdtLDWSKFrPr0aBhf9eC7EQi9dl20ZyZ/P5NqrhEpZ3tB6V+YA0Kw3Y0uPkamAwvlUefCoLOkrSKBaP4E9a5f3ldq2lvJPxMH+gtv2tK5/napvQEzJwPY50foz06hrAjOH3+xwgR1A1W0M+HeyU8wguOkyhDO/S3dzIkv2ULk4kBpkTZwlMPIwcWwAawmSLT5i1Hza6PUP2IcQUbOow+uTrgEOLzKtS8KHFOKi9Qt7IJGxyss+jH0pHqCU0Nt1Nau6aGOZVIQDbj8Q/JTNve4tKhE3ksYndc8HkhBc2i49dPoRRFiRIzc5GFYqRmsRMXchmn3m2LeEvwhYQNsMzbwy6p/0+Eo3pmlbHLh+FuxvLVymivj78Q2onhoiF43EE99bkQQcoDzl0Vu02GQiTDP475epI6j+4SCjztS8jsC3F1LioXKiy5+jh0Zce42MORUCyEyD4hOfwWQhegPAdWDaFIXwGnfK1hrv1LeionPfORPnLnqaGw4CSIj1qi9wZkzCwLQ/X2hbQhpLKSVExK4PO749j26/rB6GD7lfry1ad2N28ygZa5ItpkrkJbqDaV+Q0243sLnsSrhq52Hq2kLuk6kGVvtYtgE9WkQ4a/k6kaaKtEr+3klCKloLiG957Ht+xvKlGsNiLeSX8ZirUQZhT3U/FASEkRv4suyT/9Jqz6qLeeZTu5iQNwnQcNG0jZftrG7ppnNUw3rtknAjQ8e2gsua9s3ZZ8mFOmZ9RyJaH6Xj6xuQcFb4p/0W943Sc3j7oiqNVI2EwuJ53+ldj6NL2mFc0q/GPFy3jlp+TL7FZklon5nSBZtpRlrHcos8w8AIkj7IqqanfNXGRGj6KVwyTkaGBaqgYyW4ImEg2pDhLe5avWoonFz8JywxbTcfVuw+vz9msgdFgKDOxnWg9T/16urUFxSRmigDTUbd0QFfliH7oxbjTc0mtMGvkH0KYSXT0MSYADg4S5NUbVNvbWtlCsPtQ8CQB4YsqWAnYaPFdlvBUH8KX3KytnfnyvDXRLP26oGD94D20LVsdOCIahTcGcFNnovZUmpuQGaLO+/mm5vxb3Vb6a3m5eZbXJz1gAjyCetE60U3TMFiIDC4ntMrsWBtSnErjJSCqZHwgfEisWBrXXUl6V9eRSkc0NS1V2FK1XeBQgub+efivtVXCYwLu8cO35TY3ZBzIDITvIcTnIIhTlZqAIA/g7PPDtLJHZnY/zY+pS+HqFTrD6B/mz7n5/9ce8fsYAla0olmLUh4Cv6qcpxCeC3MoWWWqOXKFE+9NSlQm7nQ0rIKb7OFeqDgRgr9GYx6O2qReoCZY5vRcUrw40OP+ifrH++PZ29uc7crJkSX2CIMLkvr7h5MDTOyL/daUsecmaHtKkQvoIC7J02S6v+Orz0r7QgsSyJeeimWPem+13FfX8IfISHexV21KAv50QC5k+gdNJkF3MGin3R/yVJf6pfkdv7UIZjG1o1/VkLJhAzCv51dHXTIfwWW/52jut2hYm4R19/+VJjaoV8yFWKVj0/7dYVdK9hlQZSqX4gqpHXnubjLCkeVU0FYwpunYg4OgZviBDOcJMXn1yPPTZ9yhWoZTEUwdU+dAyPxgi/HO1bSZlEQONHgYg20nH4undB5tCbDchh2aFUCfJBvh8+faoyR3MtKwLSNRsOH253Q6hdrSQr88g+IakWT3Q3Msngn4DtN8L5Si3sszZVusiKtpT+P7RjPnTGuma2zEYyzECsh/an3qE37QcdhxHyxf7ljzBWYD/0jWynW11nueO8F4X3qHHBFzQQMdi1Jw/9x44z9pQqdkIS/TOOGNVwet6oyh7jx3Tx1V3ix4WVb9DPqSC6eIgnh16FcnxpnIs1WheUog4GHjfPOMAJ5tTesJH1lSykG+oFeJAMb33IcCVfjpnwb56GcX9876wwxDTuOBcAQrHY9UdZUWZvl7FOAOtB1DLs5z231wTjl44AhKDUlFWDGBFp0Hl0z3kWeE+5ZTnCxshmaEIwYftdJwE6OAg9DIGWdWSxDkOmvXzPScL9IHbqkJR5DrvYm4rTIWhv4x/enA4ehVPJPd5tsdbGuRXOLvOzIUasVF7qCdXoCucbmu3/1ihuGgsivHhqoUk682MwVDi5t12WW/p716HhNhmi4EkIZCk0YRdxHMzPtARW3AN/2zoCK2Wmb/Zwy02s27Afum3Mtm7pv/6ONn0P4Xe0gUo55RPpF105Ng5QrDpwqDEk4ZepMnezhp3DED5Mldbn5wuZ+ezyNAHZmboBauEcwV9pRPyPey2tlGk1mLSO6JWP6EgjAZGpTXsMHc0QWiOQt7eEWZ4QdMFK+x2TA88O3LYhb0a8crpZO+7vi+60Zi7vu2WzWoQJ//YoBuhng95UYb2UwcbK4Y6sHBWJEeBOrM88M+ZSIoub24gJeR8Y9FlPg1rfUrJVe+EZTAl1tN+aHcip1Y4UTMZUlxI5eEPWBAMCMl9CiOb6kuTJtXcj9wVbvtCMcjwfHch6YG2sX2FoqFkLCzJl6x8kpk/J1raV2ww06rLtRsRSJTz701jyG6HfvbQWHQjol5KhbHKdNTUVuAEeGxf0UUkanRGEFbexGTJXT7TMN9/i6Ft7a+t/SllVRWC2Mg5TOj3R8IXy9LePY7twFIM86rzCExVxCkEMTNS2qExuKCoZ7yfLVH3KQJIlpyb2ujquBbYxQlxuANu+2vQUybj+BZdRWi+fidBWYtBcUe3EZj2dqbBgh8sQ/Pwc2f4bgfkQ2fMeakFtlQApJEnu2JwEmmoNtsbq3tAtnSyiT1HRWYQyLO6KyxreRysecX+q2qBKxdXoAHcWaND7WpFnS/jhm1Bm/32j7ebOthVktq9AOF/hItRsOul429bsz/H4Jvl9N/RB9hFhthJQeHr782n/rt5arwYrDDmTbc0MotEAEUJe8oEb/HmqFh6LrJaExyP+SXkjNU73sloN4UK+fIU3TYTQX6Rf78nVe5F8hU6FUaBuwhh+W9Ypw4psDYqzxnHrPv63iseHBH08Vn/j92Ds4vc+3nodO7xTsPRHhrhf/gQhAs2YRDYpQMZE4B7k3JhHdz99veKJnlYH3LU19Q4dYL8XqlTEnZZpGaV5atVRMvlfufsJeOvFtYbvBjVgpA/Q5cErjgrySNVl2gpy6yCoFDDqTmCyGyLvGJv1ZBJTpa5H1U4aHlSXqmwklysTfNdv5x/GXPIFeP902rAXvJ5B8Hlhp6G+XymiZVvZsdOdbCwPMM36kL+JUCE/Fs3eCBeVQPcRuDje061gRKPY3gRM0xqYVLNYrpnoi/tjh4raA8Wb+iTVL1q5YTV7gadcL7/blOkqiG3CMciLNggA2OGbBA2neboadkt8tc47+iaxtOs2eD4MJorgTaHXj4gW58q3Iyu09v8Jfs7Ek8pMaVYjGZxOE4YH1wjasTqlNgAC/DFAAAQAASURBVC4jpmK1IU+MzJw4aSy5FkLW+Uj61tLJ/X0zRDByP5fJGICxI0G8kYUG4D0fXklnhAmmwcLtT7c74+FkOEajRkykejCVvlz8FsnIgi4JG97YFYPGYQsoYa3U4imJnVxgcvo1hEwiDMgYMhy1ByeiROYONpgLLC8Th5v8JwgeAMIAAACAZdu2bdu2bds2n23btm3btu36JpTHXq+EOLKSFuz1dNPfKL1HxeUYasmHz8j9TL9RbvR+xJ1pBMNTyQooW2FLoTmIi/lXgLyhg9TBEgA2RmBUkOjCCidMG86ojm63Jy1vooe6E780VUQ+gz6Z2Rmgfq0VxwwGgbPMzPewvIDc/86Q3zjWE2DT2311VBBLrgCjXpUQGg7zCzg5aCbmDiWsWKsA6HH7hoDnK5/hl4Ye56DEMkGUdV2AoiOTAXeAGmmnopWkM9p6SXs2l+ANsg8ldFiDqKwwZH5tSindNfSziigzKu6ZN4MEV1nmj2WctAQdjCy2KTSc6liZyKUUyi7kBZFCFO1psn8C2zD34XjCoGXCUiGo3juLinr37YS8bGvHpYXE8HHW9rUJFh7COorgifcWsqZBqC3pUKqSd8B/lE/C6Zi2f9GqkOIU/NsF3cvJmeNygWaFtSIKyXCN0KQa7h61H0QyrLBi3Sl2MMRCWYz2AYqF2XkFAgmgzzpI/21Kk0jQDx6GJO+FpA75HOkzwAc7D1E8/qcpBqJj11QHL9SovNwkq/UhrP1GkoKq70XPE2lLv0o3TlljZsIY2TpCqAIIOGb51boaMpMfX1t7EvxMcISOE3VKBR5+PE9frGTKrWak8lgo//jzVoip/IgL2nDxIdSvTjKgG0C0Hm4/ODLHBxPpU1JrWbalIgFZf4uZirpzCbpUZkbMEellpUmAc0YLVNF8/xekfPfGyWyhXKjYDWGSXGe9BsNQ/2XpFr6fatGwjqwyNLiV5xvww5/JMoyV86VZNkL6R7Lq3X+PHbbhAVJfqSYxNhgJIwjYTPnNRoA0/CviHnAGoNVD5ORK2gFS3PtqnCJLd3132ncXSupH/n152BpLh1tYTs6BsbaBnVG4dFwoiy0PMr13w2ZDaSpbo7aq/Y0w1BPsFn1lT+kSbwccV26np+do6BddDhpv5MZx8tdRAY/3LQJI3FzCkcjJ+X3Gl4OoYaJTavNaFFJMlJN5L1l2R/tLqNInJ+bEvf7iPJ3JGd/xMttU8pT42PEahbA9NMIKXPDEs3UwLEa7ZoVWq3KDnXP3gxZ8DAr8dAIdKyIB2Qd0yTiKBzqwP+ZrLkgFWjDUZtc0aAW64EJlg3jBI2Ls+/zMylJEiA0jdpf4I6y5TtaEi0+1nTh/xWZNsLaoBNpEzT2EQjcxyJlk+Od3dLKOAsFMS5jCM+2KMofEPqYKJPWV02xY7BKVXrRLPa7UMukSdwYALuEQpMFaY/MHDEHE5FJN6B5Rg/NvfvGZWcjxJwtNglP2JloqJzb8tUr44VvOAli0iLptv+RJjWDjyNEp1F06yTxtdeUgQnpQnnLgJf1Uo6kpCQpKMh2AW+ewHPh0UVtSUUePcaxfaaXWkSy2FeUxGBnZ0w9LrtIIsCeC5QEt49skzLodXnmmc78mdsnrYxUtfz0AgqwRww+8X1NHk5BX7/Celb0tpQ0p9g9xn+75+hvHvpwevXysfChYxnJnCBNZs9F0Kf56PGLo3IhDblbajO04uzBc6v2nYNKMhyQCVPIsnRdApGz+96IXBMA7O28txqBpF8vAmmAS0bxqCKYCdqnvGyxeBJvl2StsqE6Gi5zWEbmiAE2OBIBcPvezlASJNK+yYWpAuOVJuVQUxsFkhevCcodeXD3ypBQDkd+hD48foB0tqj2MRfZj8oafxl28aEq4T6DNV3H42bL59q0jONK4r3HZ9tfirP8neZqMKwhZZegjvOSK7RrhfjabeQRNi6ClYlJHTXsS4CAzv+eoOBICgztWZY4y+tJ5cTkrHrVhfbc2R/JTV5yAAk0lhiO4n0wEZWCHafM7/HFHCrVHOTo9Qibtb4aE/92s1tv8ym+N+B6LI5JaoUTNzuIzKlaA6p/Y+dGfDF6kBXa1GeImSFc6nvHqslIX7ALyVWgq+k/FC92cLJXGEe3Laj1XrmfuNrSImGfrLYJo497BzuYEF3W+ObDfc1TCqfokNhOZ5SAimWlHmnkO2bvB0fhiLkmtyvPOM0ihJEEFNs74p0wtc6M25FKy4Ut6uca8c2qJhW+zQhGJEjEhg9IEL6yR+RyZQXdOKY1p3t0q0N7gZ2SyAUWasbjU3HghH0hODze9y3j3+vfsmRdGY9bnXxVii0E589q+A3Qgm/yPwI8lX0npsJM18TcCgdX79UAnJTes54xP23opN9Arb4aQJ6T9bt13EN8Z+wjxQrkQKdCAYyxjdm3oFCWVCs6mrG0BdK9r/vl4F8WBfxIf5b5NetoHverH7gitDB5xuHadSgoGM31o+GEkZHfIpSW9AM9JE6S10ifuqXZJicBaVNREXqJ6oBHHU9OziPqAI8ZXOWxkPERSnvOzfkalK687yfyZMa6WrZt5rab3j4pgELFzCkL4IOZztG6/DIOwsRQQkl3UC+nX3ALbj81ARrLfNLV2leYEacy4mk05/Cctw+GwUZ6WO1iqa+/nENMPJ7wpf21I4eXw/nVcV3dWpB6OBBxk1rl0RFon0ef6GrDugfmstLFG6Mcv0OlEwyuegryOutF5BNhtAfsGb/MI+kGL5+QFVfg4w5ROcV08kZOcQL8lEmF8WDhzg5U/DRqq0GrZNng0TlH68b8DWeV4dwrVNV5lWnPVFHOWPfjZajarcEzAI/dnsbAYPN/o7m9uh9CJSwJAUpwlonqMO3Vco3s40JL+bps16ri6SipLylUXqycTGJGMmqDmZBcqJxRGx42SjRuWjTNXRWkOe1hT5yUFqVSrfyOdUoQhOryBl1YddMlLqh/BZFtOMTEKiCo0e3TFgIVIqUwxqgja2YTVULmUl+I0raiGqvyoiLj3S68I5SEz3pXo0hKpCwNzFOz+XrH3jNNIDveDU7mWmNnia9B2Dx7cM3+pGqo3u3fSI9eAQJE75D9vPTClYrHGDJLU7gpOcUTCfQyQD4ZIHPPvIPVrfx/m2dIaqzeJqQBNcz/VvmQfqHf848Smhza6oIhdpBPt+f5kY7bESqeRdrtMcuIGW8QEDIFDKhNVIx+s6Og2rFB7MBw195CSsmclCtQKziY4612+ATdlSrcPYv3DBPJNwXb25FVHXXjOgydYXVVkOU1RsDGEvM9IsFlt5XVc1eZSaZQ6MC+byD/apC0tvb9hmIH2tU3ORHSwTQ49MsFLwqcQ4rx7KoFqNDYJmtFBWOQI8uHjLrVl5+FDZAkI0su7RRG3xv7vrlujEZS49uU6bClUTs2nTVIeb60LFOU2EHrqtv/sim26B8JfAg3PFtaxPBj2Eu5ZZpLIBPuS+g2J0SCEx1AbImReulw6UyT30na2eV5bHqSbOQnLqFV2dSeEAiD3YUubiOrqZfAc77WDgQb1TxBZUv4S/0jHYjVsHp/Wxwdt2jVn+4hC+gGo0XiDTI0DMLwqtrofsHvIHaDBp+QjKkzN+u7t4e49yFLx8BQiF1QkjMnFRuGM4T22gT80swt0KapEzNaE4c//7nZxOv4AVvukBsxbOp3PfmcypyxY3udN0ots6MAlp15pZ1VhSFm9vMKx3Xd8QarGIuyJI60bRTGh7s+p6qrxc5neSNI3T61/yspMkV9NILZXQwi8lIOpmrEjhtk4AlQ+F6rDD/qpce+g42huUJy6Ef/2Como7ANAJZQdi/6uGDPi6HPtFqeQS0oPn59bscsfwFv7m3DVALffB3GLDwY0B4VYl/cRDGzaJc+lFwvPJFRQKnoegR/aJ4G+nMBslcFcKA8xUMpsY7wXvj5x5ZjI9/KiyMPsqF557bdn3ju0KqC6eyKw0e6XrBGi1DKZo5D6OnUMt7+PVdto+D57Qc9+g546B9b6cdLyCeqp2zRWZSm9a+Uid4Wz7ztNudhFYrEjEUHmYHdfwbaPzwoc0H3K06AL1vHDEB1L0d0pfrVXZNiKsnxde9WI4bdvHonB1bkYSpf7hzfGQz7v+bSJqGVXWo2d+qhF0v0+UM/wlh/11yBhPhmQ2WqzMw4mkb3nz4busmjpwrrG9sRUL+Ba/8qukWfRUW1a9HKIiceCYdEXBTtK7eq9rt6Mo9GSqzcMSdM1JuUsYmZzEnqwRZjiyw/kKURCbKM52Kkvd16vC8Hx64NlaSDSu6Cvoo6A/t6p8nGqzC/Z1idmzZaEIlPhTNMZsx4qCTRjfoiZktFLJj37M4gOywxePL/Lj4k2HK1ZwUbnX8VuqlXFicXxlp+UbjSWiD/himu3PL17PL4puB9jMr/RiOmFfQC3uZYBft3kw2zUWP2gN4SGmWqvVCImmmPyVNLVKeB9m2msmETLAk2oqvq/fcGoHx00DNYU0eqQ8uqtqFMCW+PIk/nGETxkqbv+cSgI1KSHAr9iYzVdB6GqMNPobrkol6250mR8kxvsAAIfeLvjDpgGMlHzGh7svA60iz/mL3Uy4nMI2JKARHPgYZsTkM92PkkchWyFl+96QiNPjzcdpV0NpRN4TWyTlAFFWHjlWMcsigJ7UnNZeIvyP1e54PmaI3sc47HcJSW3TPMfTpQNaUFD2pF5i8z0Nvgd/qh8fc+mmlu2VODH6hdPhwaqDzjMTRS5HpTDJyVhY6RPrzZCbv/dD/72Lf9huUCGu9X9wBc5tbksz5mjsM6lK4aP4qTrRBnD8G8GYA2q3yuJZATH4nrqTEqVCkzwn06YNoziO47rto9J/+ZBerltcuH+6YIoZJATL0zI1c/LtWTAjDO+hvyDMAJKhXrCB5lCCBJjRUHqA/nmZQYpQWNek5kwLO+OddEpzfCdcpBkQnBN+nJ8VwhIIgKwEsbC5vHkCaL2c1qoAwBWVwKAo2crs6kBSBXGEuNkTJfx0ZH22rKtidFSZL+PyBjMCr2TBSiIAJaSckpKSNG7UEVs+UMcyydZZg4nWRMGIMF4tPy1FqWAMreD1nMT7jqutXf/XmGqOuqY0Smn/WhPuKZ+Bp8poCWzOHgpEzaVKAjlUoC5CqVH44Xs8mGnUkrlUFrDy1NfhZdBTZv2ITYjE/ImvKaDwE7CUF0HGRtjT9WPYISqt9U3JlTGPNiZpmF484odUwH8pblN7WIQvP3Jgm1fHb4ghpnjARF+BtkC6PcmHAyEUdldQjO+QNiHo4bHNlrXBiyQ7Sl/65WFEzia1Oma9D0Jma0mETZiBcp3+GMjrhYejYbXKGq3dpOkFPtHmvIuXWcETOa0srvyN+3WpU4E0+9AqR5A+vz5PAjEjFBMbJVp1QR9H9PLKeIKyK/2+JI1s+D5CdJCt08IQlDnA68uH0WYIuuDZs0V8WsSvcEEwvd2gTSMRo0Ey2NXpG/LLx0LJpTWefN+DKH6FHG80GYVz/QUNx3UqumHlz8VEybhFv82VKk7Z9+kPWX1lNPCvfkR1z1igyVmhSexZRhYv+izsQurlFS5npVS+UznBQF/Ws2EUpdiupoUyigHS7snHgr+ERleZgfQq3OV1B3e9EDM6HIGoicnn63wPK5Ze5DDIy304YJUpJ/j/XPlKktD8hoJP07Sio7Wu3/2MeziM9o0RF8S+Y6UIaXIijYphBMZQF6qcJspT5PqKuxpXdwY9KwRHXjS1TkMCqgdj1nGGH2isjTjVythC1nqVKEfsM616Z+Ms7W+IkSaEj2McSSOPXLVwOrozzDZyomBOlNZEukz19lxCr/44Xw6/2saTd50MEY6eVBZyf3YIPd1HHhKcmdB6DyykvYE8PowJklUyhdJM29Xf+/HJuep4i/LZX3+9Ku16DZ9tTuR2TaQVtoNqPDbTW5E/pLwech3VP02El7w1OIg3Js/m84g77LZHZr6ZWnIQZavvBDBgw3GV/iDPAAJSYJRueO44vibQNSIFj9H0SXrUzcGsBlUJNuaQoszd/ISvchgRjr9Sg+pg/3JH8HnHD1SazvK2LW9+vlm3JiqPoCUiYv6IW7Lqk3ire7/ye7a3DR6UlqTGDB3QNIJv4ru7WM+T92O+hC7yYPASiLIRLXrvHPXt0zXCcJ07ziwr2jNd4rmYLQB0PwOu5XbrsKfEg/zMr3wVVzmw4Jch3KJKMFJ2miYFdQiuantKIKBPTUUuPFbZV9us+EKifmBLftIpFarb2h2pfJZP2OXv/8V/N7urAI/Dw2+UNsHMFunK3JLuE2USH4vo5O3xykprTdqFyEz/jzFMrJ74Bb1ALtmDS17tPfROEoc8hDa8zTrSxWO7xyCwxfzsHxhw7QwjdxV7r+X9H4BIIMGScsrNRWfnozblCzd+FzgKRkgKq3TGwz9i+XPxWD6FTvn2X6T6jn4nRQFGrZf5DBk68yTv2bLyxMIEZxotUlDm4+sLE69sHdeQLNw+gZxOkiFA7r27bCqFImSHiGDYUFp9BzCvcojeOCvyK6PINFXQaIT3Zq+73Mwmw1EC2ESmLwhHW6UImjc+YiKtyJaBZQLBZlShlepvaiafjWmLMxRvfAQef7UBbVpumchyBtpeaoz9Db2W68R/A5uFPJvyKSjsNLbLJ0Luw+wfeyTh55aWXXavoXF/OqwwdhMjen8pXB/ALiVTemJYAJI0MxLZAKs/jt5BRCtTkNlJ5lwx52c5KREiZDQ6K1T5hNveEku4sik3OO9wdce2ZlBayxthykt5/SwBQf2JDgzpyVxSOXInQmeWXih9lm12gdfHWdmev7jvg8WR8zv0436EIGBC6bD7iS+XGnjwzS1MvBt2pi/RT8TWW00vqNxFGuNRksSji2nUHSDyIU7kMkydSAg0x/rBH9VdyH27Gig560irJ7tI6luYfCKo7L0wOareMDOQoE+55edBMSFnoY3CIsdQnhdiSsldWNONZgeo/V6geEBXOyILwOAQBPj959rE+hr4irdnAMcM6SPoCPoVl3ldP0JWs4RghPpOr60F2d1I2+PWxfO2JkCJzHXmmp4j+fm/Qxh57zEljg7/G19jOG3StSxZOKOxG4StGj4EuhrjhvRRMas0gy3CR64hiQHTAeIJyeXRZCpWKxPoc2IiUlIT/UUSVVS2RXFlpU9cdrE9tp4wLdyUu81cRYBupXQLZ9B4mWMYQP+RepAJpweJcayUorUnBRge6I61adGKlnMH1+qu4SJmhi2oAUlvg/V9pDOo5Ej/RMFr5oD0lxckReJaJrV/Xd5FTM9d9AnWej0X22StkvJ+zAAD1DqcYX2iVA0SAqQmAsvs5Qjx9FdXjkZgl4+qqF8vBBw0KK4FKxJ19g1lSmCu14tTtXQsNklul51QCFDd1kJZlQX0K3xsuEEo+N9LJ5vxPUWectOTcBjOHhWX+1XYyUpBsofBffNZ88ojGIZoZ82b8RncOvHPY31Yb5DcaZ14bPX0sjni8YCEFmS6bjtPBjh4zJ6OX6bajFDlrTpBiS/cdUQBE+ULfkW11OvnM/svzjdTfuKv4pb9HUi2kLcFhDb3vAZmZ43oTPPXkAUVFgx3yugDP8OWRF1G9zrrq8SkRCPDz6YfSjLqVRFe3XtkaLDoAdSTgy+MYuTdjH017pgwbHGT7/FC8k02GhK1jg9eQqtrDao+Kr7TMPZhTwpTN17XWM31Xd5UMwKPX2L0UJ5RwciFS+0581f73qugkKRINMmXQ9w9OIoFnR6KjmOLdTTwv40Ki6grPQVPu9bcQj9yQdBD056UgYuyE8auufacTKPL1F5KM6HzSMaeq69Eb8yrbfEMOLHyUwR370uCEHu1KbO1fDcIKtecQIMZ9V1dgFHX3V9sp4rjEpQsAv/dnx8h4HECGmMccURLc77IRL5io0Qtxdc5HikOlGWWs8yIVCYxVODegsDltsYHmESfKTgDf5IWNx9dTeRt3kUnqCWBV5AgKSJHCBNhEnNPFvHsInFzljb8oWHKt+Jo38qNRfLOMJl3GmGhTp8a2Z/xkQrFIwVbD5Jy0YVSLQT0zn3czdaVpYH8f6hSJCv8pHOoQuxicXeY5g5SKLlFRP4z09/dc9kpSfKd7ZHsyVjelzt5E5E5Sj4LsSGGSL4abD9p+M+6HASUgbwT8L1CDhFbU3SqRQP6o6e56kQB3zM2du20m1jFYCqGAGxd6qpB/iztKrYaY/kBI7vdd5QZMgPwtmIExT2AEMBXNMlZKCTsxGodGYZUgxA5y5HhPZxeyfFUKLXn2d76hVEZcZdWALUpKV9S//W8XirWbM8AAeuj/OuAmvl8uTJVGIr6yKCsC6ayIq9hCrWo2eIpf/mA9Y5c277AsqzKyaJPm9urwA6lb3Ge46R0raxUs3oWSFLcVGfday9D6/X4EZUDRdp/6Gulb+CTlLRqYSQRoXN7q/WEptCBYM8ceDBPXPGP9HPmELv0vOYVfMMhFPE/iMKm7CS4S2n/PNHbU9wh0adqWoglHO9MqxPO0pzARCXuXakMl42ZStQFHmQ6sh+VE0zRFuUpS+zuVxCdUxj8GpAdhk9MR3NufExgFwe7xbYo/XxcbttgONFBPVkGqFhJN3GLciNpqxboZ0JPhYcu5yr+CYTFt/KTFfUC+oSu6ZhGxtp675TvsFtBCKXxgw544rp7suc9NejduYKYFOUSjtevDUm23eUaxhbnO76VhhLn9uw+BDBQDctFPy31bx4NvGBzjFABHj984/VLnnUFx4YokVVlKtwA3MPHpnbLJ5Mgx0MHOx+8YfI8KCL386ewyo2D1F1k1g/gjPZ8PEjOTojzRAfLJEgypiHrMzu8WYsleyIWWkJZg13CU/YoNGXp4vEjSgH0Q40p7p3dl4uNn2JBPZG1N+SU2dK+xzofXK+PQOyliD2cVV3afYTY67MSw3i2sxLOSSjqrfqFY6gtcO9+tEVaumpK+/UgKBolUa0i4mBQ58GJPiv95V534YKHB7+B3jETTuqnyGgCRAiqWQwkY9zCn0iaRVAvJzGxsT+/PDpSRlweA6tQWpRuIwt4Iavslzhlm1I4t+DNH8bbgcuE3oP8BMDPNSEjvbdodixqOYmoKd4sSPDY1oMyj0PpeoSt87luVcIkFg4au8OBO0UUgUVbaYxEd5eeparXfrUv3qQQlh323dRvbJvoA7RepKuA+HZ2puaFkoQfFdqJr8iOkM6Vjgf/XPD3npjlXgkS5AzfXh9hJykZg7ijuSw+eLgVgikghk0XgmFhUDI8945ThuBiIePxfTj6vKV3y8DBHZEuMXNqWU9PLNGlX4drbZKpP1cs3GhgYQ2aFXfJeQWOZst1J6qTz4qDz9yQ6JT/3eNXGv4FJadvo9P/F3HwZw3RumFX7OkK6DUvyLFCGzVrmD9j8xcwkG1SzoboAI5TsG7rdCXWWM9iW/ObSkwdMGtNZFyrNEDehK8y7Tx0u1WbZdGIvs37W4ldZ/ZePu5HvLoNGqnqHmOOHlr7uF66nMlI7yEM1OFaV8FebexEvdKyB+b9jR92nSdugiempwBIzD/MkVA/lq46x3uFY6UxICbkYeUY7wmJQYpEHb282ynU4vHAAzqsAkeZ4/1PV3b7Q+pouBdGsdN7mR+wuimpdJ9sKDSd4Q+5EI6EaYKrGDDEEJ1r/xJW/nbfDfm3iCxBoJOjhL0nwwlFdp/DXWJKyVxDixhPc7INX2K+VxCapk4m/pTk3RoabOU683nYCiiGy0XiPDTcgxknapuLB72CZkGCShooBV5MDlgpxOzaYsfPFaMGotcmTsJQWCXV7aJBHd74VJ6j+vrIngC0SViyRHz2Mg6SZ4wPwgd04Z18DX/PIO7NTmbZMX46XUhoGKTPO9M6oAlwvQVJb4xbi8t+P5i4IDYp6/FOfEwou2AX/1U6X7P/GVlnot5bM+T9oDX+n/AivmcY+wNVTRMrgnLXGxYr5qEVmwXug2nOr8HJde+hFzsm3QrBml7kj0beO3oupmWqDy5dRrf8qNJOUVIJRg63dtu615bnq3Wtu+BeE8ILEt70Ot40KutNWNGUCozP0qUVYQv2vGaytW+VBgJf8PTIOLVsH+AB+BGTAupZe43c1/1RNvx7CGQFysXEsHpZ4HTluYQzSWszp1spD8hv2O86iCOCuDo4d442jRiWgqZDXmFC+y34GSmrQeND53uqFl7A3kBbJXe7a3I7BBN6oceTwlcipVUi+cX/aYMo8b4C23s0ZReM1VlFrU1fcd3EO17Fm2l3fUIqibtjZm93Dwm/GhQWxLp0hqXhRA2+2mfksbO7ilGJ2g8zwryi2xuvukv4yIIerbgoWQPApg6q3qFnWVnpLioAcLKsMPG7i/5wRnPZPvguwwQFX/LXcwitnp/I6gPNNCasJvwCWtxVFqI1AXs2LHzbmAHnQSLtnpD7Qfs2BwTCjg3CmvZzZIQv4XrSmf5jmuT+u+xZX8jUGlwVn5CrEFceDR8LkTox3Xi2ThcwXXo+Psju9962Ey/VKC5ia/PoK5CQMK2OGG1pvFBpO41C2S1wR1qw166uPrRLn9tasVlYItDuFEZL4mqO8/5Z7ks6NHNoX33CfrWKhuf3BmMd3jV00AEXlBLZ1GIA9qp7qR/58UU5aska48sJMXQBKu2lfLZp8zDqgsa3/fAPZxXbdUsAOPYzm54lQjlKTk9Zmw6A4nzl2Puw7ZtU6Nuv13KhjPKEBbQzQx/DhKQphUXA6xUjUWeqlbTprXh2X9mO3a/9QuM+ksZcWRGigCgBA8JNgFzKC7mgMjVb+vMCjw3cNMLjGvV4GX4rjwjKutElkD4UoEL9qUokSQz9VWRCO9C0J98EnWrWoX6/HkRtmJA6rNrUS2QP+kvcGvV1RNNXeMF+hQjGJNOCBmZuA7HOZCp9M0pTTGgaFaQikSMfw45n36Jn97Jwy7VXtu91xh67ILytls0nZuuayD7/W25QfZg3IBXpje0gnhSc7hbz0I2UJkA8D6hT8A4Sm62pmGZtL5OCEQTlbWeAlOF0YsQXmgqEsbrHE+wsEwKeaU1zIG0tnkYTpDmduTEgTp4EvvByy0L9NQvRbYKdKvfWyeHfiV8OaMFfIIkZxApQYRp+AopeaVvv4YC0CcemVImpuM2m4rFjycpIdvE4QZFYFp5Rx5sHDlezfOf1ugpCQ0BXPlKiVT/bcwX7WJIWM7amQKhfHDo4Xe6UmbXSQ95x3ry64eBlZY5ixNMrvff/NFnfTyfcHeCXFcwqFXHLMSyaLYMfe6RbeMQkA5QrCE6q+LNfX1gttCUcYYM2zIRKAt0+mMnchtFde3hItE5xTPw4f+tcTgzh/HrAA+ovsXAdSVdFMjBwrkRkm2S4Y2l6hMCUMHgpyxpgHFp1QClPSH5xD4uAdE5Os1w6FK494GgBITlkZALthX+c6jK3dGmSl3XV1QYMGNRgPUE7MTU4UcT8bwhP0kBX3hA8/3hvbrsOZIUxtuwpxW9bK93QzexC6m44ymfe3hutWR26AWfN2+0F9qcVx9yHfTt9QZUvNrnT66x5yQUCmY+w/yMVkUeedy6Vp7zryWUmA25H3o9kMGb+TM/jaKjoO8qDKE/Ucm8jPgYqu52EoMHdITVTkDDWdLYON7EfKWG78cSqC+lgYU7SDgr/13yqWA7a29BwbWLkzqSXh8RgedcwrYNdbnWN/gyIcIqpLdZ1ycdSQ+pBURDhPRF0R3lSGpaVmuA/P23/WwWea2EzqAY/Cb8/ceX3d8E4VaiK93jkKVkjkZngv79I1VW06vHgc+9XUTeDRZWXqA30lv77KOHCJucZ2SBVpd5fsyZ2b6dFWOkLCGsboVLVD403N+Lg5N1/CNnSOv+B5e3bneenwqJmz5n85cOMkgmW83DRAoCcZ6HsJlHoxx82Z0tCEyafOPHxoORhaxCuTxClTswUksPLU+RrlPx6DbwGU1S4MFTEi0HpdhBnHs0tasoYkvltMOiuoegqTf9Ly4bFWPqmlIUyT4QlqsDaG2lCYVVrbtfig+aIlyCLEY4PQcM+c/DuvGaWnzr7qg2rLbN1ttxIOr2y3CAOFrbZz2Xk5teHdXMKPmLVVQDJx3gz6cbVF7zqIGF3u9NHU22dwtCD+m1h47S1OA0qJ4oqSx63DG394ZFCAhUjr2FI8+kypiC9hiOL+X5foQNx8OaqwzGcPrRWMfstsk8ZZSX1iVZqYhQ7XeMHhPkMkcUfu7CsJdwvxZ5rtcQaSFyIKj20/3KBVnXogFPt13U9xl3IdD4uI9IJKLWGCtxswoMAzjYWA4F9CIoi5NR+eEPdY3V1NjQkyg1KflZGSeGZ0t7jy4YzAd3pfybg9aaKub65IHys2+OvCfyISMrBQElEYDjKpNwQaP8VLAojteZxE3DU2bPKB/O9N/zzF2NIgpj6iwRoOefgtsvsncwqbnLIZtjraYnvxx13yFWq1BLxjDPulgu2+QyVKNmP3kkApjZywTq11+m/2xbXHroGj78l9Z3Uf3x2SNAU/WTbBP0k9hGy9fEzQjAXTipSKh5gfEDJXenmtmKBDnCA3OE7F//dkGcpfHbQP7nU6hWZ3LZMmF6SQSDg4FLFsBJhrHEDNJHuKwIVQNO3dJAkde2RYpe+AGAGUHvC+JgeSHFsboVG7EKdcdnLsC5QDTkO+MLDvsU/8FdeSbdzc3MV//tMblmsGbaq2QE9TpmRUdq9s+R+9nmTy1mSPLjOb53UMiJI6bsBfyQ4VVZVEJBeq1VHECW0ljGKiUT20PbEFJ2+IOOAzjyG18s0FEsmPgU9t02rnArjhqtA0t0MpFvWEVoR/bHft9tYD6OslnylBLh0wm01R6sSo49nbH+gpba5sldOu0PQ9s2BtU/l8cMYmmMWMRA+W/QoAvHVVEmBYRSwNQw4rDptlFTGiYpq/BwBW45qA8tgJGNoKGKOdXaehrhpH3C1suRhr958Kq/0RWd8jmXXpPIjMoAwOflG6DLnziaduNB7RdDl91NZJ+Nkehc+vq11X31AvaVKmVRPrFVYYTiROuyAY6EZ7vvvpuqG5jKzPqLCaavkT4s48CHXTxcj8Tk928TYxC3zB8rFK9yELsD+a/UFnatIjdE9zzYGvsXEOU29F02IBG3HKP7xPNtTiw4PuJEktR/oC/ajzv+NfR7Gwq5QT/bu5cLbTjFF5x1hb9dNCfssM+1Be8fvqiT24PsDD91M5fQ6GwS+2b5BgvesHwNCx+5DuAgefVyWzXYd5ImA+unvqghGkKAyMYAP8D8w9VeO+ET5ZNriG/uNFNopJO9vke9LNzeMy+s70al1zITQzRRQK0P9ZpKTS4s1+lliVKVt5QfPiGjiUiHjWKhgmT68wjptJGQDDfv/ZBkOKM8OC3wgMbmH1rNOepLrsxTtm3sK9qBn4jDyaDan/fPMu2/JyC7GHuaJSIpKxS+miHyAkdub/wzr1EPAw5yU+mTDWlTllUMAYl4yyIaZgl2JvnVjfO6jWIS+XYIQIt9kTX6fZegIzcaCqtzX0ED/H+J5C6IlYGXccltdfPMfjyURgKN0EgzszBtv8ND+m81+yyHPB46RWrJ69Sy9sqS+n357ZMYbyOGGOI3Nj4MeEFoThbnz8rHKR06thi18GUqypz6AcMjEfxqww+Ve+JrZkBnE4WRJKYyp7bxuezmmDoHqALToTtopZxYkEMg85USXKGjfdAV1ys1bn19xcxJzo6GiVaWhzDBCRuxc+CWzcMTpmL2fVpCGa94rR6lx81Xw0GZNJ5HHFi/jNt6waWNHHDG/twFkLb+QQOsrR7tX6Bfbl0s0/A93WobaH0DCPUzK1o1xeIEFYrgaa6dLbdtWa9RyPez/K2QrqkXgHi0PVUz+5aIbMj1fr6N/JzQ3s1s6MN9mkkZQmQ52Em+rBDpkq2kBwoYGcwKCbZKoHyWyVmNlVPR+KvmkghF7Zxn+lahs+AFWzFlOCp0078Z4/04b+l7ReBT9KVkQX1WTrb0VB2wNUHHE28L5SUNCdI+tKvFPeJLNgy1V3lMzoxIy0EQUQnmBUocZ7wbCZ4XUPB+EwNlWK8gWet8a1+H94mxDZJIYalFn75wczbRQksEDvuatdBsFa9AothiVKr3fH/KAudXNUdGaPO8dFSV8R0YUMwLeql3ddPiVYBKWAAjwOcDRbOl51HeOlpkl+SbvazTrEibKqQ0W0RMkco1PzIIgqgblBudJYgGchL+ukaj8D/kZOF7iwyHj2qC8F2LoMuPES26EJ8z8nYjBeiIuk/cOH7WYLqbF9WqQreF7WK6XgPMzSyKPMQj57YfxiZ6KmxUW4yQl8leXIT2dxzgHOGMJZGV+F+mDrDsAko23ML9p+ZOA3E9dCiM1ChnAmKk/vW6YLTysIapGwXo5Crxe1JIJXB8v+4WZ5tY4fN4YLzdOLKKXCGYZ+G5m0WMJol43Pk1h5XU+0b8voA7BETqKrqdaNTGfFov8OpUUu/G833xzp7FxQzTo5srNKPA6rmoXI5Woco3IR/7FHoGEgV2xKIyaJupEN0MWd4AEujieL2CukiMgGRdxloeZ/ubaR9HBE8oaUyaRn9uSnsOpZ8l4FDAyS/dQ/AYhGkEhA72VSmFyCv3x5DlNR1X6lfIyRoB/IlN22mY7Ztc36/z9tV2HHFIttJYvmDx+K0Ys5DNTi99IUNn5vww95upteJXbzCgB+qWM8t8ndDd6wjnNBvzAZZxJ/TgE5LGHFYrWFeGuj1VUmTAV74IP8SW6A58ZqAIFBqbS6ACItaYPqYzHpgsQTdonhuac21IiBNuHEwQQoXAyPQ+ELhYiLjI1eGX1/vjY4N8o9+FKLcf9P6yl2gAxM54sT1OTLUhvPc/v/Sho0zkpdo+5qPvsjt2bHth3FpTqEGSkJxEZ/7uw9hqcFNWGQ2PG3LEI5gSsuQnria7QpAgDIZ7872j7ElBF/2bn7jVzUBf4uTGH1erGYKxNu78HZs197OaXnzyE2N+cG16sw65xZaQCuR9fkgJLEMYfvOKgdF/+QBCquvEdLSXxLremzZfOkh/c8FvMuPmkIGBnKHOBqJWtL4rlD1KvvSVfgL+9K72x6e5FnmRZyNPaVco8mWNNyD+eHn5u69sesTchbecjsGcu+uNBFHjeWgEBTf0zMn0rdZdTA/Xo359ecDiG5+pq3YZlNDHgrO7HI8AF/yfbWXgDILsVX9gnNcoZZ0IoxZMN9ToDTilEj6xIurFItJBAkrlOJ5kjaYaH2c9/Kn40mnEr4A1RsleyZyB0NMn3FbftuIIu2twzb7JlkUM9zjt1oAR0YmBKgzizAiOqIJFfxpkUAaENJlox6bbmFNQA3QEs2dB4sRXHgH3nnHh1vq75OkGwerMwvXrqOXTJLiDG4FDOKQZbcot0lSYlJi+zyMX9WIw5DqUR7j8NhB5+zgonzDaGIjsLrGnlJZFtIngQGTurgJuNtPczajBziYaBNKw3WwvLGhEmMM6L5z8tbchLQbr15rbBb8KtrPt6YJE36M/tHHOyZs9Wg5aE6cBDyTxwn9Tuwqpftmt1r1l0jZyq6tbj90KCqYKE02KdKKXsGDXCCTy16GOkyneYLKiZdj77NpMwig6k/iaLpYbnlTwLTBCeghJq8o30Y4I3jYD/UFT1gNZ7GKH9Ub/nJiJv6GVciwh5ZdlIvdBLvWxTkwbI1qjGg7Z+MBQK75/+oIHUq3haN0KGdbQIawMXxHl8nYURsRqHVNFMY96onssnfbRMWDCN1mypT7odI3fKQ2Nf+2Vq8zAUhb8i+wvR/eHjEwflPebaB0pNBsaeuvbi9CC0xTw85X4VDW9y6b287MKtcDk3FaYXg6/Zw1k68mBRJt1RixWf8D8oVNs1Y7TiQAZbTRWX7wIa1r4qONJK1XMRtHFEA/BIye0Ma//Yp4+/5JJ6AxbQXGwNn4VnaBd5C0vSmEWUeyJmjW2fqaBEyFTW7ua4rqiypMAPrPAUK6zajef+zlco548r0zrpHnlTAvWVcWtpupdj+o9XA3yd1bbTriD84MYhSImOF5IaKPGDkeKXlnV0ykwuHkkhTv9T2zR9PGURvpVtMActYIjt/hP2T5yi8nmYhM0qGHMKa3SYps+ZFAp8CkgV5pHOw0d1PaP3+KvnA1LMJoca1dwtuMY+jRXhf7ic9kqdp1LSPYp596xEa1pQy1hXHQF8sKlzDAk8bSrLY4bf6bgdeB4AS0NQU8LrZYBW+DYHOAonSpcieuhLGP2SFtf3n7EeYFD6mbklMcdpB/FNQiTszQ27ps1GluJU9ZNKco/JPPH1RZgv1xKiBlsBJC+4GLjMwBOHO9ACtwd8rqZL9U61/FZYVVjRBj/YohihvF4WqpAr6/DyWcTuTQzJfYmEXA6XVO8Z23WHsNT1Ow5dHxPHqfmbyT6d/7VsgqN6hqP7amLywy0nShzFoz3PGpk7h1uTzQGTcKz1PufJe5rRv6gkoYZmhXJr5HCQIduvLfQgQpcmHIQGgsuyD3fCUDbPAuWfV9cTaVjZZq81xU0YY5aktEPC53Xt2iIZJTNBw5kn5ynKKwpOzA53RlfX5XlTIjSxIT6LNBmYxvjvGIr49Em+6xLnewMvJmzE/ft2XPrRqGzreSZYKhB57nYzDfPCrkauAZQP0mekmDTySxftz2d37Jhn+hgHyXpb7osZj7x/ROI8LkMdWNRneh2Agj0Rup3fWna4Y3hVBE/77+pF2EDELrLm81L+Mze1vQquzsQW5l2j8qX3Bx5Ra5Z/kE04xQrnY82VSKiZgddxndwZLcoNarwaosnVyqq8fP2iEnBMlnfymm/kI4gF3/qvdN1qgKOOGvjqYQF8sNvo2Jb+Qt4LxTgtLnSbYp87WV0z1Z5tL8L2EDLFdWE2SOHD98BPY8xoZlB0EHAc4SU0RfIh6wh4fEv/0oI9gppySmeXmfHFPxzU23bbQ+Yg/vR+wLkAqW7Q1MWVKPcHVf1oEUTs+L9PScB/6oRz4INex9K4Wl0lUh9l0mh2y/y3vYo8Hiv68F+mcaFyMnK2u6y1deaMJ8KhnmVsex9ilfaMgTA/7goszO4nmj74+rKQEbJ+ZbEOUPfWlJlM2Y6ZNLfXtBYVIl91WArBnaDCi8tM6yLzJplDX4lROpH2NPzJDDoDMqT9g5se9aYbVV0iEusXyUxGa7aoUv5lv+JxR9O0tdxQ4j2gG6gZPdh52sWX94L3FTDPlW0VZ3/NlYBTdnpNcL4WRiqb5ycZgAB7aSPWXuMhwRd7DcFSn1T39b1qtErI+/kfcquJ+U80giJVwUZ152dejgebAVAt29PPbDyx3v7zlR7nQeM34Xf6Ds/Bdujqb9Riojnqswso4Pp/1lhy3Rn7+K20Ia6Rr1BpvymEInIxEuIbnFNJwJrhU/33V6oYpGfgFARzmiT/wgvxcMZXSm710sNIFDpXxLPyMUtRwie+e3L8+3NGZe48T8WiTfeSEpZPGfiJjM2SrDm/lWBXPyF0E7wWMTFWNmwPOJOy8AHPzIg7qXfxnbZYidEfg7J3yed5ei2FaDq/2IR4xHScmtYGIrvVjaGb5JD0fjrVVv6ds+98Q2Jw5DDoRYse9OtaOiHVEpzpPL2OfOKJ2kI0MpUwloJjcAmnR4qTJ95Q8JfBANBWz2Pwg2RdhcCVqZqMqPkE+jGKzQhfl5DEtSZ7fj8fn/X/Kh0/ntDg11OpnrQQBFL3MakYwZhbPmGo87iJX/qTmo2E0zbNwH5gukrDHSTSJSmGfmzmQS+Ck1DlvwFLgwzyIFzE9lXYhCDoAjGBUKoR9wU0tVqUTZqi5EGnATNJmw7uCluXxTpmzDRXT8YQwOmF+ODOByFwEnaf6/lhGP1ejoGoNTS53YBZ3CEALPt9kWpStKTEEK4Vj7MJ1bK3wnV65XnK+B0L1tWmagHwlbUcri7F3+ZjXP5MhGynuy3rOt8gqD1ohACOZq6c0O2iBbDfMe1/xyApMKPceIXJCFe8pzxQqaM9OsRkGbwXYE3j5FwVhBEHhrfp2OOurXxeBGh6Lob0Ei1JMw/qviWpcTMtjBhxr8QCJyR9Jxo0sKrgivs6+jVdw3dACzpVgb2ApHQMEODvJVamnQ6xBTk2Ui6aa2APxX3X4P5Lm4MKHCfwccR5Y3GmrIG3GuokFRjd/incsRniRaHfS441MdPLvDpDz7nPxerY1D/UfnMoHDUHYk1qvt9HZKw4VWoMnWH62Q2Nds58kvYFSSltg9RmlSFS4H7OqUaB8cy4stBDJ2WtDo16Dt/58IAUq1Zt8E/SjCyDdNKUNmbAGXlun8i1ExolRdUXuqDRPI1W5CE90fojx7bfZmt77l82g0mC4oJUlLOiL9mEVp6NbDHhjsN0qlp9/1b7z2399zut9gkSZCe56FzFRuIAfeX+oRjVQyrEVc8Jwdq803cGgoiXOrV2UH5DdCXV/sZktgT1LrPeUh/YF3yhvW1igKeyG5EmYmpEn8byOHdIOy/InuAICgBhQ1N/cZibr02A20XOgL0pYumWrXcYv47983hJO5wtajzDbS/SgExcC9HTDocVBkHrvrCdE6SqQJS9+uVgYVfuUW+bBIDyADvtnJEJys4kOpKWoFbKQl1u1kUmyhZDf+HeBMHcT/QGyXsSCpgaDvOasyyvO2pJpar2wrSgt4csBxM2qliVVjVa3WRLkOA+sRf/sDqNcS4tzTMJWGkeAR90csHo+cbGXE1wTSmM4xCiSqRDO0hJ1GV8kHWq6CLTHBFd+3WTXF+4bIBoGhwdGmkZzEQlldB0HKMkmFnracOBtfn9BlEM/lKKlE4uG3OqIQqttEDvceTjNZF0VmmDyW0LBZGJoEp9a0wEcfdZ61UK62aEt0RJLS+wGAB1LqEyHIDNhRboQApl/Gq+wgtRVTD/TRCVHTO2s++5aDBBP+79mEy2Seq5UC7F6vvPCuZu1chBwnhycAX13td9m8X+bB6+UcZ7KQ4UV5n5F42h0PZzXqFnep9hPpiqnpSaw06HpJIUzOZN/2g32mBp6moGzy/xpFhblBMAugiiWg8RrzF97EaDyxQE8HsN4ixoIl7FTV2WaEdCl4i9uWgr/SYSRnzuxBWNkk7FHRlIQwBIv81pDbBhGl9FNg0R6n8bkF3ZU6TulNZM0GRhxmflK5sFKW33yRVyRth5uhZX55snh6ZE7s55lDdYcKTwQMYbNFcec0dcMBUqj4Zw9ZpKX3kziBimlFNiHcLw1dgB1llbTusY7QELNG/HNBxReaE+5nk6jcNEQNOXJaGUiFN9+MkUaCeERL3LX/cxTu5ODvAFVBOf402Nj3VWwuQ0vVMUeodWho7Ow9FldtMyoCQkIw+caAKDa+jAYLCKG61I4blOOitgP/sIiGj+DtmH3B4XOhqn1Wb/wCuh3O7gXdm+bBND34FoSBZHvRenmpKbGaSXiDuFJOcct9TN6Uf1Fu6oZtFqjTA0zbhaHb0Evnmzl/al3nyYhnFl17fwoufGl/u4XrL98TmT1G+4SD4tMiD3voI+FMN+ppzHva8BL0jA7SU7ZXvJ3B9gDDs6nvUIV2fIZ3kgCFf+r3uuIgqAPGE3zvcSjZv8fy5FW+9cFBRs6vwNwzzhzIM0ccOBDXsANT+3yKrzAp8N5FTwV9c2W37rjKgwNQRvko8FO5r9eUxjJwf+c2bGxejs7Ns4KkikzFsLwVrnD+x45DRo8OWDmXplAN9REyzVAvIzpxoJrEVb01IXPgXTppTm9mZCf1NZu8b6Nv5HnmgmROZBtMk88FFyUz1IkUPkRw9YvLZLDM9ezeOcGiL4DB8wwYnuWrSITiw8WvCVIHqMyOiBotHUnCfrZoRNwKc8vRYDbWgMmkYGPPwSi6fGzdN5vi5bEvir+N7Q6MOedoK6gmche9vNcIKB4ZFpY9kQutnVk+V8ItsJiTplZsTyleDhmXd+2TP52cP80fntO1pQNR1hga70cwyt8HsmcMm45orEUkREw15M9z45OPuIXunoIj/OnB403avwzh5v77I03Yt6ZZGdV97C6YWWMbgihU6bIXP6YLM9J9auU1ug6klnKxpftvsekHYWdbGUoYeKS6MKM4uGTJi3ye97JySnu8ZlpwH1OpoEKvLXuQjDreQKCaTLuq3D3MXkrYj9N4/f5zOAg29pcz0Ei+H/Lmp0Wwilj/qyHLguP7W0+pvZOSJswIAyBAZMnZunS+OGn7+nXIC0F468WsZRLiSiVNwG4gSkrl8gXiPBNLTdUmnJ3w9S5eF6J52fTKsG031xzjFZGz4+TMPiFBtUu5hhnks5XWHiMhCtCUyHnMzrnDCfGEuv1+ENGMIirh0OWqAqwstHEdy6Jz+0PV46nnW6B/Rx/kMtEdwfbwBOvHL/Xq6Ieo0eguUCcR5Xp9UagSG/MZqbB4hDMOJHkCL1yOKeXafuVVm6SPeZJdNDFNTVIKVUppvcUerahz3f9omotc9H0zEz4tfmijuyuSZXwpyTUstS8/M6xGXGAsLQj8WfZQWXJlkijNiMugxm4bidtgkP2sIoxXJXJLnju8+HY3gb75gvHB73Mu6J7zB+M9O5D4AGHHafSXddIDS0dTVY5xIfnFQjpl0rQ5IQCko1V/F7KCV1b4nmAuZXGCJqHYwEtcHFOZJETYTFL0TC7CTnUxErrD8zfec8Z5P+FLBoFLZ5JWKWaEOMuo+3UgbUubU94uWbdYyQ1q91i4kOgz+EJEphiZNfqktJGb133QQXAgI7BXOPX8YVPG/jis7eZea6AOlKWYruptWodSTSBYI4jOvFk92m0VwjIefpfbVzzUuFFbj87yJJ49lacWm/QiOOiuMENrQEQcSvXaoA8xtK1Taf5lcC0LqXHfrrhpAKfgzkbf2981W8XpBrArC+uPA/ezU6L8+odSe0Ve2dmp7vcUuzXW/yVeyYopLQ6Qd5Jt5lZYCETfxFk2p1ABYf0TImf1+WJoB0TDQvxuZSB0S8OEkAzrlHvrMcG/Leo3zRkD5IUI5Ni3GPCuycuzIvxt+hUf0LjklI+44voDP3VQ6y1gNnBxXK9egqKWpgOnu0IwOfNo1VUDVkUVPxNhVhdN3LJmMXkq4A07CTRlrVU1Ge8sObdB0jEtMazSffvrCcM2ypMzDnOI04RBytrSSquZDArNr19OUNBPDkQxteabbyanJS7aBq8QOUNUJ2nl+MF+UmQ4NHSuGTNi1mXEiRro3X6PSyYZd9Rx6jAZZtCfEZLbKYetAlk581IJQzZ+hMOyfQa8sftQJmkE07YyRFPJmJ66RjpeiCADd+HAJYj5LX9Q5rtlMd3gqK+1MS3LOFqWmnA/3Flv+Ii8FdytpOoV09yuGnY14VqSvG3NTbznqjaNOgJYhK4vugSodyzcN4JU1MqQ7eEjLa9gJZWng8Xw8QUeWZeS3YXRhTIp8Lqx+ew1oO1xUAi4cHm6DO60vGTuDfYK2v2efa0a2HypMnxmx5HSJKEO3jd38G16Tk8e5zo2fueCMXNO8Mbydknqv++3uhHq9lNBaJcEtW+tBKwmzQANnzO2yp2IqZbzLY6HX7zpqhJBbLR4lb5JP+/f+Hg/498XwajB4zZvSG8G4eKfjnkVG3IP46DDYssEOqB/PkQ7E6f2DR9DtNDdrnguLhQgNUuSFj7DPFBafNvxeXvPgMsyZEfnhj1ATjlh4tpQfMpFr8J7kzr1LJ9UTcpa6ITK3O4SspYHm574fXuDzdvHrqslg5b5N7HZlZKcWM3Qk+Q9LCq6s683VYaCeOnAFys90ZbvtyNUZrJzWW1fAaJlJ7wt5kn3syFXEUvUyKSynqRXvIpBEkFixF5KOs9+8DOwAGmosDBNDUNsNPQ18oPaPQhuDXu/MVozdpaBsVlNNjTBAKt0VdMg1f8ly3PmtGoMDkLl3nAlIfcx2hkixuhpqfcWtF012r+X00U0978C3eS+WIO8GQzQ2Vkn8csaA4hvlKUl1remm5z8bxHvZEo0JFM/AGTVCMcfyCjl1m6xIyF4q0l9qkbnXLB9/+xYw6wsERQFGD7vRux4nS2UovC7m72FJeTxnYa5+o3KeLMCawZiMeE8i4qEReC6ktroZWGJlYDXrZXE4cC0OnZu9JdgUqN+rF5PvWPCXGYDCs9DFr7UtxQxXUt3+TAfJPAmJG0p2bnOvavdqtcGlvJuVW/fDX0sivGOWP7RxThNzEon5GZrpMRQjEvNDHTXrkX8VZw+Q6uMtXVDQFWsvvsVpnNrxjhtf7yJtXmIZYyAKXMFvjL6rZ0tw/iw1KDST73ECmFr0OySt9aLfqpPNNTd/3T97gDUYRbH2rPnv+ChtLiJQsp1iqFDVY5O55xgGddmS4eBHg1ieR5+4iWkABjpz28D8TWCiEIIGjH1UHDL9kuW+VlbBTYDrkocPFBqhFLxWLKafWRwTQATItdtuyPsOFiy0HEc8BANa8s89RNovnaN5PINr7JwF9QMUfvmhFJKkRiOMCy4AADPz+YPJ62M9xZtnWOz40sn9OPx2kjYZaw0M7JO+MQmwqs4WD0Zlh/DaruSLngm5TMVwmzhNTO9kvQqn2P7OFXfv09t6P8uYHrQX9u5A2Hyh07ql9lJdOfRWo/loPKyDw5mxxZ19+OuJR2wa5aNIFuLPBZe9TIEfq51Xc87bFiaFdXDikZ+nXqJXB2DbqiN90hnRYYn/FhbUxnoCPj5UO9IWhOjEhlk/MHEemgWLbE6u4kgWRu2ujH8LarAOgcQsKF7WfIlHYUlYSho56MkhJHr7KEEsLJl0NmDLLDwLTWocogmHcpjhbVNoo1STyJLnWdyqBd3s42UGIuc8XAGBPHtSxWxFG4WzOIJQc2XO9YgvA+QeOSwuTxSsaGAVs8nxDsFJPOYG33QQiGXGgVIWCqXIKapiwL5cgXKAXD+QsMcSCmJEQ8WZAhjRfrJQYVt2Bl0GPw49AKkM6BCEa0VcEbGFLRv4GQyGDYpgh0rDLeEzFj+lPvZpOXnd9gSdN/QtLmgy9U+Inb4lw5jQlL7o9UdBMx+JDWxhUwHR5KedubmUhlbYR5ZotiaVeDCkeU9apOXxd0U0VoUSdv7uU0k2V7B5KDfTLCQzVCb5W2qSlQnim+JUdbe8E85/H2knX+4bCxhrblgwM7dFhXvRfXRLWEqz5mRdTfcRH+xm5CdyxyeLukGTOIO2KxVexecNO/XI9DubNZmueqWBg/JO3yZg5tdbhjiP8utpWae1WDKNY3FIupMY1STmshJkhTgfvWqoFo15zO4627i+cEpj5hnpVhz9cLBjtFSjCy9Q5OzEddHv0WLLn1hNxJgoHot95tX9VBmWHmm79nMheg7/eAHqpt7dKJppcCeA56hrjSY1fKjtewWkhA87qRnMnwFUyAiacfUnWxoN329lthC01fEj+AruPvQ9TtA+vdIKv6Ve79OBJvRo1lcwg/id6NpKyMr8x26OL7f8cLh7wRXSk0Bu70dXvyAXIoRXHVNwbgDs3BqnsoczAu4RDb9JI9hx5DeA4qoOCC9x65HpmYRajw3+oeoN0bt+SmFWRG0BzdGIBd3edHH+cRDeA1yIfy0bEQzvV6a5fbdNPSkEoBAwsl2OxwIw54A7KaReA16KV/+jgNeVI/eoKPR/J7LW7M6iK6DOk3RWshF/mGTCC3lhioWPLR06pRY9iiw/tcAP8ofd/6AGBT/HSA6sXwU0VyoBrR7eRhoqg8J77pIfGUfLkvRhTgltc4HPA+krw8VwYwN7H6qZ6ddVnw842Mmx4xOmQDphxcNcPPI8MS4+UUbTUdOFgjioOTN5NyqSTVVfUZzAL5YPjoRZGZAKEeUBQSAYCHwFQqjX7zruOd9LZYM/jdHLkWqCkyN70CKFjZqhuiToIwkFG+rOxGQlHrKxBsldon7/tTCLtU5IJ8hi/ZuX4MsxZ31ToDHS2xbCOvg8qxejQPSZSEM6xNa1WNY+x0U4DFy8rFWpX5eFtQ+eL1YAVYW5yRcwRWYSVb1SgZJKLVPUAEhpc5UF6+NwTomtF7JjHDvweKmUrJVUqhpgc6JPIcfnooSbcyOaWlSbLhok9agIGnefTUWkYJKjolW3r516P6UBrR0NjOH29ggQjGdTwgvoVJFvr7ivQBtDnCJfkKbyH+h/K1C4jRrnwePYQacFFKJbXiZIiBOZG+E6iLn7HOvgyKr5XsjiKT7SDJlJSfVlIbRxGpOHy2y4IWiXysWfHqIrre8h8wTHQNLoH+Db3FgTWOc/N5iUZjuC8gSRaOOzjqMKEw4aBJCprfsgr2YGIeYlSf0QcYIBrqrE0pdM6WHTuKIr7NbkeOfPa5eD9c4eKapC6CS6APBs4ypf6V/CWoIgL4B38kdA0wisL9VfY4YFHFiSDg5vEWrAYyt1eZyvHdmWzfkV84/Om6oluOLE6C3gV22V9j+jVGoXfcb2WRPf1MMFT37doUcUgRYSjeR0xRPiAGqFxKHPzvnC+YCMOI3ZjVgs+hGIwsvoYor5+mO7tnVAgY2hbzHciA0E8sIlKyI+Bc9PS5T/kJQLFlxSScoUsp4gTIZv/oCMYYVFwVMwVtk3mjp90r4EPOMkpBKdmt6CkqjGRJM7MtmPPZ53EW6DXkhRe4zCVcnHR5OWub+oKw7TS6bxrgIef8Zw5oz3jKGcHGVCVctRmU/onYYiqW8vq19ouGJCtIEgwDhil1c/DkzHb6CL5jLOWw2d1sdb/1FxpbFZE7lbEHoqYPeCoAwv8uH5Kq28ZGn79kJ7k7wgBYdOXpA1xAfywkYjSwQPcOwD41CB0nxG2km7/FHJq42fGj+XC3qF5yb0szDdu6EDIFIj8JuTzXsEl6z5d0eZPYLhLGNWAS1YVhsHj+SanRAa/8TGqhi/pN1nUaqz1bFdy8wUKMgu49+tbRLWJJC71eDHQrARbuXhZejtwNTlvuqEqv8QHsEKAR9QV54JuKhS1MZN3+jih0P6HEk5fQUrNImkE/HJJoSIgutRZkSTRwNLhgLOYE/Cqd+4VYMIoLKvatAxbs6zG2ZM2mISHKqeA0MtgXvuWswk74SU6p5U1H5mMuiyGp4rxSMa3ne2muKg5pbBi59ctGu/MtPBwGdMwwcrHhpvsmxCuisXjvmt90YMFDSuXQNZZTfjl+kXntZJcUWSTP3DOj4TJPuNFWRBqOG5UdYzzNjWVLNX7dILjH21oplEZ5nYELAmglzHZ9YYRhybtDcPEmOt70vioWYByf0meQcvS7IMaPoOAaUSNdbsWgXiSCPqaDfihwTAxokAyLDSVhixTirBEkFNAH1OpZIkhXbzxKPjLKYkarzeSQrJDQ6MyD6eZ6TBCXj08sJ9KgJPV6G4BgGVnNcSVC9SlgUOIUBIKuwGKib2olehJCK9T7X1inZGeDg33t08fwZHLDNfdnFgABlfO/RhtrUGoL8MntmwHZ+To3NU2vw7BpUKJD7APgglV8R8N0uyomfa0NPt4nc9Oc7uJmLB1IwGhX2gvAq0Yx4cv4FlqOFa3pMvUBrc+v5DsFA+oz1xAilbeNnBycgj8oNKN2RXR/09FhA/srJGwSEHxTrP3S5du8+AAq6aXfdARE7EVjfGZJUzHG+AhCZS5e3k/Y6SwKqDL7ISOkLlkCRiRan2MV7dxlMkWngb6iEfivjKrQ9ZMyD4GTXHGjffQk1K3pOZyFyb+4WR9MM2HDtNMeCe/nY1bmy5dI9P1k4Cpd47v8mu1WHDoNtnowh+On5xsy4pm2clmEl20qfSMbuKWkKPo4gdbcmYk5gUt7C/pRfDNRNJsdgZjBiL/qPgKnporoCUc67f+dytsNBYc5cEMEOUdrmIDh9kaOmfOYX64M60JzbYhtpJJGjBNol9cSAXwt/+RYmNZ3gwIWUrGCYGMVdGdHXJYu8H1jIjJMtDP/0KQoXsiMf7FBbNsw3S5vxhv//7+RJHOqrTzh4a1BgrxwyDOM9tt39zuvpumpPRJOIF6P6EJFd4oYrs1fXcNMpWZUX6YdQt3WCygkz5JP7+rL8lu4ld5I0/l/jl9oBCR1938w/SEZ+QO27iKkJEUplzVfRO4VDLJVfYZM4bfdoVh5n9OqfGPIcgMN6ZhGxsGYQMwnh1Tf5yWom12P53VExMwHEtBm796tGJTq/DGGl5UD0JHm8LQjVfGbSmuho9iiMCsndvdTR1aXeSC6UsuyGrCnMfWlxpM+S85jG2PRD/IL6Mz6l5KpdE1fPq22JWRPCSEcSH/vPuPscO+vC4OnFtdCttdomikErkTCXbyRq0zxEAh/9SrC94+429Q5/NF22cZU/TLuVCYzz5mnkx/jQz2bEx7BCQZbXAmOdll3ULrq4gWa0kuYg6g2snEdVJqKiaq8rWrIdtFiLpNrtEMRqNGJbbYZgK+ZX+834+FJ/gPvSQxjZZFbdmt/BNc+r+OBMGCG2vaw2pbLxqfXrkadPjq+lFiAXW3IW2rnuMqXgsPecEtK/hSjxTj0iyJP7Dxj09sLnzeorMw0PGBz9qkXFEH6QsNwNwQ/ORHPoTaPJHvNQUF3M/lTWAzyJFqtFQmQSFcoHjwtQQ84XmuI5wvsFomCa5TZdsBBnBacMBqFQU2bJ9c9/tlCUSW45pYKx6kabzpKrGFzZLvuk6kqGnJ0rONAJVgIMPW+HsBMNeWb9ZNPVgTn9n1XGKvZAXnrfs8Q6U7FpH7anGR+oY6JIdvkCuQ+qYzSNEJrq14obuH2BssP3UME9ugRHuXMPgR7uRkK9aPdV8UsC/YkK2ceL4xDs/89mRX/KVjCVQYgomSKO8lC9BU11KaAugOKdcXC/ZJmBysLKiq9tzWvTMJkoLFkHKhNWEPh3PiPRt3Pfq/a0LxvadVuJUteyzBRjhxZtFQofBu0a3gZilvvQ1220Coo9T2jQuru15mbRmsmpF+D5w0mHHVbtXhjrzxQtSC23V3UPdf6D5ZyanlNjPB8PcuRsDJxdrbRmZwvhGd4z1ke1QEBKurj2r01Dv15rP7qsXjdHbfyDqSLdgWmErNHbMoFUN4TxUUOqNv9Frp6xpUTwdWlUJMu7YBAF3kwea+jxbcWSjNg0WC1345NjKEwKOtuteULLwYfvvBH+XJjSm5jLECAPu1RbCQHNo7thy9aGnc08lYJh4yRkDWulrlnm7itb4ZW4P983zXMSITgjpYxad8tV/uIP+SeFUZgWHaA85vYmW/qQG1Ff8IAXidgDpbDCMUQjXG7dW49pXYRXEHYQhGaAKcTsf0lI+c1PP0m7fLU07wrLW/7ZodYVrmZM4QJFw7R4xQni6f/XFjg7x9lj2ij0sSOJDx3CderI5R3ql468Zb2cjZuRC0Gg+XVRuBqvTli7VgGpKHb/++aQa+Kxp8zblW3Q9w0NFJe9OYnL+++6yEYtm5LDSMsYB58YHGbnw7JoUsqJKzcJzjbnw59m0BmoVgcQKtDF8uqDupOMn955cCkAN/QS91BBxTKMIPU7sjT880dVVsjEjcc+qMBxh2MGWVNRB0RfwrRFuKRyrSHvAZ4oH+SS3o5sqnATW0Uo31XSTSmS8FyblplJ37ANQHmHoKvCFTMKLC+puKUy20GQ6Lpoa7nFq65W5YPII/2SpQJ0mdeUwSE5qtl3EYZ2PqgdNxeunMycWoEeks8YRaGeG0+b43IRDZEiknIovnk1YI8eKMIe6ge2tD5c3LRZp+A2wckv9oE8plrTKy+y95fzJZ+trD0bUun5RiDyq4JXjhojiiTF2gv+eoqu1Ss+XaC7+k5VpLiJdbYQ1JH6SmnIaZcXZc7JfxD18nk7j6jy/GwAOh0V0ObogUq2qIFmThrDRnbxwc/51p6OI99dX16KvQk66AqjXJsYKO+9+GE32Lqz2crUNeADsPHTDwKmenhy6UkiEthgc2vUlGbnH4uYSB03TB/Ncl89hIon2tzVV4Kx/HYXg+7IeFkxLw2gndO7RhEZhMSmA63GFhzerURniBUDKsSkAKUKgDUMRMKeVyLEVL17Myab/AL9yY3caKjwfgAO7MSEB1FiTysfURCe/4fmZ7pzviR7CTfKzMFnjlCcv2soitnAnezp+PrRylzsBMFDSe2Vku9rRIdVCaggc2Q2BV0T2nFGYyH5+sCAAg2iIzPOBCM37EwBD4KDaPl1d3RkXnfHCWvVLfFnKlUnU+/yHGBPxMMmq6yVIz0gVTuPGDi9MGVycBNlag+s6t2jCWNT/VBTqIkU7VK0EUw5OfIOadClmS1wmtV2OY4wkMaUXHRPKjSHGgIaxwKZ65Ul6yvj42/5RJRA6Sid/4vf8d0G+xIqL0PmKMkxbonCr4ft5xubSnpFP+qy/l3D9nV82aZhRlAX6K/BBPWlNzDUPs5BpAoVXy177GwKN3lEGkyAh+FFZwdELBBC6MzEAFNCEzZxt3MvG90rpPf5qkKMNgLDyY+Hm7ppc0CBUbpCG38hLSwAu+lPO+zrWEMpgzHA1dVoi5EiH9c0xuCMNZhYC6zohK/viAFxF++wkrc6GQTFJZedptlYbfxi/2my3w/bM0YGX6CkvUPGaJnkBhap371zXd8MCyYTHJWrUJ5sXso7ycMm66eoQN3+te9ALksftfo+N82mbDYM07qY7Dh7j1AeYv7EcxvrtBOS2c4c2jIxybGe04x0BteXC7AbDhuX9lmR9/KD4oGj2Evmlpd5LiiOTeolAvbw195txOl2gerTea0J8Cn4xCpaivgN83FdcGKiZCFo7jydv53+Qq0MwoC9AENpzv2oKGcnEwbWZ0qBm2Usj3/Q+o18WAysxaQUk0Qr/wxwFCl7oW1zFtNshGWZ0/y7Kia48/JQahya+7FZJbyqyxNlUwkGzYR7SzRoK7RPYouIZls61gXpz22ZHsgsgMcnv0b6KP9I65dMBVUN6qs1COQAYX0IeLAANobISGJcvMjILBuBS+AfozF7Bu4dASKw1uzbDsNIjF2G8A0SvkwaedTGixRhph7/YXcj+uc9rXEC+ZbJsI5FtSc9zQMmoOwdGRCXGEy2Strh49YO1W+8rMjAfuylG1OGKU1E+jv619P4eBMhVl4tsZnmvHoe868XIwKw8pmCxgc6rUI5T8pE4pLLlg6d5bcOwrpVjHwA78EqjKYl2QiP+5WvUXtA/BogiUGCLo3VBP0owVX51qemRuD4Sl2z/oTUSTgaIxS7ynedDiEzcIDqIma/Esm0oNrFiTzvLiDQ2LhZkPwLwc2UDtyPLylXgK/3tFtem7kxAKnEzU9R2p8G96ANL9kb5ra8LfWNpqjvxiVx+jA0o8rMPi2O0Yj7uP+DHza4jc2iNPIRkkBoeCTT67qaw3vxtIKhfrDAaaoVy+uwaKSdG/FtI277X2iAI82hTjzTNxVhuP/hsZF3Gc1wGUHrX1cjzDK8Us0FVMXPMS0cCoe0pR7QLETWNqxMZ72J+OHQuYhWTXbzDfBQE/LF0Nl1P7M27hyq+mVRoPNEOdSOKks45v8ezcwVLMrHbBRjCxnh/gNAH9Xmsq+M7f/mUkG2RCyrYhRgpXy5u1vMyGmSgvs+ea8guklhRld3wQlsxXJiACdGq5pXhL22UNhfqe5E55qPXuO0/oZ3SRtDN1JKsa5ZKgcBOaBjsaVHKDANHD1izz0xug28NTfNozEh70tNcRxtLHXbC24cgVsfAO1IlX8G6pHqVLDuh7k4yxZvM7LoVanK/JDZ3bqrbmD+NN7aM5bU4KSzSlos02SXDz/lg2U6nP76S0JjFl31za9TIQ4LKr6EwPWQi6GQKfI3Gx97Mywu6GDRPwE9VEHwVED18+phc1g8KM1EuD2vvSljhnrJOIKOfKDrbBFgfrFEPzSSKIQxBimPwtdOtw/aKQDyM4nAwscKfedR9z5KyDOyVSa/NHdb/GYQJdmDBzFXJRoQt3TzoEyD0qycl+otZdPGPFM5C53HNAVirYndkPyscIIzYHkUzADNKt8a8QRT2H24oUlXQHN2ebZ7QCjTKbOWJ7phfckHDXRJXkT8/payeZhO82hwllFXcW0RYsvJm7CX78fxnwsnOZpaEcEQ0CCmqmSxm7BqozEWynkKhjCSJG30l+DG0spKAhFNXesqhCNiAEM7HJviG/pTA6eB3IZADjDB2z8F+qk63Cx13pcB3b5OxxjCa3giyytGGv8lS6+SF+Amz+rGkK6cX3E0OlOfjPfmfYtlXdiJ8ZHmLiCwGTi4+9xas5jz6wiOr4S53nGq3W4ZYkhehprovmFIRmMH3nRzfmiMilTuVKJrfMJA7gNn37bn51pcIglptDxZWDOGZNrm/BceE/BILo1BGDb0LK65m9FjmBrMN4GpfuCAEuqAA2r+ClzG+NiVWzdI1EfI+83ne8hZ0Bb/IaOICAL1KSNcPvR+dwnVYmP0VS4KfXWNm/WNQ6UhN07RDZBFr43/NGU5E6W3FUIrUfto5JQaUzIyxTJ6hM/KSgjpL2/FN78K2dr05Aylo+09JcdocLTTyrpn/nIq7P3CElbBWyF1NmXiQcZDbxXhmd2gHH90RYtpL66CySRUKb5F7yeOF41rJbGSc6vVfsR7xiinyCA67W0PfGsoRuQmQEAFYBnQahaXuZdRrVRykfK2qdvJEJTMIYgfR3AlsY5FJXLmeMXgnv9nghtKxQN/VT2kBZJEg9moBZU8pu4kzjnF3lmCaoYTPk3uNsliTVCgDTgGa94VH4q+8EhC28bWEIen6s4urSBlJyGcigQoUnsP2yLQ2nS+EnReX6N+RdJ8xhUOn6bqrHbSBKL7dXyY+NIpwUZWxllqiyw0tktqkCKHdtO7DvKWCZ/z40PNpgoqjpeQgJyNwc331+s9qwasZn/HpyImTlTFXFNyEQseurrzDL052QzsP8sKJTMiuBC+zUU003SBtXt81wiHsP+SO7FK+8rMlNcWxtAzUuzi1SFB+uf6l2W0wWw1e6cVjJRnNXleEIrLkQNHJj16HOgrs/Snu1SdobezuRl0L6+YtvkEhjjBq9MzXlnma3bzBlqhUlVF+qqHWCRi0lDaBFIbLZso8q6k+yfu39X1esbqsM+j3jQwQoTWHIfYUavs1K5gyiAY+Mc76Lni40rbQTX6qR8MqkD4JVkOgS8BzO4ckN7bdB0lu9X1NAYhgeVCJMi7oHSqaHCDYFvhOMLV05HFsTAMRAt/qSHEhGFLNdTaPgTnPH3fT2yl/Q9024z2RYMR6IdKybS46WnFtBQ2lPHfdVqLnRl3caBXIvye1iNSw4CtXmLSRTBmC2QLfD1W3hFFfBVA3O9xtD8xuj40+1w/JOxdZkCTg+wIlyxlR7RKhNtFYSSFEoPwrEeggSn8qyrZmn7DGWCq7j/yXYP9CKab83PMzONsTj6F0TIf8mDbD5s0M0hzB/BopEO107PLYgasNlKR2uMLTAGXIrTOFuRcIeYqxNV1qZ3U5DrhlXv3yLK96pUNCWxg/q5kh8MCOxkria4VKM4KFzn6wU5Q27Qf4sO2AUp0w9dJsuFZavguPUoqacO/inMokkQyV6JozRxZ0Hv9RQjtP5vPGl9h/yjUNrv5TcWrBfiZeo/6PWXxlZfSbtm2vngsWfuYd4Bttj3Ty9ezKj8n8NWmfOzGOxVSPmLYj9pORGFuLySHG7LwMy0NTQu2MByzA/9sZj6FpBIx/Jx4It6QIXRtIRZ2ZDKalCeWV0HdqQg1yBwhhxACxiBei25ODpmV7WdN1KpZDfidYoeEF14QP3QxT0DyuWr2zvCdWod/edDzOnwqj6jhAV8sTwuokhWTqRkO9Z1q8i0YoqMvXoM6p/2zCK6gt93OcTr2BR4dhkXVhBUSPLIpqhYXvRno8eGR93ls+152IOYSgvQHMtHc+EuJO4QI2FFD8C/2ciOXMIxNrwko3AgipjdhDolabCdJlZdodDX+eNyXyJDL17gAUGhFNr6URk6P6JbR2iruQq2zj5AfRrIR1JfFD+ebKJl6WHpu95KZs3TUrv3VxRQiWul/3lvygx815z/b9yaB5G1S9R9ebKTUD4sNfP1F0CrzawHx1xALutAlu4nK0kjrHgdD+LOQ+KweKc8WDeZyC/VrkDt34owhzlfHLaG2s6CF3iu5fN/IJvW5adzvhxRUWtF/IEpIkwXQYq5U1EXtHnffbxABEqzsicHvbCpJjsRNxI+ssM92dTOHr6s9fml4opNGSBHwnyKggKzStb1WRcbeYEHUnerxczlDj1r9tGJAfOI9hk2JoSoHtlaW/zER1mXcj9uJpjEwjKY9nTlTgT8q8rrApNHL63OnXuSrO1KEdnd7ty4WIH0f1tjNQ7H2QivsXaLZ26vcWvLhI1x/DN0uQ7FAMBKZSXahVVeg7kIQIpT7QYBocg+3tcw++MLt+a2F0uEL2kuHmNScwU5YCiCynAdSBewoKLbFQQMdGdaS1ZhtqqVNAvmKu9R+Jzt8wDo1iM5xk0NDK2H52092/K3+gF5omyZKYTXPziaJf3JNWXBvOSnI11NKmDQxUxb06JwaFomqWLo5+wHUdY3/UA9YSH061H7RtYp8It6TbzzNLXUSWK8iaHkiCrZVEpiu/kiyk6vxBl63Rg4F/zYdJkDt9tCsmvAs00ULr8PvBuiuNqV3BOadbR/3CHEM4ZrW/abte3ez2LHkXeViTkHfxeMk+PjkT1wK1izkMqWmNqwIW0PVA8wbWCYpSxUPeApOfOuEcPFosm3Y7OQSYIbO566+zfIuAOS9USOsaLydl0zuNbNbHFGiJ2S7mNBsdnuHPf8zPA1rIgEY/E82Hhpf8jtbO6NIM2Bf3SOpxoKr+0IA1MZol4TkPw+yXMT6H3OjMgON3wwGprvc+rVZBWZfmz/9eKfgqMEHTpRWB/LRIJkzLVA5jzcC5V1H/WJ5uiynC/coWKaNNCuwEf6gNJPWchZmQmJsbqyWHtEbEG5fDwjy4r52P1UUMKzORMZKm5OWTNWjmBUSvKu5NNM2Cs0BpbUJyNTIRb+vXyDAwwE1gECi/K+CpCsLKcBQFbVA3q7Y+SS8lz6uJedd5ZKsU37Cg4cU5mbatQTG6tX5vwtexqjPl2ehgxpEhH7zqIApOfeyLZ0KfIsLOpuKEqnWu/gU5VESssp0Nm4C2D6EUm2uHhglQ+pEJ8ptENG9gAf1E6EjSP7fJLAT0ve4pM/HPCRtdsro/u5t7+LlG1RnKVLA/a7n8qqmN7FuvkBW78PdTgszFw6GdyBOYfSKlS3zKs6ULG++WYktLrhgObE8VMr0YSm9ktzdIzKXEJzivZBX3UZKez/M4YRSv67htK8n/dOii2ZdYm8Uo+FkbaSiCnuziEZVNUOzsLVekppcVvW5dSqCmZixQoeAtcexZHUZnesKZ9Pctbfc8iYm2p847oiBlxPHaGSk8vYh77Q0xTZd/NpofU4PknH0QEHh03g/+ziycpUFKTLKw0ZVELtJ/soAbtyFjlUQJTeWjdF6EzXz/60hRJiv+DGTUN9P7XgxjCehAyxl93vIs+NeEVqlX5wN2iD/D04HhXR4RWS9369/SBYiO+tttGltQ9f5hN3MJvuVUCrqhWAixXcWLMKk4BTHJCy5fOY4H+kVJxCCiqUR0D4UZTl8Y2mi+BIaQ2U8BMKldJcvIIiwy8C8b/S6FVIPP/ajjbROut4Mg/OEVv55RgCAaf2FGQYIWkjIKrxZyW/dBPrxFj0INo3Nn6ltId/LzTnsWTtKx6wonVOW9fZbLUDjQrrpeugs1K0R1n9l2JdNOKTw/xdxPC8SadfRvr/otHWXFM0FzpTpru9bJFA4xRISHaDS212dx3B+mJ7LYpugqOIzdOF9Ep0YalC3JJvspdQcI0M0aZ/jGpcCxtuYqLH2e/WlnLFz6dX+f8kP9F8ofqguyW19UdEF91nAw5qqaE7JjSV+YcaH485N/DhMngRYURHZHIdNZPeGHOoxPDNxSXS1D7shboitLXHIiH9cb7zIdaiH4BgwM9MAO5mbKWWxpPHzmDAEvhj6vOzCMqlOB0EvzrIG69sy64Je7PRkHnU76Z+e8wFjZi0GIDAC/ijaGajw7qoE/DqMzDlbxcg5Ff2F+OInDUbuiJBnnntfgLbZONi50rsxPEXHZyBN9VelG9gmqHSaJc0KWrZJSVsXtStUUa4DeH00YniK09SkjVxy0ffxvhx0xPmZ5/XOprBd3jnFDa6bUygD0t7M8VoOlcH1/0KyxEHtA/RDeruCYjeFQWmRK+VIBh5zulyN6IZU7HnPOjD7uD+oGX9+k1riXWaouhmovdZETgDh4/JcBdngrTgKb3AfovPtdyx/7bKxjWSX2aCXXr4dMAvmMzojX3lBQnZRf7CQubKFSNdDX3YHCcmg3k3IFwpKr65GilQt0Qh3fDwheiuBiqjkO8SyKn3FypY7svNfRMfkPdNdIIRaSQ6WwPVdj/xpNldVlcm+UDpCFjFzow271mlWXd3aJxztbG3RminOrnEXv1LTDGilJQExbjg29EiWcF4iPfEv+gzeJShFLC6ahkFQxKxmk0ov6OyJtUXMN5n7GLLLOcZTOsISb2Gc0hhBubRiEQiPoRHm6EVxzA7QPWfHdDfFjI4rEeipGJ6sKQLGuo+LbE/TlJwce0t8WADxUG+Q0um4VNkvZizDscNDnXzzXMRPvPTud9qFbjqsUygMihhsiyFPlHBMSUHr2QlN38kpkWWd87PHiVRjvUbvV9tcIsOHKEzs4+SG+51793RzU1hd1e8t7qmeFeu4WdcqNuuavcLElaA1018OPCpmRQuqzoGayOs8fmKDfOxbAiptZKD2lSag8XNX4sWDNxgs0GVZFySMEFi8AGMTeah679aObaJf1MRv7dIPr1QIR/fezJy+HXrxZsAhA4eZ0JkiIM6RbfwYgSm/7FHm8R0IuoRcor2WOU2xYtmWCQEI9VNOANzyb3tlaq/yZNcwnkOBWa0+b0xFOEhnUuYeoDawJlktvN2eJu2uALrv2GrnxXhJ4kAXc04AFv2NsZzvijUglHFh9wNYE3bcA/fkeEnVcqSkxc2tb7Rpa5qTwIg8i0bTfVohH/KKRpdMqStd1D2b4PgRdhJO1I/lo+qUdFKCjoSm5OTd1xsU5NqoTfNckmRzxOZe3gfQy5HGOSMHzrejvHDmAUX8Su4DBdG4hpPL13YfZKkD7oD0effsRSPOTmlnGasynmyLya8AtB2GmefRfG1pKwGQS0BlP1f4MHmwLO2eCyoFHSfr9Ud3yKNPfF2G9oih31NUpIjIcR5QpEKDp8c7XyF4d/pjUpgunIYgauYF0RZp5gZ+URKl/bMbKfvzPIrJU5X5LaJNn3Q3/cZqU12a0NmEp1X2ojoVmkfiqXFYbxTiV93YIFDARCYMyfzpJd0QYtaFqW2BwyqmFyiAgKAdWwH6sdbyMurZZCQTGlAd96DV1H5nsC8BZF/n7/RUE0OYnAuuh606gOV4YXmvq4fn7e6JQ72HKyflHu0vZU+DrPuk88OzgZGbTVQOsBwoBCVeNK0HglhJesVpnQPfOzMR0ocjL3wrXCz5tenPtCumD++ue7YwFmuET2hY0lEGynwRgFNuXfrDpMhXTCxB7rlJ6zTqhpJ/e3Xw8uTjpMWPFq5MtlvFE/Mwg8pLV3bLpyTsqtLIwTVSvtjyHTXVDUKcQVZ9/L5HdlWIdAMfXGd8uXenU4j+WaCXjEK1AiagvYL5gljEU8BZGSG1GR4kFCMaPsM8+/XIgXIwGk9GUaeQWkx5rZrf4IC55Ce3EsuVcCiEVqIaC+d09j9rEoTR2rr+cszRFj8gtzNGvhMTFgRqwVcW6D/Wi7qmC0eARINZ7OPCD03Qykp0gxRwsu0KbVQ7cvzmmdRIqW4AVbbUztK5cNkzb4M4vac92L0PEyF1IVwpLMptj35aHzHkqvUfyV0EYWMCOP1rzVm2EyOuoh7h+k4E6v6Zez7GKO5OCdveIh1IhCpCSXHOC1x0hnpxdRooUF1RemoNMgdAuAOogOe7WDXoUknZ40ZLn6JEqBeFjNtheXOsh1gINZsgk/bXx1rWu82/rmwDD9w9xTsNr2SGvqZrWs3+zbSfp3SEtCAhuOZ9khtkByEYwnP5p+V6fqEfEx647nCsFAoGd7gcQp1Jb1gt19MqhZXwDcVl3eSrcb6/d9vgdJFC5pvQVQVV/8O8L5UUkLBkoxZMi6YCe3f64Ltr4sJxq3PdYbaoXcjVzCJNAoFhukmKtiWLL5iw0SdNr8qiBQ1TjKmjvBczUJasC5nQ4W05sA0SOGyu9TOjau/IDzchp51T7LiwNHVgrHY1B2+iY/xgHOWzzTvD8zdNz4+zTqOocFsjJMJw23MydQzlwU8cSso99xPhETwGw54g5N/Chv5PhLyH2ZbKRtYYnuhGgaq59IG4FSLrgiiP9ftpGIed5yGvnEF6M4oOPbX/Sd3ldM9lGhpvvsXKoglIOrtQ7bPlrKd/WMN7RbyC+X7pRdAtyPZ01ObeFjM6/U3rtLOgzW6PE1OBesDzaUAvE+LWNf/3fSLSItfH9yTVKiboJeJnd9uUYq+K5KVWSSYV6O/fcLNTEXyPBiN/c05ySp607w/ljwyCNjH3ed/AgHpOsNNXUnTWe/rnUjUt1kQYSmcCsn6+7Ap/JExy66BNmZvSipjScHMVmD8I/QaA9aEp1dePdGzGqmp0ezqfBYQd5IfxrYEalO03plPgy4nr7R5SNYo0nKe1tq3ipjiFsn2DN25bBvsURPqLMgmEZiwRWUqnOsPey6swOC9ZoOLgAWXBoG2KYDA9G8HKAX1a+2B9dyeHAWV4sfmnqiIUiaqJKP2FhHXmRoClCpBUAsDmIGpgGHclVtp4iZfnuswnieJDN2SM/0/lGlHx4kR1ptTSsXTHwzs1g8GNlKtzuWeAROfBh+3LbQ5Iq0gd5N+H4y5JQIOTHaPxpHaBhYw7JUGwAKITgDJP3xbtEtehaZZ73WdEQKINLiAIaLxJICaoZ0bFzcRAUCiQzr0YRjAjYDSVd7YbhRlKwY2jC8zS0QDMZk+E2EGwOTKihgd2IYPCicR4KyhigduelACuEjEoOUZs78/A4BEG339UKIIPFxjcJpp//SkeYwWRh/hhJFgmBPku+g1H76yB2I6tvODL2Na8RcOFhm6yzjAnztAHBg4sNkwpLp6g9v8c/mtl7dXVofYWeNwIWPtZdMMtnZrONxeJB6H+JpmTfxsv9anNtGvvDmLWjtdRfwSp6jH6Fh6DNYIzq7eUU4BZlv5P2zyb8/oxMiy6YQRR7IhjTGCrRz5TME2BWQouDO6KkPXFRTH5qOTJO0j2NmXy78Lzp92X2BCmFbJcKAras/N9DyFYNRH/ut0C7BT87k4ZQ31l/b6jexdSP7aEWWwQjZhv+8+0h7ftpTxg3rTeUIOtj0KfMXiNN0oepnNiX7GCN3VRqg3y/5E8YCn6wahqHFqMJkv+gQ6hKMZq75j51oE3fXOtVhCtLKDFyuLAC1up8nuvs7FkJ1ov6fipoG40ahoPwtBxhWrSam5H8wJ1qrjp6nbDAy4lAgbgDYj2CeUanYAa/M57YP273+BMlLN1J7VCFdGO49VhIDMyxzmIZ+t03XHyJCKW4J8uhIZ6dS/rMK7TlB1MnZs/F4MLCXYau7akd8VVD933/U6qqgTwcXKqBJM1ev78kzC7MayAu43AALfGtw3OCt207JaIjupNvsadOjjX1aWf8PWJmv1GV8spBOvm0qrOoYnM+sFvhVRK5JqJipHnZGsZ6MWwJEOk3Z8DZR+zp4IwUScA//a5kc1URyNOr6rRAO/f3qMzX5nDfqRNNCia4+m7a6yf9uoGqyK0ScvixhJvnAWvwnSie6LmOroVjj72PbmkpVGjVJZAM3dN44nMKEFNGQYSOPiDBazx0MURiyjQ3aF5SRlV2JIizPGPE0QvSE34dYEcvKD/BGhheiQH2gVSLn4dKw3tT+Pyk9JLHM3tbJEq8EiYdeb/s10Sc0t8Ufq1988Pwffun+WL7bpfkUnDlO/tbGIUp2bzFLpWbvUNtzqgj6AEna5B5Z0U0As/m/VdkcFnEb0qCyiroGrYN/MCqVGSfcpklhfwmVEI6kxe1epjHGXkRVgvFrKx15i9eX5oHsniLUOeXcmh2+PxgClrra/24REHGfzDUwEeA4qvjASLO4a9AWHPMgR3GooPHT9kdslN4wrNHL+yRL+wSePQQsqBfhQJmRXzM8IsLEGXx/eshTIjKwr1VKS1zLqrFjv7fdMCD+6STl8nq2+vOHBk458aXUvbzwr5Z5mEIV5Px4KKZNeLzsoeEApI3tG9CI0FGaXyaO1wTsEtI7U5AXqW3b0AfzqaEOMHLneZpG+l9+0mVvEPeT7p3ljXShMeF90g+TBHB0d+gSfjjNxCdNhwBAYTdKpPet8NZbrUdn5AurFUF42MA/WRyvMuS5a5H9aZu7XwUmnPjzCTyTKMrpbZcmFhVQH88Qdw35NjnL9uQaxgpDYVn4bpzxpOuCRTMphy/B2fpKt8PQHLAnDu3NQtcX77PZ1qqGhiQ//ZJDnEpvgl54YRm/6r0MaTkmGVxLxmB9mqdU2PX2r+6gUk8Y/o316KJEW1vEzbSSBKGOMsa8ZmufjMOnNpmZkMR9cHuyFfFRUnOiiLspifbeEjZJ/Bm8Q8y/iFc9C8suQI4wWxNM4ZX0hnA1kweABKKcn18XPm+hXsg0KSCbAafqjrejXNm7xpsL/09JDE+rqg1bLxMgX64KjX+/vPsJWJ5jZVG0vv+FwmuyAa8eOVffCbxzfUAWnq5WyIcfbWNSBjYbdWTPX42cQ/DNxrv2bAjhHm3EixWvOOUpU+Ca/yyaOj9TjJj4nuPtrA+OmAQwqVDXbrOunEPDwrLn4HmVrC784wcn8OYFjD5eZvldyDL3keIYAkYDXUN3knZko+uN6fe252gJwz5czTw027bzk3ffdKpsQ8xHa2OS5VxkEYCPzJ6lYr8ZUt+d3GJ2KsFuIKtwbVMAd5V+SkGSPEasGkmIX4VziaUZdZsIUt5byaZGm8VJ+TiVTZay53j7w2KlZotWBuk0+9WQnPn5QrDqnbF2j3JXCSy91cW8fVuydvjJ0TNfscwdj2biLXDjBkJc4mjyOUwhIkFFMh9Im367zuwuQSqjK3tI4g/aCii12nY5e6vlI0uVQDz8/N9GBBmkdDgIbLyxmc8carEYrNWFiWtKlZ0ow6uTD05GyqsoTKSX2e7IrjDyirhaRXMSthnPmc/Q2KpcqS/Y5VbtaG7bM8eyhbx1bDrLIm+W0C+hDIC08wv6ptclB3nvBcvT4bBlE/h8zT/i9aya28Fe1yZw+Jz6VE+I75FsPRvpNU109fiotatHI3QbtrLbj2jefmX0sHDx8QyitqS3qZAcX1zwOiwDP4mvgqeBPf1yLlLpobh4v2Ya456RzAuGFKEvcFNHYF5hDDzy2poY5flPmcwIA9DLL2fgEmP/vRLYX/UL+HRDSOz1GK+oLJEguK4wLj8AljQH/aXuujuGxLzHkxuz5htxnNTYdD8qikJrhXUaQXFFZPy+1ICeQ6w9okg6KyV7LPSsBNhmSKLPRh32hFWxmV0jIfeZfjcQEVueceyKRjibPOLaeMAt2p1vdQsB/GyDoo/G7PDEbDtMWu8Rlr+6TzRyJxtKPfAHv314NgJ31efTL9bykufuAj/DEawzEAr6+l0YmJbFZM0g1Jbi9zLubT1WJZNrJrjkgXJ72Egox1TrBupsvWeBcEV9yO8rMbcsZr6o5yrmsRxLnVq0lKNljhdlJ/OOK6NhwQVRHpCdyI9NshlAhWuop2U5SNL+Da/LGINAzx+iDnjkbO/335u1PfOgECV11W3gacP6w6AHVIZQNSJNmuXVhLl+ivpB2nG5s1ulCY0z/wXEoJz01tp2+1KnX3WDqa5mj15+/mIN1zTkoH1fM4EcKy746A+V2kAUdjqaPUnMcPsnOiZw7aiaMfDDRnSqfPvi4+O3xCIXsoIplPKeeBaqNKGTtrfDgjo4tM0/gqrCdvHCQ9+2bH5KALuqAvgfHR9D6pGFptp5wEqA/KEO6u9KorNzQqiwq8XZg1HSoCwDRzdfWR7jk7XSGVS8WIuAhlS1R44P/J2kZje7fcT9gJrj+6V0OWeA68LI0qu/EkWXo2qIFt4a5xefXzVykqL2goRTCBpE5J41InWEDEISH8Y/wYy+WcNTZAQA0OFrJMzbTAgSnnPQJEnxIcdeQdGV1PFjKRXxI9KK8C1OzE7Xj5V70XM+kMWB5f3WKZ3jtqheHqaAKJpLdEBHONfYOfdMFHY8XWtLUaYGv1DlxeSLEcrEG12rqon7SHqwChrunJERTPnTUh5V3u0TiJ/7QQttDbE9o/kUrhfMYdoSH4yCgQimB73j2k8A9Ut1aFixL99USb45Sh0mSR7hYEaVs7IS+jRoAI9BS1K0q+Kac+vDkeYIRrekdHMO+oafHa8MM8fd+SFoCIw5x5IPChI59hbWtJPopeClTE4w63O6fGIOgisP4QLzHkopIe8aFC47mrJHeGdj6u80dBlDD7xXR69eTHvvJZj3CvqDDDXggmxvfyh631L1/PMyiQXlDMfDzZVwgi4A/D23XF/uAboyfzy1IlHygZRxUHpkk92sbMDUNX1hFNSpz+AJdHMSr9hW3mGA5kQTkeB7A8q+4AwLaB6M7Pnn5ShEWo7+qLAT+M6VlJ/6ATc80Q8T6Ya+1XRoJIYb0oNDKeKP6OjamPdY6JkP0k/GgUFy/N1lzZPqYG4ubvHa0ZbhBsDQUibzSjZKa6P1NK8UBdMzAuUPSXYGKYO4DfnEAt4BELuz7rTzcVkJB0xPN9X8ZHzUMgGxCQgq+joRZA6cOl8pw/7GJxbKlIUS8Vf7yfsQbgsyShvlzggIx3Vf4SEX+RvPmLJt3xk/OD0euydjiROF0JzE5BLOii93PDKedVPxu6D024sYN4fLegXdQBeCQktR9mJK8TdS8B8PQN13NTPxen65UZZcKbqzA2a3XHyWTAjTnZRF8laFeRuUES4MK7kT0tx4GTY9piNd8YYMDpeNSiopqXL+vUIt0nYU0LBXQ8R1ZPCOnnHxouIN0oacMlBN10RYkhC9hyXidRCbRiJKncJg7JL24IhlJjj85mXkQMb2ndSWlAwh4p7Xb8AblaoITkZ5EU4osWbUDXdoBfWqF2Ezb4A8PFEBEe/WgXYQHeymV1h/1hPTeA4kEVhm2NeRycTCoaSuMQiSdC+JCsQ+gWEXmJXywqo7CTW16cI4WWwOySJ7bnnWCPUJoD1p0Rz7xT3BdlxeHvcFrQfGG8YfeFIOesp/tVdWI69MhUIQ+44sK35sfl22SBjPeQCATsL9QAZaUtFO5/VhUFZkJe/qDiKx1s96pEV+SVlVipBHzE5Ib4IIF6OMC/xTyVoUWfC/eSWkM67ANy0NuBA/kntaSMCws9SZ59W3NqN5LTj5gJsvZDGT8JecT0jTdRoxHA/VhVxitosuXVC3dhPsUyISBSfuWrIsQv15o9nvYxP3QEwKiJr777eKGWPsFvQ91UMmonf3+UMRgyUNQWHK2nmWzRTPX2e3XsL7+vEUP3b6lXkEOkt+bDgxYxG8+MR3EV56supIldr7iffzvvh3JbBRS9BX7lyB7DiXb0NrGgBKZIAp3VLu+YpP8MYQepFYrMgMgEGgaPJ89hchCOh1TJPftAj1DJPeE2LLS0/k25eSywjrm0ajfEjztySJjhPf01++Fn9FyqU6P2Pi1uyDFD5+mgTG/zSPHn0uwN7/NatPTbryoVi/Q9BijSHees5WgLqZsBzVp6/473lX30dXjdbL5PZGxBgWod2Xh9s9rqfiqWrFZhKL5pvedQ/4M/59gDjJRhBNr1HCYUua3LbX+K1HI5HMgcfENWxla9TI+VNeABT1t63D+HYXTWIpeTxM/XvqFezeeiMM/dBVaAhA+3em1W9KyllzPxB1ZGEnhm4pAt9oQHYOzVGl1Nob0kbIB0++we0ydJt1bJ6ZU9UGPwnNRAVY1at7OAIP7gZl9vs0kkBmNe7da4Aw1F7+sBXeYzCkeRoTWn5YGu1nGtHwfbCgFOrBnqIW0Rshfe0MVnvpoCkrodMoahZ5p/bpw9cbpVvjXapqjve28JuJV7YlWoXWIFlEJIsbd/2SUqN2uQCMTVFExZsZsyN5JJEJeLvUuouiM/qBKcf3oAOx2KXNpGyt7EKcOKYGTBHoBd9viq52EyJ0AFUjR+8BX3JJY2PyoFD+0tBEtLJ0wrVaSm+8GJJDXH2UYxMa/T79Cbm69/NeOFvk6uVD1zeiImpPG/oLt7+Gttjy4KMPlp2tl1dsCujHuAzgtlLL4ibOxorUzROU9AgDKn+fFUFIJy7lgT/ee8eQ6Z4hmRxwVWeLDs0F7SrQRou0wAGamEtdCAva2isyfkXExhWoHkpGVhJKTthp16W5ftSRhAGVf+4LIhKY7eVqxQPQuVyec3bePDsP0x0LO0hdGuqfdytpkAGmaxtcAXszghQWYL5odSONz/8C7Gvyl7bi+4fKx4syxSDhjl6fT/oSxU0HrRzGhAmfyjm9wf7LS2+87satxUwWIW58eGP56uk2dF+oX1xqT2q9TrLFRlNUbFBXeqQ0jaLAnr4UNOy97/Lo5INpm2P5iDvlaQ2/l+a8uLl8hCvRtN8pylrIyz7G2wLfP4obDFrjH1RLmQZjq7gJOtHQO48jZdjbI5TKa4EZdjpE85kWYPvCHb97WiMBGH+1Z4QOhWfnTqAlWx2lXro3TEB33/qVUp7nh88h8ormqs1FHWyqAjd2d9xHJE9XAAM6VjvMD98UjOxO8SgmBiRorK5bMUbfLrqTO91i30UuJ/Lht2N8Smi2BtHOkO43erS98XKFJSQD6bKzYN6tzU4nbSIm8Jmh3cE++C03f3ZOo0dfxuENxtKvpf4/QLdkkFQS547NdIvDmSTlnsxJy/QwDSUIVL6KXXwXs8KJsH3RkBfRgwFAh/7LBXmjcjxFdcjhYyu4FcFaYnKMiBjmixa94J7oL1TqVsQDRrd0c9eSDyTZjYw0nIUmiuHyDBUmE6VHOO4p71Q9/LwkMElw3gb5P4nwrR4v04fyBTl8hD1YatEn2IcpktndwRLXVJscQ+bpfEJFKCCeU8d9PmlL+WQiZ6SSO7WsqCqrkM6JVWMGrLTOEZKAzAv5CFdK5KQQ415+y4c+g0L0CrWl9Po4Xw0sIqn1GbUU8Nzw0VLBJRaS1acOqxGG3Etm+AuBod29KYei3EPWTbl5863I+R902CvSAHYMy4ISsdOK36ImXIevQZdQhuJmc8697fkUpccioM0EwFdfRtYRZaK1sTJ+WH8Ep7NYjcq/YyzOMY90q/oOQsL3wDTKPLUF/s4oLDwBlYTs9OTi4+isLJ66ex6SpKFGWekP/lfsnpLNiwN24IwwUgSH+4v5R+6A+KXN2G68YViJcwVQPfI7VqJVJp4Kd3UBIONPg5z205ecu2NGOfPIpfCk26YhuYrdzgxFvpEvPKKDM8yTyEqMyna+M5SCmi5/DVXrM3y8+rOrZNnXzTS4A9D3AoVvVoP5zRAPxg8w+uD9aVKEV/SG8h0rz4+H5r0ccc99B1GQG59BmOGNUncCJ8/5/V6fZmb/GOkFwtjAqgA6Ul9gNChev5Z+Pyd7G1pVlAWSAht71OrTNKwRlDGyTLD6FZZUHF97Yj8RgwqoPlKY3iULloYFIoDdZg1DitAXcimGlhaIJk14DxCHHsFcLHzX+JJY9n3mSeHH56yScrk/wZY6dcBRebNfeZgOJZZ0eFq68Kd5H6ri8kBrrRxULWV2BLvTx5QMYqdU8zyYl52dXDKe3M9Ijj166OfnZ6pLYBki76fFdDKFEFopFB5cmwSKonBdJ7ywFzz8TqqyhAW/bVPNqHVMSHjpn+SfgG4a6/6nC6bZa133m5ZLhDB4iDLDm5Y5Oqkoty40x2Nd3NMAV7M1zve3lYeZTXJcAAqGim4jsWZ1OWnyvchtTz3fuEz3tzpmgYH7JWEA/mXmctYLFKpVzWVIb/EhzhAVqfF678e4ezClJgMWlG3Vl6Ph1dwXPg7qsbPQ3U+Jq1CsBPtiv8TBA8AYQAAAMCybdu2bdu29Wzbtm3btm3bNr4NsSRTlXYBoRCaP6yNK9K+UCYGp7+r6/Esf36HfRk7Fvz0hQULtMM3gs72Z9rZf1CeaMT5+73zLXSGw7ubwIK05IgXHo/G3EcddY6WuNdLW6PdXry2kTYMUsIziGCX1ZGSfC2u6J30mTRP+L/Mse5Awp0/z36m6h4ehIXdMGSmBATEW14AG9Jp4H+Mty9t0rrPS7XmIdGfehoNJsOiEEs6H+YoboXnc3uKXD+eoNBCOJ08Zne4q7UrjuQuxqOaXyIz2OsM7Qwb8YwgQojDRuaTj9AW03Rh+KKv2hWbgY2MGdvF5QFscY6PFo2Hzn49p2q/I/u7RrF/dr6eDOJVH4IIGAYSvQBBgMS6Ytyg2yUbK7cyzKSr74OMrkgLXkRen9rKlRnyiChlED/UaG7LYvzYoJYZYIYapovNMd+TNdQIBqEzq9+y8XrjDbqPbgATTGf9a92MmctkwfiXEQk0wSvEr3EeQDHN9cK33FEYCB7mjcMoTaD8huPTKzQJKKuc9QW87G++inYxi+03v8d2ygeygwV/L7cUvpv2K0vFroGifW97NPiwuZibapIqscXNvOrxOgQ9AKSGsVvYTnO+EhDbhpYLmPBPh6BlZZbLabJl/XOaZD6PSz6I+N106S+Kvf6AACykHIfaejDrbOjuHTPkIctbkGP1z4rUb6suS4nx8NlgMJc1CqauX85LceR3EQaVztIVomejWbDCjYt2Aj1vBSozdBb4rd+7wF1VqBpDnPgu9JQD7N30gt2NwvlN7v4fnXKNNUsBZPdkslYYoUQBdSQH3ivzN1MX3FnxT1M6sZDg+nDyPT/1d6pY0eGtccyIOpmJ4Nlb+jq5UDv1fc5HftGhDaeIE6hVJAFOPNwrkqGkwTa3M6unlbUAGUUP3ntkz0Gwo5+JVt+XZXxiW3Fr2bxC3FKxn7r66Aw1oybIKdkXULmC0xZYmm51Ncki4aS8pwuMhgpgdUqbOSegqkkUlcLqQFpFY2H94qPKVpbi0v5Y4zWINJbwqN0cd63iUrslhwuCvy2c1rvvwY0n4m0VM6njWHN82DKmN+HkcLV+Z1yC46UUHFBfiEVG4cVEMTtDNUjLaQlAy54RX/yMG2Wj8c1K7Xk8VefLapJZf1ENaf+p9UkMybr5/CUfHLBPhIEcvwi/2HBEDN5bqBQXY4Supv1FsCQx8SZqlFYJ66tL48zFpODflV7EafhL73Pj/E7fOqF7iiLA3/jHlp3eWs6GY1DJg6qKTDmqoQdYrF7rQky6cXkvQ5f/nVzDU7y8XLGD/xyYQiMuBITgfDcMDmgBGxE9kZw5rD7z9VRdwk+L0G/eS7lWTRjuoXxEmuZgmkgUrEzFSxVqdTKZeBKoDA9U3b2YNxKujI246/UspPEkPdIPSJyVjIit7R84lKBb3SB4ihP+ybDeqZsc9sKrK+LeAmMxcf0Xywd93Z9h7Vmkq7V/fU0yJ49NRtwicmIzsptiKSUIl/Hi6ltVz52mG8zfEQnpz4Fqmpx51DmsOKbtAMbUNAE/qTZR6XsSmlmG+7uq9BuJYoD1xgZAhH4MZZhUO9Hz2HuFNpoDCsMTg7ykZtpi6VriRmWVMqGPIR7pgRYhH3TeHUjTlPPS6jTu542yK2+2Uy0Np7pWNpgjKokHyUmGfbRqvMZxOH7svdppW06m5GaMpK5ITSkEFZS+Xud4VhNfSl5msPpf1Ed6yY+GNR/hxVh0+uYZL6CVgCyGrgk6z/p7zWYbM5b8FeX9uMzyTNf+NP4QCAVdqiaw/Q2PgRApYo2B6t/0ZCcAFrE59SkK3fviD3sAZnxv6TsYF5KSHFg8rld7TvzGucU86sAFmsrvl5BV+bW45E7i5rs0Ho732HokSNscBf20Wdns1TPaUi2LE/okWvKryk1qVLeAqQ97nH9xv7F44g1v++Fff6JqCICjQc3mBY947I+sJ0riMwvtU4iKrON7xps43PTrJhXZcfx8e5ZwSCvhpy4izYBnwmUw5KW1gwnN490tCKB0dcRZ6+KDjOOafk5pl47dwcG2802IY6eye0pgtrBY07CdGdcabuFMhQOzs4Kn6Pr3qNwjVcq4ApqC4C2ehe4137HoSv4uxtNfKpF+/pvjS+uQNCGom8zQpppkpKcYJ/OFyAcMU8rWaxSt2ABaAXSiumujYfaJyDd6eBXfyy3aO23Q5yeN9TkMQI72FLzmkBDlskVhOxe8LsrFPlL/g2koTe3QBa4uOUJOm89nMQdMvw7vtxAi+7Qvj+BntxpmAjcNguKRMfdmV5x5nopBLmMiurs00dVNqCtxyF7HNcb25LhXpuy7P1B4W9KlwYrdLM429RvjW5Wq4DyRyz9oaFOktKFoGl/f0EeJaYQ/LtM7Atdc6vF2ipVyvQzWkMk8Tyqifn2aLK4MbKiTu8O4G6/9sq8wKaB+k1BwftCvkfZMJ/4o6DpKwrUgqm2k4rHJIT3nO5/1xibWOfIacmLCRAHI5gJCkJvFonrg5z50dgpXo3YUSqOgJmg3UP94s+jnyzpr592BBAsmJ38wuvMaFUqBayANEgjK7VGf2syCLWfkD7kR69J4YHjbD7v9HTzf/pQhrxVN/JR6pG8d+0yaX++9nFgwhCtaai9u4zNtnS+XuaaOhpHt3942O3QjKYeoArJOsPF7k6SKon0DWwhKvmOtFYhFlHd/Yzf+9kCLkRJ6WBkQIv5rJgIdz9cPbuETJjMTHVqSzkkogo+9GqTMVtUxjKR5h8bZWiLywXojhYjescVa0ooI6Ni2bduqS/eUfLLDaRL+FAcUlSqeqS3LmNJPeKclqLSizkvhEzlJTJxlAx0cJqNzQZyGVEPVg4g7DhiDvvf85n5lynzjyXzFrAPvx3ev0E4zsgo10yA9vo7yyfrZvx/l6pt52+ifK2mrJH7YWXFXVBLsRHFQdUkCArGFz+9ZlCB/XV1zidEXFIJdBCt/7xZ34TOaFRmuGrnKbOC+uNqlOqtPvFgHRCIXvkK6LgvQBigrRcqGExy9DhtmgeYDvLF03YfRMbUhcAPDdkD0wpYdVzWseuh98ehnKimQlJ5MaLTL3ys1V2X/Li6AYZqu3Uzo3Fz4G6hSmEVX7sNXg3ULUNhU7HnQI1MGEBWsW5ddElBSTX1IXAm4XZfCArx/L/LuaFB51rRU7v3/qEbZO/BCNlZKaQ6gUc6ZOFwenTWxfMaEaFzLCFS1/aVUD5KQE0s3K5+qii4VnUdfOZDIghBR6PT3BHeVCWbnkT7ohjSpvrtTGQjVgy5ahVM8n2Y3kEtE4sNPBKROJQHKVcwOa7aIdGzAIAtquFttWVCTgsWric+FhJ/MjN0oQ+oMRwGQzSi4BYc63Fr08Vz0Dcmny7rhJR77h5dHS3CagM3AQ+FVixg5lH5t00YsStfoNH9uQrOdKjQbhidXWT5LFIpQ+92ANNNwI3mu8mHbwxthU42+/66xwNfJG8//UhbhxrEhpQ0NnonZ+JBqGJrd1Ixy2hNIJh5WKTivd/ZS6zY7COSie3UbvueIJ6zQL1r2sWvDP6y6qM2w0MHVDMZYgAYU0p5pmL4+YJ94xJz2aAnxFduWP8qwfWX2KA0u2wLXxWUBrt4WoS7WmldlfBBPbKqMgiXXKMytz5QtK8s/zU9NwawlGGGd85y2cAOk5oL9Hmp1lhLpzJ9np5OkKFOdUJRlsKpBD7dcDt4lJX+jIPtLAKHWw7V224zWjWZWP65VB9BCdtc2H66njVtVtFrvrXe80JrnKJz6O8LJJoV5EcVNqQCCyJ34ibIK+pOGuuAmmCElC6FHRR5VjwbNo8CpA5/MDQYK0Z1Yu6t1jVhtqx6m5csJvMSMm9UqWtnUYIp9gJSEZe2MwiLIi0LK2FAevn3bHHBUUjyfs+VLgeOsGyxZuc40psR+S7wvnHlqvKPmlyvnz4Mggn5AanIKvS86t2MECgofQ37zVIdEmml+TqAuKbHtuwhToeZzNqdbExrxgDRWCVkcjVj0c9gom4FrPDDbcAQUsdG0nlrNLxYNQoV1S1jAlwxsUKVvH5s37D+kD5/vGcKZWkOFd1ZdNy61wt+B/Gja8hvof0agLpOy/CP3bbd/BLFavmnB6GAsrsc3h4QTQFNTADhSHCSIH7sr8PIf5gAiu/Lj7s4NqPcnh5EzduG5Bpqn+Va9IW3Ftxeg+IWEq50CVocrcdxwEV6A8v1XXzeuvrFCH7OMJLF41WuXY6TdfebHEPSjhalZhX0H0Nw72ocGSXp9LrjUWJEX7sFIs9smFkvb6nUAOX5XckMIrswv/fWAZNyRLj3FtMF31alU/B4+SupQsTK24CWYrfgfpL2nlPPqMj2PzXvewyBDocdpDJT5UkR1/YJJh0aFtPJRazZcS9FZszZFkkWI37Fxz2+AM/JoR79YbVkrn6u52QpiU4ZmhFGcJhB1cpD49e281/xbp0TcDq9N4GBolM8ig5+YNNsYVHFyvrZFaFa0MdQfUUe6Aay1jdEvVJFYqnT7OB3+yruA20aHsLlcmzl0ZJsZBGrngrdvfAHnwWUWAtMYfryVVRIeJmod5mHFhr2FSDbta/L4Inb2a4JtVyEwpS+ctlqcB+EO13/NtbanXBrWQOt+lCcdikuDmdEkHvYgU1IMUI/9YXJmqXUucs8WbuDLTEhczGtNTwo5hfTVISQ3lPRaoy2qPLprRitwpArW8VbYlO9A/+3gAwRIGip9DJTr2bD04PcrKU/dEqH3rg1ebkBLgov3m02ryDtsD/0ZqPpBkpIR6S3RYcEdJ7POya6cGWvMhvOIqUQIJTJJP39ZZl+YhiFvBYnaTIj/Rc67JDzkFftuHlnPRsJYMjfihiePi9V5eHMkhayM5otpYurXaEYeqnW9cHREn7eRs8DXrF28tar56raxNVsefKpDKdBdfl/yxJ3Eba/NdyuiXav0L9ge+IzxZIAYPOwIBDCc87HK//4Iasct9tfwOWEQAHfsPhFkPaxSiTn+uRIjBLGl8l3q3bLWGvvDTqBmkq8Awp4giEckJJ4e3TmsUSlzkPmkZmsJ9cFJxWyFB7aX13fsWDqiOFYT7fV94u8580Z9Ei71icsXMseUs26fmkfdA/zJUgeUVxv3QklTZmAttyJD2nr6TWPe1pZESFHUyKOT2IBY4N/qtkEMTzlGCXCeepeESUDoZRgro3My67v2jp4Zo2Kxae6CudedhApkuRNitwYNTfTy7rEb0ovbd9+XQj/sq52ztwJGO+VsaR1609d7dga/nvsoX7m2t0InvsLKk6kw9L3liK4X6iRq9I1UFm3nocDtmkbZKmUCx9WX0REoo1IbBKPRDQSUjAD10LTsNM+nJXvrGa4gvipz2Lj8hMuHugCo3l7iRDxrGAlmOom2VZsAopA5GCmMf06Lp0qCgzgRfFBys5pXHw9AgOiUWCFXUXNVHjcvyQrPNBY0BTozxmnZn0xQTkIRWCsL//ZAFodQp9z4/DIeO6LSIq0Hehk8iY299DtUqRM8N/NqV3c3LaIDl3Sa61UBjCLjk2tH9CJWiC3yVMiK+ont8GZvrrZrpVeCaWIUrvinBzijaD7MXZe4FV1Dp8YY14uz7QiZlzwHsXvhQSJ8LByoL+TmKo7QfSwS2XDkeSo5DMa9KhQwb8aco2bGYSFUpJEU55xbCIFocBXvKgEUX6tctTflU/JefnfAKiCkfSLN2pTO6Va8vZ+t2LS9JhqLZTuZqKEjlEynpFtWb9nLLUjhwDFFc6aUEwoObQgxe1FFvU/mjc/l1fdz1Y/d08gUxkYKKx6rMZ49Mj/KH96WgyKK8QfL7mZl7CHH0KbgLFQ86h0JXTS1CrbD0jrlJWBjM1xpZ1I8+wsv8Qt+uISe6CY1r4NibuEl+ZJDAs1LNuJrJq3sqhxpHovBcvqGo+Ck3rUgxRzJaCpPQJmyCuq3tbeoEV6hBlVVZaDaZ6xcLn2ilPCNMiWlXXJ+5VGH2X9Kn4iZkI6ZFFiCCAShpqmFADGdsVYhrSEyrAbzDsMi77dxYifJnzIn3KT69lFxFzFayfl9QD9KhZLWHdIoBoMzCHcyPlp1tgmpY1SXQ9/ENqXR7fjW3Tdky3153rg/unvkJdikjEhUKuCAAhDjsx1r/hFJ/APoCAEk3thEcCcMKZRSjeaWv1rXmsRcFCRC+ApTtlyVOC7ivftLDhgzo09fC2M7SHO86hznykpa5o19ZF/aenfJujPukgw0iX3JjUBZ3LBZP6fpXcByLEz90ocjiyT9qF268qRf+/AZjbcUmKjFRrrsdlUHh/kZr3753THRJyr2glWKIOO4nj7IdsLbu7Qj/rPodpjANP/pta9FRtezARPgQmlS1pM+vmmPuO/nHWWPW/nW6eTSYQCjbl1TRVOuiy/CALumGftlI62tNQgaCT/wl8/LBkycMDO3jd5X5PRMTOBlVkVdrrUhiJryO0FNV5IMWvIuqVRRMz+/ADp9IOxgyewAxUfflTc0GExxABngdFkPmUlnOs3rFOqfOcnfddu0o1HK5d1aoROMUOZm28ui0do7HlLezaaJ1JuhNOfpFJRLSUVrJ3gmcRL3mAbehD0JmQZ+5gqIAaDW703xEugZHpgjcLKsd7MqAsfUkSSlTEAH+opCtTx5ygehR7dbVwFqQe0OVJwnH2+u8G3QrdlvJOTvQBZlMRTTxn7W9T//O42nRL7hgDXq+0fXcuR5iwPEDqFqOVVgIJqZTEXzi7wJntFiNLDyiUuukNCoEO6FRbzBjORgZp/1S4a2DCPDEShWMCOeN/cP6NcgIXhNvwqY8JI5F0Q2F+jQ4aDcvYo0Hxy5OOdnA0vMB6EwqA2buR4qLoLGcklp5F1Qk3NYpox+MuLsYWgQAvF0fcMN1PKd29j5yAep5YemyyDprNI6a2gEVeCN8MfiV+aCOFWLXP5BM4v0pd2vL2TbLv146UuQSCAmD+rjF/EWSZUabvqASDdSay2LYoKGnS3HSfEQOWgNiYM/Skyz/gDPt1tqwD9ejICb6rWwBsHv8OACyYGkl1whzOUtMlPhtLGhq/iDivREcEOOjWFaIGPBqXT/XcgyER4fCrHNCJRXjdsAF81+VYRtDlfBiSTee9KGl2sbmcU69oSKTEv3TbZuvLPSBtEAc/HyawYLHldpBWRDWl51QtJCulITUIPeg/EQ/4TyKFbsxvAnXV0nM1ErqKstgCm7jGD0qwVtxTK9v4CGSMpFTT0fREUYG5T/xZIERU4SgcpJ521uJhwe2a4QFkjk2B0ohU1uC7f54T0LpTexBDTF9OfXA982Pe8M6Z6Wdlc+kSPcsiy68yGA24wTU50p3ZEAaC8fCBJ8t6haDZsdZos5iLu59Hi1SO2g3n2qOUkH8CNTOkgRrYhSWZgr8t7drcso8Uw/rx0Ss7dCdZjjsnBqsMP76DXqNtrvdJh+ZdkQnmSSw+z+K8WZB8orBKP46GtMN6gLgA+MbWTMgW9l4yxiV8ipXqpp9UVdiQlb7i01Up4giaNzNarP4plEJPI5VLGZPSi5vyWluxP3X15uxlT+cny/+ESmO8fy/m6riEAfNO0LQikPgIjwnfUqepRnFerZI+2cezof/PLjYGA5g9c8dsH9CIFugpMQy3Ea3uVvc+tscmRlN1UJe/y/7KSChlR+zVd6LB+s29MvfqHzT1e90azyCRn/pUK4eobO4hBLog6mm+ueLmzSgOmowcOl6nsX1LYKpKPOOzF4+I2TTzkqOSwPRSRi0sHg+m2tEdb6SN0e1pRJxRORL1uzCy/QFNP1Qhm87RnJ5tnF3p1l0YipTdPYxbZsYXwBHvA0sqEdBbL+VjW4QMLLSsWBxWhMmNbwt2J+tJX9cFu0UyMctJ5/t6T8oqhS55Nl9BCQhY101Pxzvd5u2+aVrS25gsuQbSzVxzFRvWfpuygrFEeco7v8syyJpxYLj5tLAbI+4hA5zo9FtK7ssCdenJnxJHycEvzOIjx/cRbG2lkgPtuH/uxJSc8lI0PxE+60fRHv5ZOR2dNWer2Pcs0Xbx3uZ3XnP8Tfs1BPdejcK1ij1GDhj5JWeLWXkNLuUi+MzkExYMDu2DJq8IRUljxomnls+KVMhuVNmjtnNmZZHzGc3TGShTALqRAgAGybUFfTiPBf37MrEg49FGc1ePP9B3ToICMuABt+XFCNMeeqmatZFMY1cnELMrK3WL1QzoCgLN9nD+x3EHBzK63djV8DkuL/pAq8aQ08W2lVnxigA+Dt/TeMqvay49qUPD8CIpzuDITUe4gWKU+Ycg3VSOeO0MFz7BWNmfabZ14bZNYIqN+JeLPJj6F3mzLNTRx8c7SBktXOB3wLuuPZzuSo4MlDG18yMnPtctpBuDWCFMDzSahiOyx22pC85EXcov6loU/in6MbzQnWmmUgZmhPLUCtBNImS9XEG+X+Qj5ybAZbjM/DwZ+cIOX5+ds/2mTiQp4Krkcrof9meTxf9MDW1gmkRdNBzI0K3UUe01PCro65SxeLkT80AFttyuX+y/qGh086vEBrZuVfDIb1jLX7Vm1ymGgs7Rt8tr9V6XRkILM8J5SvOzq2AoDjuiqdNTotoFVWrM9KmAdBGUh3L1ISWVRunIBXidkXBGHewoYX2Vp8XeQRXnuLN1n73mJSFbvxi2eW3a/H6/2aScUaL/RveJkn5okO7U73HOOMhw4xEYbImBF4oAAji19iXNCF2tXDnF0pIHhrCChCFTUDc4WJo7OvIx+MzXj/N5z8MMkq/vcN9vmpOXDjnNvHX2I5BkhVLiJLWEnmlyGUnlrpKcbBY1E2S2+orkuHdNb4+P1aG0/mmVc3ZHDGczVgi7fxoE+8/sHaXy+1OlO8Ei+VEM5gKR/INPhwM1gbn4cqBQb24KX450Ti7SEmvMNoZ0Ifw3zQqmRkjBE71XKf/g8ZEqWq+ANpc0GuNvw5rviyxJwbvjnFQWrm4ewTuDuNCVqmWqw665xU4QYPqEXLVx+YBsvauM7OQtjqN7yHI9a46VpQo258TP25hmmCE4hP+UokO7V1W7X5MOfh/ud1zfJDNmIPdcj0EAU6IbUfk0NL9oc8c/5HC/SYAoRPI9H9W6gR7Hab2HoSWqB15OPrYK9qt3cZW29j+0ohxpJjeczs2sIw9gHRnela6lu8D6EisWC6ju2aahvRRYuiDgoZx0r/dO+7mrz/8tgPH7/Ixr3V1QuBZMOH5BDO2EC99Nu8vEbTUWJpb8YLyAIamCBM0+KCFX8BAX5k37y+lAS+ZTnt1NAlOEcMV/oVUxliTvXb071fGFUym+nePhBpKDzJC/rVIvDiIOlNG4miI32PJgDWrz8UfDyyTNceOQu6fCdRbxN9X1Psz/1zr/4cAHLXue8IUefuo/031V0iF5MdXF17chp/zIgIPJnH+P2D1zKky2ljTD5wMBbweRHSAYmnVuvZuGnZsk+5gpZu+Qi9dtgwLzeR5XuIsNp1G3UPuCFZykGEejZAEM/LuCXRNwJrFID2JJ6A58eySH2oNDr3ZQHHah37/d6pLnMUASPNjw4Ul4aQz0MBy+dqYEi9gopFplaw8XJ6dw9ykMp+rvtlLrn+5Z71sHYx3Hk4dyQemuXrrdt6sgJsHBYqVJxRrxaXXnr33DUoz3P8U/cli07v3HVHFaKrDUP162K84XYH4SeeAv0hB8S82j4vQRio0gbokAM87bSDtSowZj00YlZdfxopTTcngEnlMCxphS6U45M0SynIUm9MusENZ00m4WBDbCw89lDDNzhHdI0mvs1/ge4VDKQFbwfXSW4eBcpgg2j/t7z7jDxKEBJs58jWac9GWi6cs5qLzeUWiPu7LfKRHB46soFstvfRBO+kNpkft3S/Yq4/jSvBfb7lFpO6AFGZE1tIkaR8ur7dhweojdeGLtbwI18jhVjPGRc4/S2XqCu19sqUYefJ1zyULxh95wBp6klEyfDzkLU5qv2guidiwIje81snCIx3hlNC9JANG4q3F7z+2r2GLp4e+HBcKTW83mw7cKH0NApk5eCPLkisKlF7xXyTYhKmokQ6us5AHDmPd33btFBlBgI5R5t60E7OzOpuuIJxX+44/BkLsz76BijOBAUNJAO0Bw3AX4w/A/5K+SLjUg3qF4hpd9+yntdlaNtSKgKqDLKVMjaAM7gjhWn6SGUWHpzxdGniC5EZD/EOAnQc1JWBPd+FRwk9Isd1Gz9sj3QAUjDhdwBBdb/Bx/aQEWeKRl4cjaM+sn2ypX3e9kweXYahJzylHfCrzxNLcpAsneFNCDIW5HHVcb56/AGc5NFJIt/t3pmwApwGUEc7LyjSHJJhtvKX45tlgTXzya8wxtzG64y4zvPVi8zOPUB7XEwdRKLoGpMPgFJLvM/RRHE+I/N1ciclglZS2sahmX9o+kcBQYrJxWqY/m6IfkkcqU6/DIZ0Uti/rOrpo8GNOHmwqPYU37yYPBYvMV2afeVQwJVjWfHPifa5DGPnV+leTlgmjWLpLuida7aUh4WHnk6UrNmHaY3TBoWA4ODDfjFr9puJZ3Ytkldr40j+FNVE7h6/uWbjthULc3OqFre6CwzSWJf8yA7XqglF0vi8YlcaryFBY0+oT5ugi2DB2QOqIC5CW1jtA1LCF04a1sBDURy5YQ4+fWobzo+uSuk8xCajD2xg85RnAmiT43lG49lRjOxecdPQG0wnYsNEHJKMenwX0m+tIsS+OqhrvHk4ZBWDp7bKTx9KHLAJkYsV+Kx0SSqP5egKGj1VS4csuviaumT5J6SfdCuDg47+BE7eQ+6Xb/dz0lXkr5Uy+OGThYqa2x3qsqVA//vdQVcA5lN3TASGLnG0Sxnv9ex1I8aHmzreluRBOwE/V4mVe+TslDfsRD6xR9QuRfFbvCdfdFWssgvpp6OMpEen/5ZpLMXtoAUpJJEFHzBYrdRuNJMVnxgdwu1nIDZwCbQq5MB8Xu1e6bClN8hWa/IL8bQHNzGgKShnioQzXYoacSYloQJbV5SAm/tlKouy5RR1ngxWIZ3DULmg/0lqp4lkH+MI8TtX83i6gMcmJG0tt4hMaMY54w3RyKwryCrrgfRUWwAsVMOcM0DCMeu8H/XExbS6LIALwVkAcZZPsHZtFfvgj361AzeqlvyNRy08equC+MHj+6SQcY5NPRk2+1H3OLk9JFpsLKYK5pScNgbLrpWEAZUNNMeDIWw6Gf1MknqLvv7vSmm8iBonyVrpAka3zuawXiZomr0gj74YxrtoZf92NqP4q3uIntFWT4yQpAL3yeQrGazLoGmay8lr3+2hmRlg73AfxNOWx6hF1cZZLNwADU0bO6XlW+HpMvGpV4alBMDDJ+vK5bOGOEqGWSOk/OgYFcMVm8eFlCeGOL1GAa+L1gWOuGLZa6iz6rdRrgh92/G3A2n0qcD3COTdbUCfDbvKdMstCYupVu3ScW3Uu1sQFSq5zOcGXpaxhwcM9/vcT/6KzglshIkgjiFkygxAPPHK7+wA2RzxSEABf8JZGMQkB+fyQn7WouK4n0kUVBSnA3dVoQLE0tEbzLp7VojjgwrgaBkD6EWRczu5bBkFvLDL7eVoTfQbrenU4Nt66rl67OIb6xWHa5X9kOYHl+1D4nDe2I3RR92qw7xL/NX+jh+APQFjZfvVs9EIMLErXLi+XoFLQgZC9DwFeRrbzGCVzeA3UbvZb5tBpTu7IW5EjZ0ErvX6oZ2A4pIyYijtH3YjtixFt9C046fZwwdFMygGplUi7CdOTcix1yHHJG2WwbGdUdOFx8Ejt3ZCbf7hScSAsIMshCmWKRZOZ2+5ODa5hPjbMwpPHb7yIw/FPzt/OHNR6yYB3TcSrh2HnscB3Ch8osYHa1EQbK4JmmbynOPZQY9KftC49FR3xlLob+7NXm1UJ6uGHeVSgEeR5126aT1gMNnutLwwcZsS7SnKsB4GQYbvsxCBWZnmKlwgWN6sYtASwHhE+4FHoBdTMWXbKGhN7L75NhxqzK3a1qeDhPxVDr0CHODt6NOUmSvHf6d1pQXBLCFyIgiwPxklEx0nv+4mjhUgxYRm2KMVm2Q0dt2QQsPZUiCfENBvePcdZsIbnk+x5vecPZ0D/URbeLDUc1r94lo4gZKoDdcBaEMmp76uvGy3d4iCiAx9aQln0l5Dh5182PjbxtNH/whPIROmcjgdRDIO28Vsz4wdZJGko9gY0eWQ7M2wgnTXQYcHAtjDWcaXJuSMs9CMSONbg5qtHasvy5vOL0gLgfVcXF+xQdFZgdDIWweT3NX7D7kFwP2uGbBFOsw7ySSplYH6WP8iV8F8KShR7NEifu1ds4nt16X/oex5AUrNgaQT2QKHo1GgQIRLIDxxMmkXN9SAHTo9FB/r8NgNbfbSCRm9Y1YxTmqC8EjT9dnfy0qKmoYRQaatM6q31SY27LUieWxgDG3T9W1cBQVfkXWqCFXIgLp2/ESi0qjnHODTusXLIdxDOYabzPD6a41N4RDitMomRKEbjy88PSsIREQWOEMy8S3ZNYX/9LJVjUtdK2hX8sCO3PnQSqxunA01QK9Zbo485UIHzV3FXj33r6vK06mAtaviGZVNwTabuR3g38s2W8jHoWzQ4F2PkWfQjoO/Lm5ptqsP26r47bQfO6vqfL4LfNdAc8/jEC3riu3aQQVTQSwx8Ajys9lI4Z98n/4VvmyOoFOUWiJ1r/+OiObSGbN6efGCfQRJ16qxHA4qIC11HHm2ej3JO2CBR4fetpuscNBjGHh1Hz/Bpk+7Z3wvqZVHZCi+0cqe5au+IWCIdhQi6N/Zq9yGDs7XOAdd7QA2z2F3sip16RPEYN2Y+tLLMJbki36xdO8IwJQYTX2cn+83fECiHzJ5iSlHSSYdlDTxc6W7GvtfFmyLT1TgtWyKtdIGWhSyqBG1JaSBnBANMVEekaQZ4aeOcIqoYIRPvmXLV6xBPin5l5tj1K7qXnf72xZbAJhXgnFc9pbDE3gYWrSU1QR2ehspEZfaaBVfgppIsO/wMAcpwUDnedanCuDHo33hPu067GFrr9uG4swkvi+EAXmtmTssDDJg9asWO7vMQchuUEE0vNd1qPYLvc3+QxJdu7M7kPPY2XWpSxMMBaSquiHoBsy7G3Cn0Gg2Hbb2kDCK41hXUa+Q9VsdEhDtab8LE9zfpWYY86qlk4oOxd0IUtAnwmxa8elIwD59wUhrVQ9ZJXdmgohLX/sXqDhmSyO6r1UIG3gixB9Io/t0CfAvd3urV4YVIiBsGUvzS+V9M47huoHOdwnC8awoDSSWdrp6WokHF/CEv7RtjAimlvWpCXwYpxpXArp0arflBv7EBMc2ik7x5tN2EgjLtKvh06G9yq0OFRWmoflN/QkQ64OkpIW18pMf7QH+o7E8guGWW59uM4/qQKe7Y4ETK3TV+Hw3/RZzaXnOoxbtqLXJNwZDP8WULHUDUMuUe0jIKnxxVpB0BYGjCGxfXi5c4hgPyhKaX4yKEyjvn4UJe9Vrx1suxuyGBnfpAkZ01VibLFpV0wIyPHptgsmFA/ut0RrKKdRd5qMUPpOl+RuA1ivYZSrP4H3ya0lKhLGb2D5t05yL6lLDEtR4hR8+bjJtb/cFqQUP17elhhzP14qqfHqnHPgyhckj3UDyOLfZYPJq6nwXBDV0Z9pLaKwUvS4zQqxOEZLRwmGvfbbcAYiX2+8XpmNnZuYjjULc7orHbGv+BEeVj5Kdbi6Tn/J8woXZaDZU6i8TOBZb0vHAEwvybd67LPzpv6nw+nFopzcyZemm9v1/t35dqQk2H48rH8PIo9CXuAAPedco23Ug24XL9l5qi3KfoXVSk+4LXT88LiBw6/ysf2sOEECO2W33/bcU/D9ozskEgrIA9RC4ldmB0+shVoORQBiGj+mhhPmsAGTUnuy7NfvaNJiSg8/ldTa+jJ1Z1h4mMZtPLqbSOov3Co12IG36dld5mhfmnfwYu/P7YavKs7aLmgwUoTtcFTDySZzYBE/PF3K0YW37mifgvrv6sn+PopLCE0UJbibfvabbhkE2AdMUNL5Mj1PNJv3dsLbSqDeeJs6x4HGC4tBub0djDs/7D29rYsQ5qzEkr6Mo1MBysQ7AiDs0rpimQul4p2tJEObTZNM88jQDR9BHON7f+yYeBcfPOljT9Ym31Y+DVmTjemCbu78JyyquKL52M/sCLUe9ead59oyVz772TzLyqmf7oEJ/cBSkpaO1/GkE7K/PEaxcpaa9kCJQHxPo/5SLYuIj/uLao6reLGsdITNh6nvmmQ8Ix2XVTLZqBwpTQQtU+enwDlLeJn5ZPqKUbx8cqPlbQ8MjYPh7T3KxmOsdfIFXUKYtuIS1yYY2HKt6qGtYfdGdHSMnIFpXfezFKHoFdh+y/8DnZWZXordDILCSQa5y5qQO9Q2tmdpdXE6Vg6yAIUm/NBkC+oG3xN5zW3JEwcIOoZAGW6/zMf9CJi6QOxveY7BX+WzLFAtfMHco+zC2q5g/ZVMb8ioG3kx98dPEnpv2OnFSfM0CGz8ZY3GC9phT6uWAmarjlrN8hPcpRVnHXggCKwMdW1+L82u86Q3txkPPPm25U383109oBpVL4nOa76+A8QcjiYwrgIC2c8flfMQfY+3nSnm5rkKmwJfyhCCgxD3r8ZYU+BuEtFn2z+kSRpSAD1p8dGXf6dUaRnka6n3NK5kruvrUZJLlWkH7OQlJSFWhC6Dl3Z8QfXd0XL69792qEGn5Ockk4JdhWOAshcNiVSsILpGuYCHV+gr2Qhqtv92KQUy/esxOCLfe1FQs1bussdbVPcXmeq1IwqlcgisXIa1QysnQMBZL2+DMCF6iMJFRXwJ/pIfg1W/70+yP1Ad+nxsoos8XiBYnC2BLCgLEN70zhQ8xqwlLkoNUeBq4qzkrwKSfbI3fhLEQEuaKk7SlNk9dQqIJCBYvYUrpcGlZKn/ZSuN4pLhWZRp4G+E/PvjrBTNPpxYETcP+OBq530mr73BIKssI2EDqJtig85Y7AJqYqqy+nl6b1a7hiRAfbfZZ344/Pc956iKvL9etbKZJ4lybG6WAjw0vq0PJky03uUfwCUIpTQneiLqV7yS2aNzHwPPgEBdzdQJKOH/nnV600jREnoUMOT9WJVISbD2zxK4B+Kwl7QK9ln+wQTn7lyHG57rejlmxEVUyWSIBMrq2BFYdhV4q3ogKIlnWNUvJxWrFDGStcUEmicI5XittzUosmikDgOm5ZRwa2SHamIOhtWN+kGK7XwvQuAnAR8NNc0k0wpaKkSZyK2nzuNpnfoUdKK/URvDQalauZ+TDnqiOxrEbQWBVkP1c43FysHyhkq0pCshVU6u/0E3WOTAzV5bFSFabPKwQO3GVoxLxJzuPTcd90JC23D6Su+/UXjyCbz/Nf01PG4I9DiXK7Rm0ZWpHM/+RnNlJgFpkOoO8v2Z7A1Z5R8k8axg49MFmE0uQSWzjTLyinRi6KzGros8VCwKEpuQwaojtqp7/9j84MbcsLYvGeQF2japP4pjydHOhzpEMfdSpTQImh5kdqj50EJyfIxIp+xoBitIpdU3QzWXpjq6r6vqTz7iBqOviE2raxL+pDlEcZiu93lqIMYsvmE+kBhde+uERdZ+mfHrfNVuOVnkXaJ+2jZxcWWj2KmMz1g2ugI74b8MrstUvl7+a34wMeUZ+qco5HUERcskkSwbYfoF3Q7HuyoOjdyfHWD3CgmCOUqLFNIdCiLUVxLCzncsKaLeuzHmNSNRzm38+tnjZzmVOgIq9FF8kPwAeQIhRY05d5vcg4BFLctgpxh056NpMRv9EnH/UIq1j5Im+9udycrX/koQK+Lxw9EwCjwsOHMiJ4fXkHSxsSteUukAL3FoVGUe9RGeGgorfixB1O3Hn2P4FKXX65JEghQ+KySimc43IEBHHR1ikt6EVAk4pu/+SvFQ9EvAoKw2nwnlN4eG10BTjWKhDsGTFegjMX/pMhHlpw7JPwgapOv1CcovudoZYYDCUMjBeLgiELD86BWaazfUvl7xeZCVvGj5JtJ8yvajHvr8fEiHBB79GoBURsuPRGWWI+axcRwjTWEEaATaqVEzmhaHptyukXOydwIP9SroByyMJWy72Nbm/IWccPwDY2AyrEEmrY2BRaV3EiFL2Ma6hugesk5hlcQl6FkIgR76Z7sj5J8yCtPMpWo4AblFDgfrPsg2vjCMbuidQDoWmkRl2VUPWa6QpCenEZASQ1DLYkRUBnWgCsGAlUVpPHU5lZhoA1U7VgNarRz/Cn/IUh7CFbiJg8UyMtGIDd7qd5kI0jr7O+ILOKtRqTGhsHfitIy4EonYcjDc0zbPCWN4JVQR/j3zFPWo18H5jCiGaK+FFzGT8bJUvBAdIABRTWrJjOklXq6qXob3UIK69Qr7I9EGCtd0rDRIS7Gj0LHMGHlg4tT4zgE4RGOybNq2bxwJ25JudPA2HSPtm4bkBrq1mixdcQAjOSiWLO0PeNWKq/7n85LOQheV1UfksonocBhpGas6o3H8xPkScanHVEJNbs3cd3aeLGcu912ttlLl7KIifnzdXm3KzKopd0DBXlJN8/T4wb8kNKrG+2TtL4lb0aV2Se8pb8TngDqXP3dn2TteJzLhosfW+Byhgp+BawC9Nfjxl7/9e7/lycwNIzOnqyIRvOS8Hwj5Cbb+6wDgeC9zX8FN/fM8qBTG66VLUaBJZaadVrphZPpmdCn6QXzSZCivNxeFvaH8aDx60gTXGPbtt1pqfxjRxZupIM0pFX6iBGo4UfpCy1y8ux6ZnPKndxKXU97Fhzh8MrbmVbViqlMgx346RA88FldOaTYIR9Mo83t2g9qcGf/bi9hlMXXRNkMgorWTfvsR++xCgbscWw9PKFTRDDw8i12wKYW0MHIc4we87PZPhI9hl9a4Y3enbp5q3V5FDYgFQnshmi+E8bqDJDSEtuHJhovtjZszngRUeLowbd44xnWDTCyMfEAJwtosin3NR/kU4yDjZOtEy9UIgOREZMqugby88HcCVH/Ytgne3hyxhIsqcKrVwvq1+yXkhpxyS2EwG2Mvw16oQOhnJIEDRIwKt95AKStoEmHB0x4ON0tXIs2mgEp2L3mQ2dwpCpXnLDgQ84FsSB39ubIDXJ81D3Ei1SSc7u1w+Uyrheobj3yhwCHvh9tEiac5ZrBMPU5CoQs4HZXVyFxR+gk23+gs+XnLeXvpSSl1g9tSol82pJxRyxV2vltmkXYE0EgCbEAoK9IKVhNITm/sYsrsf5ii6lThQ3104L1lzUkpBzFqETxwgxFSVUn4NSG+BUVR3LEYNT1ASXVRInX7gzo9h8f6oPmSv2h3RVkfVopQuIqo74X8A3LA2hS64vHpH/N2c/FIaKO66+K1aGfU7/9AjN+Cip7+wCVUswZKvpOlSMpqb72mSdiTnN0nBMYGfzmTQsoUZVD/ZVp2KVJ4NLZb0kgwiD/PNxCjA4WDQGTYmRmK7umZfhGye0eTlA7rtXSG/ZmdemPK4iq3O2mL4bCtFhNUmBQEcTvrq9jEHHc+XjfDv7fnnh8pxtUEPRJHpWJ8y0TStHLOP0nPgb6qjZs8UvoUAISo7RyJm8etzQb5nUrA+eLKvzjfCi926XI0dCL+s8tm+OdPJbBFR31z/eIcVqFPchUgWofpXT7ZzeVIvevGc+x0S3M5//cHcU0n0Whtel+YadSImX/RxnbvsXZ0Nq59E3Wi0teaS8UXvxkZ2ylRd1zWNPZTwov2GHT4JT7SlQnuXr1q1448uoBsEY9vD39pLVlW003B/4DnrAyU8/lWgHiOhMAFlSZeNdVe1XYadzTpQ8VtrhX7+qf2y8eEOaa6IghJ9A+EvD6aJtcQBu6rAfYYB/RNRAj4bMMx2u1UhGq4kTOJmPQlwCyzIzfT0bfaJTWLWTOROao5XEmTeohfbwrA54JqyQWeMb8V0qfd+L7jGNKGsBvR+l7m/g8JMiIfnQSmLol/qyVFUhB9ZtMUh4kHbjU0DRQdBikVK21c6FGZ8mxA67m5AEnVR0aKLY4554XjClNJaSn1Vq3xwEBr1bm+MpstMazIP0Ao15RY0J2wxEKnK/ppvVFJUyOo+lYWmFeO/qa49Sqeaa2Q9ltQlv0zke0dJsBtQeC2snB4zHG//PLlABq0UyxwISahpj9efJmxEV9uhT3QyDItApoT2Ocjc8QSqakBbdN8oos7fsgjwBYSjt0PkYiqycPeqa+TsqLX1nh8HsdkYS5n9V1Ryf7LttP5AV5jNxx0pjwMHLTj6wzJ8yXs2PAh8KJ3lI9qc1/QNpVaLQYK409EExMajSjbWoIGJGUxKoTZLBF/ZWJtSB9mj8Z7jZkkK3H1DFhi+PccKXYAifLom91ys3wi5bM6GET1ie8wkaXM5HAwxJmB9BzIConURFaAIxkNRk2jCITTfmrMPkOqH/ZxJG05bSxj74prLUsGixOktfgJ9vcFd/sdqpCijKopxApAGYs1tJZpNR7TtV65I4M5exVX/0K7356QraV1Krgei6YTmiJFnXm8icMvq9nv92xVXWeQGk/XMsKsmuHTze/MPhVV7uyHzFYWlJ+IUcL9BiE8sl1a3fNIUZVQs8NiOuutxxvmKHuAr3knvcaFMnaizBLWEQ/k0U0gWIxysqmaNjh2MkiZtMg09vAtviNpnZ7B1k4HRwrBpil+kJT/HzNZojhppOxTBw6QFN4mfGtqzMCTZ/lqIXj1EycIaI2WoGY67HVdmyJmRHZDZkTUH/mo7b7S0FGqWBuQ47jodw7/SshLHXQXYRCOrtRdWgvxyo9vtiw2WNd3ltdmNkjbZsDDguUvvpMbDqzeDbjYdMNv9pTtMWfbsla9wh96SMWCad5VJO9KkiHbPMt5bGILPcbB0Iz49JEVje4Pfh9VoFk8lL7jY+fv192UaviB/OdXJl+6A9BmdZF16byK/Um3qjTAx9TbFqDY+C7wpLSXHDC7jxjQsk6yuV4umK4ybNE0VsGnf+ngI0AXKQBo/EPULJo1U1hNzLMzoeMfp2fv3gvHi3081/kewToU888mgFIpiKBLQ/Hts8OWeAn2RAmryKPAb2t9hmZyUBhwuRcwPsizLELZH5633VYY2c8gLkdqMIErIpS7amph5lablLphZ0a1MWgOODs7pOQN8QBmoKnjUNbQR8Z/oHLsEa56rDF37h7c+K0u0E1wejCEb1uXLj33SU3HEmM0qkEly9GQiHYthVr6fH+TeTGO5wB9bXqUlx4ccJ9ErLSjYTtP8WkH/ezp+LRNz68LlaE9scZxstVwoTyie1/t37fHuTPn3ToPg1vmcdc5rXH5wkPk20ojoKYTgYQDBR8fB5wO6XXttW3my1PIPqGwd1rnPh7XKStTScJ9yy+N58r7fy5g5XNfPBXXbkspW4lt/ec9BAbepDImYWp3tbS6b8A40o9RdpVTSozTgGsu/XISV/iRq4h77I6n59uw0gr4Uc1t3lRtwc+G76YYSggfs8aD6sB0Z8cKImyXjAorSlu4pDtZ0SsJUBEOyLSom+VNMsoFbCsX+NgpNoHYRtHDfKgqxVRPMd9s5F/J1n+LtHsLsDGalY7yoM7M8jOWh1paGq4n7AG8EY0ltxxVgaVXriNgUJcObjdtXdpchGZ4eMn7ryWzxT2qfuNzGzIx/g9VQJg5tFsNer/W420oknhddICr0W7oqqPuQXoXIp+V6hEF7mCQHf9CTwc/2GZQVGCnfJOLE2WOwuU/Xf/IqErcXP2GX4c4+7pPckNraMKXMjUgtWKHR+lE6ulQrgTlmKWEHXStgYvRTYJqMQ5Vk2tLLNi7vFW6b4Ip5BCLeTKkAP58b5XlAIYoNfzQsXJ7Cj3eYyhJInJm4OVeotK35i65BoLo6zB4JjxFZU0EH6XhghQrhwXjbatDpzNZZzTHSaF08V8e7R0i1fMVAKNZ0V6cZqkF8XS2KYQl1xK9Cn5Djo8IHhubylvQaxy772NRQv+EPv57IngMMaVZxiy3DBZiDn0PXByNtqn99ZJRfaHVlQU+zo1fzOXIugOfjS26yqJmmJtBgdHKdpn+L9tyxdfzfNt3wocCkYNeazlIntgH25cg3CXUXQ7s510dMDBVWAcqzXTJJoG4/DMe/je+Ab5fTBUTm06FuVWdJavgkx/JvKjQBu/HpcxnBs644VWmzRZTX3qXWxs1DF+4Zffqny4CvOTFGZhmvfXm4XPrl5zanqkjlw4P+l5Z/f+l/te4V1TdCRhkMgzItImzXB+ByC23Wi133S1qStExG2z2PtxbzalMH06JIq8dLKkX6kOmrSgJD1JDphRVhf7IFd3kRQv7e337afC1SAnJJZ9Sp2pxQudBcwKrzCR7WQODCpVa3KoqCQonpk2rT1rt0mpjZa2JWD3C0MOWa48d2J3xZ+axQRxrQHAS2l4nKCtUgNINflfIO5qwHF/oh7JkIaFkKfahXZ23c+UjdWsAQTUE9rdymKrTy19wgL8kW0lcJOMEWOZabHp3onX6HtVO8VQavKCOdmN2WsrnPc5rQc3W4c9c0gGnDUfuDTA0j7RfSQi6wFoaF/DEmpeW8fTLV2YD7fKYxpz4tDHDzOzkf17UccZCuiWu8aINjETPuXkOenCMXkq/T9JbvwFOrzyc9Mp1T/EJFKIwMc9z+ie/9QpUplTap8yznuM3EKZwYzB2EhHWgALXysTyolzGoLNJa1GtIpQeAFN96je59/rKIu3L346qaBc7LxrSI1FryxCTndWzUnuv3MXfJ2XfLu2IspcLx52aEBzsLPxc3NFkYRwyE6Zv+qX+VFFvMMDOmf2HvPC9/htuSeIXbtRZHdTeCEdZ/AO6HEo0GnizTj0NX+lHjD3LbjnT3hZnKb7yxFy99vgMWSz/RejZYY24rpxBAak8Q2ZQvEWM+Ti0DN16XOXd3N/jPWSQSkFpka3lTuQVNShpYsyplrAle36RlrgHBz79UXmqJ46f5I/YE5Uhx0zU98rzxd62+tl4hBZ5WfQ9vmHY5fvksz5FGs8Uq3Hw93YJU/py/eHCSYjd6kXT2j+qp4cKAiytIibhtD1UjfI7BSEC/xfDWCuPJ0V61OgrNB6kLr+xPdQjBlXCC3Z9lDPbB5TgNJRz/yjWs+szQw+6kyix7NPRL8/jZ14zKYE638TFeTiVZO8uvua8y4Bo9hnSBif2olQC9CWG6umtsJ/CNbj6nb3iihKzyZiQRjLIvyD9135U0bJ2/YPt+9a1CE4XN/4ZRUnWLaAEEo/QJb7GX9ZD30ieD4EtNbig6lvU5wb+9+Blv4VgYK9YS34+yuhbvyIamJMfxrfqihcNnGQjTkzLTwpaCyF8rTbYa9bQaWlr4XwS83YhxRmae9+BnQRPW/MkJOY1DSvLTBy2zM/F0NMENYIrqwJCw8NV+My+SDvTZksK2LN6I2ngPDNzM1oooT4RKHhC5OPQ+YofJos4xJ18QoEApz3e9JH05YQI2wiJuD/b54zu+e5gKChPiJeBMP3tDbJw4LtjPZg2ZaT2LEf/WiLuiavyfG3k6oJKuOd9xmoPl9hKmTuoMNdR4vw1Jjno8BRLJGMkEYC15xFQq7Sf4yS1lSuR9F0O55nGlGvaA2Gd+0BmGwouC1yrYKnGUf9S08MB5LdOZG2BIuvP1pGFhV2BHs+3MeJJkT3Y1EIBCS9VODRz263ZBID8UINby9Fo67IhM15mofZ+2DfjdJ6muZergbQYMqy2HttCoacLkLRFFn7u1uaAfSJYvSXcHi2IUC+M6vWAvtdrxXXWFIE8jNaJ8EGsbPitJ1blvEtrVRCu5JgHzv4uTItbznw3V1Bk7WE+3vQtTeu2MZCzr/hHZAmqpGiPXP6xf1FZIpOTe6keaMbFglSq06x+7LGlpBD1tx35FEVMTJvHpa30ifeN2YOUrprIkzRVHihTTcnPfpms8aeTyvvBLIaIo61Qa8e8jYWOgch3rzOJSnnrix1Po6VS5Dmy8Kg+NOqyynuztH1JgSQ5mhhlb3N16E8+70xtp7B2xc/VBnIszyUP00XvCd8pHUsV6eN6IF8ym3r1UzUq97Qxogx6sl20JjJkEtJ43GUyYQF7Y6BGKLpujvjs+zg+B7CU6waSqqH4EkmhIKTsfnTmLG5kiXLYkZ4yf/OooxVNaKf1DW795CyS9wtxMhuWq1RP8VWSUdEdQkGtyEyw4AUyK+FeA2unMSlfRBaMHTjnqaphywJC1fMp21kcIhvOGEGBCqa/NEj1c9zrC06z9kCsprIgk3xMTRA8FtYyyjC1U3uG9+jcE9XWwUwTvy5/pO5tiBiMotvd2cMeKvs/elqyS+ABpk8/EZGxNjOHIe6AkfNZMU/vCo5iwNwUfrfb60nqoSNsQjWLVXsHqbT59QFvtSJqclliifsFPZyxqE4E6VwGQyBjR1ysQ2VW3j6y7FDFb7YJdx4Ms4fuVl0W6tLRgLACVmfRIUDDwF35G3N/phUiuGrqFHDWkZ0511Tx8RLFoxLA4+b2ereyQm3HHfgGW9TzldKjPFT4ja5zya98uZ5dwzCZdu6ZK1jCRI7tmVF0/J/Fhp55OrErxQhbBVeVe+CwBKMdsUxuSpJlF0gWW3cXZZMxsPotvh55EDRvMZdVXd6h862AAdTksA7DsGoAWDgjcBoxiBTkQKXWLWhlAAbBvJ2PjLUYPx3hg8g+FNFlGfqVioNZ7Ud/FAAOtbNKY5TTLWTL1axycwrvz8JLvFnLQAc7g09qsDPvNaKeN+WjOHMPQvnowzrLnT4iBlC/6ju3aAuGRlXTxukMZqMKT7c1DQtZAGAHA4VYFqxX6FbB6wjQoBnx/9xlcWCUvql6gok9Mz4BXElZJ24MH/rdspX1KaGoiaYGA8Z2nFM/X+bzQJiLIN4nhRJsPrdBgvR+niagiQIcEFv13FiSEG0tT9dCEpAL22R21mc5eitILpDh/dkl1BwR2wuUdTYbzyM45pCz0L7r/6PYQvlT/9S761U0yLJ2rSKI06ONDkLbFHmL4E4rUBPdUGPGGE3+evtH3x+TJWPbrcL2no1Y6DCcKRIHfDpRDV5j2UcDVi29+tpINbUJ0N376hZbZY2kTHAPR44wbEuFRZtbVrbKXWqaNJIFBZBMij33nx9pmnM6FBCjzB3LLoUgHeHiuS2QkBjka7wm8X2BjoFAZ0PzagYxGz1Gbko/vtApLu8fLqpLDksWo5jyyyMXKK+MAC3zcA0cBcrvspVZsCNyx0jrIcDNGip/it9v4EqYxMnjl4goFIGDDAqSfpwG+j5rzQegdzJ/C/9FDc3Pfo5XHHa7VjD0hU6pPBflzUbIzwQ8MeyNxWD+p2VL9QHF5gl69UxX4fuSbbettdLOBwTCkhS+dbFIgXsBCYU5HJaGj9Oiq13BV8GuuyVrxRlvP6nGUCSCylrROZbhuWnDVDogEAHVmhdQEGOVYp8wZNunf7uqOlycxuVg4/5Fd1V7Ogiz1ikg1GweuLnYm8ZNYewZcTAEnnNQYFfo+YeCyMCbMPn2fBdMPG540kTSJTsTRa4CkQI1InSLLjZB4ijqNO0IrjRfSIfaHbUGYqvoRSBz3N8WTgXz/u3NdUlSb/fUF7D3ZdhlskoUYojbYIW62AC5gDYwxN3DTPrPuGyXwvTUFf+S82FGy2CcC3fv6AAtSH9896hPIGbUSqyzaB2gdBcXgBo0K9SQMw2MQhNd9vzCn69OLxyepDSyCUoVp3Ny0eGfCUbw6/VaINik2NDA9VGQkiw5J9Z+26mt9sVIjXoZ6dfjjv8vkYGZqZ5M+cD8CMUfOfFB+vbyp9E3xLDoq8sJ2A2sE40tzEKMgIg3UizjQs1susz/XF4h4Geq+7p/gDEekQbgp5TbSP3O7qO82um5q4DVEUM/4+eRgHWQybZab7P1wuexRbQBiB1S2sfLzadE6pEEGu+fbSPcjxQLecJcZSB6UL0vjes01cxp2mPN86UR04JhAERGDLk9DFECHjjwFady1CZglzNfwC6mXqzxZKq6rpLsUDThfxHvO31QezoNd9Ex7z/JoUe4Q8MBjlVBKJnuMKrr8rQl4CaWatJOr3pDnHZINkq+OSNDF/DxLgM0OtlC3emf6RmWShHtKskrU4a4wd43hLPuJW/LVDMB6gouF9jVNatP5Ggc6ZH8j5lwuKMSqbyN8HjrIXbC6CB6LWOytbQoL9Qkd7rXGPST9aGQ+92sPFIvNd7iPb7dMiD1yyXkeAIAWrd07RsNuCrK2aNgmMpX1WEDm5O4b5wDB9+0/3queyjfoKqpAyehGPjJ/6hcbkj5edNPe0KaSsL52PR38hkHdmDO39u5w65I32+J196WnZqYxJiAJId2HGNYRcLaCHf17JJ+4RU7NWLSRa+7BfWpFZKKYLslQ7O/v8/2QeO84CM/Vdi2gupRYhC4NNLS94FDqboxSIBiFYdvLbQ98PPDA83hMwLXrecro7u2wb235/UtORIC7LTYo0uPbCjCbobsymM0UUQQQIh/gx1VMZ1Vt0p1Wz0TmVAmSi121YFI6kzCviOlHjqEHoPW4c75WaOuhNWaHAmSahVvgZSBRLXqXyR2+I5IeoKglvZt08TF1Be4PlQjWpQccGIZSKOS0XVA8XqwEb5LLkAbgSkTJqn71fyVdKEM7+vRbnJbHP3ZqzsZdyrFmo7bhvuiN36OdDlY5di1AUdxy5uMutNt1AAazKwZXLXDTPLosjzuM2ok5AL1Sc97Yu9DXa06YTPYuTiuDOu9NAioIxl33KGm6KvKrBf5vt5Nx2wBAjxZXIPt6ZbKx2N830vNJZu8Ke6Rnz3MrDD0eKkzPraZ0EmvxCQJT26YA8XLhb8ao18VKWTyhy0j6IYM3NmudCiciBykiyvohq3tGijznsb7LZNR7DhakRSbMmp0Nf7U0zwqFkpXSFuMJs7feOQQcJynnlSCxa8wAiDX3kwD5Pv+EOOGkxOUTJ98AVCTYofBeJRHfxXDR+SNBe2mjylzm34sGTogFYMTq45YvpTIMLdu76MQcphVIjjgXPE0iLD3MAlb+YYM5gsjj2Z79IY/ogvX5CwtL//pCufTk0NRtBOtwMD1j7u8+a4ETLWsoAUiFKurZb3ASV38FRtw1Q+HPxk7ffxbngmD5CCu39+C5T3HHZX2pAlfFvI2mWgwt8Fblqc/G1nJ7ZCWX2nrW583NmYhAgchz8MgZ+PArIAWIi8AlFeoPvuz0K97qpNwQK+YmMHE1lz0Kj6+tdDfjFfZeinLvQayk3ydbPmBADb9HgXo5xyrh8YXGWrJ9WF2D44GLfgsJZkYvlQO5kfps/iYvdq3rjKWhkhNUNjFzdskif2KwA9j0HzZJLP3cHQ2xB+9LAfpcD6idBjGXFrXSorWKIVlB6PI8gitGukwLZdfLRM8L6osiBxtJvGLzQKlfgVIfgbXrpI72lWH6vokOCKG1Kov459OPohbKZsy7Rbuk08f9ChdTAaF/NDTCe0gA8VuGwAOuDYNfYYVaeE+JyfdmPBdQLvqMTlkhyG040QVF/1I2vuov6zP47y24ISKrXnPJ4LU3A5ueTUXSplPijl1XgtF8nCklgZjN7QBs7EetXApyy66af7t3UxiHq4MKhCI0KUwYivNV4kmxCaICGX67trrbKmx0qIRX/fGIy40syULAJLEA6KoKqP9qWbTn3bxRYGslgnxa2f5rBDmWuwHQQ/RPLBrib/q4OF3pUThMsoA3BLiIXwS6GBw978EtzWWasrEybXvE3JezYdCTzIc+XiL4UBZgTlGHkvJrWevKEDFFphGEWFb4hdMkN0EZgRubCCpEIHnpajpsvYZwwFnfUF2H24ZQnWPGX2W73XDCTPFhEemm2Vuh+L1rvihqNsRfr+Kn2atNFXlV6PdYX3W6kUH10W1g4ud/EETxqiJHcxte7ce73VStn+lepFmief3a3M+9ejiPCbiiO1kTmE35xz6wi7uNIE5nSoi3c5I5q3QRoRcMPWTGi4hUZ9R41l9SlrDwnCkwxk5xTo9YAj/JvFRPgiwZ+kPfxPzgOf8HOPnRI33bwQ2xMrtLSgGz+Qkpz10UWXAPxJt9xnIuoZnOJNf0r4iNTGadUvhnGdL0+92uJ+JGzRS+2Q/1k3a0KkEYjULSimS7BIxVqqMTcap3zMkKpkI81Z6BKP/IaY3XrGe0U2MbmIQfxxu9CHGHjFJuwITc5XzjOtlllnyotiGQDiwhuNcg58TPnatHpG3TzwqbU91wc5rbBjuHIf7wLEUwh3GVCG56+pxTFC+HJ+fnOeYNgBTc6g/jjEE2KZdBBSIdjxQoEGLWybcXjNZiwojj9bGltzg8On1IXQlh0/aPJh8oy5mIXLRKc6327K3BVuXL0N+5rrjn3sP4lfuFFdEYpGERlonXvUfHn9zT8Xm9JRglPC60BRO+qe8Ldu5w1h5rlRzK2Uxa+PgYbeqG5kC38WLwYv3Ap2T4nQ/a+/RPH1ngaX8T8IkIQsQz6iBVl4MyJ/rIh4EHElqlWoaFSfcGCoZa5lp3Jje6WvHawYm46yieI7BCCnL2YZaTI9dcW0K4cwwaXK4DlkYL8C+pLlPpnw3xyLcKawGkwLmUV0E/RrNHOZpzM0gVVIat7+lkjpPT1WanlU/gMme+fHFdutfelXRpDDFkV+/8yguu952h2lfWPCQGsYeGnvgJFwyeYPclC+tVzQcuE2DNl9JnCe16TxIx5IevHLOBKVFVpPAPJNcv+gfSZWwMjEcYPEsZHmrcgejzO1w3SI4Db7upX8oJTWWXanoqz5wfNP2Yb6LLv+4rGIRo5T4PEtKrcdOTtM7HQbGdLedQEtLC557b4eCY5uOK3LrsLdO+GnFDE0I+SVdaISudU8yWZrCmotF2GxQXDbAC5q75wqHwD0uqxTmCFYEaw7B+Ea7NF16G3fHrlsOJCgxjby2H4JooxVK1KCxCmD5zNitQIKSbO6jpm/VB4hgFGOlLZ5siQN1fr2COyteJXNxMrJYBKZPHF879l4FbvAI3LgX1fE93N1okLG9Bag+9Pn01bt/ByXtZwvJMi7quAVbkWhDeCpp6YFPnwcnT8HRcZhZgPPi0ZfZy40po1uj+WoI2W94+SEoU5548Z/JhLP8/fFJN7VXSiApJ5d9NaB/81iU0SojD91i26WleF04gqwr9C11J2QRTCONphu8FZ3BVBofg8RxQcYBp3rSTWNl5n5jB2Y/n+kjg/tSPJfUf4UskPjO7pmeU3TzhEGsXrxMAv7KmunayWz0ufA/ly/jHN8ChtA9JZ4j2xJNcu44TfwfdV/TwwFgUrgOCc5SmSQA9D0XO0x/kZHoUGtxT5s7ds7mWyI4iacfgfhB0fI53GzPckaEbXD73nUgAm/L6elSM8BVmCCTOo48jObJg43AWj8Sn2Qa8IaVPo6Cc1lzjp9ND9Pirfr5Z1HTvaxjxRUgCiirrZtPlTYcTS0nE6elTBF6g9O/LvCfq2lzKWoulUkLqJAfA8iEIcn0LSr8bFLfK/gEZC69qVdB7o7V7eDN/3ctOz0JBXzlI+Mf9FhpEWiLxu+jz2WlQyNnaZ3qIUh9UJy3PBfl+Z0ThJG3ZVxbkV07Etpbuctc4MaOX3h0sjeCjwHHBwUFcEbnn9V9mIvYGI0awUStIssi6bR+l8+9K9A+W9BRedMijgC3oXi1ikuwNPe+6x51t7RcyY0j11hWh/MiB/AT0UteZzvvSYf+sREtJ8iilflbDzaLMNHNy6VMgHXEeYwVXgsDObW2XPboF5ybgAYgEmUHRfw9leJAI5fouFECDNm3kfmX+GemFihuSO8/Mt7r5DwPUaurVFh660ervbzAzq6JKY2xAoCdHv/RQHWZrmXXzWgEmFXXLRjXK5QUr9s5oAByD4wFlXYxhPMqQMk3rWEBrWnIKX/s6W7+wS1jwdTrBtRwr9IHy9+mwurQhNDtJMLYbe0S9+6DM8E9MNKFglk1eVyUh7ItNnAhT6Z7R69Bu7mT3EDU2BF3V9LKXR/MJsx7tD2XH6wOua37396qD4HQ3Nwu+QdEOZ8TUzVUCyzZtAn39mbasJUXquJcrqEKiy2fY/DJK0Ze0J6grPGwwyJOXCkvBMFW7ZwfJLWzJ5UT2X/bEWM7l/AZYRSCRv3M0vcDBGcwCnD0IeGwpREzi450FkOYVLRQnn5dz0uxidlqQvyA+teeXEwNJMr58UASaIkDlvhtFre9+l/LRxkABk/fzxYKyUCdA2YmzuUqA77XozGh0sfczNLAjgtnEkE76MM4DVy22z8kcZPgb8kKcSHv25E61nyBJPgQLGXyRtm0/hDTWhtge60zV5sRBSXS4ZwVkBJJZYFUCwE+jwszACxf2GkYLbnqJEBIUc7eA2Usohf5R6fPJ9XvowYFky7wVWpeeL3urBif1yTnYA6GVgn8+ciU7PIE6B9xLE2EfyYDXtwJn5HbMtPBFGWeFkoODdSYWBDXgm4g9fq8MrTgFpZDI3AyoAuS+jhiwcNiNyB/uepBftej3Ty5lLk81R8yN0pqHZbSSWq1rPKX05pdG/JlyA6vltg8w1mr1ah5xbs99owfA8agTPGTpyswUbPYMl5XHU7F/HP7IJcztOIEvUeBQYScrM14vhF0Ba8GypRsj+Sjq/D1LGc/hGp4VE6yg9a6xBtv52XnsiAsSkjPOzdY5fAQRGH2JRcSPq5Iiq23Qo7dEw8Qwde72I/CDqq//rvahxGh7nPHO+2ULh0S/MawR9wq6WRD2oE0p2RuUVYfIbYWKdiPkQknMqw6ghSCVtBeEmcMvIOEgLc7MeGVdnn41cD2OTqrVapbsKHgBYMn3mjc2F6tWQc+clZyXKMC/sQi1wPQYHNblFdHnZwHcBn9h5sot4G9KnfockRIa1Cho3QUza6QeRe86iphCwhd2xYlPASEDZLcCGp0goH+PrzRGIQD5T7/nhyWsaFTRRRWZ1yj8PzsLqPAKbNAAYGluSADmuyy/Gy7KGc459W0R5naXE242/NPtjqKylo8G7RbpB4qIEIPH1xjd43dv9zr7s9y+ZUa5huaAZ+ZIOosbABcwk7AI3DWbcl/OhKcy5+kY0uMyitoF2QBWOz8bqnqIcDOMyTmXROiCEe8JvHXzkdEWSwyIFtheSXS5i9mLPw4ZW+/PVpZ8SYbJ/Igq1lXXbwK/pZEIvfcYZMJass5az2GxXBxCQGtj122PsiPx+6amUHKonC2jk4VVBF9RTv3iUfywu50Wfe4uplaYDJKI1nW383Sei8XSDpDch7Oij3uKpBE0wM6nHRhaPo0TnSfvyO76cdCvd2060EZCVLLd8aNYy5SPak+AuLGJGuPERnCMwJxw9/v4nQhbHC1H8k4CPRzTJy3ubamZvVuc+3aK4NmGrJXAm0TqWaaa5oVHUOuHHR5VHYRAbSpW9q5poK13R023EPVwN4FfXn+8IxPUA9xcOzujDu0mnF8um8R0QuzqxtWNQoaJa0v4reBhwvrPun1bf0nn3hdnehFYWOh1uFseSjaMhtF0v8LGG2KqvFcNssu38VBRg2BYMTraYGXMqgFELqb2T6Q+0jAmOU4uJbq4K5540n/5dZPAHi3FkjEQq7c6HHNrzF59JfPQAshnoPAtNqu3qn9+wV9VcM37+JR6Wwu3irty/dx0z9TQRpewSXwt6Sv2ywBg67mlFXkzFT+fM0F2503KNN2CLfy0Z9zxLvT5fVqjKwDuB7S8AknJ6SFMX4RiuxvMPr+N3g3vxQBdBENkEHHtcwM4IQ+y4ntFXG3zEk+3rZ4brkMKKt1f58T9Mx3bpZSJ3h0pZ9/JrcFQhPssuoFXd+Jg7/Ce9nj8gkhc4fqdzUvBh1+mAPXI9STskGThLaT7V9gg0LknWrX1NxLoaDRMGYhx5uOY22IbJpU3XFClCBBm4dXrQuMJF2Z5Vl7pSEzlnq0quCTUCBYev2B0VSZ0jIRQmf9a/6leO2TQrVfxFW2JWF25aLspSYiaB9M7WZ1XvKdmvcxCnOr81AdmPDF6q5PWQ3R7Q+OrZMss4e6iMVy6QXI8msUjvPtsTQNV14PlvKGE9Qh49eLc4D/43oJWut08VA2DKSFiZqLD9GuRAEPBNqm9oZLzQTYhqPq4VcRj+J/VaKeQrvKevxpvVdIp7b69/WG8YvWdOYw50HPjvYEcBLoxrUejVQszIbUiWHVEgkOceMh2xyEjCkWlS/ehb0FBi5dIFbTrFOMVUqPDrTndhbO/rEBbmc8h0PIOoMFbM+v6CwbdivQaOzgfT/e6/uSH1slkca7jxjntg6VDsX9HjwpTiJ8idSYWLKi4LR1+0735xVFnBG8Ivw695ZslJNLqRsyk3T/dJDJ2avWWYormS7hk6DUrJ+Q2Mbsmrn+wyj29uvl7yZd/tZ/ODXjDEX/WHtKBOV8nH6xfO5/tj1tlu43/Q8D4PmDU4ExIaE8Fm353wHS+SIiK2TOZld/p8YU2rmyQowLRu+k2zFHk1Oq1XcaFRr27lTtg+ZaDGl0TwFaL8gcgKilo0OQC8Bjk83wqopfN0U+ywOtkhFWsj/SdOhe+LzjL6jRKvARdciOXaoheK3uh7mIPGMfSg0IJpxj11tJ/kzgEwDZTqUB/3GSKFouxt1oGuw94bX2sQE9FDiDZ3z+q9m37lg2OsOkT6y+BYp3FnESW8Wb7wH5u5/FMo4gS6tpOE6SvllfJIk48EdDfcfMVIQV4b8FC/w0yRaU3ZV2iBz0o5itgNmDkCBTxX3h2OgawEQWFgR2AwjJ1Yia81qKnoI4OyFNW/NddZQju4EM87CNp8XJQIVN9SS2F+0vMj2huMqM5zsTPxhUdIm7gswiYa3KkqUpC6VwjPfW11pQW/1gJF97mf2ARET1oU/t9ZwdQdkJGhpb0gWfxC0Pvlg1bTbjS9brwFwFx7OuyhZUsWmgD7Q/doPHzCZH3lAnu5EFnGzZ2xl5Nk6raObtrFCSNQC1vxpLXIVtHvq7SOK2ZJ8YPa2m2kS80jvMB06G5oDFE5NY+xtMFI70JacXaUyyQm1s95z4xYHBRbxedxitBfHRaBk7UZ241xdBl4njY39xTmsYLQFG7eay5HLS4I0kY3vhYY5aKt0CzYZn5dHoTXnGN1AhCTSHWAIopBUBXnx1avs6/qm06W4pGAcFrHTj2JNFsGNuHDkVK483v9R4UnM7+HbzwwEI3vSAVqJ86JqFbN/DJDoHpqD9sogHhUyIpdpgMCSD+Xb6AhnQkoVskClE8SbXgzQjDCfdTK96ZITin4a27dHwm4SLVcUqDZ/cDi4ME8M+hSZcpcDZVyXVD8Jx2sIixrSvaRQsKiMzZYV9hi1+F0ehkpIpw/9eWzjoRYY/4IgkLPkvGd01YwbqsxNOp5QR4Zaof8jX769Wpjv/dyz2a0okgQ8aIsxFVeafKqbYZEvWQDlq707/eGcDU0S30m77KSeKUEjpmA0sT6e9KChVksm5GmAlen/mnuUPSaY9jY17ewsV/mZfc8BLzsULL9kh8JEmJZy6kynQUuXMdl6b8WQDQWLPvb4gN8aIgbSmtGA7cRBSD+unrNXygoGIiDnSeMbhmBixJdxEFmkxdw/mYszNU9XZrSbTLoRN8RCnDcEYz265AhGzoQc7DZNYIoIB4o4dKVlxx6TWHkiOAXaA1/yBbli6MJRsZWFglVRsJCjtekZ8BgtWbO1sZvaCE2+rUHXg2SMVum2oLvlhdd2WTPBU+r/0BFeesN1wyHFEDIKgJm5M2JQluS7h5cepHb1u2dsZPY+QO+cTxXQHz5EozE9GXI3CWIzpH0AFijZVH4NI75Yrrhi8j+wGfdS2Pbw97YRpyqcvZbhVFfvm4KjVG76rsgt3ySd196BibyhjfrpZoO4+lIkSciAt6i66w6R1INJqV5YSciEPHfLoE5NXNh8aW11HnCl+I6Y8VRtEdcT0ebz+k8yjwxFqQnfesA3QACM9Zd9igd2Y4Xy7yWNCICMTvBZeVb/29jGa6kPfyGdNtr6tWM3kQjCf7pg+7KbS971YSsDi4pbb5mE+/1Af0huXz/Hhcj+9PMDuLiJVyPWQqWIkcO6V0GpdRLc6MoWK78I/YE5vr5h8QdCVwqBImZM/zk7qcRClWSHVc8Y5Pwb2YwI8Sw99ON9Bn9bcwVUbxg8pz5q9FWN04bqxMLssl+dK5wMO+uSkLA+Ns41AJVIT09e4AQLhJ20BQ9dtlnMt0N5IoEyNJVVCE5LzMLfYWrKEd/57Dp4B/Kfx6i4MFYQurc7VhA3yt6F8U2XfZ/b2mkA22j1pVWxiYgbMZjAy22ZZUCvR6sUoMqkGjXCT2bB9iDhQxSBI5HW6So8LPZ33kJ7YM2CgYNGwxVmgc+/ZMLCTfTrW2Gltud85+10khF5sJevugZtH5DwFtR9TksGde3IGLP2sFZD9+/XXug0yaFvYQWLYzqmcrCY+cv+Lor+kFZxZXnK+Yja0iriTUZuGdcpKLLmmF0BCc1/RDfZelc3slPdgW2QYVZ3VbM2qS3Z9ihxY3NDTtIgkxsJNzEaEaTQZ2nYfXFHQgnsPLK4ZfC2rEhJODnJAAwCQ+vWh11douhdMkbIAMR/KeFdw9/wgKNSMtujYbVBn7iWak1Ip3XYwwMuwBCDWP6rgoxlcnTt8Uhq261wpuRiOhqmo/OZjIV7QANTLYWB6HFYLtoOJgCW1RCCFZ/ZG5YgZolEPUX/qtAsE2Pr/3wviJHgF9PCyQsqCAEwD4JvFl7VmHT+r+gIUpK3o7sECW5tlXyw3UOoFuv14ae6M1AUfFQzgtKnOxVtJBITPPrr2Sta4zDbz8s0S6PR7TNEvvrwZf6xODNoP0oWezVhiuOLxEQ05Pc/k46Z5mBaxlQIw6poHQTdDjuktbw0l1nKT97fngfRkNq5oMNQrPYNNyu3S/n2c5WQTh0+z3dpAxuGw4btDvIRDl8cV266BHqEySGb/c8S0u/H7lzSwpK2uR0EMOcMcsrrRRXTW5Aq64w3XzfWPzbK9yW1y0s+6BSZrc6av/Zsddpd1XchQ+cNe9GY1XngqbInt8YEHoml10LykRKeXjsOsnIjnrXxjvtJBkXH/ZrGD6Ud/KqjJLJSAE7qWZMWCO0yYAhN6WksrV7aermigtvW975ynQtLCHrrAMNMpvRIahKv7ckSo9f/fWPADdtFlbtoVauPdlVGdh0stOJ57tgpY9y1RwijVaztpSfPQvPZiy67X87xxafRH2ueGSg8vHpKOdQ+T5LzW/rJNrZ3BjNLQg7hNICH3hKb2tHRbmruREm+EQ7ujVXspc77U5b6a2q0D8psxtxS7cECiUwH1nzs9cvM6Ip9oOzq9Olh1t3m7JO/Znbg1UZBeKZ235kZ8GhQYiZtnuo+rFzsl4VR5AQzfxmgjhDjnTJhk3IAukK18Y8D4nd3jwQqo9XGELoacwJxUBqHdSICpyAgMfcQzL9PZqIkfLLOjm5pO7QJ2lN+EELrBKuDIVfb5kUEF2jkYoMSlJMMs3I32Ea7FDse0MTdYpbDnc8z6s2WE3cwPbwKbZRH/oHb4knsIh0oBrKQqadT3f7AcfYDgpxsV7ZBeMr7qQ1mUDtJHlpAEUexOhl5XuP7N2GREZ11vFDcibERqMLwIVbOGNcrMwLDmajMXPAhL4K2B5iJifBI57Zmnxfgb0qphVlbdu1N2aaXUTPhaGxe9vREundMkY80hA+I0U0mrNDjGiNxPdbrka8zcQ7QbkOauoZZ4ntFXjxZb7laZRnBxBd8pi9s948ROKBjWmBDIPWUQ3juJKNxS1T4sjzKDxSIf96pH3GDXSfOubwGVlXAjTjtR/E9suKZKzF+03LAn7zZ91VsrqHNgP8VyjT92GWDyl8sSyi/CZ18E6uWYCCo2f1H8/LDpiW64JNS+ik+q65Z99FFYQSYPFAtA23DN9Hds1aD+frjvL76X5ubL/1LvQFegVIyoj0u32bz76d+6B1AqogErU6eHb/yR9/96wOXW3yOLhRv4Sff4lsP0DIunu8AomQz5A7W+vWXTEz40/s2nIOrd+9pg5e1Pao+HQFtfqH7g4yESbDwkQue6cOAGlp08oA4KwgAw0Fg0JUfKqUQwwYHN0el+Nq6Np3E2fG028QSEq0gUbf/2syuTo/rZ7bVoL/ohOmotN+25Cw1jt/WIILN9dvEqCQdnv7T+1M1vYuU73DsrjwpxafpaJjB92hGhdgSEOkseEi+HFZa2dKJPCVSvtdEBk7X00Tnc6HSdk2V8vdC/nu+TYKLGZp8pMmIgwzhismJ/yhYPH/mc9YkrxDgSbtQWmS53h+tjWoNDSbveG48eD5raITKrbwYl4ZoFST7cJePVVI0oqDYMtoR34USEovjq5O0Le8tXACyA4RHWXliaP2PJlSe5x3qYxa1Vid1jdYnkei5d6WDfqJYY0TBSoluT8lykIGsSgZCqaxI6IQhCShCyoYovJTeLBlwSFOGdeFRwsXkI6WLKwSD5y6kn3/M/5FcdkGcVaiEt2ghdjb+H07LkESGKW5I+yBtEzEI33O1j6xRym7dqO2NRwEb+7n+mmawswWTMYX7fjvFvM4tTDJh7eKKbKMN8PYeNvab5nbfvQMWPLKPFtGwjrVtLJiC+NIe4WnGCVc1JptD2dY2EB93iiEvjV+F7McR4iAHnvUxwuI2bDiSKmE8r0GzHdMh6o+DZJwasskUSdCZIBYiIGfiZL9e86OMM1QfUhkH7zZ5DmSjdg7PVgrsLjQeki4R6TFwNm2bTEh4/nEoUhWn7IaWRwsU3FwWg7ls3DUD+3Jkpnt+WjkRspfTfyd9f8wdBg84d4822WUo2/8hGbj3uH0mmcnl5x/I+DBhA9gJz/LiVw1p4T4EAMQfKb/TD/1g2L6sOBiPN0ItevrO5KGGJp960w6wKGtfE2cqxDbxLLuw2VWRuGlgzgn2WPBhqxhjtoj2TX4VrQoBBAVYoHmWgJO19PX/kpjNKObRaSPfsw49Qw9RMR8mJrK1DBPxirFOK4Mlb+eI4X1H/Z2PW3kN/R0YGV8gqgIgBtodlUNN1No3AQKKavTMD+7n05RUB5iKVbrR5Odbuhv9y3sWG9VnqFdI0CkKilpPZKZf1YIXtXNpqw63AJZJFwa6o+VbYnQuw6ElsCIJ7lRwid3GDiH4bOLbm5hwlk2EzjOUlP7XC6UEBQzupJ/+iHU7Hqv0pz25bZ+5A4nx5nMeOSuD0J8iS+ytlL6wHibTf6oLixdIUBGpRLnFFRsf2f1upYPLj3XIKKM6dZN/mFDGXjDjjpZ5OEGyp/5UIaLl8mpvO4/FVQgdTk5bbLtyIA+qiNPP4KjAB6eTnsuWnhnIehv/zyv1YtKVSr11pg6sBKVieCOvMHqOac4j4PLvEojsQ4baPnnHc/gwPfZAU6IRRz8F+JiVqPPcjuMEGQ/rZRhOcqCeiF4FOxE+AiGCtw2+5Zmv2P8Wj6fg6VdyZL6AEMZSxRp5nWlAwVmrnunB2rp7nnvOue5pV2gLRXz2GUZT3r1BINaL3Hk6boUqm3tAuzt43ZEx1P1RYq/kpId9ZEt81afTJcDN+8ITVrt6tK1bnkjgOuGar9TROy00lAZ76kkm1KapUvHM/ZR1SPQLsR7lpdPMo3NyiAe3twP9gU6f3s+OXdg0wqcTj7ZrvNNpgYBJLKgEAj5Zjn2J0rAYqXFhlXTKctPvg3XD/7Oay4/m1zh+/S65Bmcxeih4lPj1Ce9VKSi/89/XjNVuCgzwDS/lTI6VgZVMoOARdE1q51QCn2XbDgHBUogdzmQ9rEkdjgOz8dR4GcAusSXADau+HPtv7m2DGpnwIkKZcq6gg8rxquU+9dO5X0JsAEIbn9DBDI42BhP5w587lleKLH/nYS1pu9KH73Q1yJ0TeKAwP1DbWZHvDWROudIxireU9mz6QeShvtxpwTfY/F5VRgF3F77VnATs/iCqmflNhqTuza8nkMAwkjmEUgRu6MeXiX9gIHnr6uDCVNSUNR5KuZzPiTNNZKHUICvBnVBQwvRH5tn94CxKOrr4q1R1Pkr2Erke4S0u4fmbpkp6rfupej6VnKI30kVfvi3MvJxb95em5B1RLZSILz/W3BZypscP3bHxA6lRUPA1VC7MslrfeCj1l9roAxq2Cud9MxLiKuBnH4KbX3dBXi+ETaplowNEfTckW7PV2cWzWz/5hvKq3YhbEuHMQCkq/8gRxmi75vWqixVDV6zonuOLe+CNFQtBMngFGY/M5Tc+6GSgXVpGva6e+O51hzr9Tf29yuPHlU1zlKLseWEJfrHfRUxM8gva+sZcUP28dNtPAObHwh6DzX/od9CoD41dN3eszBp+nz61f5ZSUbQyjVShM5ty+nl3N9pIpqA5SvrwqG3JhfLWqEI4HwFBLzj5PworUDNUfWiJAvBLjwODFUWCmH85kl017Il7N/daZUS/nBzb2rcdedd6pJ3XiU9QsdSmqmxWSHWVLamiCx6NqNMqyPYO2Hi4/9pwHgQWCu8CY++QLpij6cGPiil7fbwFxCaSb4sqj2tRJPJK5p6lyzMiPIkQcZVwoecCvzmEjh8Ms5n+n9gRmqWSe031BBh2+VuuNKJKN+BQUu4dxyU3D+bj0vd8eVUQkOOQcjl1M7J5TEIdhPfnvx37hL3pV8HHxkJDXAAU0nS+gilQbTVyAxgBZY9vsuC2hiRzUkZB8v1v7Pc3kPf5WZlDWAou33dfZd0rzFlEajCS8ZmlISQJTWsU3geqHAVuTydeaMs3HcAFVzP7kWj1NVsBHnI10/8kCi5WXcLghOKkINxzbE1esuNo8aikdPxsxRHH0GudxtvBKvqbhhEpiYB8/Pd2wL2uQpzZ0vWNa6tgs4gdU1u3f4jTd7VHnEbXyTPSrXJt2TPFzEIzX/7e0kw0ef+AVUKCOBuOwWTIeD38/bJ/4g+TICg6rufAv1MXWYT/ousmE4gT9PzS0IPlaxkk0d9B1fHhBZ+wVo39cTcohTiQkl5Tsy/ByvKIssRv1uwtB+IZd9JBZzoRO8oLkRZhAclAK74uIWqNRQEVkpECzEDPtWOKBaEHmS8YuRU/QlOFK5kfYyo3JJs6f2fQZvC2Vp9vOKHqpdxcNS925nCc2JjiOVEQ9989Qi8FtMjxfmYVmkGdxJqiYqXW++453i+zPgKwTJ61Eqp3b8OqJYbqOkdC9m5dSO4mKPdI8Fqcj792D+E1Cu6wKHu5HTGsTeLUYeZSIF/ZKgnNJrsDJEqceejOZJxcOAHK2nnd0nz4h/6Pp9MRH6/hbOUKBKlRc2lWnSMdqN4KiQB6YJuigA0yGlI0yEKe5cufuIAGawlda5ReG4TFKs8so5qkZdnmhsaOwuqGsGjqteOS6ws7sZgiG0fBh8/9SU1ncLFIOABg2Q1h9QTcpIHcj6m0MoO0kJUF7xZKxA46l/In0wr7UeM9eekLNPnuVD0fWkAgdgg0hhRz2G3SPYQCtndRDa/sztDRsd9jg3HjV1eLbFG5YJg8D1SoMFkO9Gn/w6AOuPd3Ub05q1Q9SV5r6liTqXqasx6QUtacuiMh0aE0e/Y1lo5HeNvO6DJNTBVEdtFVEpZwToHNqs+JbF5AZWz1RI2Bsoo2i2z4uokCa5x86s96wYxdram4GGIHPlQP6+2kUZ9ZaQBc0Hkxe9cR2qOLi5JsSs8AQnw7szNCtWJAM5S4RxjB77ApapWB8acmaIpiV/QpyOslMbDOK6F4VxZnDcHTwQ7FX0UuLzo2CxKYxt9ByncEK98U4eQnAzwZ37/u2iPn00EYQ13HD61s5k50KSAcPBLWPcdAfrl3ZzKlXZOOYFS3DvMj86FsA8ic5d4E2bsyYE0rBDmv5lxTEb+C7dBZjpJWmGujkaVgQTdLUYwBHLsdmCowd++4xZKMiJg9d2zqV/e4MPqmwyGpg9rUkmx9KwfUdIAqvaPs+LoMcI+85JHNH0J9qEKv9tenqIz3YDxhj03YHe2ypkmU2r7ujOce3c5ofcSekpBxkgSvm1VFKvAgpO8ys66PD25hm0TPoD7Gl9aDp8KQrnqXpNG9X+yNxC9VwVuoaZa+GTrQlNKEQPVLstILu0mieQDD4b0zlAkaEsEaZfeAe8y8WyKqfwvsLbQpK9v9agj/Q+zujo8CJZ9uvNZ6mk5KYErAig14nHhfwwXyJUf0QaCW5kt3aMtbmZzyQy8uYyN8ineXp3pihq6zpNcelSEV3aRtLGFUmcgn90bD63rUs3Ct5Kf4r/shTF98zcDH4t6Mj6058dmX6pl6pkVb2+zxMbdqZXSWEoLyXvP2BkmVVJt4oaA2GujI5iDn6e8zyGlA3q6FilM/brbQll+mEDVvI1DfN0k5Cu1VlfmlaRP2DJt+2xSQmo2jyuXWgsS15q1abb4M0rUd1H8fTN8cOcuppE5MpJactQmvFWpg+B1+a5Dfy2GtVngmYnuB1qdT5FdGFXYBE/dI9WdfA16VrdcN1nTJwLSQzgncHSYXuvZHAo2LktL/v8Z9suXrccxTgrBxBLk5xhIL2ljrm/KGiwwjxJSlKIc/WPwi2LquXNXYwu6lLwl2r9CQGfClWaeloX1XrLddRp8llxca4fPnxU29lagFa5DRpBkxm/VSbEPVd/7jm85c8Z79cgjK8b5pha3Q4OgvZkp202S7aE6aPF0Lt3Lbii+GtrTHH0rxaX/EMg0pO4ymzkAsa97VREIUv4suTkOiT5oSFBsYcRY540dzpTkhKMt+re/ehMWETb61FPsTRL9gNZUlkJ83sMZedsCGKH7TlZO4DB5T2CzFleiwCkHxZARyZrqfYLfMBFvLFCiaeslfh4qS/qRsYXEmngtep/RK2PrrjMh2CHy9QiBiAQn51Gfo/ReIUoKdZ/nWxNyWbRAqutDn5OaoGFWYJSU84PrEhDLw66L2zSyyGcupHkExWKllt5nmwDjBw92tcxC3EfTC/2fjcqV6USR5Uh78qG09AggtLb5fLRC33ghLf6H73j3MiEsjiQAlZTlv56Y+Tqo+hHrYkfXgRPwbLfv1ZnPitLuS615rGaMGrfrArtO1BNEoxruWf54pXu8ppoIldekha0khK5IMwlESflt2Kw+nfc359ijtUU66ULSA0oQChuwDY+T12IdjPItDzucWmNNCm8CzLy+7xPvb7EbzsbCQCEoJy60FPhUde/Xkj76QypAC4U5RHH9+0gCw5HNDXGQELWDPlMVmocl3ld+MJ6D5VewC1Tpyd9XieEuGASCaasCx9rgrMc7uqjP5wpx8FS6NsOOx5TFwVVBdYRESmaiY3XEWTiwYWw4NXJDDgQS4TS/mAnNJwQcUqsuL40f6illE8nKhesMw8Zjvti/DDFVow3/V2Rr8K6ERyb2vB7Lc8R4tuH04crpHkGhAiJywcgrqapy8s13PyN/0KrRunbb+KuZvCnTizhpwrkhcsQOfjCz2mdsMEIGTZW9t485AwwiBK/EFmF3Ne6x4W3Nc64jpeM3DNfW61fiYbHQtyi3qZN6r5o7a91J84x5DsJtt2J4EwzDrou3nlQ+OhsCGzbeiJAVa4ATp2lXrVEtisvd1tWNWGtTkjJTcqYmqYX9JG3+RP40/YXYfMMwuYC+1Ym2lwRUC3mFIGsDM4u/gTxvyfHO1oRY0ML4vt4Vqja68rSr2OPZN3fAXVdssFBmzKWhE+ScHVfUh9xNxk0lKe9Wc1SXb6NoXpcDDU3ttJSNfaKR95XdB36isnfylIJ5HmBG/cBblJ6Dim4KBJvv0xrtDLMB6FJ8tidCUeBoyI7kjw3XD6iS84W/a7Bi/y4wV2Ub3vy7SLpSdic7yEXnFIEEYCJ2A0pKPZdkNEQPhJfGjlfoxDrSeqx2Z+bsV08dnyjS8HTvCL+VCRjlrlo6BG05PPTd4F9A5naYCIfpqo3PyWER5crOsDDFSZRQtaIHr3pk9a9ytgDoIvPNJjHY4VGwOfdIjxTjGTQSDpIJBBVmJ0XekFV27cKoesKiebkoEFwTWMDsj6pyiU0Efb/8GawS+fe0epF96rmRCWgWFI5amc5VSl16PVTcm57nFiEyHGmPXWpnOh+YH4TeJ+h6cU7DM8Sc/mrxDnEVHAjOHBAc8ZSOQ9ITxfrlCO/My87Hm/yUhWDLftMMF8VCAbJ8itAawE7NU/1s5dGdmNS6M6VUowlGrLfZRK0uCcAz8nyzIXh/ZxVWcm8PA99Q1I2e8wF/xbxakFdt+FKL90+ktE0GPwZQtN0eqKiDcs64OtkQG3dSwad8fDYTCxZapL+MR6/c9BGq76tC0lEzycPEbUnfnrwOH7a3yIUJQlaNP4rKEX4G3tI//F2FQuzXQbYKY5x4l/zWZe/OZ7o7h0uAcjn3phr3TLaVJI9UxeR3LDy9Oi0Q8xaN+ZklLjQtD04H6zP+7Yypft4iqy3bFn28qjlzH1Qf+jLPmot9WXnlr4i1CQHqnkiDr7+9JjOhvsj3A427hu9vA37lnZ+AHk0Uc057TCM29X7WoRcyh0VtoMXFCqxLmK5bha9oJaoRHKOmuu0nA2IuKJLxBcFS+14f815CZqysw+UMaNw3S/V42GnW0SbEvyN2imCXNsFVbSnGvU5Eti0UQjmnIrdiMKntArRw3Ptc8PDEIAFKKU++7WifB3bsWO1zopUMUjCU2QjsKj9qR2XTvMgjdw/kleznxbWVjP28ru3Is2wnqxJ35P7lTSGU0LSxN/GXJnSw+daeu5Y/p6ocg5Gn6z3FTjO50PpfGwrKD4cw6Df1UgEQyO1O4pZE+OfNcj0zxE8uQoy0WnqAxKBtuKXeUrKUVZB73a0K1BwhSrfw2T1AXj4a0SYR4qHywWoYgH3OzMlXTCrFz54Spo15Rcm6jGB+5YDeII+3QCBotPo6aASF1hcaoSdx619/yrC/hrobxx/B76GplwHFsxQ0kDtEaySoK138Wi+ndZ5sScVeD7XqoEmUag+8xBKlFCzkR3JMmGqYMFnNtmHzmdgV/tBOU9IPOSvwph7mr++nBzk3BLFHg4BfFEqvoagZRJrBXP5toWIi5kzQcoVhDK92riQqy9xkmosx8cwptdqDPgRB3MpSTrbH3E7A6q1+wfqiWIvJGT/hq3wLIpYu5zrQYtzaHbf833UsbGCgIoV/Brfas2xxj32dOhzzP9WEpbkccEvKFz0+Mm3/oJKYBsEiBQntVnifOT2aXfl5G9sjCiMJF/tCAZ8SeFpRzn0b79ji7qnf7OqpUhD9FRH9elM5AXFwzSA+tDEEeIKzdXIO8eQOGoypIbHO9c44R9PO0GEP8Bpe0hVk7K/6UB77dnWnhWhktVPswbiMepni3cK/X76xTTVOT9e59S8m/rldpeEV7W7XIfXs786kbedYKsb7s/U0MdT0yGDmATnQuE3ou0kEf0kXVKtjLt6mZoXEI7VHxpnjBvWqQNuVecaN7MKPkuQ+sywF5LLMe5zC2L4RxbUJbJEpNfhbmVTNl7xzzGFsGYZ4yjuwoDktUeBuQnusKBhXs5xQ1s/IXDy3sCDSFHd7z8e1Ll1sRDk0x9JQ4+FX7xMbOj+Bn15xTqkU9AqN30faJcHFrWRk8trJwjETipgoBeC9oCxZtWglGvi4oj8TOexzO3LR1Uu2zBOfiW4rO6BFwDnWSoriAXYtU6+h2i3qiFX3wJx7lLWzH90cojrtRkiVxjCMf7y9KQbZZoLjHCu0QL8oCJTXH/V0kNZRkYGL1RtgQeKru8VlWBWnz4iUffeTs1DnLuUQupKA230lEMfeY1QjY3ZlgZ3gtOKYkNgEKGDXKceGDszlPSQs8W0N8WW3ZSXb/uTIFwwVoY3YxkQ6KGTzO3LfXgMSGpu9H0kl7rH6tFsiJgrbIqEZnorxfjMZqrnP7gcn4UWDhu1UHYlXNhxsDDhMCDO2o+LT57UzlNEp+w/o9OvzCUBz0aEGK2fbCt5fdigEQUPotzPWMqzeXgk5xO40mXz/sk47yLbQLWp/6Tq3TEP6s047zo+DB8nxlLjsB7cJsY+KbKm1kJN9gMS1PsVuqFZGQybyK0Od0slij+CX/B/t7Qb+6+ACIECGqZViz1Fjlw2GV8T6P1Tly7siGiCC8irlB+03wHNV5mcQF12lMKUW02n/6liHq0/u8YMGsCtemh6Kv75iWv6Ekd5/SPmdpZF5UT6Oq2uNnBxA9LTY8dHPfTji0ERFxNpYIfHYCqSsc+ll/ux1gNwl050x8peuTh7fRCHZ/AYnopvReUZ63PN92zTvGkMvtSPokS/WGQBfRV7LoCOEuMG8GRNQ66pFz+C/6Bdfy8/EuUZ6+5TdN0ONXArEaPPibniYwakUT+A9SUcppx4tescFZq56CFz799Xf0KEnmzK6VMSjvE0Uf5TDmses9mfKAIJNfuaaIHyHWTle9G7Re4VAog4RVDWYZMYZoclaX5wgoiHdja5lvqV3jyKVwi790gppi7yc/VReacP5wOl8bMBxLggVTuZmCOr+IcBY6pb0M0+/gdiiUrE+5yyKYDJle04xZQCIqRKxXJXpap2LrIIESi6eRFSBCFY8MUxQkhyzYsZ7frEdu5r5AKRBJG8EqRE5NegJwyBXpsTxfPxHekzo032CQRtUrTqJZlQwglKfV+qMbAXCLx/benKRWPyddJW+GTtcIM6YVHEgTxK2jzdKN/nD1C6XQyatO04XXqbvcOoh+uH6zlQtPvjnGsKxpwr+RzcqVe8BSTzCybIiZu16WsImh3QqjH64Byds1R8W2kc35/MLc180Jt8trwMPIftgRR8qK8U0JJ+82B/4os8TEveF5R7/9Eks1UPir3ohqzFM5PxuY6ot/HulmZzjLorQy2FtT+VWsx3cHe6fL0E2PhM4zbLxyXylZdSqgC62X8j/vRW73PDwuNkhfbL4qZK9guBQgMxHo2IgPvuWXXesUga5lQsullhiqK5cMV7gu57nZDj3jVP6kE8TgWzhdr0eTkdQuOzEUCmP5wE4B2gUwXB5pH+xfEdJGC/M3Uh9aW5M4GaDQOyWLC6iR3m5QGde/XRoBHQalu6NxmX610kwH38dGVwMCrgVLbwJsrouE631OCSXuR+YxnFiyQ/iv2UIwIosWIpGpWy3lHRVCRNZBjeAKdfYKnj7Lis8LUBTMJ71dVtRijZmsqpzdj2Mc4NOwHavcvcU44zq8YBN4ULn9MG4WVEB2JSiaak5l2PKQ0jzl+cKrZgeqKjNOdEzAehB9i86mHbFGbZwlNQ8koGUHEdBHVlV8Q6fYIxJhJ3bsXR1dNJFCVArYSEIpo+buVHEP9DZC0120aZ4BIBF4YFYdRBNLSllUooW1188plNQmtSxwL8IpLXk+/xtV9arXOxmKRixG0YaiBBBHksnJP4PUz8ZIPVIa0lJuFI4uCQnft3SR0ZbF3O0x4z5OJNBmvDHNYD52Hfi6SIsGag4hP1c5l5wiaCupkMqWVfAtbTghChcsSV15U5iS53L328PNxM5R9BI230zfFb6okEKAbjBVBMEQPgp2g8+lqPhQ84FtIIOVSLaoPJ0DbS6uzZTCfHp7Ry/sD/yV0Azuz/EFPMF3G9V+lEmDuDFNEdWDtdXnhrvLjLn3Mys+7WdhE7YixLAxkQ3LbHT2KC5aynxqOTA7XmISbd0FtGJPOTMDU6XPPM22c5dLM7AAAB1klEQVTTHgPzpwOC0na0tNN5Rz9SAxncFh0GnC2meKrl1Fav91X05b8seYMeStVksJcqhTIBnH5iu5vfwI4T039Xz2g6CePxklMiMCM36/QCbJLp/o6JGOz2KOWLnkUk92xTUndTmKFCSVyj9zz6YbTS7HPUncD3mrKP6Mrl50LwbHZX1k4SUciz/tP1nutLChiZ0dMbec0JgiEEzCAxu5rpwejZUIRmepqWICZI5j4N+OygsQ7EpmtJgmLEsY9Wg/VOJFipk4kibR77PwYB+f7rMKBv1zf+NkH/zxyNkAf6IEWmsnELfXlZn/bW2BSJ83UqHs6skNH8cFhsBoHRnIyXLjXSl3cN0MXw1iteO6kOIcnQOuY3EyuyTK+xow4fLT/Yxogq/k5/2D6eu+Y8OIkvWhyMkKCVpCoAp27ngbCUpKVdHC4SqTlVh4yfJs24ApB4AVZDR052zg6nbtZx8/pEYKe5lnfK+JctQqz9Whv0r+A8B0WgUAtLlLjh3F4torYRbj04gGMT2MtCpNaeDX1jd5U+qB21pbmZM/mpJQIr7AiTd9Xm6zEJEzQoTxxcQL000jqK401BJCoZRJlvBF06aqk/I6r+s24E0cWVeotgh83NwxHl97GJkEiXOeQAAAAASUVORK5CYII=\n" 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\n" 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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "\r100%|██████████| 50/50 [00:02<00:00, 21.60it/s]\n" ] } ] }, { "cell_type": "markdown", "source": [ "You can see that the image generation is indeed much faster - a mere two seconds - but also that you pay by giving away image quality in exchange for speed." ], "metadata": { "id": "SzVVhCudBGkP" } }, { "cell_type": "markdown", "source": [ "Cool, now you should have gotten a good first understanding of the schedulers. The important things to remember are:\n", "1. schedulers are *parameter-free* (no trainable weights)\n", "2. schedulers define the algorithm computing the slightly less noisy sample during inference\n", "\n", "They are many schedulers already added to `diffusers` and diffusers will be adding even more in the future. It's important that you read the model cards to understand which model checkpoints can be used with which schedulers.\n", "You can find all available schedulers [here](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)." ], "metadata": { "id": "V2Rk_bZeBTVq" } }, { "cell_type": "markdown", "source": [ "To end the chapter about **models** and **schedulers**, please also note that we very much *deliberately* try to keep *models* and *schedulers* as independent from each other as possible. This means a `scheduler` should never accept a `model` as an input and vice-versa. 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model that is 6 times faster, 49% smaller,\n", "and performs within 1% WER on out-of-distribution evaluation sets.\n", "\n", "First, let's have a super-fast recap on Whisper. Whisper was proposed by OpenAI\n", "in [*Robust Speech Recognition via Large-Scale Weak Supervision*](https://cdn.openai.com/papers/whisper.pdf)\n", "and is based on an encoder-decoder architecture that was trained on >600,000 hours of audio data.\n", "\n", "To transcribe speech, the audio is passed through the encoder once and then the decoder auto-regressively\n", "generates the transcription. OpenAI's best Whisper checkpoint, named *Whisper-large-v2* has both 32 encoder and\n", "decoder layers. 32 is quite a lot. Let's visualize the model.\n", "\n" ], "metadata": { "id": "ZftMWEt3pQis" } }, { "cell_type": "markdown", "source": [ "![img](https://huggingface.co/datasets/patrickvonplaten/images_distil/resolve/main/whiser_arch_old.png)" ], "metadata": { "id": "3qHYg-MnqkGS" } }, { "cell_type": "markdown", "source": [ "$\\mathbf{X}_{1:T}$ represents hereby the speech input that in run through the encoder (shown in green) exactly\n", "once. The encoder ouputs, *a.k.a* `encoder_hidden_states`, $\\mathbf{H}_{1:M}$ are then used to condition\n", "every decoder layer.\n", "\n", "Starting with $y_0$ the decoder (shown in orange) then auto-regressively generates all tokens of the\n", "transcription. In the visiualization above, there are 5 decoder forward passes (one pass for each $\\mathbf{P}(y_i | \\mathbf{y}_{0: i - 1}), \\forall i$).\n", "\n", "In practice however, the decoder is run up to 128 times (depending on the length of the transcription) which means\n", "that there are much much more passes through the decoder then the encoder.\n", "\n", "Now, how does Distil-Whisper look like?" ], "metadata": { "id": "lVmCb1L5pTFs" } }, { "cell_type": "markdown", "source": [ "![img](https://huggingface.co/datasets/patrickvonplaten/images_distil/resolve/main/distil_arch_old.png)" ], "metadata": { "id": "B1M-XAGxqvpX" } }, { "cell_type": "markdown", "source": [ "Just two decoder layers! That means to generate a transcription of 128 tokens, Distil-Whisper needs to run\n", "only 256 decoder layer forward passes, while Whisper--large-v2 has to run 4096 forward passes." ], "metadata": { "id": "-pxtZf5EpkRc" } }, { "cell_type": "markdown", "source": [ "## Benchmarking\n", "\n", "Great, now that we understood why Distil-Whisper should be faster in theory, let's see if it holds true in practice.\n", "\n", "To begin with, we install `transformers`, `accelerate`, and `datasets`." ], "metadata": { "id": "pu7Hr-yQpm5v" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "iokFYXGhmqMB", "outputId": "c0090b75-b165-4f49-9b9a-36eed9fed954" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting transformers\n", " Downloading transformers-4.34.1-py3-none-any.whl (7.7 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.7/7.7 MB\u001b[0m \u001b[31m49.4 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tokenizers, accelerate, transformers, datasets\n", "Successfully installed accelerate-0.24.1 datasets-2.14.6 dill-0.3.7 huggingface-hub-0.17.3 multiprocess-0.70.15 safetensors-0.4.0 tokenizers-0.14.1 transformers-4.34.1\n" ] } ], "source": [ "# !pip install --upgrade transformers accelerate datasets" ] }, { "cell_type": "code", "source": [ "!pip install git+https://github.com/huggingface/transformers accelerate datasets" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VZ11lR78nRI_", "outputId": "8be92f36-70c3-4317-9ac2-3acb3a6e9f2e" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting git+https://github.com/huggingface/transformers\n", " Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-1nplx1mj\n", " Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers /tmp/pip-req-build-1nplx1mj\n", " Resolved https://github.com/huggingface/transformers to commit 8801861d2de1568e8ca8f81d96a7ddf3964f6373\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.24.1)\n", "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.14.6)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (3.12.4)\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (0.17.3)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (1.23.5)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) 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python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n", "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n" ] } ] }, { "cell_type": "markdown", "source": [ "In addition, we will make use of Flash-Attention 2 as it can both save\n", "a lot of memory and speed up large matmul operations." ], "metadata": { "id": "QxD2pDW6po36" } }, { "cell_type": "code", "source": [ "!pip install flash-attn --no-build-isolation" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LShRnQZJm4AG", "outputId": "4587884e-c9a2-4fa3-adab-d42c6c6c25b1" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: flash-attn in /usr/local/lib/python3.10/dist-packages (2.3.3)\n", "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from flash-attn) (2.1.0+cu118)\n", "Requirement already satisfied: einops in /usr/local/lib/python3.10/dist-packages (from flash-attn) (0.7.0)\n", "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from flash-attn) (23.2)\n", "Requirement already satisfied: ninja in /usr/local/lib/python3.10/dist-packages (from flash-attn) (1.11.1.1)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (3.12.4)\n", "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (4.5.0)\n", "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (1.12)\n", "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (3.2)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (3.1.2)\n", "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (2023.6.0)\n", "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (2.1.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->flash-attn) (2.1.3)\n", "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->flash-attn) (1.3.0)\n" ] } ] }, { "cell_type": "markdown", "source": [ "To begin with, let's load a dataset that we will use for benchmarking." ], "metadata": { "id": "ov9zV9TpptMM" } }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"hf-internal-testing/librispeech_asr_dummy\", \"clean\", split=\"validation\")" ], "metadata": { "id": "L7qRiTmjm_te" }, "execution_count": 3, "outputs": [] }, { "cell_type": "markdown", "source": [ "We start by benchmarking [`Whisper-large-v2`](https://huggingface.co/openai/whisper-large-v2) to get a reference number.\n", "Let's load it and make sure to cast it to `float16` as well as setting `use_flash_attention_2=True`." ], "metadata": { "id": "4M-WGWIppv9f" } }, { "cell_type": "code", "source": [ "from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor\n", "import torch\n", "\n", "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n", "torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32\n", "\n", "model_id = \"openai/whisper-large-v2\"\n", "\n", "model = AutoModelForSpeechSeq2Seq.from_pretrained(\n", " model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, use_flash_attention_2=True\n", ")\n", "model.to(device)\n", "\n", "processor = AutoProcessor.from_pretrained(model_id)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 322, "referenced_widgets": [ "af2bb919a6f24221ad1f8bfc68306896", "dd1afd32f8ec4088a9ed0b61e2aa65be", "e6efc274708a40a9a9f1cdf47219c062", "18130f4ccea349d3b5737875249b41ae", "0cad9485c0634cdd949a102b069399b0", "8579b61f27f54701b53ba6a3ca7823f8", "b30a8f1f32a04b99a1d92e9f8a04fb36", "46019f268495405498d5822cea750c09", "f35016d8f52f4299addb48844190ff25", "9de504654b874ce887b4c384fd88d242", "ebc4723dd20b45fa98610091c22305d7", "d7f50d64e7f64bb5a3bbb7a38addfb14", "e67b624a752840458e8eef80f46c7dcf", "109a33abcc0742a4a3a3802cd7f7d6be", "295175d80d684810ab975aac902586db", "03384ae9b9fc4c5e98cd96cc395b13e3", "5d876d3f93d94b0fbd275d7e2c66087f", "c4c0adf0935643f38febced7ddb2b667", "c11d25e7766a485e9e05794a6d6c69be", "116f6663e6354a9e8a135ebcd02c7cd9", "626cdeb86a744866b1b6ed76f17baed0", "3f56da40c12947e99af5b76c0ced96ba", "e9d19630bd9b48bb93c9a1530b537e60", "6413e90761814fc4bd3d60accf6d70b8", "fdc1a0ae8d5a4bab9179ea0a7e8232c4", "8ef76004c31d45bdb39e5e56303f580b", "ff215213ce1d4eabaf86edf7a002dc55", "148ad7bc29ba4c198071876fec79ab78", "15b30bbeb39043d895a63488ffd59721", "4b7c456b21494359807acb9f8c0bb403", "d4688972a78d44258594b09935dcde8b", "1b75f187f3ce4becb604e6f68600aff1", "66243cc6c0ea4e419b591e4014edb347", "a3d848ced14a4631be7091943e9b56ca", "5c55b4aa83e044dbbf62cbd8ead1827c", "295492ab48a649ee979138fe2e0ea137", "fe79be0d13734908843b94176546ccc1", "e920f99bceff401e8e8869ba88eb0f4e", "1138ec467dd94cde9120f41c4008f71d", "579769ff2868473288dfe345375f7461", "21ac1e9bd98d4c3aa2d58579f9cc9f37", "7ac9792537d84aa9ae3b305451ab83b8", "667e9d866d164e5cb700683bd046cb7b", "b8f30f32666d40738585696b1a495e6e", "f2f5125522a845c08225bd85705c28a0", "a152dec25e954c869a7ed0376422e27a", "1de31f0585c8469abb22569b9222c4e9", "8a50a687abf540cda0730edfe2ea3dc1", "9249027c9e5147069d51add4b078c62a", "c5784e5ae20948f489193bd8b4202276", "c39d268f3e434d80abb97dd3f61b5026", "1ceb5e9ddb1143b3801bccbc6847f18e", "36191367dc0a4b6cb204500fd12ef2c6", "c671c9cc908f45c3af5de363f1ca529e", "857a3b9622404505be355d69bbdc02b4", "edbe3df3733a4dd1a88cab9ec94d4011", "ccddc9a2d5884be6a7655250e3252ea1", "b516fb5ed39c443f8beec84c824a2ed2", "5884ce489aac4a9ea0dc75a41da59a18", "d6fb7885b6fb4fa48aa4c0b9af6e3b95", "982bf2c425784ae7a2fccdf832625351", "3fbc7f85dba24b88b7cb451bd610d8b5", "902ea6d443314d47a7227c7413a4d251", "e741e1ffac964a93ba9e473745d73e31", "7d0216ce058e4253b10c5c4b81e4214a", "43e34f8bc1bd4349b159d555545fb126", "57f19cba0fa14202812a27cbcd787357", "424547316f464d2881151190fbec3695", "05160e0b5599435cb1b39f9ebc620d02", "fccd93e8e53e4afea8cb3d7864146dd6", "1238c8cfa11e4e9fa3d30e498bf1652b", "c26b271022da40298a175cbc224a8033", "9e093df13ede403cba47069905788809", "9d114fc2905748c7aaa80e9ec02fec96", "585c3828a66843aea8222c983178b567", "76ddd39414244330b90f4b0572ee92f4", "bae254d545384f7a946d5585108b2cd1", "157e412710b24d31a37e11b345f80de3", "9e332e689490451a875b975b98ee0c51", "e13dcf5ecb8c44d4a5ccce811eb4c936", "b45c481de0da4bb99be2c2e407e52aef", "7579c75f68194be6857984429db2583e", "0594dfe7f92b4e3ca2008205b54c7b48", "f68e936c67c44e438fb8cbdde032c937", "9e798d06d98b4b7885b4b57fa9ed5292", "6f18bfe59a3f4a81b012b2f042bb2914", "416f14b1709647899cd1599e7428239a", "aefdfa3e9b9b41ceb78bf9b8e8f96428", "1bd6d110fc9e4725af438879a669cbfd", "bd639a3f3be64ef2a27f0be94bf02a40", "00df622b2840416cb246400521d723b3", "d7a97f156e224d819edec2a7a3df2476", "6523dcbb83b442eba8d3edaa2d1014cc", "cd91511533104f8cba06b8afc0c79617", "189dbeed74e744c3ab29788d8143fbb7", "69fae077fa624777973366b9289f39a6", "9b494082ece0425f9ddd768bfc6aefbd", "87273986db6c4a5782d062f051241325", "a70ae69876814a32a2f372e0c1282a44" ] }, "id": "3TKgiw8PnBK0", "outputId": "494e557c-8690-4618-de10-bdf42d76b99c" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "You are attempting to use Flash Attention 2.0 with a model initialized on CPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)neration_config.json: 0%| | 0.00/4.26k [00:00\"Open" ] }, { "cell_type": "code", "metadata": { "id": "DRzbFeCCZBw1" }, "source": [ "%%capture\n", "!pip install transformers==4.2.1\n", "!pip install sentencepiece==0.1.95" ], "execution_count": 10, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "myjTH2wFeWTU" }, "source": [ "# **Transformer-based Encoder-Decoder Models**\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "U9YCIjCPInpe" }, "source": [ "The *transformer-based* encoder-decoder model was introduced by Vaswani et al. in the famous [Attention is all you need paper](https://arxiv.org/abs/1706.03762) and is today the *de-facto* standard encoder-decoder architecture in natural language processing (NLP).\n", "\n", "Recently, there has been a lot of research on different *pre-training* objectives for transformer-based encoder-decoder models, *e.g.* T5, Bart, Pegasus, ProphetNet, Marge, *etc*..., but the model architecture has stayed largely the same.\n", "\n", "The goal of the blog post is to give an **in-detail** explanation of **how** the transformer-based encoder-decoder architecture models *sequence-to-sequence* problems. We will focus on the mathematical model defined by the architecture and how the model can be used in inference. Along the way, we will give some background on sequence-to-sequence models in NLP and break down the *transformer-based* encoder-decoder architecture into its **encoder** and **decoder** part. We provide many illustrations and establish the link\n", "between the theory of *transformer-based* encoder-decoder models and their practical usage in 🤗Transformers for inference.\n", "Note that this blog post does *not* explain how such models can be trained - this will be the topic of a future blog post.\n", "\n", "Transformer-based encoder-decoder models are the result of years of research on *representation learning* and *model architectures*. \n", "This notebook provides a short summary of the history of neural encoder-decoder models. For more context, the reader is advised to read this awesome [blog post](https://ruder.io/a-review-of-the-recent-history-of-nlp/) by Sebastion Ruder. Additionally, a basic understanding of the *self-attention architecture* is recommended. \n", "The following blog post by Jay Alammar serves as a good refresher on the original Transformer model [here](http://jalammar.github.io/illustrated-transformer/).\n", "\n", "At the time of writing this notebook, 🤗Transformers comprises the encoder-decoder models *T5*, *Bart*, *MarianMT*, and *Pegasus*, which are summarized in the docs under [model summaries](https://huggingface.co/transformers/model_summary.html#sequence-to-sequence-models).\n", "\n", "The notebook is divided into four parts:\n", "\n", "- **Background** - *A short history of neural encoder-decoder models is given with a focus on on RNN-based models.*\n", "- **Encoder-Decoder** - *The transformer-based encoder-decoder model is presented and it is explained how the model is used for inference.*\n", "- **Encoder** - *The encoder part of the model is explained in detail.*\n", "- **Decoder** - *The decoder part of the model is explained in detail.*\n", "\n", "Each part builds upon the previous part, but can also be read on its own. " ] }, { "cell_type": "markdown", "metadata": { "id": "rVFKk5cyy_O0" }, "source": [ "## **Background** \n", "\n", "Tasks in natural language generation (NLG), a subfield of NLP, are best expressed as sequence-to-sequence problems. Such tasks can be defined as finding a model that maps a sequence of input words to a sequence of target words. Some classic examples are *summarization* and *translation*.\n", "In the following, we assume that each word is encoded into a vector representation. $n$ input words can then be represented as a sequence of $n$ input vectors: \n", "\n", "$$\\mathbf{X}_{1:n} = \\{\\mathbf{x}_1, \\ldots, \\mathbf{x}_n\\}.$$\n", "\n", "Consequently, sequence-to-sequence problems can be solved by finding a mapping $f$ from an input sequence of $n$ vectors $\\mathbf{X}_{1:n}$ to a sequence of $m$ target vectors $\\mathbf{Y}_{1:m}$, whereas the number of target vectors $m$ is unknown apriori and depends on the input sequence:\n", "\n", "$$ f: \\mathbf{X}_{1:n} \\to \\mathbf{Y}_{1:m}. $$\n", "\n", "[Sutskever et al. (2014)](https://arxiv.org/abs/1409.3215) noted that deep neural networks (DNN)s, \"*despite their flexibility and power can only define a mapping whose inputs and targets can be sensibly encoded with vectors of fixed dimensionality.*\"${}^1$\n", "\n", "Using a DNN model ${}^2$ to solve sequence-to-sequence problems would therefore mean that the number of target vectors $m$ has to be known *apriori* and would have to be independent of the input $\\mathbf{X}_{1:n}$.\n", "This is suboptimal because, for tasks in NLG, the number of target words usually depends on the input $\\mathbf{X}_{1:n}$ and not just on the input length $n$. \n", "*E.g.* An article of 1000 words can be summarized to both 200 words and 100 words depending on its content. \n", "\n", "In 2014, [Cho et al.](https://arxiv.org/pdf/1406.1078.pdf) and [Sutskever et al.](https://arxiv.org/abs/1409.3215) proposed to use an encoder-decoder model purely based on recurrent neural networks (RNNs) for *sequence-to-sequence* tasks. In contrast to DNNS, RNNs are capable of modeling a mapping to a variable number of target vectors. \n", "Let's dive a bit deeper into the functioning of RNN-based encoder-decoder models.\n", "\n", "During inference, the encoder RNN encodes an input sequence $\\mathbf{X}_{1:n}$ by successively updating its *hidden state*${}^3$. After having processed the last input vector $\\mathbf{x}_n$, the encoder's hidden state defines the input *encoding* $\\mathbf{c}$. Thus, the encoder defines the mapping:\n", "\n", "$$ f_{\\theta_{enc}}: \\mathbf{X}_{1:n} \\to \\mathbf{c}. $$\n", "\n", "Then, the decoder's hidden state is initialized with the input encoding and during inference, the decoder RNN is used to auto-regressively generate the target sequence.\n", "Let's explain. \n", "\n", "Mathematically, the decoder defines the probability distribution of a target sequence $\\mathbf{Y}_{1:m}$ given the hidden state $\\mathbf{c}$:\n", "\n", "$$ p_{\\theta_{dec}}(\\mathbf{Y}_{1:m} |\\mathbf{c}). $$\n", "\n", "By Bayes' rule the distribution can be decomposed into conditional distributions of single target vectors as follows:\n", "\n", "$$ p_{\\theta_{dec}}(\\mathbf{Y}_{1:m} |\\mathbf{c}) = \\prod_{i=1}^{m} p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{c}). $$\n", "\n", "Thus, if the architecture can model the conditional distribution of the next target vector, given all previous target vectors:\n", "\n", " $$ p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{c}), \\forall i \\in \\{1, \\ldots, m\\},$$ \n", "\n", " then it can model the distribution of any target vector sequence given the hidden state $\\mathbf{c}$ by simply multiplying all conditional probabilities. \n", "\n", "So how does the RNN-based decoder architecture model $p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{c})$?\n", "\n", "In computational terms, the model sequentially maps the previous inner hidden state $\\mathbf{c}_{i-1}$ and the previous target vector $\\mathbf{y}_i$ to the current inner hidden state $\\mathbf{c}_i$ and a *logit vector* $\\mathbf{l}_i$ (shown in dark red below):\n", "\n", "$$ f_{\\theta_{\\text{dec}}}(\\mathbf{y}_{i-1}, \\mathbf{c}_{i-1}) \\to \\mathbf{l}_i, \\mathbf{c}_i.$$\n", "\n", "$\\mathbf{c}_0$ is thereby defined as $\\mathbf{c}$ being the output hidden state of the RNN-based encoder. \n", "Subsequently, the *softmax* operation is used to transform the logit vector $\\mathbf{l}_i$ to a conditional probablity distribution of the next target vector:\n", "\n", "$$ p(\\mathbf{y}_i | \\mathbf{l}_i) = \\textbf{Softmax}(\\mathbf{l}_i), \\text{ with } \\mathbf{l}_i = f_{\\theta_{\\text{dec}}}(\\mathbf{y}_{i-1}, \\mathbf{c}_{\\text{prev}}). $$\n", "\n", "For more detail on the logit vector and the resulting probability distribution, please see footnote ${}^4$.\n", "From the above equation, we can see that the distribution of the current target vector $\\mathbf{y}_i$ is directly conditioned on the previous target vector $\\mathbf{y}_{i-1}$ and the previous hidden state $\\mathbf{c}_{i-1}$. Because the previous hidden state $\\mathbf{c}_{i-1}$ depends on all previous target vectors $\\mathbf{y}_0, \\ldots, \\mathbf{y}_{i-2}$, it can be stated that the RNN-based decoder *implicitly* (*e.g.* *indirectly*) models the conditional distribution $p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{c})$.\n", "\n", "The space of possible target vector sequences $\\mathbf{Y}_{1:m}$ is prohibitively large so that at inference, one has to rely on decoding methods ${}^5$ that efficiently sample high probability target vector sequences from $p_{\\theta_{dec}}(\\mathbf{Y}_{1:m} |\\mathbf{c})$.\n", "\n", "Given such a decoding method, during inference, the next input vector $\\mathbf{y}_i$ can then be sampled from $p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{c})$ and is consequently appended to the input sequence so that the decoder RNN then models $p_{\\theta_{\\text{dec}}}(\\mathbf{y}_{i+1} | \\mathbf{Y}_{0: i}, \\mathbf{c})$ to sample the next input vector $\\mathbf{y}_{i+1}$ and so on in *auto-regressive* fashion.\n", "\n", "An important feature of RNN-based encoder-decoder models is the definition of *special* vectors, such as the $\\text{EOS}$ and $\\text{BOS}$ vector. The $\\text{EOS}$ vector often represents the final input vector $\\mathbf{x}_n$ to \"cue\" the encoder that the input sequence has ended and also defines the end of the target sequence. As soon as the $\\text{EOS}$ is sampled from a logit vector, the generation is complete. \n", "The $\\text{BOS}$ vector represents the input vector $\\mathbf{y}_0$ fed to the decoder RNN at the very first decoding step. To output the first logit $\\mathbf{l}_1$, an input is required and since no input has been generated at the first step a special $\\text{BOS}$ input vector is fed to the decoder RNN.\n", "Ok - quite complicated! Let's illustrate and walk through an example.\n", "\n", "![](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/encoder_decoder/rnn_seq2seq.png)\n", "\n", "The unfolded RNN encoder is colored in green and the unfolded RNN decoder is colored in red. \n", "\n", "The English sentence \"I want to buy a car\", represented by $\\mathbf{x}_1 = \\text{I}$, $\\mathbf{x}_2 = \\text{want}$, $\\mathbf{x}_3 = \\text{to}$, $\\mathbf{x}_4 = \\text{buy}$, $\\mathbf{x}_5 = \\text{a}$, $\\mathbf{x}_6 = \\text{car}$ and $\\mathbf{x}_7 = \\text{EOS}$ is translated into German: \"Ich will ein Auto kaufen\" defined as $\\mathbf{y}_0 = \\text{BOS}$, $\\mathbf{y}_1 = \\text{Ich}$, $\\mathbf{y}_2 = \\text{will}$, $\\mathbf{y}_3 = \\text{ein}$, $\\mathbf{y}_4 = \\text{Auto}, \\mathbf{y}_5 = \\text{kaufen}$ and $\\mathbf{y}_6=\\text{EOS}$.\n", "To begin with, the input vector $\\mathbf{x}_1 = \\text{I}$ is processed by the encoder RNN and updates its hidden state. Note that because we are only interested in the final encoder's hidden state $\\mathbf{c}$, we can disregard the RNN encoder's target vector.\n", "The encoder RNN then processes the rest of the input sentence $\\text{want}$, $\\text{to}$, $\\text{buy}$, $\\text{a}$, $\\text{car}$, $\\text{EOS}$ in the same fashion, updating its hidden state at each step until the vector $\\mathbf{x}_7={EOS}$ is reached ${}^6$. In the illustration above the horizontal arrow connecting the unfolded encoder RNN represents the sequential updates of the hidden state.\n", "The final hidden state of the encoder RNN, represented by $\\mathbf{c}$ then completely defines the *encoding* of the input sequence and is used as the initial hidden state of the decoder RNN. This can be seen as *conditioning* the decoder RNN on the encoded input. \n", "\n", "To generate the first target vector, the decoder is fed the $\\text{BOS}$ vector, illustrated as $\\mathbf{y}_0$ in the design above.\n", "The target vector of the RNN is then further mapped to the logit vector $\\mathbf{l}_1$ by means of the *LM Head* feed-forward layer to define the conditional distribution of the first target vector as explained above:\n", "\n", "$$ p_{\\theta_{dec}}(\\mathbf{y} | \\text{BOS}, \\mathbf{c}). $$\n", "\n", "The word $\\text{Ich}$ is sampled (shown by the grey arrow, connecting $\\mathbf{l}_1$ and $\\mathbf{y}_1$) and consequently the second target vector can be sampled:\n", "\n", "\n", "$$ \\text{will} \\sim p_{\\theta_{dec}}(\\mathbf{y} | \\text{BOS}, \\text{Ich}, \\mathbf{c}). $$\n", "\n", "\n", "And so on until at step $i=6$, the $\\text{EOS}$ vector is sampled from $\\mathbf{l}_6$ and the decoding is finished. The resulting target sequence amounts to $\\mathbf{Y}_{1:6} = \\{\\mathbf{y}_1, \\ldots, \\mathbf{y}_6\\}$, which is \"Ich will ein Auto kaufen\" in our example above. \n", "\n", "To sum it up, an RNN-based encoder-decoder model, represented by $f_{\\theta_{\\text{enc}}}$ and $ p_{\\theta_{\\text{dec}}}$ defines the distribution \n", "$p(\\mathbf{Y}_{1:m} | \\mathbf{X}_{1:n})$ by factorization:\n", "\n", "$$ p_{\\theta_{\\text{enc}}, \\theta_{\\text{dec}}}(\\mathbf{Y}_{1:m} | \\mathbf{X}_{1:n}) = \\prod_{i=1}^{m} p_{\\theta_{\\text{enc}}, \\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{X}_{1:n}) = \\prod_{i=1}^{m} p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{c}), \\text{ with } \\mathbf{c}=f_{\\theta_{enc}}(X). $$\n", "\n", "During inference, efficient decoding methods can auto-regressively generate the target sequence $\\mathbf{Y}_{1:m}$.\n", "\n", "The RNN-based encoder-decoder model took the NLG community by storm. In 2016, Google announced to fully replace its heavily feature engineered translation service by a single RNN-based encoder-decoder model (see [here](https://www.oreilly.com/radar/what-machine-learning-means-for-software-development/#:~:text=Machine%20learning%20is%20already%20making,of%20code%20in%20Google%20Translate.)). \n", "\n", "Nevertheless, RNN-based encoder-decoder models have two pitfalls. First, RNNs suffer from the vanishing gradient problem, making it very difficult to capture long-range dependencies, *cf.* [Hochreiter et al. (2001)](https://www.bioinf.jku.at/publications/older/ch7.pdf). Second, the inherent recurrent architecture of RNNs prevents efficient parallelization when encoding, *cf.* [Vaswani et al. (2017)](https://arxiv.org/abs/1706.03762). \n", "\n", "---\n", "\n", "${}^1$ The original quote from the paper is \"*Despite their flexibility and power, DNNs can only be applied to problems whose inputs and targets can be sensibly encoded with vectors of fixed dimensionality*\", which is slightly adapted here.\n", "\n", "${}^2$ The same holds essentially true for convolutional neural networks (CNNs). While an input sequence of variable length can be fed into a CNN, the dimensionality of the target will always be dependent on the input dimensionality or fixed to a specific value.\n", "\n", "${}^3$ At the first step, the hidden state is initialized as a zero vector and fed to the RNN together with the first input vector $\\mathbf{x}_1$.\n", "\n", "${}^4$ A neural network can define a probability distribution over all words, *i.e.* $p(\\mathbf{y} | \\mathbf{c}, \\mathbf{Y}_{0: i-1})$ as follows. First, the network defines a mapping from the inputs $\\mathbf{c}, \\mathbf{Y}_{0: i-1}$ to an embedded vector representation $\\mathbf{y'}$, which corresponds to the RNN target vector. The embedded vector representation $\\mathbf{y'}$ is then passed to the \"language model head\" layer, which means that it is multiplied by the *word embedding matrix*, *i.e.* $\\mathbf{Y}^{\\text{vocab}}$, so that a score between $\\mathbf{y'}$ and each encoded vector $\\mathbf{y} \\in \\mathbf{Y}^{\\text{vocab}}$ is computed. The resulting vector is called the logit vector $\\mathbf{l} = \\mathbf{Y}^{\\text{vocab_size}} \\mathbf{y'}$ and can be mapped to a probability distribution over all words by applying a softmax operation: $p(\\mathbf{y} | \\mathbf{c}) = \\text{Softmax}(\\mathbf{Y}^{\\text{vocab_size}} \\mathbf{y'}) = \\text{Softmax}(\\mathbf{l})$.\n", "\n", "${}^5$ Beam-search decoding is an example of such a decoding method. Different decoding methods are out of scope for this notebook. The reader is advised to refer to this [interactive notebook](https://huggingface.co/blog/how-to-generate) on decoding methods.\n", "\n", "${}^6$ [Sutskever et al. (2014)](https://arxiv.org/abs/1409.3215) reverses the order of the input so that in the above example the input vectors would correspond to $\\mathbf{x}_1 = \\text{car}$, $\\mathbf{x}_2 = \\text{a}$, $\\mathbf{x}_3 = \\text{buy}$, $\\mathbf{x}_4 = \\text{to}$, $\\mathbf{x}_5 = \\text{want}$, $\\mathbf{x}_6 = \\text{I}$ and $\\mathbf{x}_7 = \\text{EOS}$. The motivation is to allow for a shorter connection between corresponding word pairs such as $\\mathbf{x}_6 = \\text{I}$ and $\\mathbf{y}_1 = \\text{Ich}$. The research group emphasizes that the reversal of the input sequence was a key reason for their model's improved performance on machine translation.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ILBApEhcMGy1" }, "source": [ "## **Encoder-Decoder** \n", "\n", "In 2017, Vaswani et al. introduced the **Transformer** and thereby gave birth to *transformer-based* encoder-decoder models. \n", "\n", "Analogous to RNN-based encoder-decoder models, transformer-based encoder-decoder models consist of an encoder and a decoder which are both stacks of *residual attention blocks*. \n", "The key innovation of transformer-based encoder-decoder models is that such residual attention blocks can process an input sequence $\\mathbf{X}_{1:n}$ of variable length $n$ without exhibiting a recurrent structure. Not relying on a recurrent structure allows transformer-based encoder-decoders to be highly parallelizable, which makes the model orders of magnitude more computationally efficient than RNN-based encoder-decoder models on modern hardware.\n", "\n", "As a reminder, to solve a *sequence-to-sequence* problem, we need to find a mapping of an input sequence $\\mathbf{X}_{1:n}$ to an output sequence $\\mathbf{Y}_{1:m}$ of variable length $m$. Let's see how transformer-based encoder-decoder models are used to find such a mapping.\n", "\n", "Similar to RNN-based encoder-decoder models, the transformer-based encoder-decoder models define a conditional distribution of target vectors $\\mathbf{Y}_{1:n}$ given an input sequence $\\mathbf{X}_{1:n}$:\n", "\n", "$$\n", "p_{\\theta_{\\text{enc}}, \\theta_{\\text{dec}}}(\\mathbf{Y}_{1:m} | \\mathbf{X}_{1:n}).\n", "$$\n", "\n", "The transformer-based encoder part encodes the input sequence $\\mathbf{X}_{1:n}$ to a *sequence* of *hidden states* $\\mathbf{\\overline{X}}_{1:n}$, thus defining the mapping: \n", "\n", "$$ f_{\\theta_{\\text{enc}}}: \\mathbf{X}_{1:n} \\to \\mathbf{\\overline{X}}_{1:n}. $$\n", "\n", "The transformer-based decoder part then models the conditional probability distribution of the target vector sequence $\\mathbf{Y}_{1:n}$ given the sequence of encoded hidden states $\\mathbf{\\overline{X}}_{1:n}$:\n", "\n", "$$ p_{\\theta_{dec}}(\\mathbf{Y}_{1:n} | \\mathbf{\\overline{X}}_{1:n}).$$\n", "\n", "By Bayes' rule, this distribution can be factorized to a product of conditional probability distribution of the target vector $\\mathbf{y}_i$ given the encoded hidden states $\\mathbf{\\overline{X}}_{1:n}$ and all previous target vectors $\\mathbf{Y}_{0:i-1}$:\n", "\n", "$$\n", "p_{\\theta_{dec}}(\\mathbf{Y}_{1:n} | \\mathbf{\\overline{X}}_{1:n}) = \\prod_{i=1}^{n} p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{\\overline{X}}_{1:n}). $$\n", "\n", "The transformer-based decoder hereby maps the sequence of encoded hidden states $\\mathbf{\\overline{X}}_{1:n}$ and all previous target vectors $\\mathbf{Y}_{0:i-1}$ to the *logit* vector $\\mathbf{l}_i$. The logit vector $\\mathbf{l}_i$ is then processed by the *softmax* operation to define the conditional distribution $p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{\\overline{X}}_{1:n})$, just as it is done for RNN-based decoders. \n", "However, in contrast to RNN-based decoders, the distribution of the target vector $\\mathbf{y}_i$ is *explicitly* (or directly) conditioned on all previous target vectors $\\mathbf{y}_0, \\ldots, \\mathbf{y}_{i-1}$ as we will see later in more detail.\n", "The 0th target vector $\\mathbf{y}_0$ is hereby represented by a special \"begin-of-sentence\" $\\text{BOS}$ vector.\n", "\n", "Having defined the conditional distribution $p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{\\overline{X}}_{1:n})$, we can now *auto-regressively* generate the output and thus define a mapping of an input sequence $\\mathbf{X}_{1:n}$ to an output sequence $\\mathbf{Y}_{1:m}$ at inference.\n", "\n", "Let's visualize the complete process of *auto-regressive* generation of *transformer-based* encoder-decoder models.\n", "\n", "![texte du lien](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/encoder_decoder/EncoderDecoder.png)\n", "\n", "The transformer-based encoder is colored in green and the transformer-based decoder is colored in red. As in the previous section, we show how the English sentence \"I want to buy a car\", represented by $\\mathbf{x}_1 = \\text{I}$, $\\mathbf{x}_2 = \\text{want}$, $\\mathbf{x}_3 = \\text{to}$, $\\mathbf{x}_4 = \\text{buy}$, $\\mathbf{x}_5 = \\text{a}$, $\\mathbf{x}_6 = \\text{car}$, and $\\mathbf{x}_7 = \\text{EOS}$ is translated into German: \"Ich will ein Auto kaufen\" defined as $\\mathbf{y}_0 = \\text{BOS}$, $\\mathbf{y}_1 = \\text{Ich}$, $\\mathbf{y}_2 = \\text{will}$, $\\mathbf{y}_3 = \\text{ein}$, $\\mathbf{y}_4 = \\text{Auto}, \\mathbf{y}_5 = \\text{kaufen}$, and $\\mathbf{y}_6=\\text{EOS}$.\n", "\n", "To begin with, the encoder processes the complete input sequence $\\mathbf{X}_{1:7}$ = \"I want to buy a car\" (represented by the light green vectors) to a contextualized encoded sequence $\\mathbf{\\overline{X}}_{1:7}$. *E.g.* $\\mathbf{\\overline{x}}_4$ defines an encoding that depends not only on the input $\\mathbf{x}_4$ = \"buy\", but also on all other words \"I\", \"want\", \"to\", \"a\", \"car\" and \"EOS\", *i.e.* the context. \n", "\n", "Next, the input encoding $\\mathbf{\\overline{X}}_{1:7}$ together with the BOS vector, *i.e.* $\\mathbf{y}_0$, is fed to the decoder. The decoder processes the inputs $\\mathbf{\\overline{X}}_{1:7}$ and $\\mathbf{y}_0$ to the first logit $\\mathbf{l}_1$ (shown in darker red) to define the conditional distribution of the first target vector $\\mathbf{y}_1$:\n", "\n", "$$ p_{\\theta_{enc, dec}}(\\mathbf{y} | \\mathbf{y}_0, \\mathbf{X}_{1:7}) = p_{\\theta_{enc, dec}}(\\mathbf{y} | \\text{BOS}, \\text{I want to buy a car EOS}) = p_{\\theta_{dec}}(\\mathbf{y} | \\text{BOS}, \\mathbf{\\overline{X}}_{1:7}). $$\n", "\n", "Next, the first target vector $\\mathbf{y}_1$ = $\\text{Ich}$ is sampled from the distribution (represented by the grey arrows) and can now be fed to the decoder again. The decoder now processes both $\\mathbf{y}_0$ = \"BOS\" and $\\mathbf{y}_1$ = \"Ich\" to define the conditional distribution of the second target vector $\\mathbf{y}_2$:\n", "\n", "$$ p_{\\theta_{dec}}(\\mathbf{y} | \\text{BOS Ich}, \\mathbf{\\overline{X}}_{1:7}). $$\n", "\n", "We can sample again and produce the target vector $\\mathbf{y}_2$ = \"will\". We continue in auto-regressive fashion until at step 6 the EOS vector is sampled from the conditional distribution: \n", "\n", "$$ \\text{EOS} \\sim p_{\\theta_{dec}}(\\mathbf{y} | \\text{BOS Ich will ein Auto kaufen}, \\mathbf{\\overline{X}}_{1:7}). $$\n", "\n", "And so on in auto-regressive fashion.\n", "\n", "It is important to understand that the encoder is only used in the first forward pass to map $\\mathbf{X}_{1:n}$ to $\\mathbf{\\overline{X}}_{1:n}$.\n", "As of the second forward pass, the decoder can directly make use of the previously calculated encoding $\\mathbf{\\overline{X}}_{1:n}$. \n", "For clarity, let's illustrate the first and the second forward pass for our example above.\n", "\n", "![texte du lien](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/encoder_decoder/EncoderDecoder_step_by_step.png)\n", "\n", "As can be seen, only in step $i=1$ do we have to encode \"I want to buy a car EOS\" to $\\mathbf{\\overline{X}}_{1:7}$. At step $i=2$, the contextualized encodings of \"I want to buy a car EOS\" are simply reused by the decoder.\n", "\n", "In 🤗Transformers, this auto-regressive generation is done under-the-hood when calling the `.generate()` method. Let's use one of our translation models to see this in action.\n" ] }, { "cell_type": "code", "metadata": { "id": "BMTXlLtB0Wc-", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "c1eab154-eca6-48ad-f455-c45cc1517c80" }, "source": [ "from transformers import MarianMTModel, MarianTokenizer\n", "\n", "tokenizer = MarianTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-en-de\")\n", "model = MarianMTModel.from_pretrained(\"Helsinki-NLP/opus-mt-en-de\")\n", "\n", "# create ids of encoded input vectors\n", "input_ids = tokenizer(\"I want to buy a car\", return_tensors=\"pt\").input_ids\n", "\n", "# translate example\n", "output_ids = model.generate(input_ids)[0]\n", "\n", "# decode and print\n", "print(tokenizer.decode(output_ids))" ], "execution_count": 11, "outputs": [ { "output_type": "stream", "text": [ " Ich will ein Auto kaufen\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "dO6KM8GQ1Bbd" }, "source": [ "Calling `.generate()` does many things under-the-hood. First, it passes the `input_ids` to the encoder. Second, it passes a pre-defined token, which is the $\\text{}$ symbol in the case of `MarianMTModel` along with the encoded `input_ids` to the decoder. Third, it applies the beam search decoding mechanism to auto-regressively sample the next output word of the *last* decoder output ${}^1$. For more detail on how beam search decoding works, one is advised to read [this](https://huggingface.co/blog/how-to-generate) blog post.\n", "\n", "In the Appendix, we have included a code snippet that shows how a simple generation method can be implemented \"from scratch\". To fully understand how *auto-regressive* generation works under-the-hood, it is highly recommended to read the Appendix.\n", "\n", "To sum it up:\n", "- The transformer-based encoder defines a mapping from the input sequence $\\mathbf{X}_{1:n}$ to a contextualized encoding sequence $\\mathbf{\\overline{X}}_{1:n}$.\n", "- The transformer-based decoder defines the conditional distribution \n", "$p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{\\overline{X}}_{1:n})$. \n", "- Given an appropriate decoding mechanism, the output sequence $\\mathbf{Y}_{1:m}$ can auto-regressively be sampled from $p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{\\overline{X}}_{1:n}), \\forall i \\in \\{1, \\ldots, m\\}$. \n", "\n", "Great, now that we have gotten a general overview of how *transformer-based* encoder-decoder models work, we can dive deeper into both the encoder and decoder part of the model. More specifically, we will see exactly how the encoder makes use of the self-attention layer to yield a sequence of context-dependent vector encodings and how self-attention layers allow for efficient parallelization.\n", "Then, we will explain in detail how the self-attention layer works in the decoder model and how the decoder is conditioned on the encoder's output with *cross-attention* layers to define the conditional distribution $p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{\\overline{X}}_{1:n})$.\n", "Along, the way it will become obvious how transformer-based encoder-decoder models solve the long-range dependencies problem of RNN-based encoder-decoder models.\n", "\n", "---\n", "${}^1$ In the case of `\"Helsinki-NLP/opus-mt-en-de\"`, the decoding parameters can be accessed [here](https://s3.amazonaws.com/models.huggingface.co/bert/Helsinki-NLP/opus-mt-en-de/config.json), where we can see that model applies beam search with `num_beams=6`." ] }, { "cell_type": "markdown", "metadata": { "id": "3jsJBw-nOIE_" }, "source": [ "## **Encoder**\n", "\n", "As mentioned in the previous section, the *transformer-based* encoder maps the input sequence to a contextualized encoding sequence:\n", "\n", "$$ f_{\\theta_{\\text{enc}}}: \\mathbf{X}_{1:n} \\to \\mathbf{\\overline{X}}_{1:n}. $$\n", "\n", "Taking a closer look at the architecture, the transformer-based encoder is a stack of residual *encoder blocks*.\n", "Each encoder block consists of a **bi-directional** self-attention layer, followed by two feed-forward layers. For simplicity, we disregard the normalization layers in this notebook. Also, we will not further discuss the role of the two feed-forward layers, but simply see it as a final vector-to-vector mapping required in each encoder block ${}^1$.\n", "The bi-directional self-attention layer puts each input vector $\\mathbf{x'}_j, \\forall j \\in \\{1, \\ldots, n\\}$ into relation with all input vectors $\\mathbf{x'}_1, \\ldots, \\mathbf{x'}_n$ and by doing so transforms the input vector $\\mathbf{x'}_j$ to a more \"refined\" contextual representation of itself, defined as $\\mathbf{x''}_j$.\n", "Thereby, the first encoder block transforms each input vector of the input sequence $\\mathbf{X}_{1:n}$ (shown in light green below) from a *context-independent* vector representation to a *context-dependent* vector representation, and the following encoder blocks further refine this contextual representation until the last encoder block outputs the final contextual encoding $\\mathbf{\\overline{X}}_{1:n}$ (shown in darker green below).\n", "\n", "Let's visualize how the encoder processes the input sequence \"I want to buy a car EOS\" to a contextualized encoding sequence. Similar to RNN-based encoders, transformer-based encoders also add a special \"end-of-sequence\" input vector to the input sequence to hint to the model that the input vector sequence is finished ${}^2$.\n", "\n", "![texte du lien](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/encoder_decoder/Encoder_block.png)\n", "\n", "Our exemplary *transformer-based* encoder is composed of three encoder blocks, whereas the second encoder block is shown in more detail in the red box on the right for the first three input vectors $\\mathbf{x}_1, \\mathbf{x}_2 and \\mathbf{x}_3$.\n", "The bi-directional self-attention mechanism is illustrated by the fully-connected graph in the lower part of the red box and the two feed-forward layers are shown in the upper part of the red box. As stated before, we will focus only on the bi-directional self-attention mechanism.\n", "\n", "As can be seen each output vector of the self-attention layer $\\mathbf{x''}_i, \\forall i \\in \\{1, \\ldots, 7\\}$ depends *directly* on *all* input vectors $\\mathbf{x'}_1, \\ldots, \\mathbf{x'}_7$. This means, *e.g.* that the input vector representation of the word \"want\", *i.e.* $\\mathbf{x'}_2$, is put into direct relation with the word \"buy\", *i.e.* $\\mathbf{x'}_4$, but also with the word \"I\",*i.e.* $\\mathbf{x'}_1$. The output vector representation of \"want\", *i.e.* $\\mathbf{x''}_2$, thus represents a more refined contextual representation for the word \"want\".\n", "\n", "Let's take a deeper look at how bi-directional self-attention works.\n", "Each input vector $\\mathbf{x'}_i$ of an input sequence $\\mathbf{X'}_{1:n}$ of an encoder block is projected to a key vector $\\mathbf{k}_i$, value vector $\\mathbf{v}_i$ and query vector $\\mathbf{q}_i$ (shown in orange, blue, and purple respectively below) through three trainable weight matrices $\\mathbf{W}_q, \\mathbf{W}_v, \\mathbf{W}_k$:\n", "\n", "$$ \\mathbf{q}_i = \\mathbf{W}_q \\mathbf{x'}_i,$$\n", "$$ \\mathbf{v}_i = \\mathbf{W}_v \\mathbf{x'}_i,$$ \n", "$$ \\mathbf{k}_i = \\mathbf{W}_k \\mathbf{x'}_i, $$\n", "$$ \\forall i \\in \\{1, \\ldots n \\}.$$\n", "\n", "Note, that the **same** weight matrices are applied to each input vector $\\mathbf{x}_i, \\forall i \\in \\{i, \\ldots, n\\}$. After projecting each input vector $\\mathbf{x}_i$ to a query, key, and value vector, each query vector $\\mathbf{q}_j, \\forall j \\in \\{1, \\ldots, n\\}$ is compared to all key vectors $\\mathbf{k}_1, \\ldots, \\mathbf{k}_n$. The more similar one of the key vectors $\\mathbf{k}_1, \\ldots \\mathbf{k}_n$ is to a query vector $\\mathbf{q}_j$, the more important is the corresponding value vector $\\mathbf{v}_j$ for the output vector $\\mathbf{x''}_j$. More specifically, an output vector $\\mathbf{x''}_j$ is defined as the weighted sum of all value vectors $\\mathbf{v}_1, \\ldots, \\mathbf{v}_n$ plus the input vector $\\mathbf{x'}_j$. Thereby, the weights are proportional to the cosine similarity between $\\mathbf{q}_j$ and the respective key vectors $\\mathbf{k}_1, \\ldots, \\mathbf{k}_n$, which is mathematically expressed by $\\textbf{Softmax}(\\mathbf{K}_{1:n}^\\intercal \\mathbf{q}_j)$ as illustrated in the equation below.\n", "For a complete description of the self-attention layer, the reader is advised to take a look at [this](http://jalammar.github.io/illustrated-transformer/) blog post or the original [paper](https://arxiv.org/abs/1706.03762).\n", "\n", "Alright, this sounds quite complicated. Let's illustrate the bi-directional self-attention layer for one of the query vectors of our example above. For simplicity, it is assumed that our exemplary *transformer-based* decoder uses only a single attention head `config.num_heads = 1` and that no normalization is applied.\n", "\n", "![texte du lien](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/encoder_decoder/encoder_detail.png)\n", "\n", "On the left, the previously illustrated second encoder block is shown again and on the right, an in detail visualization of the bi-directional self-attention mechanism is given for the second input vector $\\mathbf{x'}_2$ that corresponds to the input word \"want\".\n", "At first all input vectors $\\mathbf{x'}_1, \\ldots, \\mathbf{x'}_7$ are projected to their respective query vectors $\\mathbf{q}_1, \\ldots, \\mathbf{q}_7$ (only the first three query vectors are shown in purple above), value vectors $\\mathbf{v}_1, \\ldots, \\mathbf{v}_7$ (shown in blue), and key vectors $\\mathbf{k}_1, \\ldots, \\mathbf{k}_7$ (shown in orange). The query vector $\\mathbf{q}_2$ is then multiplied by the transpose of all key vectors, *i.e.* $\\mathbf{K}_{1:7}^{\\intercal}$ followed by the softmax operation to yield the *self-attention weights*. The self-attention weights are finally multiplied by the respective value vectors and the input vector $\\mathbf{x'}_2$ is added to output the \"refined\" representation of the word \"want\", *i.e.* $\\mathbf{x''}_2$ (shown in dark green on the right).\n", "The whole equation is illustrated in the upper part of the box on the right.\n", "The multiplication of $\\mathbf{K}_{1:7}^{\\intercal}$ and $\\mathbf{q}_2$ thereby makes it possible to compare the vector representation of \"want\" to all other input vector representations \"I\", \"to\", \"buy\", \"a\", \"car\", \"EOS\" so that the self-attention weights mirror the importance each of the other input vector representations $\\mathbf{x'}_j \\text{, with } j \\ne 2$ for the refined representation $\\mathbf{x''}_2$ of the word \"want\".\n", "\n", "To further understand the implications of the bi-directional self-attention layer, let's assume the following sentence is processed: \"*The house is beautiful and well located in the middle of the city where it is easily accessible by public transport*\". The word \"it\" refers to \"house\", which is 12 \"positions away\". In transformer-based encoders, the bi-directional self-attention layer performs a single mathematical operation to put the input vector of \"house\" into relation with the input vector of \"it\" (compare to the first illustration of this section). In contrast, in an RNN-based encoder, a word that is 12 \"positions away\", would require at least 12 mathematical operations meaning that in an RNN-based encoder a linear number of mathematical operations are required. This makes it much harder for an RNN-based encoder to model long-range contextual representations.\n", "Also, it becomes clear that a transformer-based encoder is much less prone to lose important information than an RNN-based encoder-decoder model because the sequence length of the encoding is kept the same, *i.e.* $\\textbf{len}(\\mathbf{X}_{1:n}) = \\textbf{len}(\\mathbf{\\overline{X}}_{1:n}) = n$, while an RNN compresses the length from $\\textbf{len}((\\mathbf{X}_{1:n}) = n$ to just $\\textbf{len}(\\mathbf{c}) = 1$, which makes it very difficult for RNNs to effectively encode long-range dependencies between input words.\n", "\n", "In addition to making long-range dependencies more easily learnable, we can see that the Transformer architecture is able to process text in parallel.Mathematically, this can easily be shown by writing the self-attention formula as a product of query, key, and value matrices:\n", "\n", "$$\\mathbf{X''}_{1:n} = \\mathbf{V}_{1:n} \\text{Softmax}(\\mathbf{Q}_{1:n}^\\intercal \\mathbf{K}_{1:n}) + \\mathbf{X'}_{1:n}. $$\n", "\n", "The output $\\mathbf{X''}_{1:n} = \\mathbf{x''}_1, \\ldots, \\mathbf{x''}_n$ is computed via a series of matrix multiplications and a softmax operation, which can be parallelized effectively. \n", "Note, that in an RNN-based encoder model, the computation of the hidden state $\\mathbf{c}$ has to be done sequentially: Compute hidden state of the first input vector $\\mathbf{x}_1$, then compute the hidden state of the second input vector that depends on the hidden state of the first hidden vector, etc. The sequential nature of RNNs prevents effective parallelization and makes them much more inefficient compared to transformer-based encoder models on modern GPU hardware.\n", "\n", "Great, now we should have a better understanding of a) how transformer-based encoder models effectively model long-range contextual representations and b) how they efficiently process long sequences of input vectors. \n", "\n", "Now, let's code up a short example of the encoder part of our `MarianMT` encoder-decoder models to verify that the explained theory holds in practice.\n", "\n", "---\n", "${}^1$ An in-detail explanation of the role the feed-forward layers play in transformer-based models is out-of-scope for this notebook. It is argued in [Yun et. al, (2017)](https://arxiv.org/pdf/1912.10077.pdf) that feed-forward layers are crucial to map each contextual vector $\\mathbf{x'}_i$ individually to the desired output space, which the *self-attention* layer does not manage to do on its own. It should be noted here, that each output token $\\mathbf{x'}$ is processed by the same feed-forward layer. For more detail, the reader is advised to read the paper.\n", "\n", "${}^2$ However, the EOS input vector does not have to be appended to the input sequence, but has been shown to improve performance in many cases. In contrast to the *0th* $\\text{BOS}$ target vector of the transformer-based decoder is required as a starting input vector to predict a first target vector.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "-21mDkdwHusE" }, "source": [ "%%capture\n", "from transformers import MarianMTModel, MarianTokenizer\n", "import torch\n", "\n", "tokenizer = MarianTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-en-de\")\n", "model = MarianMTModel.from_pretrained(\"Helsinki-NLP/opus-mt-en-de\")\n" ], "execution_count": 12, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "KUSpvzCs0398", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "764528e6-a722-4180-ca91-430853aa22a3" }, "source": [ "embeddings = model.get_input_embeddings()\n", "\n", "# create ids of encoded input vectors\n", "input_ids = tokenizer(\"I want to buy a car\", return_tensors=\"pt\").input_ids\n", "\n", "# pass input_ids to encoder\n", "encoder_hidden_states = model.base_model.encoder(input_ids, return_dict=True).last_hidden_state\n", "\n", "# change the input slightly and pass to encoder\n", "input_ids_perturbed = tokenizer(\"I want to buy a house\", return_tensors=\"pt\").input_ids\n", "encoder_hidden_states_perturbed = model.base_model.encoder(input_ids_perturbed, return_dict=True).last_hidden_state\n", "\n", "# compare shape and encoding of first vector\n", "print(f\"Length of input embeddings {embeddings(input_ids).shape[1]}. Length of encoder_hidden_states {encoder_hidden_states.shape[1]}\")\n", "\n", "# compare values of word embedding of \"I\" for input_ids and perturbed input_ids\n", "print(\"Is encoding for `I` equal to its perturbed version?: \", torch.allclose(encoder_hidden_states[0, 0], encoder_hidden_states_perturbed[0, 0], atol=1e-3))" ], "execution_count": 13, "outputs": [ { "output_type": "stream", "text": [ "Length of input embeddings 7. Length of encoder_hidden_states 7\n", "Is encoding for `I` equal to its perturbed version?: False\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "PnRx-IamHnvN" }, "source": [ "We compare the length of the input word embeddings, *i.e.* `embeddings(input_ids)` corresponding to $\\mathbf{X}_{1:n}$, with the length of the `encoder_hidden_states`, corresponding to $\\mathbf{\\overline{X}}_{1:n}$.\n", "Also, we have forwarded the word sequence \"I want to buy a car\" and a slightly perturbated version \"I want to buy a house\" through the encoder to check if the first output encoding, corresponding to \"I\", differs when only the last word is changed in the input sequence.\n", "\n", "As expected the output length of the input word embeddings and encoder output encodings, *i.e.* $\\textbf{len}(\\mathbf{X}_{1:n})$ and $\\textbf{len}(\\mathbf{\\overline{X}}_{1:n})$, is equal.\n", "Second, it can be noted that the values of the encoded output vector of $\\mathbf{\\overline{x}}_1 = \\text{\"I\"}$ are different when the last word is changed from \"car\" to \"house\". This however should not come as a surprise if one has understood bi-directional self-attention.\n", "\n", "On a side-note, *autoencoding* models, such as BERT, have the exact same architecture as *transformer-based* encoder models. *Autoencoding* models leverage this architecture for massive self-supervised pre-training on open-domain text data so that they can map any word sequence to a deep bi-directional representation. In [Devlin et al. (2018)](https://arxiv.org/abs/1810.04805), the authors show that a pre-trained BERT model with a single task-specific classification layer on top can achieve SOTA results on eleven NLP tasks. All *autoencoding* models of 🤗Transformers can be found [here](https://huggingface.co/transformers/model_summary.html#autoencoding-models)." ] }, { "cell_type": "markdown", "metadata": { "id": "FYItWQClOCX2" }, "source": [ "## **Decoder**\n", "\n", "As mentioned in the *Encoder-Decoder* section, the *transformer-based* decoder defines the conditional probability distribution of a target sequence given the contextualized encoding sequence:\n", "\n", "$$ p_{\\theta_{dec}}(\\mathbf{Y}_{1: m} | \\mathbf{\\overline{X}}_{1:n}), $$\n", "\n", "which by Bayes' rule can be decomposed into a product of conditional distributions of the next target vector given the contextualized encoding sequence and all previous target vectors:\n", "\n", "$$ p_{\\theta_{dec}}(\\mathbf{Y}_{1:m} | \\mathbf{\\overline{X}}_{1:n}) = \\prod_{i=1}^{m} p_{\\theta_{dec}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{\\overline{X}}_{1:n}). $$\n", "\n", "Let's first understand how the transformer-based decoder defines a probability distribution. The transformer-based decoder is a stack of *decoder blocks* followed by a dense layer, the \"LM head\".\n", "The stack of decoder blocks maps the contextualized encoding sequence $\\mathbf{\\overline{X}}_{1:n}$ and a target vector sequence prepended by the $\\text{BOS}$ vector and cut to the last target vector, *i.e.* $\\mathbf{Y}_{0:i-1}$, to an encoded sequence of target vectors $\\mathbf{\\overline{Y}}_{0: i-1}$. Then, the \"LM head\" maps the encoded sequence of target vectors $\\mathbf{\\overline{Y}}_{0: i-1}$ to a sequence of logit vectors $\\mathbf{L}_{1:n} = \\mathbf{l}_1, \\ldots, \\mathbf{l}_n$, whereas the dimensionality of each logit vector $\\mathbf{l}_i$ corresponds to the size of the vocabulary. This way, for each $i \\in \\{1, \\ldots, n\\}$ a probability distribution over the whole vocabulary can be obtained by applying a softmax operation on $\\mathbf{l}_i$. These distributions define the conditional distribution: \n", "\n", "$$p_{\\theta_{dec}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{\\overline{X}}_{1:n}), \\forall i \\in \\{1, \\ldots, n\\},$$ \n", "\n", "respectively. The \"LM head\" is often tied to the transpose of the word embedding matrix, *i.e.* $\\mathbf{W}_{\\text{emb}}^{\\intercal} = \\left[\\mathbf{y}^1, \\ldots, \\mathbf{y}^{\\text{vocab}}\\right]^{\\intercal}$ ${}^1$. Intuitively this means that for all $i \\in \\{0, \\ldots, n - 1\\}$ the \"LM Head\" layer compares the encoded output vector $\\mathbf{\\overline{y}}_i$ to all word embeddings in the vocabulary $\\mathbf{y}^1, \\ldots, \\mathbf{y}^{\\text{vocab}}$ so that the logit vector $\\mathbf{l}_{i+1}$ represents the similarity scores between the encoded output vector and each word embedding. The softmax operation simply transformers the similarity scores to a probability distribution. For each $i \\in \\{1, \\ldots, n\\}$, the following equations hold:\n", "\n", "$$ p_{\\theta_{dec}}(\\mathbf{y} | \\mathbf{\\overline{X}}_{1:n}, \\mathbf{Y}_{0:i-1})$$\n", "$$ = \\text{Softmax}(f_{\\theta_{\\text{dec}}}(\\mathbf{\\overline{X}}_{1:n}, \\mathbf{Y}_{0:i-1}))$$\n", "$$ = \\text{Softmax}(\\mathbf{W}_{\\text{emb}}^{\\intercal} \\mathbf{\\overline{y}}_{i-1})$$\n", "$$ = \\text{Softmax}(\\mathbf{l}_i). $$\n", "\n", "Putting it all together, in order to model the conditional distribution of a target vector sequence $\\mathbf{Y}_{1: m}$, the target vectors $\\mathbf{Y}_{1:m-1}$ prepended by the special $\\text{BOS}$ vector, *i.e.* $\\mathbf{y}_0$, are first mapped together with the contextualized encoding sequence $\\mathbf{\\overline{X}}_{1:n}$ to the logit vector sequence $\\mathbf{L}_{1:m}$. \n", "Consequently, each logit target vector $\\mathbf{l}_i$ is transformed into a conditional probability distribution of the target vector $\\mathbf{y}_i$ using the softmax operation. Finally, the conditional probabilities of all target vectors $\\mathbf{y}_1, \\ldots, \\mathbf{y}_m$ multiplied together to yield the conditional probability of the complete target vector sequence:\n", "\n", "$$ p_{\\theta_{dec}}(\\mathbf{Y}_{1:m} | \\mathbf{\\overline{X}}_{1:n}) = \\prod_{i=1}^{m} p_{\\theta_{dec}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i-1}, \\mathbf{\\overline{X}}_{1:n}).$$\n", "\n", "In contrast to transformer-based encoders, in transformer-based decoders, the encoded output vector $\\mathbf{\\overline{y}}_i$ should be a good representation of the *next* target vector $\\mathbf{y}_{i+1}$ and not of the input vector itself. Additionally, the encoded output vector $\\mathbf{\\overline{y}}_i$ should be conditioned on all contextualized encoding sequence $\\mathbf{\\overline{X}}_{1:n}$. To meet these requirements each decoder block consists of a **uni-directional** self-attention layer, followed by a **cross-attention** layer and two feed-forward layers ${}^2$.\n", "The uni-directional self-attention layer puts each of its input vectors $\\mathbf{y'}_j$ only into relation with all previous input vectors $\\mathbf{y'}_i, \\text{ with } i \\le q$ for all $j \\in \\{1, \\ldots, n\\}$ to model the probability distribution of the next target vectors. \n", "The cross-attention layer puts each of its input vectors $\\mathbf{y''}_j$ into relation with all contextualized encoding vectors $\\mathbf{\\overline{X}}_{1:n}$ to condition the probability distribution of the next target vectors on the input of the encoder as well.\n", "\n", "Alright, let's visualize the *transformer-based* decoder for our English to German translation example.\n", "\n", "![](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/encoder_decoder/encoder_decoder_detail.png)\n", "\n", "We can see that the decoder maps the input $\\mathbf{Y}_{0:5}$ \"BOS\", \"Ich\", \"will\", \"ein\", \"Auto\", \"kaufen\" (shown in light red) together with the contextualized sequence of \"I\", \"want\", \"to\", \"buy\", \"a\", \"car\", \"EOS\", *i.e.* $\\mathbf{\\overline{X}}_{1:7}$ (shown in dark green) to the logit vectors $\\mathbf{L}_{1:6}$ (shown in dark red).\n", "\n", "Applying a softmax operation on each $\\mathbf{l}_1, \\mathbf{l}_2, \\ldots, \\mathbf{l}_5$ can thus define the conditional probability distributions:\n", "\n", "$$ p_{\\theta_{dec}}(\\mathbf{y} | \\text{BOS}, \\mathbf{\\overline{X}}_{1:7}), $$\n", "$$ p_{\\theta_{dec}}(\\mathbf{y} | \\text{BOS Ich}, \\mathbf{\\overline{X}}_{1:7}), $$\n", "$$ \\ldots, $$\n", "$$ p_{\\theta_{dec}}(\\mathbf{y} | \\text{BOS Ich will ein Auto kaufen}, \\mathbf{\\overline{X}}_{1:7}). $$\n", "\n", "The overall conditional probability of:\n", "\n", "$$ p_{\\theta_{dec}}(\\text{Ich will ein Auto kaufen EOS} | \\mathbf{\\overline{X}}_{1:n})$$\n", "\n", "can therefore be computed as the following product:\n", "\n", "$$ p_{\\theta_{dec}}(\\text{Ich} | \\text{BOS}, \\mathbf{\\overline{X}}_{1:7}) \\times \\ldots \\times p_{\\theta_{dec}}(\\text{EOS} | \\text{BOS Ich will ein Auto kaufen}, \\mathbf{\\overline{X}}_{1:7}). $$\n", "\n", "The red box on the right shows a decoder block for the first three target vectors $\\mathbf{y}_0, \\mathbf{y}_1, \\mathbf{y}_2$. In the lower part, the uni-directional self-attention mechanism is illustrated and in the middle, the cross-attention mechanism is illustrated. Let's first focus on uni-directional self-attention.\n", "\n", "As in bi-directional self-attention, in uni-directional self-attention, the query vectors $\\mathbf{q}_0, \\ldots, \\mathbf{q}_{m-1}$ (shown in purple below), key vectors $\\mathbf{k}_0, \\ldots, \\mathbf{k}_{m-1}$ (shown in orange below), and value vectors $\\mathbf{v}_0, \\ldots, \\mathbf{v}_{m-1}$ (shown in blue below) are projected from their respective input vectors $\\mathbf{y'}_0, \\ldots, \\mathbf{y}_{m-1}$ (shown in light red below). However, in uni-directional self-attention, each query vector $\\mathbf{q}_i$ is compared *only* to its respective key vector and all previous ones, namely $\\mathbf{k}_0, \\ldots, \\mathbf{k}_i$ to yield the respective *attention weights*.\n", "This prevents an output vector $\\mathbf{y''}_j$ (shown in dark red below) to include any information about the following input vector $\\mathbf{y}_i, \\text{ with } i > 1$ for all $j \\in \\{0, \\ldots, m - 1 \\}$. As is the case in bi-directional self-attention, the attention weights are then multiplied by their respective value vectors and summed together.\n", "\n", "We can summarize uni-directional self-attention as follows: \n", "\n", "$$\\mathbf{y''}_i = \\mathbf{V}_{0: i} \\textbf{Softmax}(\\mathbf{K}_{0: i}^\\intercal \\mathbf{q}_i) + \\mathbf{y'}_i. $$\n", "\n", "Note that the index range of the key and value vectors is $0:i$ instead of $0: m-1$ which would be the range of the key vectors in bi-directional self-attention.\n", "\n", "Let's illustrate uni-directional self-attention for the input vector $\\mathbf{y'}_1$ for our example above.\n", "\n", "![](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/encoder_decoder/causal_attn.png)\n", "\n", "As can be seen $\\mathbf{y''}_1$ only depends on $\\mathbf{y'}_0$ and $\\mathbf{y'}_1$. Therefore, we put the vector representation of the word \"Ich\", *i.e.* $\\mathbf{y'}_1$ only into relation with itself and the \"BOS\" target vector, *i.e.* $\\mathbf{y'}_0$, but **not** with the vector representation of the word \"will\", *i.e.* $\\mathbf{y'}_2$.\n", "\n", "So why is it important that we use uni-directional self-attention in the decoder instead of bi-directional self-attention? \n", "As stated above, a transformer-based decoder defines a mapping from a sequence of input vector $\\mathbf{Y}_{0: m-1}$ to the logits corresponding to the **next** decoder input vectors, namely $\\mathbf{L}_{1:m}$.\n", "In our example, this means, *e.g.* that the input vector $\\mathbf{y}_1$ = \"Ich\" is mapped to the logit vector $\\mathbf{l}_2$, which is then used to predict the input vector $\\mathbf{y}_2$. Thus, if $\\mathbf{y'}_1$ would have access to the following input vectors $\\mathbf{Y'}_{2:5}$, the decoder would simply copy the vector representation of \"will\", *i.e.* $\\mathbf{y'}_2$, to be its output $\\mathbf{y''}_1$. This would be forwarded to the last layer so that the encoded output vector $\\mathbf{\\overline{y}}_1$ would essentially just correspond to the vector representation $\\mathbf{y}_2$. \n", "\n", "This is obviously disadvantageous as the transformer-based decoder would never learn to predict the next word given all previous words, but just copy the target vector $\\mathbf{y}_i$ through the network to $\\mathbf{\\overline{y}}_{i-1}$ for all $i \\in \\{1, \\ldots, m \\}$. In order to define a conditional distribution of the next target vector, the distribution cannot be conditioned on the next target vector itself. It does not make much sense to predict $\\mathbf{y}_i$ from $p(\\mathbf{y} | \\mathbf{Y}_{0:i}, \\mathbf{\\overline{X}})$ because the distribution is conditioned on the target vector it is supposed to model.\n", "The uni-directional self-attention architecture, therefore, allows us to define a *causal* probability distribution, which is necessary to effectively model a conditional distribution of the next target vector.\n", "\n", "Great! Now we can move to the layer that connects the encoder and decoder - the *cross-attention* mechanism!\n", "\n", "The cross-attention layer takes two vector sequences as inputs: the outputs of the uni-directional self-attention layer, *i.e.* $\\mathbf{Y''}_{0: m-1}$ and the contextualized encoding vectors $\\mathbf{\\overline{X}}_{1:n}$.\n", "As in the self-attention layer, the query vectors $\\mathbf{q}_0, \\ldots, \\mathbf{q}_{m-1}$ are projections of the output vectors of the previous layer, *i.e.* $\\mathbf{Y''}_{0: m-1}$. However, the key and value vectors $\\mathbf{k}_0, \\ldots, \\mathbf{k}_{m-1}$ and $\\mathbf{v}_0, \\ldots, \\mathbf{v}_{m-1}$ are projections of the contextualized encoding vectors $\\mathbf{\\overline{X}}_{1:n}$. Having defined key, value, and query vectors, a query vector $\\mathbf{q}_i$ is then compared to *all* key vectors and the corresponding score is used to weight the respective value vectors, just as is the case for *bi-directional* self-attention to give the output vector $\\mathbf{y'''}_i$ for all $i \\in \\{0, \\ldots, m-1\\}. Cross-attention can be summarized as follows:\n", "\n", "$$\n", "\\mathbf{y'''}_i = \\mathbf{V}_{1:n} \\textbf{Softmax}(\\mathbf{K}_{1: n}^\\intercal \\mathbf{q}_i) + \\mathbf{y''}_i.\n", "$$\n", "\n", "Note that the index range of the key and value vectors is $1:n$ corresponding to the number of contextualized encoding vectors.\n", "\n", "Let's visualize the cross-attention mechanism Let's for the input vector $\\mathbf{y''}_1$ for our example above.\n", "\n", "![](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/encoder_decoder/cross_attention.png)\n", "\n", "We can see that the query vector $\\mathbf{q}_1$ (shown in purple) is derived from $\\mathbf{y''}_1$ and therefore relies on a vector representation of the word \"Ich\". The query vector $\\mathbf{q}_1$ (shown in red) is then compared to the key vectors $\\mathbf{k}_1, \\ldots, \\mathbf{k}_7$ (shown in yellow) corresponding to the contextual encoding representation of all encoder input vectors $\\mathbf{X}_{1:n}$ = \"I want to buy a car EOS\". This puts the vector representation of \"Ich\" into direct relation with all encoder input vectors. Finally, the attention weights are multiplied by the value vectors $\\mathbf{v}_1, \\ldots, \\mathbf{v}_7$ (shown in turquoise) to yield in addition to the input vector $\\mathbf{y''}_1$ the output vector $\\mathbf{y'''}_1$ (shown in dark red). \n", "\n", "So intuitively, what happens here exactly? \n", "Each output vector $\\mathbf{y'}_i$ is a weighted sum of all value projections of the encoder inputs $\\mathbf{v}_{1}, \\ldots, \\mathbf{v}_7$ plus the input vector itself $\\mathbf{y}_i$ (*c.f.* illustrated formula above). The key mechanism to understand is the following: Depending on how similar a query projection of the *input decoder vector* $\\mathbf{q}_i$ is to a key projection of the *encoder input vector* $\\mathbf{k}_j$, the more important is the value projection of the encoder input vector $\\mathbf{v}_j$. In loose terms this means, the more \"related\" a decoder input representation is to an encoder input representation, the more does the input representation influence the decoder output representation. \n", "\n", "Cool! Now we can see how this architecture nicely conditions each output vector $\\mathbf{y'''}_i$ on the interaction between the encoder input vectors $\\mathbf{\\overline{X}}_{1:n}$ and the input vector $\\mathbf{y''}_i$. \n", "Another important observation at this point is that the architecture is completely independent of the number $n$ of contextualized encoding vectors $\\mathbf{\\overline{X}}_{1:n}$ on which the output vector $\\mathbf{y'''}_i$ is conditioned on. All projection matrices $\\mathbf{W}^{\\text{cross}}_{k}$ and $\\mathbf{W}^{\\text{cross}}_{v}$ to derive the key vectors $\\mathbf{k}_1, \\ldots, \\mathbf{k}_n$ and the value vectors $\\mathbf{v}_1, \\ldots, \\mathbf{v}_n$ respectively are shared across all positions $1, \\ldots, n$ and all value vectors $\\mathbf{v}_1, \\ldots, \\mathbf{v}_n$ are summed together to a single weighted averaged vector.\n", "Now it becomes obvious as well, why the transformer-based decoder does not suffer from the long-range dependency problem, the RNN-based decoder suffers from. Because each decoder logit vector is *directly* dependent on every single encoded output vector, the number of mathematical operations to compare the first encoded output vector and the last decoder logit vector amounts essentially to just one.\n", "\n", "To conclude, the uni-directional self-attention layer is responsible for conditioning each output vector on all previous decoder input vectors and the current input vector and the cross-attention layer is responsible to further condition each output vector on all encoded input vectors.\n", "\n", "To verify our theoretical understanding, let's continue our code example from the encoder section above.\n", "\n", "---\n", "${}^1$ The word embedding matrix $\\mathbf{W}_{\\text{emb}}$ gives each input word a unique *context-independent* vector representation. This matrix is often fixed as the \"LM Head\" layer. However, the \"LM Head\" layer can very well consist of a completely independent \"encoded vector-to-logit\" weight mapping.\n", "\n", "${}^2$ Again, an in-detail explanation of the role the feed-forward layers play in transformer-based models is out-of-scope for this notebook. It is argued in [Yun et. al, (2017)](https://arxiv.org/pdf/1912.10077.pdf) that feed-forward layers are crucial to map each contextual vector $\\mathbf{x'}_i$ individually to the desired output space, which the *self-attention* layer does not manage to do on its own. It should be noted here, that each output token $\\mathbf{x'}$ is processed by the same feed-forward layer. For more detail, the reader is advised to read the paper.\n" ] }, { "cell_type": "code", "metadata": { "id": "8iZC1-5bb_hQ", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "32959489-4040-4bb1-f5bc-92342de793c9" }, "source": [ "from transformers import MarianMTModel, MarianTokenizer\n", "import torch\n", "\n", "tokenizer = MarianTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-en-de\")\n", "model = MarianMTModel.from_pretrained(\"Helsinki-NLP/opus-mt-en-de\")\n", "embeddings = model.get_input_embeddings()\n", "\n", "# get encoded input vectors\n", "input_ids = tokenizer(\"I want to buy a car\", return_tensors=\"pt\").input_ids\n", "\n", "# create ids of encoded input vectors\n", "decoder_input_ids = tokenizer(\" Ich will ein\", return_tensors=\"pt\", add_special_tokens=False).input_ids\n", "\n", "# pass decoder input_ids and encoded input vectors to decoder\n", "decoder_output_vectors = model.base_model.decoder(decoder_input_ids).last_hidden_state\n", "\n", "# derive embeddings by multiplying decoder outputs with embedding weights\n", "lm_logits = torch.nn.functional.linear(decoder_output_vectors, embeddings.weight, bias=model.final_logits_bias)\n", "\n", "# change the decoder input slightly\n", "decoder_input_ids_perturbed = tokenizer(\" Ich will das\", return_tensors=\"pt\", add_special_tokens=False).input_ids\n", "decoder_output_vectors_perturbed = model.base_model.decoder(decoder_input_ids_perturbed).last_hidden_state\n", "lm_logits_perturbed = torch.nn.functional.linear(decoder_output_vectors_perturbed, embeddings.weight, bias=model.final_logits_bias)\n", "\n", "# compare shape and encoding of first vector\n", "print(f\"Shape of decoder input vectors {embeddings(decoder_input_ids).shape}. Shape of decoder logits {lm_logits.shape}\")\n", "\n", "# compare values of word embedding of \"I\" for input_ids and perturbed input_ids\n", "print(\"Is encoding for `Ich` equal to its perturbed version?: \", torch.allclose(lm_logits[0, 0], lm_logits_perturbed[0, 0], atol=1e-3))" ], "execution_count": 16, "outputs": [ { "output_type": "stream", "text": [ "Shape of decoder input vectors torch.Size([1, 5, 512]). Shape of decoder logits torch.Size([1, 5, 58101])\n", "Is encoding for `Ich` equal to its perturbed version?: True\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Fb5k_NzGgr8y" }, "source": [ "We compare the output shape of the decoder input word embeddings, *i.e.* `embeddings(decoder_input_ids)` (corresponds to $\\mathbf{Y}_{0: 4}$, here `` corresponds to BOS and \"Ich will das\" is tokenized to 4 tokens) with the dimensionality of the `lm_logits`(corresponds to $\\mathbf{L}_{1:5}$).\n", "Also, we have passed the word sequence \" Ich will das\" and a slightly perturbated version \" Ich will das\" together with the `encoder_output_vectors` through the encoder to check if the second `lm_logit`, corresponding to \"Ich\", differs when only the last word is changed in the input sequence (\"ein\" -> \"das\").\n", "\n", "As expected the output shapes of the decoder input word embeddings and lm_logits, *i.e.* the dimensionality of $\\mathbf{Y}_{0: 4}$ and $\\mathbf{L}_{1:5}$ are different in the last dimension. While the sequence length is the same (=5), the dimensionality of a decoder input word embedding corresponds to `model.config.hidden_size`, whereas the dimensionality of a `lm_logit` corresponds to the vocabulary size `model.config.vocab_size`, as explained above.\n", "Second, it can be noted that the values of the encoded output vector of $\\mathbf{l}_1 = \\text{\"Ich\"}$ are the same when the last word is changed from \"ein\" to \"das\". This however should not come as a surprise if one has understood uni-directional self-attention.\n", "\n", "On a final side-note, *auto-regressive* models, such as GPT2, have the same architecture as *transformer-based* decoder models **if** one removes the cross-attention layer because stand-alone auto-regressive models are not conditioned on any encoder outputs. \n", "So auto-regressive models are essentially the same as *auto-encoding* models but replace bi-directional attention with uni-directional attention. These models can also be pre-trained on massive open-domain text data to show impressive performances on natural language generation (NLG) tasks. In [Radford et al. (2019)](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf), the authors show that a pre-trained GPT2 model can achieve SOTA or close to SOTA results on a variety of NLG tasks without much fine-tuning. All *auto-regressive* models of 🤗Transformers can be found [here](https://huggingface.co/transformers/model_summary.html#autoregressive-models).\n", "\n", "Alright, that's it! Now, you should have gotten a good understanding of *transformer-based* encoder-decoder models and how to use them with the 🤗Transformers library.\n", "\n", "Thanks a lot to Victor Sanh, Sasha Rush, Sam Shleifer, Oliver Åstrand, \n", "‪Ted Moskovitz and Kristian Kyvik for giving valuable feedback." ] }, { "cell_type": "markdown", "metadata": { "id": "MF0rjj-TmjYa" }, "source": [ "## **Appendix**\n", "\n", "As mentioned above, the following code snippet shows how one can program \n", "a simple generation method for *transformer-based* encoder-decoder models.\n", "Here, we implement a simple *greedy* decoding method using `torch.argmax` to sample the target vector.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "KSSQdRoOZG-w", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d54985ae-10ab-4738-d2e2-26affcc77128" }, "source": [ "from transformers import MarianMTModel, MarianTokenizer\n", "import torch\n", "\n", "tokenizer = MarianTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-en-de\")\n", "model = MarianMTModel.from_pretrained(\"Helsinki-NLP/opus-mt-en-de\")\n", "\n", "# create ids of encoded input vectors\n", "input_ids = tokenizer(\"I want to buy a car\", return_tensors=\"pt\").input_ids\n", "\n", "# create BOS token\n", "decoder_input_ids = tokenizer(\"\", add_special_tokens=False, return_tensors=\"pt\").input_ids\n", "\n", "assert decoder_input_ids[0, 0].item() == model.config.decoder_start_token_id, \"`decoder_input_ids` should correspond to `model.config.decoder_start_token_id`\"\n", "\n", "# STEP 1\n", "\n", "# pass input_ids to encoder and to decoder and pass BOS token to decoder to retrieve first logit\n", "outputs = model(input_ids, decoder_input_ids=decoder_input_ids, return_dict=True)\n", "\n", "# get encoded sequence\n", "encoded_sequence = (outputs.encoder_last_hidden_state,)\n", "# get logits\n", "lm_logits = outputs.logits\n", "\n", "# sample last token with highest prob\n", "next_decoder_input_ids = torch.argmax(lm_logits[:, -1:], axis=-1)\n", "\n", "# concat\n", "decoder_input_ids = torch.cat([decoder_input_ids, next_decoder_input_ids], axis=-1)\n", "\n", "# STEP 2\n", "\n", "# reuse encoded_inputs and pass BOS + \"Ich\" to decoder to second logit\n", "lm_logits = model(None, encoder_outputs=encoded_sequence, decoder_input_ids=decoder_input_ids, return_dict=True).logits\n", "\n", "# sample last token with highest prob again\n", "next_decoder_input_ids = torch.argmax(lm_logits[:, -1:], axis=-1)\n", "\n", "# concat again\n", "decoder_input_ids = torch.cat([decoder_input_ids, next_decoder_input_ids], axis=-1)\n", "\n", "# STEP 3\n", "lm_logits = model(None, encoder_outputs=encoded_sequence, decoder_input_ids=decoder_input_ids, return_dict=True).logits\n", "next_decoder_input_ids = torch.argmax(lm_logits[:, -1:], axis=-1)\n", "decoder_input_ids = torch.cat([decoder_input_ids, next_decoder_input_ids], axis=-1)\n", "\n", "# let's see what we have generated so far!\n", "print(f\"Generated so far: {tokenizer.decode(decoder_input_ids[0], skip_special_tokens=True)}\")\n", "\n", "# This can be written in a loop as well.\n" ], "execution_count": 15, "outputs": [ { "output_type": "stream", "text": [ "Generated so far: Ich will ein\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "TfU7AqfHcDBS" }, "source": [ "In this code example, we show exactly what was described earlier. We pass an input \"I want to buy a car\" together with the $\\text{BOS}$ token to the encoder-decoder model and sample from the first logit $\\mathbf{l}_1$ (*i.e.* the first `lm_logits` line). Hereby, our sampling strategy is simple to greedily choose the next decoder input vector that has the highest probability. In an auto-regressive fashion, we then pass the sampled decoder input vector together with the previous inputs to the encoder-decoder model and sample again. We repeat this a third time. As a result, the model has generated the words \"Ich Ich\". The first word is spot-on! The second word is not that great. \n", "We can see here, that a good decoding method is key for a successful sequence generation from a given model distribution.\n", "\n", " In practice, more complicated decoding methods are used to sample the `lm_logits`. Most of which are covered in [this](https://huggingface.co/blog/how-to-generate) blog post." ] } ] } ================================================ FILE: Evaluating_Big_Bird_on_TriviaQA.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Evaluating Big Bird on TriviaQA", "provenance": [], "collapsed_sections": [], "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "cc720864436943c98de25e071a26843c": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "state": { "_view_name": "HBoxView", "_dom_classes": [], "_model_name": "HBoxModel", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_view_count": null, "_view_module_version": "1.5.0", "box_style": "", "layout": "IPY_MODEL_d65d4adf92ee43b6873859c52471b3ea", "_model_module": "@jupyter-widgets/controls", 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"grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "lbNZdYkugq7-" }, "source": [ "# Evaluation of a model using 🤗nlp\n", "\n", "*This notebook shows how to evaluate **BigBird** on **TriviaQA** .*\n", "\n", "- The [`datasets`](https://github.com/huggingface/datasets) library allows simple and intuitive access to nlp datasets and metrics.\n", "\n", "- **Big Bird** is a transformer-based model for long-range sequence modeling introduced by *Manzil Zaheer, et. al* (see paper [here](https://arxiv.org/abs/2007.14062)) and can now be accessed via Transformers via the [TODO: docs]().\n", "\n", "- **TriviaQA** is a reading comprehension dataset containing question-answer-evidence triplets (see paper here [here](https://homes.cs.washington.edu/~eunsol/papers/acl17jcwz.pdf))." ] }, { "cell_type": "markdown", "metadata": { "id": "ckDfoClXj-mm" }, "source": [ "We will evaluate a pretrained `BigBirdForQuestionAnswering` model on the *validation* dataset of **TriviaQA**. Along the way, this notebook will show you how `datasets` can be used for effortless preprocessing of data and analysis of the results." ] }, { "cell_type": "markdown", "metadata": { "id": "PGg2btE2mY0v" }, "source": [ "Alright! Let's start by installing the `datasets` library and loading *TriviaQA*. " ] }, { "cell_type": "markdown", "metadata": { "id": "KLb6vCflSqcI" }, "source": [ "### Installs and Imports" ] }, { "cell_type": "markdown", "metadata": { "id": "jALyuLQLHj4e" }, "source": [ "Install `transformers` & `datasets`." ] }, { "cell_type": "code", "metadata": { "id": "FXHkpUVuft4o" }, "source": [ "%%capture\n", "!pip install datasets==1.5.0\n", "!pip install git+https://github.com/vasudevgupta7/transformers.git@add_big_bird # TODO: replace with new pip version eventually\n", "\n", "import datasets\n", "import torch" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "TVQCLkYbS0Nm" }, "source": [ "### Data Preprocessing & Cleaning " ] }, { "cell_type": "markdown", "metadata": { "id": "gpOqRdcjnaBT" }, "source": [ "The total *TriviaQA* dataset has a size of 17 GB once processed.\n", "Downloading and preprocessing the dataset will take around *15 minutes*. ☕\n", "Afterwards the data is serialized in *Arrow* format, so that subsequent call can reuse the processed dataset.\n", "\n", "**Note**: If you want to evaluate *BigBird* on the full validation set this notebook will run for *ca.* 6 hours ⌛. On 5% of the data it takes around 30 minutes.\n", "\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YusOKo5FgkYH", "outputId": "6d98b5eb-1e05-4b42-ad8d-4a9f75e50475" }, "source": [ "validation_dataset = datasets.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:5%]\") # remove [:5%] to run on full validation set" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Reusing dataset trivia_qa (/root/.cache/huggingface/datasets/trivia_qa/rc/1.1.0/c1b17d80bfdd2f43b13e4b5741847f704115379ecebcb21e0917d2e8ddaa1ff1)\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "G_LFkvD_sx9H" }, "source": [ "First, let's get an overview of the dataset 🧐." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mJTko97rwQGF", "outputId": "23fb02df-0868-4131-aa7d-eb479bb8c524" }, "source": [ "validation_dataset" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Dataset({\n", " features: ['question', 'question_id', 'question_source', 'entity_pages', 'search_results', 'answer'],\n", " num_rows: 18669\n", "})" ] }, "metadata": { "tags": [] }, "execution_count": 20 } ] }, { "cell_type": "markdown", "metadata": { "id": "QHsmt1ULwTug" }, "source": [ "Alright, there are ~19K data samples in the validation step.\n", "\n", "Next, let's check out the datatset's structure." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DjAVcrAitsU5", "outputId": "07f7ec7d-3cbb-43cc-e657-10fb834edb85" }, "source": [ "\n", "validation_dataset.info.features" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'answer': {'aliases': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n", " 'matched_wiki_entity_name': Value(dtype='string', id=None),\n", " 'normalized_aliases': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n", " 'normalized_matched_wiki_entity_name': Value(dtype='string', id=None),\n", " 'normalized_value': Value(dtype='string', id=None),\n", " 'type': Value(dtype='string', id=None),\n", " 'value': Value(dtype='string', id=None)},\n", " 'entity_pages': Sequence(feature={'doc_source': Value(dtype='string', id=None), 'filename': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'wiki_context': Value(dtype='string', id=None)}, length=-1, id=None),\n", " 'question': Value(dtype='string', id=None),\n", " 'question_id': Value(dtype='string', id=None),\n", " 'question_source': Value(dtype='string', id=None),\n", " 'search_results': Sequence(feature={'description': Value(dtype='string', id=None), 'filename': Value(dtype='string', id=None), 'rank': Value(dtype='int32', id=None), 'title': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None), 'search_context': Value(dtype='string', id=None)}, length=-1, id=None)}" ] }, "metadata": { "tags": [] }, "execution_count": 21 } ] }, { "cell_type": "markdown", "metadata": { "id": "7XqHiM5mt5t-" }, "source": [ "Alright, quite a lot of entries here! For Questions Answering, all we need is the *question*, the *context* and the *answer*. \n", "\n", "The **question** is a single entry, so we keep it.\n", "\n", "Because *BigBird* was trained on the Wikipedia part of *TriviaQA*, we will use `validation_dataset[\"entity_pages\"][\"wiki_context\"]` as our **context**. \n", "\n", "We can also see that there are multiple entries for the **answer**. In this use case, we define a correct output of the model as one that is one of the answer aliases `validation_dataset[\"answer\"][\"aliases\"]`. Lastly, we also keep `validation_dataset[\"answer\"][\"normalized_value\"]`. All other columns can be disregarded. \n", "\n", "Let's write the corresponding `.map(...)` function" ] }, { "cell_type": "code", "metadata": { "id": "GpEkZy_LvhEX" }, "source": [ "def format_dataset(example):\n", " # the context might be comprised of multiple contexts => me merge them here\n", " example[\"context\"] = \" \".join((\"\\n\".join(example[\"entity_pages\"][\"wiki_context\"])).split(\"\\n\"))\n", " example[\"targets\"] = example[\"answer\"][\"aliases\"]\n", " example[\"norm_target\"] = example[\"answer\"][\"normalized_value\"]\n", " return example" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DdELPXpidIPZ" }, "source": [ "and apply it so that the dataset is put into the format as defined above." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HPw6rzPIb1s5", "outputId": "53bd648e-a8da-4d25-bb1b-2421890987fc" }, "source": [ "validation_dataset = validation_dataset.map(format_dataset, remove_columns=[\"search_results\", \"question_source\", \"entity_pages\", \"answer\", \"question_id\"])" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Loading cached processed dataset at /root/.cache/huggingface/datasets/trivia_qa/rc/1.1.0/c1b17d80bfdd2f43b13e4b5741847f704115379ecebcb21e0917d2e8ddaa1ff1/cache-d5b916196656bd02.arrow\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "jRhh7AJrw3fM" }, "source": [ "Now, let's check out a couple of data samples. \n", "\n", "For this we define a function that picks `num_examples` random data samples." ] }, { "cell_type": "code", "metadata": { "id": "7I730mW9wNeo" }, "source": [ "from datasets import ClassLabel\n", "import random\n", "import pandas as pd\n", "from IPython.display import display, HTML\n", "\n", "def show_random_elements(dataset, num_examples=10):\n", " assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n", " picks = []\n", " for _ in range(num_examples):\n", " pick = random.randint(0, len(dataset)-1)\n", " while pick in picks:\n", " pick = random.randint(0, len(dataset)-1)\n", " picks.append(pick)\n", " \n", " df = pd.DataFrame(dataset[picks])\n", " display(HTML(df.to_html()))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "G8AVj9Lsf1w3" }, "source": [ "Let's run it." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "O6mpmTkef3rz", "outputId": "bb7675de-e5bb-42a0-daee-92e33a8752a2" }, "source": [ "show_random_elements(validation_dataset, num_examples=3)" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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0Peterhouse is a constituent college of the University of Cambridge, England. It is the oldest college of the university, having been founded in 1284 by Hugo de Balsham, Bishop of Ely and granted its charter by King Edward I. Today, Peterhouse has 226 undergraduates, 86 full-time graduate students and 45 fellows. The modern name of Peterhouse does not include the word \"college\". Despite being one of the smallest colleges, it has one of the highest overall endowments, more than £250 million, including property in central London such as the Albany apartment complex in Piccadilly. Peterhouse is widely considered the most traditional of the Cambridge colleges. It is one of the few that still seeks to insist that its members attend communal dinners, known as \"Hall\". Hall takes place in two sittings, with the second known as \"Formal Hall\", which consists of a three-course candlelit meal and which must be attended wearing gowns. At Formal Hall, the students rise as the fellows proceed in, a gong is rung, and two Latin graces are read. Academic performance tends to vary from year to year due to its very small student population; for example, Peterhouse came 25th in the Tompkins Table in 2007 but 7th in 2010, 12th in 2014 and 6th in 2015 (out of 29 colleges). The college has five Nobel laureates associated with it, either as former students or fellows: Sir John Kendrew, Sir Aaron Klug, Archer Martin, Max Perutz, and Michael Levitt. History Foundation The foundation of Peterhouse dates to 1280, when letters patent from Edward I dated Burgh, Suffolk, 24 December 1280 allowed Hugo de Balsham to keep a number of scholars in the Hospital of St John, where they were to live according to the rules of the scholars of Merton. After disagreement between the scholars and the Brethren of the Hospital, both requested a separation. As a result, in 1284 Balsham transferred the scholars to the present site with the purchase of two houses just outside the then Trumpington Gate to accommodate a Master and fourteen \"worthy but impoverished Fellows\". The Church of St Peter without Trumpington Gate was to be used by the scholars. Bishop Hugo de Balsham died in 1286, bequeathing 300 marks that were used to buy further land to the south of St Peter's Church, on which the college's Hall was built. The earliest surviving set of statutes for the college was given to it by the then Bishop of Ely, Simon Montacute, in 1344. Although based on those of Merton College, these statutes clearly display the lack of resources then available to the college. They were used in 1345 to defeat an attempt by Edward III to appoint a candidate of his own as scholar. In 1354-55, William Moschett set up a trust that resulted in nearly 70 acre of land at Fen Ditton being transferred to the College by 1391-92. The College's relative poverty was relieved in 1401 when it acquired the advowson and rectory of Hinton through the efforts of Bishop John Fordham and John Newton. During the reign of Elizabeth I, the college also acquired the area formerly known as Volney's Croft, which today is the area of St Peter's Terrace, the William Stone Building and the Scholars' Garden. 16th century onwards In 1553, Andrew Perne was appointed Master. His religious views were pragmatic enough to be favoured by both Mary I, who gave him the Deanery of Ely, and Elizabeth I. A contemporary joke was that the letters on the weather vane of St Peter's Church could represent \"Andrew Perne, Papist\" or \"Andrew Perne, Protestant\" according to which way the wind was blowing. Having previously been close to the reformist Regius Chair of Divinity, Martin Bucer, later as vice-chancellor of the university Perne would have Bucer's bones exhumed and burnt in Market Square. John Foxe in his Actes and Monuments singled this out as \"shameful railing\". There is a hole burnt in the middle of the relevant page in Perne's own copy of Foxe. Perne died in 1589, leaving a legacy to the college that funded a number of fellowships and scholarships, as well bequeathing an extensive collection of books. This collection and rare volumes since added to it is now known as the Perne Library. Between 1626 and 1634, the Master was Matthew Wren. Wren had previously accompanied Charles I on his journey to Spain to attempt to negotiate the Spanish Match. Wren was a firm supporter of Archbishop William Laud, and under Wren the college became known as a centre of Arminianism. This continued under the Mastership of John Cosin, who succeeded Wren in 1634. Under Cosin significant changes were made to the college's Chapel to bring it into line with Laud's idea of the \"beauty of holiness\". On 13 March 1643, in the early stages of the English Civil War, Cosin was expelled from his position by a Parliamentary ordinance from the Earl of Manchester. The Earl stated that he was deposed \"for his opposing the proceedings of Parliament, and other scandalous acts in the University\". On 21 December of the same year, statues and decorations in the Chapel were pulled down by a committee led by the Puritan zealot William Dowsing. The college was the first in the University to have electric lighting installed, when Lord Kelvin provided it for the Hall and Combination Room to celebrate the College's six-hundredth anniversary in 1883-1884. It was the second building in the country to get electric lighting, after the Palace of Westminster. Modern day In the 1980s Peterhouse acquired an association with Conservative, Thatcherite politics. Maurice Cowling and Roger Scruton were both influential fellows of the College and are sometimes described as key figures in the so-called \"Peterhouse right\" – an intellectual movement linked to Margaret Thatcher. The Conservative politicians Michael Portillo and Michael Howard both studied at Peterhouse. During this period, which coincided with the mastership of Hugh Trevor-Roper, the college endured a period of significant conflict amongst the fellowship, particularly between Trevor-Roper and Cowling. Buildings and grounds Peterhouse has its main site situated on Trumpington Street, to the south of Cambridge's town centre. The main portion of the college is just to the north of the Fitzwilliam Museum, and its grounds run behind the museum. The buildings date from a wide variety of times, and have been much altered over the years. The college is reputed to have been at least partially destroyed by fire in 1420. The entrance of the college has shifted through its lifetime as well, with the change being principally the result of the demolition of the row of houses that originally lined Trumpington Street on the east side of the college. In 1574, a map shows the entrance being on the south side of a single main court. The modern entrance is to the east, straight onto Trumpington Street. First Court The area closest to Trumpington Street is referred to as First Court. It is bounded to the north by the Burrough's Building (added in the 18th century), to the east by the street, to the south by the Porters' lodge and to the west by the chapel. Above the Porters' lodge is the Perne Library, named in honour of Andrew Perne, a former Master, and originally built in 1590 to house the collection that he donated to the college. It was extended towards the road in 1633 and features interior woodwork that was added in 1641-8 by William Ashley, who was also responsible for similar woodwork in the chapel. Electric lighting was added to the library in 1937. The area above the Perne Library was used as the Ward Library (the college's general purpose library) from 1952 to 1984, but that has now been moved to its own building in the north-west corner of the college site. The Burrough's Building The Burrough's Building is situated at the front of the college, parallel to the Chapel. It is named after its architect, Sir James Burrough, the Master of Caius, and was built in 1736. It is one of several Cambridge neo-Palladian buildings designed by Burrough. Others include the remodelling of the Hall and Old Court at Trinity Hall and the chapel at Clare College. The building is occupied by fellows and college offices. Old Court Old Court lies beyond the Chapel cloisters. To the south of the court is the dining hall, the only College building that survives from the 13th century. Between 1866 and 1870, the hall was restored by the architect George Gilbert Scott, Jr.. Under Scott, the timber roof was repaired and two old parlours merged to form a new Combination Room. The stained glass windows were also replaced with Pre-Raphaelite pieces by William Morris, Ford Madox Brown and Edward Burne-Jones. The fireplace (originally built in 1618) was restored with tiles by Morris, including depictions of St Peter and Hugo de Balsham. The hall was extensively renovated in 2006-7. The north and west sides of Old Court were added in the 15th century, and classicised in the 18th century. The chapel makes up the fourth, east side to the court. Rooms in Old Court are occupied by a mixture of fellows and undergraduates. The north side of the court also house Peterhouse's MCR (Middle Combination Room). Chapel Viewed from the main entrance to Peterhouse on Trumpington Street, the altar end of the Chapel is the most immediately visible building. The Chapel was built in 1628 when the Master of the time Matthew Wren (Christopher Wren's uncle) demolished the college's original hostels. Previously the college had employed the adjacent Church of St Mary the Less as its chapel. The Chapel was consecrated on 17 March 1632 by Dr Francis White, Bishop of Ely. The building's style reflects the contemporary religious trend towards Arminianism. The Laudian Gothic style of the Chapel mixes Renaissance details but incorporated them into a traditional Gothic building. The Chapel's Renaissance architecture contains a Pietà altarpiece and a striking ceiling of golden suns. Its placement in the centre of one side of a court, between open colonnades is unusual, being copied for a single other college (Emmanuel) by Christopher Wren. The original stained glass was destroyed by Parliamentarians in 1643, with only the east window's crucifixion scene (based on Rubens's Le Coup de Lance) surviving. The current side windows are by Max Ainmiller, and were added in 1855. The cloisters on each side of the Chapel date from the 17th century. Their design was classicised in 1709, while an ornamental porch was removed in 1755. The Peterhouse Partbooks, music manuscripts from the early years of the Chapel, survive, and are one of the most important collections of Tudor and Jacobean church music. The Chapel Choir, one of the smallest in Cambridge, has recently attracted wider interest for its regular performances of this material, some of which has not been heard since the 16th century. The restoration of the 1763 John Snetzler organ in the Chapel was by Noel Mander. The first person buried in the Chapel was Samuel Horne, a fellow of the college. Horne was probably chaplain. Gisborne Court Gisborne Court is accessible through an archway leading from the west side of Old Court. It was built in 1825-6. Its cost was met with part of a benefaction of 1817 from the Rev. Francis Gisborne, a former fellow. The court is built in white brick with stone dressings in a simple Gothic revival style from the designs of William McIntosh Brookes. Only three sides to the court were built, with the fourth side being a screen wall. The wall was demolished in 1939, leaving only its footing. Rooms in Gisborne Court are mainly occupied by undergraduates. Many previously housed distinguished alumni, including Lord Kelvin in I staircase. Whittle Building The Whittle Building, named after Petrean Frank Whittle, opened in the western side of Gisborne Court in early 2015. Designed in Neo-Gothic style by John Simpson Architects, it contains en-suite undergraduate accommodation, the student bar and common room, a function room, and a gym. Its design picks up on that of the original screen-wall that once stood in its place. Fen Court Beyond Gisborne Court is Fen Court, a 20th-century building partially on stilts. Fen Court was built between 1939 and 1941 from designs by H. C. Hughes and his partner Peter Bicknell. It was amongst the earliest buildings in Cambridge designed in the style of the Modern Movement pioneered by Walter Gropius at the Bauhaus. The carved panel by Anthony Foster over the entrance doorway evokes the mood in Britain as the building was completed. It bears the inscription DE PROFUNDIS CLAMAVI MCMXL — \"out of the depths have I cried out 1940\". These are the first words of Psalm 130, one of the Penitential Psalms. Alongside the inscription is a depiction of St Peter being saved from the sea. An adjacent bath-house, known as the Birdwood Building, used to make up the western side of Gisborne Court. This was also designed by Hughes and Bicknell, and was built between 1932 and 1934. It was demolished in 2013 to make way for the new Whittle Building. Ward Library The north-west corner of the main site is occupied by former Victorian warehouses containing the Ward Library as well as a theatre and function room. The building it is housed in was originally the University's Museum of Classical Archaeology and was designed by Basil Champneys in 1883. It was adapted to its modern purpose by Robert Potter in 1982. It opened in its current form as a library in 1984. In recent years, the final gallery of the old museum building has been converted into a reading room, named the Gunn Gallery, after Dr Chan Gunn. Gardens While officially being named the Grove, the grounds to the south of Gisborne Court have been known as the Deer Park since deer were brought there in the 19th century. During that period it achieved fame as the smallest deer park in England. After the First World War the deer sickened and passed their illness onto stock that had been imported from the Duke of Portland's estate at Welbeck Abbey in an attempt to improve the situation. There are no longer any deer. The remainder of the college's gardens divide into areas known as the Fellows' Garden, just to the south of Old Court, and the Scholars' Garden, at the south end of the site, surrounding the William Stone Building. The William Stone Building The William Stone Building stands in the Scholars' Garden and was funded by a £100,000 bequest from William Stone (1857–1958), a former scholar of the college. Erected in 1963-4, to a design by Sir Leslie Martin and Sir Colin St John Wilson, it is an eight-storey brick tower. It houses a mixture of fellows and undergraduates. It has been refurbished, converting the rooms to en-suite. Trumpington Street The college also occupies a number of buildings on Trumpington Street. The Master's Lodge The Master's Lodge is situated across Trumpington Street from the College, and was bequeathed to the College in 1727 by a fellow, Dr Charles Beaumont, son of the 30th Master of the college, Joseph Beaumont. It is built in red brick in the Queen Anne style. The Hostel The Hostel is situated next to the Master's Lodge. It was built in a neo-Georgian style in 1926 from designs by Thomas Henry Lyon. The Hostel was intended to be part of a larger complex but only one wing was built. It currently houses undergraduates and some fellows. During World War II the London School of Economics was housed in The Hostel and nearby buildings, at the invitation of the Master and Fellows. Cosin Court Behind the Hostel lies Cosin Court, which provides accommodation for fellows, mature, postgraduate and married students. The court is named for John Cosin (1594–1672) who was successively Master of Peterhouse, Vice-Chancellor of Cambridge University and Prince-Bishop of Durham. St Peter's Terrace This row of Georgian townhouses houses first-year undergraduates, fellows, and some graduate students in basement flats. It is directly in front of the William Stone Building. Arms The College has, during its history, used five different coats of arms. The one currently in use has two legitimate blazons. The first form is the original grant by Robert Cooke, Clarenceux King of Arms, in 1575: Or four pallets Gules within a border of the last charged with eight ducal coronets of the first. The College did, however, habitually use a version with three pallets and this was allowed at the Herald's Visitation of Cambridgeshire in 1684. The latter version (with three pallets) was officially adopted by the Governing Body in 1935. The construction of the arms is that of the founder, Hugo de Balsham, surrounded by the crowns of the See of Ely. Grace Peterhouse and Jesus College are the only two colleges to have two separate halves to their grace, the first being a standard grace, and the second a quotation of 1 John 4:16. People associated with Peterhouse Members of Peterhouse — as masters, fellows (including honorary fellows) or students — are known as Petreans. Nobel laureates Peterhouse has five Nobel laureates associated with it, either as former students or fellows. * John Kendrew - Chemistry (1962) for determining the first atomic structures of proteins using X-ray crystallography. * Sir Aaron Klug - Chemistry (1982) for his development of crystallographic electron microscopy. * Michael Levitt - Chemistry (2013) for the development of multiscale models for complex chemical systems. * Archer Martin - Chemistry (1952) for his invention of partition chromatography. * Max Perutz - Chemistry (1962) for determining the first atomic structures of proteins using X-ray crystallography. Public servants * Elizabeth Butler-Sloss, Baroness Butler-Sloss - First female Lord Justice of Appeal, 1988–1999, President of the Family Division, High Court of Justice, 1999–2005. * The Duke of Grafton, Prime Minister of Great Britain, 1768-1770. * Michael Howard, Leader of the Conservative Party, 2003-2005. * Christopher Meyer, British Ambassador to the United States, 1997-2003. * Michael Portillo, Cabinet Minister, 1992-1997. * Nicholas Stern, author of the Stern Review on climate change. * John Whitgift, Archbishop of Canterbury, 1583-1604. * David Wilson, Governor of Hong Kong, 1987-1992. Academics, artists, writers File:World Debate - Niall Ferguson crop.jpgNiall FergusonHistorian File:PortraitThomasGrayByJohnGilesEccart1747to1748.jpg|Thomas GrayPoet File:CGreenwood2006-06Radiohead.jpg|Colin GreenwoodBassist, Radiohead File:David_mitchell.jpg|David MitchellActor/comedian (Mitchell and Webb) and writer * Denis Brogan, historian. * Herbert Butterfield, historian, Master of Peterhouse, 1955–1968, Vice-Chancellor of the University of Cambridge, 1959–1961, and Regius Professor of Modern History, 1963-1968. * John Ashton Cannon, historian. * Thomas Campion, 16th century composer. * Alistair Cooke, Baron Lexden, official historian of the Conservative Party. * Patrick Cosgrave, journalist, author and advisor to Margaret Thatcher. * Maurice Cowling, historian. * Mike Dash, historian. * Roger Deakin, naturalist writer. * Robert Dudley Edwards, historian. * Ronan Fanning, historian. * John Finnemore, writer and actor. * Thomas Gray, poet. * Colin Greenwood, Radiohead bassist. * Luke Hughes, furniture designer. * Harold James, historian. * David Knowles, Regius Professor of Modern History, 1954–1963. * Denis Mack Smith, historian. * F.X. Martin, historian and first Catholic priest admitted to Cambridge since the Reformation. * James Mason, actor. * Sam Mendes, Academy Award-winning film director (for American Beauty) and four-time Laurence Olivier Award winner. * David Mitchell, actor, comedian and writer. * Michael Postan, historian. * William Ridgeway, classical scholar and Disney Professor of Archaeology, Cambridge University. * Harold Temperley, historian * Hugh Trevor-Roper, historian. * Adolphus William Ward, President, Royal Historical Society, 1899–1901. * Eudora Welty, Pulitzer Prize-winning American author. * Desmond Williams, historian. According to college tradition, Thomas Gray left Peterhouse for Pembroke College after being the victim of a practical joke played by undergraduates. Gray is supposed to have been afraid of fire, and had attached a bar outside his window to which a rope could be tied. After being woken by undergraduates with a fire made of shavings, Gray climbed down the rope but landed in a tub of water which had been placed below his window. Scientists and inventors File:CharlesBabbage.jpg|Charles Babbage Inventor of the difference engine, \"Father of the computer\" File:Cavendish Henry signature.jpg|Henry CavendishScientist, discoverer of hydrogen File:James Dewar.jpg|Sir James DewarChemist, physicist, inventor of the Dewar flask File:Lord Kelvin photograph.jpg|Lord KelvinInventor and pioneer in thermodynamics, electricity and telegraphy File:James Clerk Maxwell.png|James Clerk MaxwellFormulator of classical electromagnetic theory File:Portrait_of_Sir_John_Meurig_Thomas_by_John_Stanton_Ward,_RA_1995.jpgSir John Meurig ThomasChemist File:Frank_Whittle_CH_011867.jpg|Sir Frank WhittleInventor of the jet engine * Christopher Cockerell - Inventor of the hovercraft * Peter Guthrie Tait - Mathematician * William Hopkins - Mathematician * Edward John Routh - Mathematician * Klaus Roth - Mathematician and winner of the Fields Medal Essay prizes Peterhouse runs annually three essay prizes for History, Science and English. The Vellacott Historical Essay Prize, The Kelvin Science Prize, The Thomas Campion Prize are awarded by Peterhouse to members of the Lower Sixth or year twelve in a British Secondary School. Gallery File:Peterhouse College, Cambridge.jpg| Chapel and main entrance File:St. Peter's College Cambridge 2.jpg| Part of St Peter's College, view from the private gardens, 1815 File:St. Peter's College Cambridge.jpg| St Peter's College, Chapel, 1815 Anthony Frederick Blunt (26 September 1907 – 26 March 1983), known as Sir Anthony Blunt, KCVO, from 1956 to 1979, was a leading British art historian who in 1964, after being offered immunity from prosecution, confessed to having been a Soviet spy. He had been a member of the Cambridge Five, a group of spies working for the Soviet Union from some time in the 1930s to at least the early 1950s. A closely held secret for many years, his status was revealed publicly by Prime Minister Margaret Thatcher in November 1979, and he was stripped of his knighthood immediately thereafter. Blunt was Professor of the History of Art at the University of London, director of the Courtauld Institute of Art, and Surveyor of the Queen's Pictures. His monograph on the French Baroque painter Nicolas Poussin (1967) is still widely regarded as a watershed book in art history. His teaching text and reference work Art and Architecture in France 1500–1700, first published in 1953, reached its fifth edition in a slightly revised version by Richard Beresford in 1999, when it was still considered the best account of the subject. Early life Blunt was born in Bournemouth, Dorset, the third and youngest son of a vicar, the Revd (Arthur) Stanley Vaughan Blunt (1870–1929), and his wife, Hilda Violet (1880–1969), daughter of Henry Master of the Madras civil service. He was the brother of writer Wilfrid Jasper Walter Blunt and of numismatist Christopher Evelyn Blunt, and the grandnephew of poet Wilfrid Scawen Blunt. He was a third cousin of Elizabeth Bowes-Lyon, the late Queen Mother: his mother was the second cousin of Elizabeth's father Claude Bowes-Lyon, 14th Earl of Strathmore and Kinghorne. On occasions, Blunt and his two brothers, Christopher and Wilfrid, took afternoon tea at the Bowes-Lyon's London home in Bruton Street, Mayfair. Blunt's vicar father was assigned to Paris with the British embassy chapel, and so moved his family to the French capital for several years during Blunt's childhood. The young Anthony became fluent in French, and experienced intensely the artistic culture closely available to him, stimulating an interest which would last a lifetime and form the basis for his later career. He was educated at Marlborough College, where he joined the college's secret 'Society of Amici', in which he was a contemporary of Louis MacNeice (whose unfinished autobiography The Strings are False contains numerous references to Blunt), John Betjeman and Graham Shepard. He was remembered by historian John Edward Bowle, a year ahead of Blunt at Marlborough, as an intellectual prig, too preoccupied with the realm of ideas. He thought Blunt had too much ink in his veins and belonged to a world of rather prissy, cold-blooded, academic puritanism. Cambridge University He won a scholarship in mathematics to Trinity College, Cambridge. At that time, scholars in Cambridge University were allowed to skip Part I of the Tripos and complete Part II in two years. However, they could not earn a degree in less than three years, and hence Blunt spent four years at Trinity and switched to Modern Languages, eventually graduating in 1930 with a first class degree. He taught French at Cambridge and became a Fellow of Trinity College in 1932. His graduate research was in French art history and he travelled frequently to continental Europe in connection with his studies. Like Guy Burgess, Blunt was known to be homosexual, which was a criminal activity at that time in Britain. Both were members of the Cambridge Apostles (also known as the Conversazione Society), a clandestine Cambridge discussion group of 12 undergraduates, mostly from Trinity and King's Colleges who considered themselves to be the brightest minds in the university. Many were homosexual and Marxist at that time. Amongst other members, also later accused of being part of the Cambridge spy ring, were the American Michael Whitney Straight and Victor Rothschild who later worked for MI5. Rothschild gave Blunt £100 to purchase Eliezar and Rebecca by Nicolas Poussin. The painting was sold by Blunt's executors in 1985 for £100,000 (totalling £192,500 with tax remission ) and is now in the Fitzwilliam Museum.[http://www.fitzmuseum.cam.ac.uk/opac/search/cataloguedetail.html?&priref 2874&_function_xslt&_limit_ 10 Fitzwilliam Museum – OPAC Record] Recruitment to Soviet espionage There are numerous versions of how Blunt was recruited to the NKVD. As a Cambridge don, Blunt visited the Soviet Union in 1933, and was possibly recruited in 1934. In a press conference, Blunt claimed that Guy Burgess recruited him as a spy. Many sources suggest that Blunt remained at Cambridge and served as a talent-spotter. He may have identified Burgess, Kim Philby, Donald Maclean, John Cairncross and Michael Straight – all undergraduates at Trinity College a few years younger than he – as potential spies for the Soviets. Blunt said in his public confession that it was Burgess who converted him to the Soviet cause, after both had left Cambridge. Both were members of the Cambridge Apostles, and Burgess could have recruited Blunt or vice versa either at Cambridge University or later when both worked for British intelligence. Joining MI5 With the invasion of Poland by German and Soviet forces, Blunt joined the British Army in 1939. During the phoney war he served in France in the intelligence corps. When the Wehrmacht drove British forces back to Dunkirk in May 1940, he was evacuated by the Royal Navy. During that same year he was recruited to MI5, the Security Service. Before the war MI5 employed mostly former Indian policemen, for it was in India that the British Empire faced security threats. MI5 may have known Blunt's views, for an officer later claimed that it had been virtually running the Communist Party of Great Britain and complained about the cost of pension payments to its retired infiltrators. Blunt passed the results of Ultra intelligence from decrypted Enigma intercepts of Wehrmacht radio traffic from the Russian front. He also admitted to passing details of German spy rings, operating in the Soviet Union. Ultra was primarily working on the Kriegsmarine naval codes, which eventually helped win the Battle of the Atlantic, but as the war progressed Wehrmacht army codes were also broken. Sensitive receivers could pick up transmissions, relating to German war plans, from Berlin. There was great risk that, if the Germans discovered their codes had been compromised, they would change the settings of the Enigma wheels, blinding the codebreakers. Full details of the entire Operation Ultra were fully known by only four people, only one of whom routinely worked at Bletchley Park. Dissemination of Ultra information did not follow usual intelligence protocol but maintained its own communications channels. Military intelligence officers gave intercepts to Ultra liaisons, who in turn forwarded the intercepts to Bletchley Park. Information from decoded messages was then passed back to military leaders through the same channels. Thus, each link in the communications chain knew only one particular job and not the overall Ultra details. Nobody outside Bletchley Park knew the source. John Cairncross, another of the Cambridge Five, was posted from MI6 to work at Bletchley Park. Blunt admitted to recruiting Cairncross and may well have been the cut-out between Cairncross and the Soviet controllers. For although the Soviet Union was now an ally, Russians were not trusted. Some information concerned German preparations and detailed plans for the Battle of Kursk, the last decisive encounter on the Eastern Front. Malcolm Muggeridge, himself a wartime British agent, recalls meeting Kim Philby and Victor Rothschild, a friend of Blunt since Trinity College, Cambridge. He reported that at the Paris meeting in late 1955 Rothschild argued that much more Ultra material should have been given to Stalin. For once, Philby reportedly dropped his reserve, and agreed. During the war, Blunt attained the rank of major. In the final days of World War II in Europe, Blunt made a successful secret trip to Schloss Friedrichshof in Germany to retrieve sensitive letters between the Duke of Windsor and Adolf Hitler and other leading Nazis. George VI asked Blunt, who worked part-time at the Royal Library, to conduct the Royal Librarian, Owen Morshead, to Friedrichshof in March 1945 to liberate letters to the Empress Victoria, a daughter of Queen Victoria, and mother to Kaiser Wilhelm. Papers rescued by Morshead and Blunt were deposited in the Royal Archives. Suspicion and secret confession Some people knew of Blunt's role long before his public exposure. In 1948, demobilised army officer Philip Hay attended an interview at Buckingham Palace for the post of private secretary to the Dowager Duchess of Kent. After passing Blunt in a corridor, Sir Alan Lascelles, the King's private secretary, told Hay: \"That's our Russian spy.\" According to MI5 papers released in 2002, Moura Budberg, known as the Russian Mata Hari and suspected of being a double agent, reported in 1950 that Blunt was a member of the Communist Party, but this was ignored. According to Blunt himself, he never joined because Burgess persuaded him that he would be more valuable to the anti-fascist crusade by working with Burgess. He was certainly on friendly terms with Sir Dick White, the head of MI5 and later MI6, in the 1960s, and they used to spend Christmas together with Victor Rothschild in Rothschild's Cambridge house. His NKVD control had also become suspicious at the sheer amount of material he was passing over and suspected him of being a triple agent. Later, he was described by a KGB officer as an \"ideological shit\". With the defection of Burgess and Maclean to Moscow in May 1951, Blunt came under suspicion. He and Burgess had been friends since Cambridge. Maclean was in imminent danger due to decryptions from Venona as the messages were decrypted. Burgess returned on the Queen Mary to Southampton after being suspended from the British Embassy in Washington for his conduct. He was to warn Maclean, who now worked in the Foreign Office but was under surveillance and isolated from secret material. Blunt collected Burgess at Southampton Docks and took him to stay at his flat in London, although he later denied that he had warned the defecting pair. Blunt was interrogated by MI5 in 1952, but gave away little, if anything. Arthur Martin and Jim Skardon had interviewed Blunt 11 times since 1951, but Blunt had admitted nothing. Blunt was greatly distressed by Burgess's flight and, on 28 May 1951, confided in his friend Goronwy Rees, a fellow of All Souls College, Oxford, who had briefly supplied the NKVD with political information in 1938–39. Rees suggested that Burgess had gone to the Soviet Union because of his violent anti-Americanism and belief that America would involve Britain in a Third World War, and that he was a Soviet agent. Blunt suggested that this was not sufficient reason to denounce Burgess to MI5. He pointed out that \"Burgess was one of our oldest friends and to denounce him would not be the act of a friend.\" Blunt quoted E. M. Forster's belief that country was less important than friendship. He argued that \"Burgess had told me he was a spy in 1936 and I had not told anyone.\" Blunt was made a Knight Commander of the Royal Victorian Order in 1956 by Queen Elizabeth II. In 1963, MI5 learned of Blunt's espionage from an American, Michael Straight, whom he had recruited. Blunt confessed to MI5 on 23 April 1964, and Queen Elizabeth II was informed shortly thereafter. He also gave up John Cairncross, Peter Ashby, Brian Symon and Leonard Henry (Leo) Long as spies. Long had also been a member of the Communist Party and an undergraduate at Trinity College, Cambridge. During the war he served in MI14 military intelligence in the War Office, with responsibility for assessing German offensive plans. He passed analyses but not original material relating to the Eastern Front to Blunt. In return for Blunt's full confession, the British government agreed to keep his spying career an official secret for fifteen years, and granted him full immunity from prosecution. According to the memoir of MI5 officer Peter Wright, Wright had regular interviews with Blunt from 1964 onwards for six years. Prior to that, he had a briefing with Michael Adeane, the Queen's private secretary, who told Wright: \"From time to time you may find Blunt referring to an assignment he undertook on behalf of the Palace – a visit to Germany at the end of the war. Please do not pursue this matter. Strictly speaking, it is not relevant to considerations of national security.\" Blunt's life was little affected. In 1966, two years after his secret confession, Noel Annan, provost of King's College, Cambridge, held a dinner party for Labour Home Secretary Roy Jenkins, Ann Fleming, widow of James Bond author Ian Fleming, and Victor Rothschild and his wife Tess. The Rothschilds brought their friend and lodger – Blunt. All had had wartime connections with British Intelligence, Jenkins at Bletchley Park. Public exposure Blunt's role was represented under the name Maurice in Andrew Boyle's book Climate of Treason in 1979. Maurice was taken from the E. M. Forster novel of that name. Blunt tried to prevent the book's publication, which was reported in the magazine Private Eye. This drew attention to Blunt. In the House of Commons of the United Kingdom, Prime Minister Margaret Thatcher revealed Blunt's wartime role, firstly on Thursday 15 November 1979, and in more detail on 21 November. Sir Bernard Ingham, Thatcher's press secretary, suggested, \"I believe she did it because she didn't see why the system should cover things up. This was early in her prime ministership. I think she wanted to tell the civil service that the politicians decide policy, not the system. She wanted them to know who was boss.\" For weeks after Thatcher’s announcement, Blunt was hunted by the press. Once found, he was besieged by photographers. Blunt had recently given a lecture at the invitation of Francis Haskell, Oxford University's professor of art history. Haskell had a Russian mother and wife and had graduated from King’s College, Cambridge. To the press this made him an obvious suspect. They repeatedly telephoned his home in the early hours of the morning, using the names of his friends and claiming to have an urgent message for \"Anthony\". Although Blunt was outwardly calm, the sudden exposure shocked him. His former pupil, art critic Brian Sewell, said at the time, \"He was so businesslike about it; he considered the implications for his knighthood and academic honours and what should be resigned and what retained. What he didn't want was a great debate at his clubs, the Athenaeum and the Travellers. He was incredibly calm about it all.\" Sewell was involved in protecting Blunt from the extensive media attention after his exposure, and his friend was spirited away to a flat within a house in Chiswick. Queen Elizabeth II stripped Blunt of his knighthood, and he was removed as an Honorary Fellow of Trinity College. After his BBC Television confession at the age of 72, he broke down in tears. Blunt died of a heart attack at his London home in 1983, aged 75. Memoirs Blunt withdrew from society and seldom went out after his exposure. His friend Tess Rothschild suggested that he occupy his time writing his memoirs. Brian Sewell, his former pupil, said they remained unfinished because he had to consult the newspaper library in Colindale, North London, to check facts. He was unhappy at being recognised. \"I do know he was really worried about upsetting his family,\" said Sewell. \"I think he was being absolutely straight with me when he said that if he could not verify the facts there was no point in going on.\" Blunt stopped writing in 1983, leaving his memoirs to his partner, John Gaskin, who kept them for a year and then gave them to Blunt's executor, John Golding, a fellow art historian. Golding passed them on to the British Library, insisting that they not be released for 25 years. It was finally made available to readers on 23 July 2009. Golding explains: \"I did so because, although most of the figures mentioned were dead, their families might not like it. It covers his Cambridge days and there are a number of names. They weren't all spies, but communism was common amongst intellectuals in the Thirties.\" In the typed manuscript, Blunt conceded that spying for the Soviet Union was the biggest mistake of his life. \"What I did not realise is that I was so naïve politically that I was not justified in committing myself to any political action of this kind. The atmosphere in Cambridge was so intense, the enthusiasm for any anti-fascist activity was so great, that I made the biggest mistake of my life.\" The memoir revealed little that was not already known about Blunt. When asked whether there would be any new or unexpected names, John Golding replied: \"I'm not sure. It's 25 years since I read it, and my memory is not that good.\" Although ordered by the KGB to defect with Maclean and Burgess to protect Philby, in 1951 Blunt realised \"quite clearly that I would take any risk in [Britain], rather than go to Russia.\" After he was publicly exposed, he claims to have considered suicide but instead turned to \"whisky and concentrated work\". Career as an art historian Throughout the time of his activities in espionage, Blunt's public career was in the History of Art, a field in which he gained prominence. In 1940, most of his fellowship dissertation was published under the title of Artistic Theories in Italy, 1450–1600. In 1945, he was given the esteemed position of Surveyor of the King's Pictures, and later the Queen’s Pictures (after the death of King George VI in 1952), one of the largest private collections in the world. He held the position for 27 years, was knighted as a KCVO in 1956 for his work in the role, and his contribution was vital in the expansion and cataloguing of the Queen’s Gallery, which opened in 1962. In 1947, Blunt became both Professor of the History of Art at the University of London, and the director of the Courtauld Institute of Art, University of London, where he had been lecturing since the spring of 1933, and where his tenure in office as director lasted until 1974. This position included the use of a live-in apartment on the premises. During his 27 years at the Courtauld Institute, Blunt was respected as a dedicated teacher, a kind superior to his staff. His legacy at the Courtauld was to have left it with a larger staff, increased funding, and more space, and his role was central in the acquisition of outstanding collections for the Courtauld's Galleries. He is often credited for making the Courtauld what it is today, as well as for pioneering art history in Britain, and for training the next generation of British art historians. In 1953, Blunt published his book Art and Architecture in France, 1500–1700, and he was in particular an expert on the works of Nicolas Poussin, writing numerous books and articles about the painter, and serving as curator for a landmark exhibition of Poussin at the Louvre in 1960, which was an enormous success. He also wrote on topics as diverse as William Blake, Pablo Picasso, the Galleries of England, Scotland, and Wales. He also catalogued the French drawings (1945), G. B. Castiglione and Stefano della Bella drawings (1954) Roman drawings (with H. L. Cooke, 1960) and Venetian (with Edward Croft-Murray, 1957) drawings in the collection of the Queen, as well as a supplement of Addenda and Corrigenda to the Italian catalogues (in E. Schilling's German Drawings). Blunt attended a summer school in Sicily in 1965, leading to a deep interest in Sicilian architecture, and in 1968 he wrote the only authoritative and in-depth book on Sicilian Baroque. From 1962 he was engaged in a dispute with Denis Mahon regarding the authenticity of a Poussin work which rumbled on for several years. Mahon was shown to be correct. Blunt was also unaware that a painting in his own possession was also by Poussin. It has been suggested that Blunt could not accept that Poussin may have produced inferior work. Notable students who have been influenced by Blunt include Aaron Scharf, photography historian and author of 'Art and Photography' (whom Blunt assisted, along with Scharf's wife, in escaping McCarthy condemnation for their support of communism), Brian Sewell (an art critic for the Evening Standard), Ron Bloore, Sir Oliver Millar (his successor at the Royal Collection and an expert on Van Dyck), Nicholas Serota, Neil Macgregor, the former editor of the Burlington magazine, former director of the National Gallery and the current director of the British Museum who paid tribute to Blunt as \"a great and generous teacher\", John White (art historian), Sir Alan Bowness (who ran the Tate Gallery), John Golding (who wrote the first major book on Cubism), Reyner Banham (an influential architectural historian), John Shearman (the \"world expert\" on Mannerism and the former Chair of the Art History Department at Harvard University), Melvin Day (former Director of National Art Gallery of New Zealand and Government Art Historian for New Zealand ), Christopher Newall (an expert on the Pre-Raphaelites), Michael Jaffé (an expert on Rubens), Michael Mahoney (former Curator of European Paintings at the National Gallery of Art, Washington, D.C., and former Chair of the Art History Department at Trinity College, Hartford), Lee Johnson (an expert on Eugène Delacroix), and Anita Brookner (an art historian and novelist). Among his many accomplishments, Blunt also received a series of honorary fellowships, became the National Trust picture adviser, put on exhibitions at the Royal Academy, edited and wrote numerous books and articles, and sat on every influential art committee. After Margaret Thatcher had exposed Blunt's espionage, he continued his art historical work by writing and publishing a Guide to Baroque Rome (1982). He intended to write a monograph about the architecture of Pietro da Cortona but he died before realising the project. His manuscripts were sent to the intended co-author of this work, German art historian Jörg Martin Merz by the executors of his will. Merz published a book, Pietro da Cortona and Roman Baroque Architecture in 2008 incorporating a draft by the late Anthony Blunt. Many of his publications are still seen today by scholars as integral to the study of art history. His writing is lucid, and is based largely on art and architecture in the context of their place in history. In his book Art and Architecture in France, for example, he begins each section with a brief depiction of the social, political and/or religious contexts in which works of art and art movements are emerging. In Blunt’s Artistic Theory in Italy, 1450–1600, he explains the motivational circumstances involved in the transitions between the High Renaissance and Mannerism. Works A festschrift, Studies in Renaissance and Baroque Art presented to Anthony Blunt on his 60th Birthday, Phaidon 1967 (introduction by Ellis Waterhouse), contains a full list of his writings up to 1966. Major works include: *Anthony Blunt, François Mansart and the Origins of French Classical Architecture, 1941. *Blunt, Art and Architecture in France, 1500–1700, 1953 and many subsequent editions. *Blunt, Philibert de l'Orme, A. Zwemmer, 1958. *Blunt, Nicolas Poussin. A Critical Catalogue, Phaidon 1966 *Blunt, Nicolas Poussin, Phaidon 1967 (new edition Pallas Athene publishing, London, 1995). *Blunt, Sicilian Baroque, 1968 (ed. it. Milano 1968; Milano 1986). *Blunt, Picasso's Guernica, Oxford University Press, 1969. *Blunt, Neapolitan Baroque and Rococo Architecture, London 1975 (ed. it. Milano 2006). *Blunt, Baroque and Rococo Architecture and Decoration, 1978. *Blunt, Borromini, 1979 (ed. it. Roma-Bari 1983). *Blunt, L'occhio e la storia. Scritti di critica d'arte (1936–38), a cura di Antonello Negri, Udine 1999. Important articles after 1966: *Anthony Blunt, 'French Painting, Sculpture and Architecture since 1500,' in France: A Companion to French Studies, ed. D.G. Charlton (New York, Toronto and London: Pitman, 1972), 439–92. *Anthony Blunt, 'Rubens and architecture,' Burlington Magazine, 1977, 894, pp. 609–621. *Anthony Blunt, 'Roman Baroque Architecture: the Other Side of the Medal,' Art history, no. 1, 1980, pp. 61–80 (includes bibliographical references). Depictions in popular culture A Question of Attribution is a play written by Alan Bennett about Blunt, covering the weeks before his public exposure as a spy, and his relationship with Queen Elizabeth II. After a successful run in London's West End, it was made into a television play directed by John Schlesinger and starring James Fox, Prunella Scales and Geoffrey Palmer. It was aired on the BBC in 1991. This play was seen as a companion to Bennett's 1983 television play about Guy Burgess, An Englishman Abroad. Blunt: The Fourth Man is a 1985 film starring Ian Richardson, Anthony Hopkins, Michael Williams, and Rosie Kerslake, covering the events of 1951 when Guy Burgess and Donald Maclean went missing. The Untouchable, a 1997 novel by John Banville, is a roman à clef based largely on the life and character of Anthony Blunt; the novel's protagonist, Victor Maskell, is a loosely disguised Blunt, although some elements of the character are based on Louis MacNeice. \"I.M. Anthony Blunt\" is a poem by Gavin Ewart, cleverly attempting a humane corrective to the hysteria over Blunt's fall from grace. Published in [http://www.amazon.com/Selected-Poems-1933-1993-Gavin-Ewart/dp/0571241956 Gavin Ewart, Selected Poems 1933-1993], Hutchenson, 1996 (reprinted Faber and Faber, 2011). A Friendship of Convenience: Being a Discourse on Poussin's \"Landscape With a Man Killed by a Snake\", is a 1997 novel by Rufus Gunn set in 1956 in which Blunt, then Surveyor of the Queen's Pictures, encounters Joseph Losey, the famous film director fleeing McCarthyism. Samuel West portrayed Blunt in Cambridge Spies, a 2003 four-part BBC television drama concerning the lives of the Cambridge Four from 1934 to the defection of Guy Burgess and Donald Maclean to the Soviet Union. Harold Adrian Russell \"Kim\" Philby (1 January 1912 – 11 May 1988 ) was a high-ranking member of British intelligence who worked as a double agent before defecting to the Soviet Union in 1963. He served as both an NKVD and KGB operative. In 1963, Philby was revealed to be a member of the spy ring now known as the Cambridge Five, the other members of which were Donald Maclean, Guy Burgess, Anthony Blunt and, possibly, John Cairncross. Of the five, Philby is believed to have been most successful in providing secret information to the Soviet Union. His activities were moderated only by Joseph Stalin's fears that he was secretly on Britain's side. Philby was an Officer of the Order of the British Empire (OBE) from 1946 to 1965. Biography Early life Philby was born at Ambala in the Punjab Province of India. He was the son of Dora (Johnston) and St John Philby, who was a well-known author, orientalist and convert to Islam. His father was a member of the Indian Civil Service (ICS) and later a civil servant in Mesopotamia and advisor to King Ibn Sa'ud of Saudi Arabia. Nicknamed \"Kim\" after the boy in Rudyard Kipling's novel Kim, Philby attended Aldro preparatory school. In his early teens he spent some time with the Bedouin in the desert of Saudi Arabia \"to be turned into a man\". Following in the footsteps of his father, he continued to Westminster School, which he left in 1928 at the age of 16. He won a scholarship to Trinity College, Cambridge, where he read History and Economics. He graduated in 1933 with a 2:1 degree in Economics. Upon Philby's graduation, Maurice Dobb, a fellow of King's College, Cambridge, and tutor in Economics, introduced him to the World Federation for the Relief of the Victims of German Fascism in Paris. The organization was one of several fronts operated by German Communist Willi Münzenberg, a member of the Reichstag who had fled to France in 1933. Vienna In Vienna, working to aid refugees from Nazi Germany, Philby met and fell in love with Litzi Friedmann (born Alice Kohlmann), a young Austrian Communist of Hungarian Jewish origins. Philby admired the strength of her political convictions and later recalled that at their first meeting: \"[a] frank and direct person, Litzi came out and asked me how much money I had. I replied £100, which I hoped would last me about a year in Vienna. She made some calculations and announced, 'That will leave you an excess of £25. You can give that to the International Organisation for Aid for Revolutionaries. We need it desperately.' I liked her determination.\" He acted as a courier between Vienna and Prague, paying for the train tickets out of his remaining £75 and using his British passport to evade suspicion. He also delivered clothes and money to refugees from the Nazis. With threats of an armed uprising against Austrian Chancellor Engelbert Dollfuss (who was assassinated the following year) and the deaths of several hundred people in the February Uprising, Philby's British passport became even more valuable. He and Friedmann married in February 1934, enabling her to escape to the United Kingdom with him two months later. It is possible that it was a Viennese-born friend of Friedmann's in London, Edith Tudor Hart – herself, at this time, a Soviet agent – who first approached Philby about the possibility of working for Soviet intelligence. According to Genrikh Borovik, who worked from Soviet archives, Tudor Hart recommended Philby and Friedmann in 1934. Peter Wright, a former senior MI5 officer, said in his 1987 book, Spycatcher that Litzi Friedmann was \"almost certainly the person who recruited him to the Soviet cause.\" Yuri Modin, one of the KGB controllers of the Cambridge Five, agreed; \"Contrary to received opinion, it was neither Guy Burgess nor one of our own agents who lured Philby into the toils of the Soviet espionage apparatus. It was Litzi.\" In January 1934 Arnold Deutsch, one of NKVD's agents, was sent to London. As a cover for his spying activities he did post-graduate work at London University. In May, he made contact with Friedmann and discussed the recruitment of Soviet spies, whereupon she suggested her husband. Philby later recalled that in June 1934. \"Lizzy came home one evening and told me that she had arranged for me to meet a 'man of decisive importance'. I questioned her about it but she would give me no details. The rendezvous took place in Regents Park. The man described himself as Otto. I discovered much later from a photograph in MI5 files that the name he went by was Arnold Deutsch. I think that he was of Czech origin; about 5ft 7in, stout, with blue eyes and light curly hair. Though a convinced Communist, he had a strong humanistic streak. He hated London, adored Paris, and spoke of it with deeply loving affection. He was a man of considerable cultural background.\" Arnold Deutsch was soon replaced by Theodore Maly (code-name Man), who was also Hungarian. Philby also dealt with Anatoly Gorsky (code-name Kap) the OGPU rezident in London and his predecessor, a German known as Reif (code-name Mar). All were shot in Moscow between 1936 and 1938 during the era of Stalin's Great Purge on trumped-up charges of being either German or Polish spies. Philby's first task for Otto was to make a list of his Cambridge contemporaries who might respond to discreet contact. He listed seven, including Donald Maclean and Guy Burgess. London and Spain In London, Philby enrolled at the School of Slavonic Languages to learn Russian, helped by his father, a friend of the director. The school trained people for a career in diplomacy or the intelligence services. Philby's Russian was never good and he soon took a job at a monthly magazine, the World Review of Reviews, for which he wrote articles and letters (sometimes under pseudonyms) and occasionally served as acting editor. At this point, Philby and Litzi separated. They remained friends and divorced only in 1946. When the Germans threatened to overrun Paris in 1940, where she was then living, he arranged her escape to Britain. In 1936 he joined a trade magazine, the Anglo-Russian Trade Gazette, as editor. The paper was failing and its owner changed its role to covering Anglo-German trade. Philby engaged in a concerted effort to make contact with Germans such as Joachim von Ribbentrop, at that time the German ambassador in London. He joined the Anglo-German Fellowship, which was supported both by the British and German governments, and made many trips to Berlin. In February 1937, Philby travelled to Seville, Spain. At the time, Spain was embroiled in a bloody civil war, triggered by the rebellion of Nationalist forces under General Francisco Franco against the socialist Republican government of President Manuel Azaña. Philby worked at first as a freelance journalist; from May 1937, he served as a correspondent for The Times, reporting from the side of the pro-Franco forces. He was also working for both Soviet and British intelligence, posting letters in a crude code to a fictitious girlfriend, Mlle Dupont in Paris, for the Russians. He used a simpler system for MI6 delivering post at Hendaye, France, for the British Embassy in Paris. When visiting Paris after the war, he was shocked to discover that the address that he used for Mlle Dupont was that of the Soviet Embassy. His controller in Paris, the Latvian Ozolin-Haskins (code name Pierre), was shot in Moscow in 1937 during Stalin's purge. His successor, Boris Bazarov, suffered the same fate two years later during the purges. Both services were interested in the combat performance of the new Messerschmitt Bf109s and Panzer I and IIs deployed with Nationalist forces in Spain. Philby told the British, after a direct question to Franco, that German troops would never be permitted to cross Spain to attack Gibraltar. His Soviet controller at the time, Theodore Maly, reported in April 1937 to the NKVD that he had personally briefed Philby on the need \"to discover the system of guarding Franco and his entourage.\" So as to assist in Franco's assassination, Philby was instructed to report on vulnerable points in Franco's security and recommend ways to gain access to him and his staff. However, such an act was never a real possibility; upon debriefing Philby in London on 24 May 1937, Maly wrote to the NKVD, \"Though devoted and ready to sacrifice himself, [Philby] does not possess the physical courage and other qualities necessary for this [assassination] attempt.\" In December 1937, during the Battle of Teruel, a Republican shell hit just in front of the car in which Philby was travelling with the correspondents Edward J. Neil of the Associated Press, Bradish Johnson of Newsweek, and Ernest Sheepshanks of Reuters. Johnson was killed outright, and Neil and Sheepshanks soon died of their injuries. Philby suffered only a minor head wound. As a result of this accident, Philby, who was well-liked by the Nationalist forces whose victories he trumpeted, was awarded the Red Cross of Military Merit by Franco on 2 March 1938. Philby found that the award proved helpful in obtaining access to fascist circles: \"Before then,\" he later wrote, \"there had been a lot of criticism of British journalists from Franco officers who seemed to think that the British in general must be a lot of Communists because so many were fighting with the International Brigades. After I had been wounded and decorated by Franco himself, I became known as 'the English-decorated-by-Franco' and all sorts of doors opened to me.\" In 1938, Walter Krivitsky (born Samuel Ginsberg), a former GRU officer in Paris who had defected to France the previous year, travelled to the United States and published an account of his time in \"Stalin's secret service.\" He testified before the Dies Committee (later to become the House Un-American Activities Committee) regarding Soviet espionage within the United States. In 1940 he was interviewed by MI5 officers in London, led by Jane Archer. Krivitsky claimed that two Soviet intelligence agents had penetrated the British Foreign Office and that a third Soviet intelligence agent had worked as a journalist for a British newspaper during the civil war in Spain. No connection with Philby was made at the time. Krivitsky was shot in a Washington hotel room the following year. Alexander Orlov (born Lev Feldbin; code-name Swede), Philby's controller in Madrid, who had once met him in Perpignan, France, with the bulge of an automatic rifle clearly showing through his raincoat, also defected. To protect his family, still living in the USSR, he said nothing about Philby, an agreement Stalin respected. On a short trip back from Spain, Philby tried to recruit Flora Solomon as a Soviet agent; she was the daughter of a Russian banker and gold dealer, a relative of the Rothschilds, and wife of a London stockbroker. At the same time, Burgess was trying to get her into MI6. But the resident (Russian term for spymaster) in France, probably Pierre at this time, suggested to Moscow that he suspected Philby's motives. Solomon introduced Philby to his second wife, Aileen Furse, but went to work for the British retailer Marks & Spencer. World War II In July 1939, Philby returned to the Times office in London. The Molotov–Ribbentrop Pact, with its secret protocol that Germany and the Soviet Union would divide Poland, shocked Philby. He often asked, \"Why was this necessary?\". When Britain declared war on Germany in September 1939, contact with his Soviet controllers was lost and Philby failed to attend meetings. During the Phoney War from September 1939 until the Dunkirk evacuation, Philby worked as the Times correspondent with the British Expeditionary Force headquarters. After being evacuated from Boulogne on 21 May, he returned to France in mid-June (now representing the Daily Telegraph in addition to The Times). He briefly reported from Cherbourg and Brest, sailing for Plymouth less than twenty-four hours before the French surrender. Hester Marsden-Smedley, a correspondent with the Sunday Express who shared the train ride from Plymouth to London, then introduced him to Marjorie Maxse, who offered him a role in the War Office. On his first meeting in her office, Philby was surprised to see his old friend from Cambridge, Burgess, who was already working there. His time there, however, was short-lived; the under-funded section was absorbed by the new Special Operations Executive (SOE) in July 1940. Burgess was fired for \"irreverence\", and Philby was appointed as an instructor in the art of clandestine propaganda at the SOE's training establishment in Beaulieu, Hampshire. Philby's role as an instructor of sabotage agents again brought him to the attention of the OGPU. The new London rezident, Ivan Chichayev (code-name Vadim), re-established contact and asked for a list of names of British agents being trained to enter the USSR. Philby replied that none had been sent and that none were undergoing training. This statement was underlined twice in red and marked with two question marks by disbelieving staff at Moscow Central in the Lubyanka, according to Genrikh Borovik, who saw the telegrams much later in the KGB archives. Section IX was often known as Section D (SIS Sections used Roman numerals). Philby was originally a Section D officer and is so noted in a letter dated 24 September 1940 written by Lt. Col. Valentine Vivian, the head of Section V at that time. Under Section IX was the Statistical Research Centre War Office (a cover name), mobilised on September 1939 on the outbreak of war at War Station No X Bletchley Park, charged with breaking the German Enigma codes. Philby provided Stalin with advance warning of Operation Barbarossa and of the Japanese intention to strike south at Singapore instead of attacking the USSR as Hitler had urged. The first was ignored as a provocation, but the second, when confirmed by the Russo-German journalist and spy in Tokyo, Richard Sorge, contributed to Stalin's decision to transport troops from the Far East in time for Georgy Zhukov to use them in the counteroffensive around Moscow. By September 1941, Philby was working for Section V of MI6, responsible for offensive counter-intelligence. On the strength of his knowledge and experience of Franco's Spain, he was put in charge of the subsection which dealt with Spain and Portugal. This entailed responsibility for a network of undercover operatives in Madrid, Lisbon, Gibraltar and Tangier. At this time, the German Abwehr was active in Spain, particularly around the British naval base of Gibraltar, which its agents hoped to watch with cameras and radar to track Allied supply ships in the Western Mediterranean. During 1942–43 Philby's responsibilities were expanded to include North Africa and Italy, and he was made the deputy head of Section V under Major Felix Cowgill, an army officer seconded to SIS. Cowgill was the SIS representative on the XX Committee run by John Masterman, which dealt with double agents working for the Abwehr but controlled by the British. In late 1944 Philby, on instructions from his KGB handler, manoeuvred successfully to replace Cowgill as head of Section. Charles Arnold-Baker, an officer of German birth (born Wolfgang von Blumenthal) working for Richard Gatty in Belgium and later transferred to the Norwegian/Swedish border, voiced suspicions of Philby but was ignored. While with Section V, Philby met James Jesus Angleton, a young American counter-intelligence officer working in liaison with SIS in London. Angleton, later chief of the Central Intelligence Agency's (CIA) Counterintelligence Staff, became suspicious of Philby when he failed to pass on information relating to a British agent executed by the Gestapo in Germany. It later emerged that the agent – known as Schmidt – had also worked as an informant for the Rote Kapelle organisation, which sent information to both London and Moscow. Nevertheless, Angleton's suspicions went unheard. In late summer 1943, the SIS provided the GRU with an official report on the activities of German agents in Bulgaria and Romania, soon to be invaded by the Red Army. The NKVD complained to Cecil Barclay, the SIS representative in Moscow, that information had been withheld. Barclay reported the complaint to London. Philby claimed to have overheard discussion of this by chance and sent a report to his controller. This turned out to be identical with Barclay's dispatch, convincing the NKVD that Philby had seen the full Barclay report. A similar lapse occurred with a report from the Imperial Japanese Embassy in Moscow sent to Tokyo. The NKVD received the same report from Richard Sorge but with an extra paragraph claiming that Hitler might seek a separate peace with the Soviet Union. These lapses by Philby aroused intense suspicion in Moscow. Elena Modrzhinskaya at GUGB headquarters in Moscow was the person who assessed all material from the Cambridge Five. She noted that they produced an extraordinary wealth of information on German war plans but next to nothing on the repeated question of British penetration of Soviet intelligence in either London or Moscow. Philby had repeated his claim that there were no such agents. She asked, \"Could the SIS really be such fools they failed to notice suitcase-loads of papers leaving the office? Could they have overlooked Philby's communist wife?\" Modrzhinskaya concluded that all were double agents, working essentially for the British. A more serious incident occurred in August 1945, when Konstantin Volkov, an NKVD agent and vice-consul in Istanbul, requested political asylum in Britain for himself and his wife. For a large sum of money, Volkov offered the names of three Soviet agents inside Britain, two of whom worked in the Foreign Office and a third who worked in counter-espionage in London. Philby was given the task of dealing with Volkov by British intelligence. He warned his Soviet controller of his mission and proceeded to Istanbul – slowly. By the time he arrived in Turkey, three weeks later, Volkov had been found and hurriedly returned to Moscow, disguised in heavy bandages. Alerting Moscow of his mission, as well as the speed of his travel might have compromised his position within the SIS, however, Volkov's defection had been discussed with the British Embassy in Ankara on telephones found to be tapped by Soviet intelligence. Volkov had insisted that all written communications about him take place by diplomatic bag rather than by telegraph, causing a belated reaction that plausibly could have given the Soviets time to uncover his plans. Philby was thus able to evade blame and detection. A month later Igor Gouzenko, a cipher clerk in Ottawa, took political asylum in Canada and gave the Royal Canadian Mounted Police names of agents operating within the British Empire that were known to him; Philby could do nothing about this. When Jane Archer (who had interviewed Krivitsky) was appointed to Philby's section he moved her off investigatory work in case she became aware of his past. He later wrote \"... she had got a tantalising scrap of information about a young English journalist whom the Soviet intelligence had sent to Spain during the Civil War. And here she was pluncked down in my midst!\". Philby, \"employed in a Department of the Foreign Office\", was awarded the OBE in 1946. Istanbul In February 1947, Philby was appointed head of British intelligence for Turkey, and posted to Istanbul with his second wife, Aileen, and their family. His public position was that of First Secretary at the British Consulate; in reality, his intelligence work required overseeing British agents and working with the Turkish security services. Philby planned to infiltrate five or six groups of émigrés into Soviet Armenia or Soviet Georgia. But efforts among the expatriate community in Paris produced just two recruits. Turkish intelligence took them to a border crossing into Georgia but soon afterwards shots were heard. Another effort was made using a Turkish gulet for a seaborne landing, but it never left port. He was implicated in a similar campaign in Albania. Colonel David Smiley, an aristocratic Guards officer who had helped Enver Hoxha and his Communist guerillas to liberate Albania, now prepared to liberate it from Hoxha. He trained Albanian commandos – some of whom were former Nazi collaborators – in Libya or Malta. From 1947, they infiltrated the southern mountains to build support for former King Zog. The first three missions, overland from Greece, were trouble-free. Larger numbers were landed by sea and air under Operation Valuable, which continued until 1951, increasingly under the influence of the newly formed CIA. Stewart Menzies, head of SIS, disliked the idea, which was promoted by former SOE men now in SIS. Most infiltrators were caught by the Sigurimi, the Albanian Security Service. Clearly there had been leaks and Philby was later suspected as one of the leakers. His own comment was \"I do not say that people were happy under the regime but the CIA underestimated the degree of control that the Authorities had over the country.\" Macintyre (2014) includes this typically cold-blooded quote from Philby: \"The agents we sent into Albania were armed men intent on murder, sabotage and assassination ... They knew the risks they were running. I was serving the interests of the Soviet Union and those interests required that these men were defeated. To the extent that I helped defeat them, even if it caused their deaths, I have no regrets.\" Aileen Philby had suffered since childhood from psychological problems which caused her to inflict injuries upon herself. In 1948, troubled by the heavy drinking and frequent depressions that had become a feature of her husband's life in Istanbul, she experienced a breakdown of this nature, staging an accident and injecting herself with urine and insulin to cause skin disfigurations. She was sent to a clinic in Switzerland to recover. Upon her return to Istanbul in late 1948, she was badly burned in an incident with a charcoal stove and returned to Switzerland. Shortly afterward, Philby was moved to the job as chief SIS representative in Washington, D.C., with his family. Washington, D.C. In September 1949, the Philbys arrived in the United States. Officially, his post was that of First Secretary to the British Embassy; in reality, he served as chief British intelligence representative in Washington. His office oversaw a large amount of urgent and top-secret communications between the United States and London. Philby was also responsible for liaising with the CIA and promoting \"more aggressive Anglo-American intelligence operations.\" A leading figure within the CIA was Philby's wary former colleague, James Jesus Angleton, with whom he once again found himself working closely. Angleton remained suspicious of Philby, but lunched with him every week in Washington. However, a more serious threat to Philby's position had come to light. During the summer of 1945, a Soviet cipher clerk had reused a one time pad to transmit intelligence traffic. This mistake made it possible to break the normally impregnable code. Contained in the traffic (intercepted and decrypted as part of the Venona project) was information that documents had been sent to Moscow from the British Embassy in Washington. The intercepted messages revealed that the British Embassy source (identified as \"Homer\") travelled to New York City to meet his Soviet contact twice a week. Philby had been briefed on the situation shortly before reaching Washington in 1949; it was clear to Philby that the agent was Maclean, who worked in the British Embassy at the time and whose wife, Melinda, lived in New York. Philby had to help discover the identity of \"Homer\", but also wished to protect Maclean. In January 1950, on evidence provided by the Venona intercepts, Soviet atomic spy Klaus Fuchs was arrested. His arrest led to others: Harry Gold, a courier with whom Fuchs had worked, David Greenglass, and Julius and Ethel Rosenberg. The investigation into the British Embassy leak was still ongoing, and the stress of it was exacerbated by the arrival in Washington, in October 1950, of Burgess – Philby's unstable and dangerously alcoholic, fellow Soviet spy. Burgess, who had been given a post as Second Secretary at the British Embassy, took up residence in the Philby family home and rapidly set about causing offence to all and sundry. Aileen Philby resented him and disliked his presence; Americans were offended by his \"natural superciliousness\" and \"utter contempt for the whole pyramid of values, attitudes, and courtesies of the American way of life.\" J. Edgar Hoover complained that Burgess used British Embassy automobiles to avoid arrest when he cruised Washington in pursuit of homosexual encounters. His dissolution had a troubling effect on Philby; the morning after a particularly disastrous and drunken party, a guest returning to collect his car heard voices upstairs and found \"Kim and Guy in the bedroom drinking champagne. They had already been down to the Embassy but being unable to work had come back.\" Burgess' presence was problematic for Philby, yet it was potentially dangerous for Philby to leave him unsupervised. The situation in Washington was tense. From April 1950, Maclean had been the prime suspect in the investigation into the Embassy leak. Philby had undertaken to devise an escape plan which would warn Maclean, currently in England, of the intense suspicion he was under and arrange for him to flee. Burgess had to get to London to warn Maclean, who was under surveillance. In early May 1951, Burgess got three speeding tickets in a single day – then pleaded diplomatic immunity, causing an official complaint to be made to the British Ambassador. Burgess was sent back to England, where he met Maclean in his London club. The SIS planned to interrogate Maclean on 28 May 1951. On 23 May, concerned that Maclean had not yet fled, Philby wired Burgess, ostensibly about his Lincoln convertible abandoned in the Embassy car park. \"If he did not act at once it would be too late,\" the telegram read, \"because [Philby] would send his car to the scrap heap. There was nothing more [he] could do.\" On 25 May (Maclean's thirty-eighth birthday), Burgess drove Maclean from his home in Tatsfield to Southampton, where the two of them boarded a boat to France and then proceeded to Moscow. London Burgess had intended to aid Maclean in his escape, not accompany him in it. The \"affair of the missing diplomats,\" as it was referred to before Burgess and Maclean surfaced in Moscow, attracted a great deal of public attention, and Burgess' disappearance, which identified him as complicit in Maclean's espionage, deeply compromised Philby's position. Under a cloud of suspicion raised by his highly visible and intimate association with Burgess, Philby returned to London. There, he underwent MI5 interrogation aimed at ascertaining whether he had acted as a \"third man\" in Burgess and Maclean's spy ring. In July 1951, he resigned from MI6, preempting his all-but-inevitable dismissal. Even after Philby's departure from MI6, speculation regarding his possible Soviet affiliations continued. Interrogated repeatedly regarding his intelligence work and his connection with Burgess, he continued to deny that he had acted as a Soviet agent. From 1952, Philby struggled to find work as a journalist, eventually – in August 1954 – accepting a position with a diplomatic newsletter called the Fleet Street Letter. Lacking access to material of value and out of touch with Soviet intelligence, he all but ceased to operate as a Soviet agent. In October 1955, Philby was officially cleared by Foreign Secretary Harold Macmillan, who told the House of Commons, \"I have no reason to conclude that Mr. Philby has at any time betrayed the interests of his country, or to identify him with the so-called 'Third Man', if indeed there was one.\" In November 1955 Philby gave a press conference in which – calmly, confidently, and without the stammer he had struggled with since childhood – he reiterated his innocence, declaring, \"I have never been a communist.\" Beirut After being exonerated, Philby was no longer employed by MI6 and Soviet intelligence lost all contact with him. In August 1956 he was sent to Beirut as a Middle East correspondent for The Observer and The Economist. There, his journalism served as cover for renewed work for MI6. In Lebanon, Philby at first lived in Mahalla Jamil, his father's large household located in the village of Ajaltoun, just outside Beirut. Following the departure of his father and stepbrothers for Saudi Arabia, Philby continued to live alone in Ajaltoun, but took a flat in Beirut after beginning an affair with Eleanor, the Seattle-born wife of New York Times correspondent Sam Pope Brewer. On 12 December 1957, Aileen Philby was discovered dead in the bedroom of her house in Crowborough. Her friends believed she had killed herself, with drink and pills. However, her psychiatrist suspected, that she \"might have been murdered\" by Kim Philby because she knew too much. \"The coroner ruled she had died from heart failure, myocardial degeneration, tuberculosis, and a respiratory infection having contracted influenza. Her alcoholism undoubtedly accelerated her death.\" Following Aileen's death and Eleanor's subsequent divorce from Brewer, Philby and Eleanor were married in London in 1959 and set up house together in Beirut. From 1960, Philby's formerly marginal work as a journalist became more substantial and he frequently travelled throughout the Middle East, including Saudi Arabia, Egypt, Jordan, Kuwait and Yemen. In 1961, Anatoliy Golitsyn, a major in the First Chief Directorate of the KGB, defected to the United States from his diplomatic post in Helsinki. Golitsyn offered the CIA revelations of Soviet agents within American and British intelligence services. Following his debriefing in the US, Golitsyn was sent to SIS for further questioning. The head of MI6, Dick White, only recently transferred from MI5, had suspected Philby as the \"third man.\" Golitsyn proceeded to confirm White's suspicions about Philby's role. Nicholas Elliott, an MI6 officer recently stationed in Beirut who was a friend of Philby's and had previously believed in his innocence, was tasked with attempting to secure Philby's full confession. It is unclear whether Philby had been alerted, but Eleanor noted that as 1962 wore on, expressions of tension in his life \"became worse and were reflected in bouts of deep depression and drinking.\" She recalled returning home to Beirut from a sight-seeing trip in Jordan to find Philby \"hopelessly drunk and incoherent with grief on the terrace of the flat,\" mourning the death of a little pet fox which had fallen from the balcony. When Nicholas Elliott met Philby in late 1962, the first time since Golitsyn's defection, he found Philby too drunk to stand and with a bandaged head; he had fallen repeatedly and cracked his skull on a bathroom radiator, requiring stitches. Philby told Elliott that he was \"half expecting\" to see him. Elliott confronted him, saying, \"I once looked up to you, Kim. My God, how I despise you now. I hope you've enough decency left to understand why.\" Prompted by Elliott's accusations, Philby confirmed the charges of espionage and described his intelligence activities on behalf of the Soviets. However, when Elliott asked him to sign a written statement, he hesitated and requested a delay in the interrogation. Another meeting was scheduled to take place in the last week of January. It has since been suggested that the whole confrontation with Elliott had been a charade to convince the KGB that Philby had to be brought back to Moscow, where he could serve as a British penetration agent of Moscow Centre. On the evening of 23 January 1963, Philby vanished from Beirut, failing to meet his wife for a dinner party at the home of Glencairn Balfour Paul, First Secretary at the British Embassy. The Dolmatova, a Soviet freighter bound for Odessa, had left Beirut that morning so abruptly that cargo was left scattered over the docks; Philby claimed that he left Beirut on board this ship. However, others maintain that he escaped through Syria, overland to Soviet Armenia and thence to Russia.Morris Riley Philby: The Hidden Years, 1990, Penzance: United Writers' Publications. It was not until 1 July 1963 that Philby's flight to Moscow was officially confirmed. On 30 July Soviet officials announced that they had granted him political asylum in the USSR, along with Soviet citizenship. When the news broke, MI6 came under criticism for failing to anticipate and block Philby's defection, though Elliott was to claim he could not have prevented Philby's flight. Journalist Ben Macintyre, author of several works on espionage, wrote in his 2014 book on Philby that MI6 may have left open the opportunity for Philby to flee to Moscow to avoid an embarrassing public trial. Philby himself thought this might have been the case, according to Macintyre. Moscow Upon his arrival in Moscow, Philby discovered that he was not a colonel in the KGB, as he had been led to believe. He was paid 500 rubles a month and his family was not immediately able to join him in exile. It was ten years before he visited KGB headquarters and he was given little real work. Philby was under virtual house arrest, guarded, with all visitors screened by the KGB. Mikhail Lyubimov, his closest KGB contact, explained that this was to guard his safety, but later admitted that the real reason was the KGB's fear that Philby would return to London. Philby occupied himself by writing his memoirs, published in the UK in 1968 under the title My Silent War, not published in the Soviet Union until 1980. He continued to read The Times, which was not generally available in the USSR, listened to the BBC World Service, and was an avid follower of cricket. The award of the OBE was cancelled and annulled in 1965. Though Philby claimed publicly in January 1988 that he did not regret his decisions and that he missed nothing about England except some friends, Colman's mustard, and Lea & Perrins Worcestershire sauce, his wife Rufina Ivanovna Pukhova later described Philby as \"disappointed in many ways\" by what he found in Moscow. \"He saw people suffering too much,\" but he consoled himself by arguing that \"the ideals were right but the way they were carried out was wrong. The fault lay with the people in charge.\" Pukhova said, \"he was struck by disappointment, brought to tears. He said, 'Why do old people live so badly here? After all, they won the war.'\" Philby drank heavily and suffered from loneliness and depression; according to Rufina, he had attempted suicide by slashing his wrists sometime in the 1960s. Philby died of heart failure in Moscow in 1988. He was given a hero's funeral, and posthumously awarded numerous medals by the USSR. Philby's comments In a 1981 lecture to the East German security service, Stasi, Philby attributed the failure of the British Secret Service to unmask him as due in great part to the British class system—it was inconceivable that one \"born into the ruling class of the British Empire\" would be a traitor—to the amateurish and incompetent nature of the organisation, and to so many in MI6 having so much to lose if he was proven to be a spy. He had the policy of never confessing—a document in his own handwriting was dismissed as a forgery. He said that at the time of his recruitment as a spy there were no prospects of his being useful; he was instructed to make his way into the Secret Service, which took years, starting with journalism and building up contacts in the establishment. He said that there was no discipline there; he made friends with the archivist, which enabled him for years to take secret documents home, many unrelated to his own work, and bring them back the next day; his handler took and photographed them overnight. When he was instructed to remove and replace his boss, Felix Cowgill, he asked if it was proposed \"to shoot him or something\", but was told to use bureaucratic intrigue. He said \"It was a very dirty story—but after all our work does imply getting dirty hands from time to time but we do it for a cause that is not dirty in any way\". Commenting on his betrayal of the operation to secretly send thousands of Albanians back into their country to overthrow the communist regime, which led to many being killed, Philby tries to turn it to his credit, even saying that he helped prevent another World War. Personal life In February 1934, Philby married Litzi Friedmann, an Austrian communist whom he had met in Vienna. They subsequently moved to Britain; however, as Philby assumed the role of a fascist sympathiser, they separated. Litzi lived in Paris before returning to London for the duration of the war; she ultimately settled in East Germany. While working as a correspondent in Spain, Philby began an affair with Frances Doble, Lady Lindsay-Hogg, an actress and aristocratic divorcée who was an admirer of Franco and Hitler. They travelled together in Spain through August 1939. In 1940 he began living with Aileen Furse in London. Their first three children, Josephine, John and Tommy Philby, were born between 1941 and 1944. In 1946, Philby finally arranged a formal divorce from Litzi. He and Aileen were married on 25 September 1946, while Aileen was pregnant with their fourth child, Miranda. Their fifth child, Harry George, was born in 1950. Aileen suffered from psychiatric problems, which grew more severe during the period of poverty and suspicion following the flight of Burgess and Maclean. She lived separately from Philby, settling with their children in Crowborough while he lived first in London and later in Beirut. Weakened by alcoholism and frequent sickness, she died of influenza in December 1957. In 1956, Philby began an affair with Eleanor Brewer, the wife of New York Times correspondent Sam Pope Brewer. Following Eleanor's divorce, the two married in January 1959. After Philby defected to the Soviet Union in 1963, Eleanor visited him in Moscow. In November 1964, after a visit to the United States, she returned, intending to settle permanently. In her absence, Philby had begun an affair with Donald Maclean's wife, Melinda. He and Eleanor divorced and she departed Moscow in May 1965. Melinda left Maclean and briefly lived with Philby in Moscow. In 1968 she returned to Maclean. In 1971, Philby married Rufina Ivanovna Pukhova, a Russo-Polish woman twenty years his junior, with whom he lived until his death in 1988.[http://www.oxforddnb.com/view/article/40699 Philby, Harold Adrian Russell Kim, (1912–1988), spy] by Nigel Clive in Dictionary of National Biography online (accessed 11 November 2007) In popular culture Fiction based on actual events * Philby, Burgess and MacLean a Granada TV drama written by Ian Curteis in 1977, covers the period of the late 1940s, when British intelligence investigated Maclean until 1955 when the British government cleared Philby because it did not have enough evidence to convict him. * Philby has a key role in Mike Ripley's short story Gold Sword published in 'John Creasey's Crime Collection 1990' which was chosen as BBC Radio 4's Afternoon Story to mark the 50th anniversary of D-Day on 6 June 1994. * Cambridge Spies, a 2003 four-part BBC drama, recounts the lives of Philby, Burgess, Blunt and Maclean from their Cambridge days in the 1930s through the defection of Burgess and Maclean in 1951. Philby is played by Toby Stephens. * German author Barbara Honigmann's Ein Kapitel aus meinem Leben tells the history of Philby's first wife, Litzi, from the perspective of her daughter. Speculative fiction * One of the earliest appearances of Philby as a character in fiction was in the 1974 Gentleman Traitor by Alan Williams, in which Philby goes back to working for British intelligence in the 1970s. * In the 1980 British television film Closing Ranks, a false Soviet defector sent to sow confusion and distrust in British intelligence is unmasked and returned to the Soviet Union. In the final scene, it is revealed that the key information was provided by Philby in Moscow, where he is still working for British intelligence. * In the 1981 Ted Allbeury novel The Other Side of Silence, an elderly Philby arouses suspicion when he states his desire to return to England. * The 1984 Frederick Forsyth novel The Fourth Protocol features an elderly Philby's involvement in a plot to trigger a nuclear explosion in Britain. In the novel, Philby is a much more influential and connected figure in his Moscow exile than he apparently was in reality. ** In the 1987 adaptation of the novel, also named The Fourth Protocol, Philby is portrayed by Michael Bilton. In contradiction of historical fact, he is murdered by the KGB in the opening scene. * In the 2000 Doctor Who novel Endgame, the Doctor travels to London in 1951 and matches wits with Philby and the rest of the Cambridge Five. * The Tim Powers novel Declare (2001) is partly based on unexplained aspects of Philby's life, providing a supernatural context for his behaviour. * The Robert Littell novel The Company (2002) features Philby as a confidant of former CIA Counter-Intelligence chief James Angleton. The book was adapted for the 2007 TNT television three-part series The Company, produced by Ridley Scott, Tony Scott and John Calley; Philby is portrayed by Tom Hollander. * Philby appears as one of the central antagonists in William F. Buckley Jr's 2004 novel Last Call for Blackford Oakes. * The 2013 Jefferson Flanders novel The North Building explores the role of Philby in passing American military secrets to the Soviets during the Korean War. In alternative histories * The 2003 novel Fox at the Front by Douglas Niles and Michael Dobson depicts Philby selling secrets to the Soviet Union during the alternate Battle of the Bulge where German Field Marshal Erwin Rommel turns on the Nazis and assists the Allies in capturing all of Berlin. Before he can sell the secret of the atomic bomb to the Soviet Union, he is discovered by the British and is killed by members of MI5 who stage his death as a heart attack. * The 2005 John Birmingham novel Designated Targets features a cameo of Philby, under orders from Moscow to assist Otto Skorzeny's mission to assassinate Winston Churchill. Fictional characters based on Philby * Graham Greene, Philby's close friend, wrote the screenplay for the 1949 film The Third Man using Philby as a model for Harry Lime, one of the characters. * The 1971 BBC television drama Traitor starred John Le Mesurier as Adrian Harris, a character loosely based on Kim Philby. * John le Carré (David Cornwell) depicts a Philby-like upper-class traitor in the 1974 novel Tinker Tailor Soldier Spy. The novel has been adapted as a 1979 TV miniseries, a 2011 film, and radio dramatisations in 1988 and 2009. In real life, Philby had ended le Carré's intelligence officer career by betraying him to the Russians. * In the 1977 film The Jigsaw Man by Dorothea Bennett and the 1983 film adaption of it, The Jigsaw Man, \"Sir Philip Kimberly\" is a former head of the British Secret Service who defected to Russia, who is then given plastic surgery and sent back to Britain on a spy mission. * Under the cover name of 'Mowgli' Philby appears in Duncan Kyle's World War II thriller Black Camelot published in 1978. * John Banville's 1997 novel The Untouchable is a fictionalised biography of Blunt that includes a character based on Philby. * Philby was the inspiration for the character of British intelligence officer Archibald \"Arch\" Cummings in the 2005 film The Good Shepherd. Cummings is played by Billy Crudup. * The 2005 film A Different Loyalty is an unattributed account taken from Eleanor Philby's book, Kim Philby: The Spy I Loved. The film recounts Philby's love affair and marriage to Eleanor Brewer during his time in Beirut and his eventual defection to the Soviet Union in late January 1963, though the characters based on Philby and Brewer have different names. In music * In the song \"Philby\", from the Top Priority album (1979), Rory Gallagher draws parallels between his life on the road and a spy's in a foreign country. Sample lyrics : \"Now ain't it strange that I feel like Philby / There's a stranger in my soul / I'm lost in transit in a lonesome city / I can't come in from the cold.\" * The Philby affair is mentioned in the Simple Minds song \"Up on the Catwalk\" from their sixth studio album Sparkle in the Rain. The lyric goes \"Up on the catwalk, and you dress in waistcoats / And got brillantino, and friends of Kim Philby.\" * The song \"Angleton\", by Russian indie rock band Biting Elbows, focuses largely on Philby's role as a spy from the perspective of James Jesus Angleton. * The song by 'Traitor' by Renegade Soundwave from their album Soundclash mentions \"Philby, Burgess and Maclean\" with the lyrics \"snitch, grass, informer, you're a traitor; you can't be trusted and left alone\". Other * The 1993 Joseph Brodsky essay Collector's Item (published in his 1995 book On Grief and Reason) contains a conjectured description of Philby's career, as well as speculations into his motivations and general thoughts on espionage and politics. The title of the essay refers to a postal stamp commemorating Philby issued in the Soviet Union in the late 1980s. Guy Francis de Moncy Burgess (16 April 1911 – 30 August 1963) was a British radio producer, intelligence officer and Foreign Office official. He was a member of the Cambridge Five spy ring that passed Western secrets to the Soviets before and during the Cold War. Burgess, Donald Maclean, Anthony Blunt and Kim Philby were the four confirmed members of the Cambridge Five, a spy ring that contributed to the Communist cause with the transmission of secret Foreign Office and MI5 documents that described NATO military and Marshall Plan economic strategy. Early life and education Burgess was born in Devonport, Devon, England, the son of Evelyn Mary (Gillman) and Malcolm Kingsford de Moncy Burgess (1881-1925), a naval officer. The Burgesses were of Huguenot origin, having changed the name from Bourgeois, and had been successful bankers in Kent during the Napoleonic wars. Although Burgess attended the Royal Naval College, Dartmouth, he failed to follow in his father's footsteps. Like most of the Cambridge Five, he came from a privileged background, attending Lockers Park School and Eton College, and eventually attending Trinity College, Cambridge. He joined the conservative Pitt Club but was also recruited into the Cambridge Apostles, a secret, elite debating society at the University, whose members at the time were largely Marxist and included Anthony Blunt. Burgess, together with Blunt, Maclean and Philby, was recruited by the Comintern. Life and career Upon coming down from Cambridge, Burgess initially was personal assistant to the Conservative MP Captain \"Jack\" Macnamara. He then worked for the BBC as a Talks Assistant, producing a wide variety of programmes. As war approached he was recruited into Section D of MI6 as a propaganda specialist, then returned to the BBC, eventually becoming the producer of The Week in Westminster, the flagship programme covering Parliamentary activity – wherein he was able to further his acquaintance with important politicians. In London Burgess resided at Chester Square and later 5, Bentinck Street, for some time with Anthony Blunt and Teresa Mayor, later Lady Rothschild. The house, which belonged to Lord Rothschild, was a famous centre of bohemian life during the Blitz. In the spring of 1944 Burgess was recruited into the News Department of the Foreign Office by Alexander Cadogan, Permanent Under-Secretary for Foreign Affairs, a position that gave him access to Foreign Office communications. When the Labour Government took office in the following year, Burgess became an assistant to Hector McNeil, Minister of State in the Foreign Office. As McNeil's assistant, Burgess was able to transmit top secret Foreign Office documents to the KGB regularly, secreting them out at night to be photographed by his controller and returning them to McNeil's desk in the morning. Burgess later worked in the Foreign Office's Far Eastern section and in the Washington Embassy. During the Marshall Plan negotiations, Burgess and Maclean provided information to the Soviets about the negotiations and the implications of the agreements. Just before going to Washington, he fell down a marble staircase in the Royal Automobile Club on Pall Mall during a fight with a colleague and suffered multiple skull fractures, injuries from which he never fully recovered. While assigned to the British embassy in Washington, Burgess lived with Kim Philby in a basement flat, perhaps so that Philby could keep an eye on him. In 1951 Burgess accompanied Donald Maclean in his escape to Moscow after Maclean fell under suspicion of espionage, even though Burgess himself was not under suspicion. The escape was arranged by their controller, Yuri Modin. There is some debate as to why Burgess was asked to accompany Maclean, and whether he was misled about the prospect for him returning to England. Much of his time in the Soviet Union was spent in sanitoria on the Black Sea. Unlike Maclean, who became a respected Soviet citizen in exile and lived until 1983, Burgess did not take to life in the Soviet Union. He could not pursue his homosexuality as he had become accustomed, though he lived openly with a lover. Unlike Maclean he never bothered to learn Russian, furnished his flat from London, and continued to order his clothes from his Savile Row tailor. He died aged 52, having become ever more dependent on alcohol in his last years. His remains were interred in the family plot at St John the Evangelist Churchyard, West Meon, Hampshire. In the memories of his contemporaries Harold Acton, in More Memoirs of an Aesthete, recalls meeting him at the beginning of World War II: \"The most vindictive of these guys was Guy Burgess, later to win notoriety as one of the 'Missing Diplomats', though nobody could have been less diplomatic\". Legacy Anthony Blunt, in his memoir released to the public on 22 July 2009, 26 years after his death in 1983 and 46 years after Burgess's death in 1963, described Burgess as \"an extraordinarily persuasive person\" who talked him (Blunt) into joining the spy ring. Although they were both homosexual and even shared a house together, Blunt said that there was \"nothing sexual\" in their relationship. Philby came under suspicion of being the \"Third Man\" who had tipped off Maclean and Burgess, especially since he and Burgess were known to be close friends and had shared a house in Washington. He was thus forced to resign from MI6 but was cleared by an official inquiry into the matter. Philby later defected to Russia in 1963. In an interview with spy writer and journalist Phillip Knightley held shortly before his death, Philby himself blamed his exposure on \"that bloody man Burgess\", who had effectively ruined his chances of becoming head of MI6 itself. Genrikh Borovik, author of The Philby Files, says that Burgess was actually tricked by the KGB into accompanying Maclean to Moscow on the basis that he would be able to return to Britain later, but never did. It later emerged that in 1959, when a British delegation led by Prime Minister Harold Macmillan was visiting Moscow, Burgess contacted members of the group asking permission to return to Britain and visit his dying mother. Informed by telegram, the then-Attorney General Sir Reginald Manningham-Buller admitted that there was insufficient evidence to arrest and prosecute Burgess for treason. The British delegates withheld this from Burgess and his mother died without seeing her son again. Macmillan also encouraged the leaking of misinformation to prevent Maclean from visiting Britain on a return trip from Cuba. Nonetheless, after his death, Burgess's body was returned to England and was interred in his mother's grave in West Meon in Hampshire. His name is inscribed in a discreet way rather than on the main headstone. In January 2014, Channel 4 News broadcast what at the time was thought to be the only known recording of Burgess. In this he recalled a 1938 meeting with Winston Churchill. It was the first time the material, recorded in 1951 shortly before his defection to the Soviet Union, had been heard publicly, after it was released to City University London researchers by the US government. Then in February 2015, the BBC programme Newsnight broadcast hitherto unknown footage of an interview with Burgess, filmed in January 1959 by Erik Durschmied. The film was made for the Canadian Broadcasting Corporation's programme \"Close Up\" and due to the archive of the programme being filed under the name of Dame Edith Sitwell, the programme's other invitee, the film went forgotten in a vault until after 2011. Chronology * 1911: Born in Devonport, England * Studies at Eton College * Studies at Dartmouth Royal Naval College * Studies at Trinity College, Cambridge. Meets the rest of the spy ring and becomes a supporter of the Communist Party of Great Britain. Is inducted into the Cambridge Apostles, a secret society that at this time is strongly Marxist * 1934: To hide his sympathies, he renounces communism and joins the Anglo-German Fellowship, a pro-Nazi group. Philby is also a member * 1936–1944: works for the BBC. Produces the programme The Week in Westminster * 1939–1941: Seconded to MI5 to work on war propaganda * 1944: Joins the Foreign Office news department * 1947: Private Secretary to Foreign Office Minister of State * 1948: Foreign Office Far Eastern Department * 1950: Sent to Washington, D.C. as a second secretary of the British Embassy * 1951: Meets Michael Straight in D.C.; Kim Philby warns Burgess that Maclean is under suspicion and will most likely be unmasked; Burgess and Maclean flee and go into hiding * 1956: They appear in Moscow * 1958: Meets Coral Browne in Moscow, the basis for Alan Bennett's play, An Englishman Abroad * 1959: Is interviewed in Moscow on film by the Canadian Broadcasting Corporation * 1963: Dies in Moscow in the same year that Philby defects to the Soviet Union Works based on his life * Another Country, a play by Julian Mitchell. * Another Country, a film based on the play, was directed by Marek Kanievska. Made in 1984, it starred Rupert Everett as Guy Bennett, a character based on Burgess. Colin Firth played Tommy Judd. Cary Elwes played Burgess' lover, James Harcourt. * Philby, Burgess And Maclean, a 1977 television film made by Granada Television for the ITV network about the Burgess-Maclean defection and the subsequent investigation of Kim Philby. * An Englishman Abroad, a 1983 television play by Alan Bennett starring Alan Bates as Guy Burgess, subsequently adapted for the stage by Bennett as the first act of Single Spies. * Cambridge Spies, a four-part BBC TV series, starring Tom Hollander as Burgess. * A Morning With Guy Burgess, a dramatic biography by John Morrison, premièred in 2011 by Black Pig Theatre at the Courtyard, Hoxton, London N1 * The Turning Point by Michael Dobbs explores a little-known 1938 meeting (mentioned above) between Burgess and Winston Churchill. The play was brought to life onstage in a live TV broadcast by Theatre LiveSky Arts with Benedict Cumberbatch portraying Burgess and Matthew Marsh as Churchill. * Blunt: The Fourth Man, a TV Movie starring Ian Richardson as Sir Anthony Blunt and Anthony Hopkins as Guy Burgess showing the events that took place during MacLean's & Burgess' defection to Moscow. Biographies, etc. * Deacon, Richard (1986), The Cambridge Apostles: a History of Cambridge University's Elite Intellectual Secret Society. * Barrie Penrose & Simon Freeman (1987), Conspiracy of Silence: The Secret Life of Anthony Blunt, New York. * Newton, Verne W. (1991), The Cambridge Spies: the Untold Story of Maclean, Philby, and Burgess in America. * Modin, Yuri (1994), My Five Cambridge Friends. * Carter, Miranda (2001), Anthony Blunt: His Lives. * Kim Philby, My Silent War (2002), Modern Library [Random House] : New York. ISBN 0-375-75983-2. * Holzman, Michael (2012), Guy Burgess: Revolutionary in an Old School Tie. * Carlston, Erin (2013), Double Agents. Espionage, Literature, and Liminal Citizens. New York, Columbia University Press. * Lownie, Andrew (2015), Stalin's Englshman : The Lives of Guy Burgess, London : Hodder and Stoughton. ISBN 978-1473627369. * Purvis, Stewart & Hulbert, Jeff (2016), Guy Burgess: The Spy Who Knew Everyone, London : Biteback Publishing. ISBN 9781849549134 Trinity College is a constituent college of the University of Cambridge in England. With around 600 undergraduates, 300 graduates, and over 180 fellows, it is the largest college in either of the Oxbridge universities by number of undergraduates. By combined student numbers, it is second to Homerton College, Cambridge. Members of Trinity have won 32 Nobel Prizes out of the 91 won by members of Cambridge University, the highest number of any college. Five Fields Medals in mathematics were won by members of the college (of the six awarded to members of British universities) and one Abel Prize was won. Trinity alumni include six British prime ministers (all Tory or Whig/Liberal), physicists Isaac Newton, James Clerk Maxwell, Ernest Rutherford and Niels Bohr, the poet Lord Byron, philosophers Ludwig Wittgenstein and Bertrand Russell (whom it expelled before reaccepting), and Soviet spies Kim Philby, Guy Burgess, and Anthony Blunt. Two members of the British Royal Family have studied at Trinity and been awarded degrees as a result: Prince William of Gloucester and Edinburgh, who gained an MA in 1790, and Prince Charles, who was awarded a lower second class BA in 1970. Other British Royal family members have studied there without obtaining degrees, including King Edward VII, King George VI, and Prince Henry, Duke of Gloucester. Trinity has many college societies, including the Trinity Mathematical Society, which is the oldest mathematical university society in the United Kingdom, and the First and Third Trinity Boat Club, its rowing club, which gives its name to the college's May Ball. Along with Christ's, Jesus, King's and St John's colleges, it has also provided several of the well known members of the Apostles, an intellectual secret society. In 1848, Trinity hosted the meeting at which Cambridge undergraduates representing private schools such as Westminster drew up the first formal rules of football, known as the Cambridge Rules. Trinity's sister college in Oxford is Christ Church. Like that college, Trinity has been linked with Westminster School since the school's re-foundation in 1560, and its Master is an ex officio governor of the school. History Foundation The college was founded by Henry VIII in 1546, from the merger of two existing colleges: Michaelhouse (founded by Hervey de Stanton in 1324), and King's Hall (established by Edward II in 1317 and refounded by Edward III in 1337). At the time, Henry had been seizing church lands from abbeys and monasteries. The universities of Oxford and Cambridge, being both religious institutions and quite rich, expected to be next in line. The King duly passed an Act of Parliament that allowed him to suppress (and confiscate the property of) any college he wished. The universities used their contacts to plead with his sixth wife, Catherine Parr. The Queen persuaded her husband not to close them down, but to create a new college. The king did not want to use royal funds, so he instead combined two colleges (King's Hall and Michaelhouse) and seven hostels (Physwick (formerly part of Gonville and Caius College, Cambridge), Gregory's, Ovyng's, Catherine's, Garratt, Margaret's, and Tyler's) to form Trinity. Nevile's expansion Contrary to popular belief, the monastic lands granted by Henry VIII were not on their own sufficient to ensure Trinity's eventual rise. In terms of architecture and royal association, it was not until the Mastership of Thomas Nevile (1593–1615) that Trinity assumed both its spaciousness and its courtly association with the governing class that distinguished it since the Civil War. In its infancy Trinity had owed a great deal to its neighbouring college of St John's: in the exaggerated words of Roger Ascham Trinity was little more than a colonia deducta. Its first four Masters were educated at St John's, and it took until around 1575 for the two colleges' application numbers to draw even, a position in which they have remained since the Civil War. In terms of wealth, Trinity's current fortunes belie prior fluctuations; Nevile's building campaign drove the college into debt from which it only surfaced in the 1640s, and the Mastership of Richard Bentley adversely affected applications and finances. Bentley himself was notorious for the construction of a hugely expensive staircase in the Master's Lodge, and for his repeated refusals to step down despite pleas from the Fellows. Most of the Trinity's major buildings date from the 16th and 17th centuries. Thomas Nevile, who became Master of Trinity in 1593, rebuilt and redesigned much of the college. This work included the enlargement and completion of Great Court, and the construction of Nevile's Court between Great Court and the river Cam. Nevile's Court was completed in the late 17th century when the Wren Library, designed by Sir Christopher Wren, was built. Modern day In the 20th century, Trinity College, St John's College and King's College were for decades the main recruiting grounds for the Cambridge Apostles, an elite, intellectual secret society. In 2011, the John Templeton Foundation awarded Trinity College's Master, the astrophysicist Martin Rees, its controversial million-pound Templeton Prize, for \"affirming life's spiritual dimension\". Trinity is the richest Oxbridge college, with a landholding alone worth £800 million. Trinity is sometimes suggested to be the second, third or fourth wealthiest landowner in the UK (or in England) — after the Crown Estate, the National Trust and the Church of England. (A variant of this legend is repeated in the Tom Sharpe novel Porterhouse Blue.) In 2005, Trinity's annual rental income from its properties was reported to be in excess of £20 million. Trinity owns: * 3400 acres (14 km2) housing facilities at the Port of Felixstowe, Britain's busiest container port * the Cambridge Science Park * the O2 Arena in London (formerly the Millennium Dome) * a 50% stake in a portfolio of Tesco supermarket stores, worth £440 million Legends Lord Byron purportedly kept a pet bear whilst living in the college. A second legend is that it is possible to walk from Cambridge to Oxford on land solely owned by Trinity. Several varieties of this legend exist – others refer to the combined land of Trinity College, Cambridge and Trinity College, Oxford, of Trinity College, Cambridge and Christ Church, Oxford, or St John's College, Oxford and St John's College, Cambridge. All are almost certainly false. Trinity is often cited as the inventor of an English, less sweet, version of crème brûlée, known as \"Trinity burnt cream\", although the college chefs have sometimes been known to refer to it as \"Trinity Creme Brulee\". The burnt-cream, first introduced at Trinity High Table in 1879, in fact differs quite markedly from French recipes, the earliest of which is from 1691. Trinity in Camberwell Trinity College has a long-standing relationship with the Parish of St George's, Camberwell, in South London. Students from the College have helped to run holiday schemes for children from the parish since 1966. The relationship was formalised in 1979 with the establishment of Trinity in Camberwell as a registered charity (Charity Commission no. 279447) which exists ‘to provide, promote, assist and encourage the advancement of education and relief of need and other charitable objects for the benefit of the community in the Parish of St George's, Camberwell, and the neighbourhood thereof.’ Buildings and grounds Great Gate The Great Gate is the main entrance to the college, leading to the Great Court. A statue of the college founder, Henry VIII, stands in a niche above the doorway. In his hand he holds a table leg instead of the original sword and myths abound as to how the switch was carried out and by whom. In 1704, the University's first astronomical observatory was built on top of the gatehouse. Beneath the founder's statue are the coats of arms of Edward III, the founder of King's Hall, and those of his five sons who survived to maturity, as well as William of Hatfield, whose shield is blank as he died as an infant, before being granted arms. Great Court Great Court (built principally 1599–1608) was the brainchild of Thomas Nevile, who demolished several existing buildings on this site, including almost the entirety of the former college of Michaelhouse. The sole remaining building of Michaelhouse was replaced by the then current Kitchens (designed by James Essex) in 1770–1775. The Master's Lodge is the official residence of the Sovereign when in Cambridge. King's Hostel (built 1377–1416) is located to the north of Great Court, behind the Clock Tower, this is (along with the King's Gate), the sole remaining building from King's Hall. Bishop's Hostel (built 1671, Robert Minchin): A detached building to the southwest of Great Court, and named after John Hacket, Bishop of Lichfield and Coventry. Additional buildings were built in 1878 by Arthur Blomfield. Nevile's Court Nevile's Court (built 1614) is located between Great Court and the river, this court was created by a bequest by the college's master, Thomas Nevile, originally two-thirds of its current length and without the Wren Library. The appearance of the upper floor was remodelled slightly two centuries later. Cloisters run around the court, providing sheltered walkways from the rear of Great Hall to the college library and reading room as well as the Wren Library and New Court. Wren Library (built 1676–1695, Christopher Wren) is located at the west end of Nevile's Court, the Wren is one of Cambridge's most famous and well-endowed libraries. Among its notable possessions are two of Shakespeare's First Folios, a 14th-century manuscript of The Vision of Piers Plowman, and letters written by Sir Isaac Newton. The Eadwine Psalter belongs to Trinity but is kept by Cambridge University Library. Below the building are the pleasant Wren Library Cloisters, where students may enjoy a fine view of the Great Hall in front of them, and the river and Backs directly behind. New Court New Court (or King's Court; built 1825, William Wilkins) is located to the south of Nevile's Court, and built in Tudor-Gothic style, this court is notable for the large tree in the centre. A myth is sometimes circulated that this was the tree from which the apple dropped onto Isaac Newton; in fact, Newton was at Woolsthorpe when he deduced his theory of gravity – and the tree is a chestnut tree. Many other \"New Courts\" in the colleges were built at this time to accommodate the new influx of students. Other courts Whewell's Court (actually two courts with a third in between, built 1860 & 1868, architect Anthony Salvin) is located across the street from Great Court, and was entirely paid for by William Whewell, the Master of the college from 1841 until his death in 1866. The north range was later remodelled by W.D. Caroe. Angel Court (built 1957–1959, H. C. Husband) is located between Great Court and Trinity Street, and is used along with the Wolfson Building for accommodating first year students. The Wolfson Building (built 1968–1972, Architects Co-Partnership) is located to the south of Whewell's Court, on top of a podium above shops, this building resembles a brick-clad ziggurat, and is used exclusively for first-year accommodation. Having been renovated during the academic year 2005–06, rooms are now almost all en-suite. Blue Boar Court (built 1989, MJP Architects and Wright) is located to the south of the Wolfson Building, on top of podium a floor up from ground level, and including the upper floors of several surrounding Georgian buildings on Trinity Street, Green Street and Sidney Street. Burrell's Field (built 1995, MJP Architects) is located on a site to the west of the main College buildings, opposite the Cambridge University Library. There are also College rooms above shops in Bridge Street and Jesus Lane, behind Whewell's Court, and graduate accommodation in Portugal Street and other roads around Cambridge. Chapel Trinity College Chapel dates from the mid 16th Century and is Grade I listed. There are a number of memorials to former Fellows of Trinity within the Chapel, including statues, brasses, and two memorials to graduates and Fellows who died during the World Wars. The Chapel is a performance space for the college choir which comprises around 30 Choral Scholars and 2 Organ Scholars, all of whom are ordinarily undergraduate members of the college. Grounds The Fellows’ Garden is located on the west side of Queen's Road, opposite the drive that leads to the Backs. The Fellows’ Bowling Green is located behind the Master's Lodge it is the site for many of the tutors' garden parties in the summer months, while the Master's Garden is located behind the Master's Lodge. The Old Fields are located on the western side of Grange Road, next to Burrell's Field. It currently houses the college's gym, changing rooms, squash courts, badminton courts, rugby, hockey and football pitches along with tennis and netball courts. Gallery File:TrinityCollegeCamGreatGate.jpg|Great Gate File:Clock Tower, Great Court, Trinity College, Cambridge.jpg|Clock Tower File:cmglee_Cambridge_Trinity_College_New_Court_doorway.jpg|New Court after 2016 refurbishment File:River Cam green.JPG|The River Cam as it flows past the back of Trinity, Trinity Bridge is visible and the punt house is to the right of the moored punts File:cmglee_Cambridge_Trinity_College_avenue.jpg|The Avenue of lime and cherry trees, and wrought iron gate to Queen's Road viewed from the Backs File:Burrell's_Field_main_entrance.jpg|Main entrance to Burrell's Field File:ISH WC Trinity2.jpg|Fellows’ Bowling Green, with the oldest building in the college (originally part of King's Hall) in the background File:Cmglee_Cambridge_Trinity_College_Blue_Boar_Court.jpg|Blue Boar Court, with the Wolfson Building in the background. Academic profile Since 1997, the college has always come at least eighth in the Tompkins Table, which ranks the 29 Cambridge colleges according to the academic performance of their undergraduates, and on five occasions it has been in first place. Its average position has been third. On this benchmark, it has been behind Emmanuel (average second place) and above Christ's (average fourth place). In 2011, 37% of Trinity undergraduates achieved Firsts – a recent record among Cambridge colleges. The College improved on this in 2012, when 37.9% of its undergraduates were awarded Firsts. In 2013, the College topped the Table for the third successive year. Its undergraduates achieved a new record with the highest proportion of first degree passes ever achieved at 41.7 per cent, eight percentage points ahead of second-placed Pembroke. Admissions Currently, about 50% of Trinity's undergraduates attended independent schools. In 2006 it accepted a smaller proportion of students from state schools (39%) than any other Cambridge college, and on a rolling three-year average it has admitted a smaller proportion of state school pupils (42%) than any other college at either Cambridge or Oxford. According to the Good Schools Guide, about 7% of British school-age students attend private schools, although this figure refers to students in all school years - a higher proportion attend private schools in their final two years before university. Trinity states that it disregards what type of school its applicants attend, and accepts students solely on the basis of their academic prospects. Trinity admitted its first woman graduate student in 1976 and its first woman undergraduate in 1978, and appointed its first female fellow in 1977. Scholarships and prizes The Scholars, together with the Master and Fellows, make up the Foundation of the College. In order of seniority: Research Scholars receive funding for graduate studies. Typically one must graduate in the top ten percent of one's class and continue for graduate study at Trinity. They are given first preference in the assignment of college rooms and number approximately 25. The Senior Scholars consist of those who attain a degree with First Class honours or higher in any year after the first of an undergraduate tripos, but also, those who obtain a high First class marks in their first year. The college pays them a stipend of £250 a year and allows them to choose rooms directly following the research scholars. There are around 40 senior scholars at any one time. The Junior Scholars are those who are not senior scholars but still obtained a First in their first year. Their stipend is £175 a year. They are given preference in the room ballot over 2nd years who are not scholars. These scholarships are tenable for the academic year following that in which the result was achieved. If a scholarship is awarded but the student does not continue at Trinity then only a quarter of the stipend is given. However all students who achieve a First are awarded an additional £240 prize upon announcement of the results. All final year undergraduates who achieve first-class honours in their final exams are offered full financial support to read for a Master's degree at Cambridge (this funding is also sometimes available for students who achieved high second-class honours marks). Other support is available for PhD degrees. The College also offers a number of other bursaries and studentships open to external applicants. The right to walk on the grass in the college courts is exclusive to Fellows of the college and their guests. Scholars do, however, have the right to walk on the Scholars' Lawn, but only in full academic dress. Traditions Great Court Run The Great Court Run is an attempt to run round the 400-yard perimeter of Great Court (approximately 367m), in the 43 seconds of the clock striking twelve. Students traditionally attempt to complete the circuit on the day of the Matriculation Dinner. It is a rather difficult challenge: one needs to be a fine sprinter to achieve it, but it is by no means necessary to be of Olympic standard, despite assertions made in the press. It is widely believed that Sebastian Coe successfully completed the run when he beat Steve Cram in a charity race in October 1988. Coe's time on 29 October 1988 was reported by Norris McWhirter to have been 45.52 seconds, but it was actually 46.0 seconds (confirmed by the video tape), while Cram's was 46.3 seconds. The clock on that day took 44.4 seconds (i.e., a \"long\" time, probably two days after the last winding) and the video film confirms that Coe was some 12 metres short of his finish line when the fateful final stroke occurred. The television commentators were disingenuous in suggesting that the dying sounds of the bell could be included in the striking time, thereby allowing Coe's run to be claimed as successful. One reason Olympic runners Cram and Coe found the challenge so tough is that they started at the middle of one side of the court, thereby having to negotiate four right-angle turns. In the days when students started at a corner, only three turns were needed. The Great Court Run was portrayed in the film Chariots of Fire about the British Olympic runners of 1924. Until the mid-1990s, the run was traditionally attempted by first-year students at midnight following their matriculation dinner. Following a number of accidents to undergraduates running on slippery cobbles, the college now organises a more formal Great Court Run, at 12 noon on the day of the matriculation dinner: the challenge is only open to freshers, many of whom compete in fancy dress. Open-air concerts One Sunday each June (the exact date depending on the university term), the College Choir perform a short concert immediately after the clock strikes noon. Known as Singing from the Towers, half of the choir sings from the top of the Great Gate, while the other half sings from the top of the Clock Tower approximately 60 metres away, giving a strong antiphonal effect. Midway through the concert, the Cambridge University Brass Ensemble performs from the top of the Queen's Tower. Later that same day, the College Choir gives a second open-air concert, known as Singing on the River, where they perform madrigals and arrangements of popular songs from a raft of punts lit with lanterns or fairy lights on the river. For the finale, John Wilbye's madrigal Draw on, sweet night, the raft is unmoored and punted downstream to give a fade out effect. As a tradition, however, this latter concert dates back only to the mid-1980s, when the College Choir first acquired female members. In the years immediately before this, an annual concert on the river was given by the University Madrigal Society. Mallard Another tradition relates to an artificial duck known as the Mallard, which should reside in the rafters of the Great Hall. Students occasionally moved the duck from one rafter to another without permission from the college. Chair legs and bicycles The sceptre held by the statue of Henry VIII mounted above the medieval Great Gate was replaced with a chair leg as a prank many years ago. It has remained there to this day: when in the 1980s students exchanged the chair leg for a bicycle pump, the College replaced the chair leg. For many years it was the custom for students to place a bicycle high in branches of the tree in the centre of New Court. Usually invisible except in winter, when the leaves had fallen, such bicycles tended to remain for several years before being removed by the authorities. The students then inserted another bicycle. College rivalry The college remains a great rival of St John's which is its main competitor in sports and academia (John's is situated next to Trinity). This has given rise to a number of anecdotes and myths. It is often cited as the reason why the older courts of Trinity generally have no J staircases, despite including other letters in alphabetical order. A far more likely reason remains the absence of the letter J in the Latin alphabet, and it should be noted that St John's College's older courts also lack J staircases. There are also two small muzzle-loading cannons on the bowling green pointing in the direction of John's, though this orientation may be coincidental. Another story sometimes told is that the reason that the clock in Trinity Great Court strikes each hour twice is that the fellows of St John's once complained about the noise it made. Minor traditions Trinity College undergraduate gowns are readily distinguished from the black gowns favoured by most other Cambridge colleges. They are instead dark blue with black facings. They are expected to be worn to formal events such as formal halls and also when an undergraduate sees the Dean of the College in a formal capacity. Trinity students, along with those of King's and St John's, are the first to be presented to the Congregation of the Regent House at graduation. College Grace Each evening before dinner, grace is recited by the senior fellow presiding, as follows: * Benedic, Domine, nos et dona tua, (Bless us, Lord, and these gifts) * quae de largitate tua sumus sumpturi, (which, through your generosity, we are about to receive) * et concede, ut illis salubriter nutriti (and grant that we, wholesomely nourished by them,) * tibi debitum obsequium praestare valeamus, (may be able to offer you the service we owe) * per Christum Dominum nostrum. (through Christ our Lord) If both of the two high tables are in use then the following antiphonal formula is prefixed to the main grace: * A. Oculi omnium in te sperant Domine: (The eyes of all are on you, Lord) * B. Et tu das escam illis in tempore. (and you give them their food, in due time.) * A. Aperis tu manum tuam, (You open your hand) * B. Et imples omne animal benedictione. (and bestow upon all living things your blessing.) Following the meal, the simple formula Benedicto benedicatur is pronounced. People associated with the college Notable fellows and alumni The Parish of the Ascension Burial Ground in Cambridge contains the graves of 27 Fellows of Trinity College, Cambridge most of whom are also commemorated in Trinity College Chapel with brass plaques. Nobel Prize winners Fields Medallists Trinity College also has claim to a number of winners of the Fields Medal (commonly regarded as the mathematical equivalent of the Nobel Prize ): Michael Atiyah, Alan Baker, Richard Borcherds and Timothy Gowers. Atiyah is also an Abel Prize winner. British Prime Ministers Other Trinity politicians include Robert Devereux, 2nd Earl of Essex, courtier of Elizabeth I; William Waddington, Prime Minister of France; Erskine Hamilton Childers, President of Ireland; Jawaharlal Nehru, the first and longest serving Prime Minister of India; Rajiv Gandhi, Prime Minister of India; Lee Hsien Loong, Prime Minister of Singapore; Samir Rifai, Prime Minister of Jordan and William Whitelaw, 1st Viscount Whitelaw, Lady Thatcher's Home Secretary and subsequent Deputy Prime Minister. Masters The head of Trinity College is called the Master. The role is a Crown appointment, formerly made by the Monarch on the advice of the Prime Minister. Nowadays the Fellows of the College choose the new Master, and the Royal role is only nominal. The first Master, John Redman, was appointed in 1546. All six Masters subsequent to R.A. Butler had been Fellows of the College prior to becoming Master (Honorary Fellow in the case of Martin Rees). The last master was Martin Rees, Baron Rees of Ludlow (until end of June 2012). He was succeeded by Sir Gregory Winter on 2 October 2012. The University of Cambridge (informally Cambridge University or simply Cambridge)The corporate title of the university is The Chancellor, Masters, and Scholars of the University of Cambridge. is a collegiate public research university in Cambridge, England. Founded in 1209, Cambridge is the second-oldest university in the English-speaking world and the world's fourth-oldest surviving university. The university grew out of an association of scholars who left the University of Oxford after a dispute with the townspeople. The two ancient universities share many common features and are often referred to jointly as \"Oxbridge\". Cambridge is formed from a variety of institutions which include 31 constituent colleges and over 100 academic departments organised into six schools. Cambridge University Press, a department of the university, is the world's oldest publishing house and the second-largest university press in the world. The university also operates eight cultural and scientific museums, including the Fitzwilliam Museum, and a botanic garden. Cambridge's libraries hold a total of around 15 million books, eight million of which are in Cambridge University Library, a legal deposit library. In the year ended 31 July 2015, the university had a total income of £1.64 billion, of which £398 million was from research grants and contracts. The central university and colleges have a combined endowment of around £5.89 billion, the largest of any university outside the United States. The university is closely linked with the development of the high-tech business cluster known as \"Silicon Fen\". It is a member of numerous associations and forms part of the \"golden triangle\" of leading English universities and Cambridge University Health Partners, an academic health science centre. Cambridge is consistently ranked as one of the world's best universities. The university has educated many notable alumni, including eminent mathematicians, scientists, politicians, lawyers, philosophers, writers, actors, and foreign Heads of State. Ninety-two Nobel laureates and ten Fields medalists have been affiliated with Cambridge as students, faculty, staff or alumni. History By the late 12th century, the Cambridge region already had a scholarly and ecclesiastical reputation, due to monks from the nearby bishopric church of Ely. However, it was an incident at Oxford which is most likely to have formed the establishment of the university: two Oxford scholars were hanged by the town authorities for the death of a woman, without consulting the ecclesiastical authorities, who would normally take precedence (and pardon the scholars) in such a case, but were at that time in conflict with King John. The University of Oxford went into suspension in protest, and most scholars moved to cities such as Paris, Reading, and Cambridge. After the University of Oxford reformed several years later, enough scholars remained in Cambridge to form the nucleus of the new university. In order to claim precedence, it is common for Cambridge to trace its founding to the 1231 charter from King Henry III granting it the right to discipline its own members (ius non-trahi extra) and an exemption from some taxes. (Oxford would not receive a similar enhancement until 1248.) A bull in 1233 from Pope Gregory IX gave graduates from Cambridge the right to teach \"everywhere in Christendom\". After Cambridge was described as a studium generale in a letter by Pope Nicholas IV in 1290, and confirmed as such in a bull by Pope John XXII in 1318, it became common for researchers from other European medieval universities to visit Cambridge to study or to give lecture courses. Foundation of the colleges The colleges at the University of Cambridge were originally an incidental feature of the system. No college is as old as the university itself. The colleges were endowed fellowships of scholars. There were also institutions without endowments, called hostels. The hostels were gradually absorbed by the colleges over the centuries, but they have left some indicators of their time, such as the name of Garret Hostel Lane. Hugh Balsham, Bishop of Ely, founded Peterhouse, Cambridge's first college, in 1284. Many colleges were founded during the fourteenth and fifteenth centuries, but colleges continued to be established throughout the centuries to modern times, although there was a gap of 204 years between the founding of Sidney Sussex in 1596 and Downing in 1800. The most recently established college is Robinson, built in the late 1970s. However, Homerton College only achieved full university college status in March 2010, making it the newest full college (it was previously an \"Approved Society\" affiliated with the university). In medieval times, many colleges were founded so that their members would pray for the souls of the founders, and were often associated with chapels or abbeys. A change in the colleges' focus occurred in 1536 with the Dissolution of the Monasteries. King Henry VIII ordered the university to disband its Faculty of Canon Law and to stop teaching \"scholastic philosophy\". In response, colleges changed their curricula away from canon law, and towards the classics, the Bible, and mathematics. Nearly a century later, the university was at the centre of a Protestant schism. Many nobles, intellectuals and even commoners saw the ways of the Church of England as being too similar to the Catholic Church and that it was used by the crown to usurp the rightful powers of the counties. East Anglia was the centre of what became the Puritan movement and at Cambridge, it was particularly strong at Emmanuel, St Catharine's Hall, Sidney Sussex and Christ's College. They produced many \"non-conformist\" graduates who greatly influenced, by social position or pulpit, the approximately 20,000 Puritans who left for New England and especially the Massachusetts Bay Colony during the Great Migration decade of the 1630s. Oliver Cromwell, Parliamentary commander during the English Civil War and head of the English Commonwealth (1649–1660), attended Sidney Sussex. Mathematics and mathematical physics Examination in mathematics was once compulsory for all undergraduates studying for the Bachelor of Arts degree, the main first degree at Cambridge in both arts and sciences. From the time of Isaac Newton in the later 17th century until the mid-19th century, the university maintained an especially strong emphasis on applied mathematics, particularly mathematical physics. The exam is known as a Tripos. Students awarded first-class honours after completing the mathematics Tripos are termed wranglers, and the top student among them is the Senior Wrangler. The Cambridge Mathematical Tripos is competitive and has helped produce some of the most famous names in British science, including James Clerk Maxwell, Lord Kelvin and Lord Rayleigh. However, some famous students, such as G. H. Hardy, disliked the system, feeling that people were too interested in accumulating marks in exams and not interested in the subject itself. Pure mathematics at Cambridge in the 19th century had great achievements but also missed out on substantial developments in French and German mathematics. Pure mathematical research at Cambridge finally reached the highest international standard in the early 20th century, thanks above all to G. H. Hardy and his collaborator, J. E. Littlewood. In geometry, W. V. D. Hodge brought Cambridge into the international mainstream in the 1930s. Although diversified in its research and teaching interests, Cambridge today maintains its strength in mathematics. Cambridge alumni have won six Fields Medals and one Abel Prize for mathematics, while individuals representing Cambridge have won four Fields Medals. Modern period After the Cambridge University Act formalised the organizational structure of the university, the study of many new subjects was introduced, such as theology, history and modern languages. Resources necessary for new courses in the arts, architecture and archaeology were generously donated by Richard Fitzwilliam of Trinity College. Between 1896 and 1902, Downing College sold part of its land to build the Downing Site, comprising new scientific laboratories for anatomy, genetics and Earth sciences. During the same period, the New Museums Site was erected, including the Cavendish Laboratory, which has since moved to the West Cambridge Site, and other departments for chemistry and medicine. The University of Cambridge began to award doctorates in the first third of the 20th century. The first Cambridge PhD in mathematics was awarded in 1924. In the First World War, 13,878 members of the university served and 2,470 were killed. Teaching, and the fees it earned, came almost to a stop and severe financial difficulties followed. As a consequence the university first received systematic state support in 1919, and a Royal Commission appointed in 1920 recommended that the university (but not the colleges) should receive an annual grant. Following the Second World War, the university saw a rapid expansion of student numbers and available places; this was partly due to the success and popularity gained by many Cambridge scientists. Parliamentary representation The university was one of only eight UK universities to hold a parliamentary seat in the Parliament of the United Kingdom. The constituency was created by a Royal Charter of 1603 and returned two members of parliament. It was abolished in 1950 by the Representation of the People Act 1948. The constituency was not a geographical area. Its electorate consisted of the graduates of the University. Before 1918 the franchise was restricted to male graduates with a doctorate or MA degree. Women's education For many years only male students were enrolled into the university. The first colleges for women were Girton College (founded by Emily Davies) in 1869 and Newnham College in 1872 (founded by Anne Clough and Henry Sidgwick), followed by Hughes Hall in 1885 (founded by Elizabeth Phillips Hughes as the Cambridge Teaching College for Women), Murray Edwards College (founded by Rosemary Murray as New Hall) in 1954, and Lucy Cavendish College in 1965. The first women students were examined in 1882 but attempts to make women full members of the university did not succeed until 1948. Women were allowed to study courses, sit examinations, and have their results recorded from 1881; for a brief period after the turn of the twentieth century, this allowed the \"steamboat ladies\" to receive ad eundem degrees from the University of Dublin. From 1921 women were awarded diplomas which \"conferred the Title of the Degree of Bachelor of Arts\". As they were not \"admitted to the Degree of Bachelor of Arts\" they were excluded from the governing of the university. Since students must belong to a college, and since established colleges remained closed to women, women found admissions restricted to colleges established only for women. Darwin College, the first wholly graduate college of the University, matriculated both men and women students from its inception in 1964 – and elected a mixed fellowship. Of the undergraduate colleges, starting with Churchill, Clare and King's Colleges, the former men's colleges began to admit women between 1972 and 1988. One of the female-only colleges, Girton, also began to admit male students from 1979, but the other female-only colleges did not do likewise. As a result of St Hilda's College, Oxford, ending its ban on male students in 2008, Cambridge is now the only remaining United Kingdom University with female-only colleges (Newnham, Murray Edwards and Lucy Cavendish). In the academic year 2004–5, the university's student sex ratio, including post-graduates, was male 52%: female 48%. Myths, legends and traditions As an institution with such a long history, the University has developed a large number of myths and legends. The vast majority of these are untrue, but have been propagated nonetheless by generations of students and tour guides. A discontinued tradition is that of the wooden spoon, the 'prize' awarded to the student with the lowest passing grade in the final examinations of the Mathematical Tripos. The last of these spoons was awarded in 1909 to Cuthbert Lempriere Holthouse, an oarsman of the Lady Margaret Boat Club of St John's College. It was over one metre in length and had an oar blade for a handle. It can now be seen outside the Senior Combination Room of St John's. Since 1908, results were published alphabetically within class rather than score order. This made it harder to ascertain who the winner of the spoon was (unless there was only one person in the third class), and so the practice was abandoned. Each Christmas Eve, BBC radio and television broadcasts The Festival of Nine Lessons and Carols by the Choir of King's College, Cambridge. The radio broadcast has been a national Christmas tradition since it was first transmitted in 1928 (though the festival has existed since 1918). The radio broadcast is carried worldwide by the BBC World Service and is also syndicated to hundreds of radio stations in the USA. The first television broadcast of the festival was in 1954. Locations and buildings Buildings The university occupies a central location within the city of Cambridge, with the students taking up a significant proportion (nearly 20%) of the town's population and heavily affecting the age structure. Most of the older colleges are situated nearby the city centre and river Cam, along which it is traditional to punt to appreciate the buildings and surroundings. Examples of notable buildings include King's College Chapel, the history faculty building designed by James Stirling; and the Cripps Building at St John's College. The brickwork of several of the colleges is also notable: Queens' College contains \"some of the earliest patterned brickwork in the country\" and the brick walls of St John's College provide examples of English bond, Flemish bond and Running bond. Sites The university is divided into several sites where the different departments are placed. The main ones are: * Addenbrooke's * Downing Site * Madingley/Girton * New Museums Site * Old Addenbroke's * Old Schools * Silver Street/Mill Lane * Sidgwick Site * West Cambridge The university's School of Clinical Medicine is based in Addenbrooke's Hospital where students in medicine undergo their three-year clinical placement period after obtaining their BA degree, while the West Cambridge site is undergoing a major expansion and will host a new sports development. In addition, the Judge Business School, situated on Trumpington Street, provides management education courses since 1990 and is consistently ranked within the top 20 business schools globally by the Financial Times. Given that the sites are in relative close proximity to each other and the area around Cambridge is reasonably flat, one of the favourite modes of transport for students is the bicycle: a fifth of the journeys in the city are made by bike, a figure enhanced by the fact that students are not permitted to hold car park permits, except under special circumstances. \"Town and Gown\" The relationship between the university and the city has not always been positive. The phrase Town and Gown is employed to differentiate inhabitants of Cambridge from students at the university, who historically wore academical dress. There are many stories of ferocious rivalry between the two categories: in 1381, strong clashes brought about attacks and looting of university properties while locals contested the privileges granted by the government to the academic staff. Following these events, the Chancellor was given special powers allowing him to prosecute the criminals and re-establish order in the city. Attempts to reconcile the two groups followed over time, and in the 16th century agreements were signed to improve the quality of streets and student accommodation around the city. However, this was followed by new confrontations when the plague hit Cambridge in 1630 and colleges refused to help those affected by the disease by locking their sites. Nowadays, these conflicts have somewhat subsided and the University has become an opportunity for employment among the population, providing an increased level of wealth in the area. The enormous growth in the number of high-tech, biotech, providers of services and related firms situated near Cambridge has been termed the Cambridge Phenomenon: the addition of 1,500 new, registered companies and as many as 40,000 jobs between 1960 and 2010 has been directly related to the presence and importance of the educational institution. Organisation and administration Cambridge is a collegiate university, meaning that it is made up of self-governing and independent colleges, each with its own property and income. Most colleges bring together academics and students from a broad range of disciplines, and within each faculty, school or department within the university, academics from many different colleges will be found. The faculties are responsible for ensuring that lectures are given, arranging seminars, performing research and determining the syllabi for teaching, overseen by the General Board. Together with the central administration headed by the Vice-Chancellor, they make up the entire Cambridge University. Facilities such as libraries are provided on all these levels: by the University (the Cambridge University Library), by the Faculties (Faculty libraries such as the Squire Law Library), and by the individual colleges (all of which maintain a multi-discipline library, generally aimed mainly at their undergraduates). Colleges The colleges are self-governing institutions with their own endowments and property, founded as integral parts of the university. All students and most academics are attached to a college. Their importance lies in the housing, welfare, social functions, and undergraduate teaching they provide. All faculties, departments, research centres, and laboratories belong to the university, which arranges lectures and awards degrees, but undergraduates receive their supervisions—small-group teaching sessions, often with just one student—within the colleges. Each college appoints its own teaching staff and fellows, who are also members of a university department. The colleges also decide which undergraduates to admit to the university, in accordance with university regulations. Cambridge has 31 colleges, of which three, Murray Edwards, Newnham and Lucy Cavendish, admit women only. The other colleges are mixed, though most were originally all-male. Darwin was the first college to admit both men and women, while Churchill, Clare, and King's were the first previously all-male colleges to admit female undergraduates, in 1972. In 1988 Magdalene became the last all-male college to accept women. Clare Hall and Darwin admit only postgraduates, and Hughes Hall, Lucy Cavendish, St Edmund's and Wolfson admit only mature (i.e. 21 years or older on date of matriculation) students, encompassing both undergraduate and graduate students. All other colleges admit both undergraduate and postgraduate students with no age restrictions. Colleges are not required to admit students in all subjects, with some colleges choosing not to offer subjects such as architecture, history of art or theology, but most offer close to the complete range. Some colleges maintain a bias towards certain subjects, for example with Churchill leaning towards the sciences and engineering, while others such as St Catharine's aim for a balanced intake. Others maintain much more informal reputations, such as for the students of King's College to hold left-wing political views, or Robinson College and Churchill College's attempts to minimise its environmental impact. Costs to students (accommodation and food prices) vary considerably from college to college. Similarly, college expenditure on student education also varies widely between individual colleges. There are also several theological colleges in Cambridge, separate from Cambridge University, including Westcott House, Westminster College and Ridley Hall Theological College, that are, to a lesser degree, affiliated to the university and are members of the Cambridge Theological Federation. The 31 colleges are: Schools, faculties and departments In addition to the 31 colleges, the university is made up of over 150 departments, faculties, schools, syndicates and other institutions. Members of these are usually also members of one of the colleges and responsibility for running the entire academic programme of the university is divided amongst them. The university also houses the Institute of Continuing Education, a centre for part-time study. A \"School\" in the University of Cambridge is a broad administrative grouping of related faculties and other units. Each has an elected supervisory body—the \"Council\" of the school—comprising representatives of the constituent bodies. There are six schools: * Arts and Humanities * Biological Sciences * Clinical Medicine * Humanities and Social Sciences * Physical Sciences * Technology Teaching and research in Cambridge is organised by faculties. The faculties have different organisational sub-structures which partly reflect their history and partly their operational needs, which may include a number of departments and other institutions. In addition, a small number of bodies entitled 'Syndicates' have responsibilities for teaching and research, e.g. Cambridge Assessment, the University Press, and the University Library. Central administration Chancellor and Vice-Chancellor The office of Chancellor of the University, for which there are no term limits, is mainly ceremonial and is held by David Sainsbury, Baron Sainsbury of Turville, following the retirement of the Duke of Edinburgh on his 90th birthday in June 2011. Lord Sainsbury was nominated by the official Nomination Board to succeed him, and Abdul Arain, owner of a local grocery store, Brian Blessed and Michael Mansfield were also nominated. The election took place on 14 and 15 October 2011. David Sainsbury won the election taking 2,893 of the 5,888 votes cast, winning on the first count. The current Vice-Chancellor is Leszek Borysiewicz. While the Chancellor's office is ceremonial, the Vice-Chancellor is the de facto principal administrative officer of the University. The university's internal governance is carried out almost entirely by its own members, with very little external representation on its governing body, the Regent House (though there is external representation on the Audit Committee, and there are four external members on the University's Council, who are the only external members of the Regent House). Senate and the Regent House The Senate consists of all holders of the MA degree or higher degrees. It elects the Chancellor and the High Steward, and elected two members of the House of Commons until the Cambridge University constituency was abolished in 1950. Prior to 1926, it was the University's governing body, fulfilling the functions that the Regent House fulfils today. The Regent House is the University's governing body, a direct democracy comprising all resident senior members of the University and the Colleges, together with the Chancellor, the High Steward, the Deputy High Steward, and the Commissary. The public representatives of the Regent House are the two Proctors, elected to serve for one year, on the nomination of the Colleges. Council and the General Board Although the University Council is the principal executive and policy-making body of the University, it must report and be accountable to the Regent House through a variety of checks and balances. It has the right of reporting to the University, and is obliged to advise the Regent House on matters of general concern to the University. It does both of these by causing notices to be published by authority in the Cambridge University Reporter, the official journal of the University. Since January 2005, the membership of the Council has included two external members, and the Regent House voted for an increase from two to four in the number of external members in March 2008, and this was approved by Her Majesty the Queen in July 2008. The General Board of the Faculties is responsible for the academic and educational policy of the University, and is accountable to the Council for its management of these affairs. Faculty Boards are responsible to the General Board; other Boards and Syndicates are responsible either to the General Board (if primarily for academic purposes) or to the Council. In this way, the various arms of the University are kept under the supervision of the central administration, and thus the Regent House. Finances Cambridge is by far the wealthiest university in the UK and in the whole of Europe, with an endowment of £5.89 billion in 2014. This is made up of around £2.3 billion tied directly to the university and £3.6 billion to the colleges. As of 2014, the next wealthiest, the University of Oxford, had an endowment valued at around £4.4 billion. Each college is an independent charitable institution with its own endowment, separate from that of the central university endowment. If ranked on a US university endowment table on most recent figures, Cambridge would rank fifth compared with the eight Ivy League institutions (subject to market fluctuations) and in the top 10 with all US universities (excluding aggregated system-wide endowments in Texas). Comparisons between Cambridge's endowment and those of other top US universities are, however, inaccurate because being a partially state-funded public university (although the status of Cambridge as a public university cannot be compared with US or European public universities as, for example, the state does not \"own\" the university and its colleges are private institutions), Cambridge receives a major portion of its income through education and research grants from the British Government. In 2006–7, it was reported that approximately one third of Cambridge's income comes from UK government funding for teaching and research, with another third coming from other research grants. Endowment income contributes around £130 million. The University also receives a significant income in annual transfers from the Cambridge University Press. Benefactions and fundraising In 2000, Bill Gates of Microsoft donated US$210 million through the Bill and Melinda Gates Foundation to endow the Gates Scholarships for students from outside the UK seeking postgraduate study at Cambridge. In 2005 the Cambridge 800th Anniversary Campaign was launched, aimed at raising £1 billion by 2012. This aim was reached in the financial year 2009–2010, raising £1.037 billion. In the year ended 31 July 2013 the university had a total income of £1.44 billion, of which £332 million was from research grants and contracts. Bonds The University of Cambridge borrowed 350 million pounds by issuing a 40-year security bond in October 2012. [http://www.tcs.cam.ac.uk/news/0019543-cambridge-issues-its-first-350m-bond.html Cambridge university issues its first £350m bond] L. Tidy, The Cambridge Student, News, 11 Oct 2012 Its interest rate is about 0.6 percent higher than a British government 40-year bond. Vice chancellor Leszek Borysiewicz hailed the success of the issue. In a 2010 report, the Russell Group of 20 leading universities made a conclusion that higher education could be financed by issuing bonds. Affiliations and memberships Cambridge is a member of the Russell Group of research-led British universities, the G5, the League of European Research Universities, and the International Alliance of Research Universities, and forms part of the \"golden triangle\" of highly research intensive and elite southern English universities. It is also closely linked with the development of the high-tech business cluster known as \"Silicon Fen\", and as part of the Cambridge University Health Partners, an academic health science centre. Academic profile Admissions Procedure Undergraduate applications to Cambridge must be made through UCAS in time for the early deadline, currently mid-October in the year before starting. Until the 1980s candidates for all subjects were required to sit special entrance examinations, since replaced by additional tests for some subjects, such as the Thinking Skills Assessment and the Cambridge Law Test. The University is considering reintroducing an admissions exam for all subjects with effect from 2016. The acceptance rate for students in the 2014–2015 cycle was 21.0%. Most applicants who are called for interview will have been predicted at least three A-grade A-level qualifications relevant to their chosen undergraduate course, or the equivalent in other qualifications, such as getting at least 7,7,6 for higher-level subjects at IB. The A* A-level grade (introduced in 2010) now plays a part in the acceptance of applications, with the university's standard offer for most courses being set at A*AA, with A*A*A for sciences courses. Due to a very high proportion of applicants receiving the highest school grades, the interview process is crucial for distinguishing between the most able candidates. The interview is performed by College Fellows, who evaluate candidates on unexamined factors such as potential for original thinking and creativity. For exceptional candidates, a Matriculation Offer is sometimes offered, requiring only two A-levels at grade E or above. In 2006, 5,228 students who were rejected went on to get 3 A levels or more at grade A, representing about 63% of all applicants rejected. Strong applicants who are not successful at their chosen college may be placed in the Winter Pool, where they can be offered places by other colleges. This is in order to maintain consistency throughout the colleges, some of which receive more applicants than others. Graduate admission is first decided by the faculty or department relating to the applicant's subject. This effectively guarantees admission to a college—though not necessarily the applicant's preferred choice. Access Public debate in the United Kingdom continues over whether admissions processes at Oxford and Cambridge are entirely merit based and fair; whether enough students from state schools are encouraged to apply to Cambridge; and whether these students succeed in gaining entry. In 2007–08, 57% of all successful applicants were from state schools (roughly 93 percent of all students in the UK attend state schools). Critics have argued that the lack of state school applicants with the required grades applying to Cambridge and Oxford has had a negative impact on Oxbridge's reputation for many years, and the University has encouraged pupils from state schools to apply for Cambridge to help redress the imbalance. Others counter that government pressure to increase state school admissions constitutes inappropriate social engineering. The proportion of undergraduates drawn from independent schools has dropped over the years, and such applicants now form a (very large) minority (43%) of the intake. In 2005, 32% of the 3599 applicants from independent schools were admitted to Cambridge, as opposed to 24% of the 6674 applications from state schools. In 2008 the University of Cambridge received a gift of £4m to improve its accessibility to candidates from maintained schools. Cambridge, together with Oxford and Durham, is among those universities that have adopted formulae that gives a rating to the GCSE performance of every school in the country to \"weight\" the scores of university applicants. With the release of admissions figures, a 2013 article in The Guardian reported that ethnic minority candidates had lower success rates in individual subjects even when they had the same grades as white applicants. The University was hence criticised for what was seen as institutional discrimination against ethnic minority applicants in favour of white applicants. The University denied the claims of institutional discrimination by stating the figures did not take into account \"other variables\". A following article stated that in the years 2010–2012 ethnic minority applicants to medicine with 3 A* grades or higher were 20% less likely to gain admission than white applicants with similar grades. The University refused to provide figures for a wider range of subjects claiming it would be too costly. Teaching The academic year is divided into three academic terms, determined by the Statutes of the University. Michaelmas term lasts from October to December; Lent term from January to March; and Easter term from April to June. Within these terms undergraduate teaching takes place within eight-week periods called Full Terms. According to the University statutes, it is a requirement that during this period all students should live within 3 miles of the Church of St Mary the Great; this is defined as Keeping term. Students can graduate only if they fulfill this condition for nine terms (three years) when obtaining a Bachelor of Arts or twelve terms (four years) when studying for a Master of Science, Engineering or Mathematics. These terms are shorter than those of many other British universities. Undergraduates are also expected to prepare heavily in the three holidays (known as the Christmas, Easter and Long Vacations). Triposes involve a mixture of lectures (organised by the university departments), and supervisions (organised by the colleges). Science subjects also involve laboratory sessions, organised by the departments. The relative importance of these methods of teaching varies according to the needs of the subject. Supervisions are typically weekly hour-long sessions in which small groups of students (usually between one and three) meet with a member of the teaching staff or with a doctoral student. Students are normally required to complete an assignment in advance of the supervision, which they will discuss with the supervisor during the session, along with any concerns or difficulties they have had with the material presented in that week's lectures. The assignment is often an essay on a subject set by the supervisor, or a problem sheet set by the lecturer. Depending on the subject and college, students might receive between one and four supervisions per week. This pedagogical system is often cited as being unique to Oxford (where \"supervisions\" are known as \"tutorials\") and Cambridge. A tutor named William Farish developed the concept of grading students' work quantitatively at the University of Cambridge in 1792. Research The University of Cambridge has research departments and teaching faculties in most academic disciplines. All research and lectures are conducted by University Departments. The colleges are in charge of giving or arranging most supervisions, student accommodation, and funding most extracurricular activities. During the 1990s Cambridge added a substantial number of new specialist research laboratories on several University sites around the city, and major expansion continues on a number of sites. Cambridge also has a research partnership with MIT in the United States: the Cambridge–MIT Institute. Graduation At the University of Cambridge, each graduation is a separate act of the university's governing body, the Regent House, and must be voted on as with any other act. A formal meeting of the Regent House, known as a Congregation, is held for this purpose. This is the common last act at which all the different university procedures (for: undergraduate and graduate students; and the different degrees) land. After degrees are approved, to have them conferred candidates must ask to their Colleges to be presented during a Congregation. This happened until the 2006, when, for the first time, a Graduate Student (Dr Luca Epis) refused the degree approved by the Board of Graduate Studies, creating a \"leading case\" on the matter. Graduates receiving an undergraduate degree wear the academic dress that they were entitled to before graduating: for example, most students becoming Bachelors of Arts wear undergraduate gowns and not BA gowns. Graduates receiving a postgraduate degree (e.g. PhD or Master's) wear the academic dress that they were entitled to before graduating, only if their first degree was also from the University of Cambridge; if their first degree is from another university, they wear the academic dress of the degree that they are about to receive, the BA gown without the strings if they are under 24 years of age, or the MA gown without strings if they are 24 and over. Graduates are presented in the Senate House college by college, in order of foundation or recognition by the university, except for the royal colleges. During the congregation, graduands are brought forth by the Praelector of their college, who takes them by the right hand, and presents them to the vice-chancellor for the degree they are about to take. The Praelector presents graduands with the following Latin statement, substituting \"____\" with the name of the degree: The now-graduate then rises, bows and leaves the Senate House through the Doctor's door, where he or she receives his or her certificate, into Senate House Passage. Libraries and museums The university has 114 libraries. The Cambridge University Library is the central research library, which holds over 8 million volumes. It is a legal deposit library, therefore it is entitled to request a free copy of every book published in the UK and Ireland. In addition to the University Library and its dependents, almost every faculty or department has a specialised library; for example, the History Faculty's Seeley Historical Library possesses more than 100,000 books. Furthermore, every college has a library as well, partially for the purposes of undergraduate teaching, and the older colleges often possess many early books and manuscripts in a separate library. For example, Trinity College's Wren Library has more than 200,000 books printed before 1800, while Corpus Christi College's Parker Library possesses one of the greatest collections of medieval manuscripts in the world, with over 600 manuscripts. Cambridge University operates eight arts, cultural, and scientific museums, and a botanic garden. The Fitzwilliam Museum, is the art and antiquities museum, the Kettle's Yard is a contemporary art gallery, the Museum of Archaeology and Anthropology houses the University's collections of local antiquities, together with archaeological and ethnographic artefacts from around the world, the Cambridge University Museum of Zoology houses a wide range of zoological specimens from around the world and is known for its iconic finback whale skeleton that hangs outside. This Museum also has specimens collected by Charles Darwin. Other museums include, the Museum of Classical Archaeology, the Whipple Museum of the History of Science, the Sedgwick Museum of Earth Sciences which is the geology museum of the University, the Polar Museum, part of the Scott Polar Research Institute which is dedicated to Captain Scott and his men, and focuses on the exploration of the Polar Regions. The Cambridge University Botanic Garden is the botanic garden of the University, created in 1831. Publishing and assessments The University's publishing arm, the Cambridge University Press, is the oldest printer and publisher in the world, and it is the second largest university press in the world. The university set up its Local Examination Syndicate in 1858. Today, the syndicate, which is known as Cambridge Assessment, is Europe's largest assessment agency and it plays a leading role in researching, developing and delivering assessments across the globe. Reputation and rankings According to the 2016 Complete University Guide, the University of Cambridge is ranked first amongst the UK’s universities; this ranking is based on a broad raft of criteria from entry standards and student satisfaction to quality of teaching in specific subjects and job prospects for graduates. The University is ranked as the 2nd best university in the UK for the quality of graduates according to recruiters from the UK's major companies. In 2014–15, according to University Ranking by Academic Performance (URAP), Cambridge is ranked second in UK (coming second to Oxford) and ranked fifth in the world. In the 2001 and 2008 Government Research Assessment Exercises, Cambridge was ranked first in the country. In 2005, it was reported that Cambridge produces more PhDs per year than any other British university (over 30% more than second placed Oxford). In 2006, a Thomson Scientific study showed that Cambridge has the highest research paper output of any British university, and is also the top research producer (as assessed by total paper citation count) in 10 out of 21 major British research fields analysed. Another study published the same year by Evidence showed that Cambridge won a larger proportion (6.6%) of total British research grants and contracts than any other university (coming first in three out of four broad discipline fields). The university is also closely linked with the development of the high-tech business cluster in and around Cambridge, which forms the area known as Silicon Fen or sometimes the \"Cambridge Phenomenon\". In 2004, it was reported that Silicon Fen was the second largest venture capital market in the world, after Silicon Valley. Estimates reported in February 2006 suggest that there were about 250 active startup companies directly linked with the university, worth around US$6 billion. Cambridge has been highly ranked by most international and UK league tables. In particular, it had topped the QS World University Rankings from 2010/11 to 2011/12. A 2006 Newsweek overall ranking, which combined elements of the THES-QS and ARWU rankings with other factors that purportedly evaluated an institution's global \"openness and diversity\", suggested Cambridge was sixth around the globe. In The Guardian newspaper's 2012 rankings, Cambridge had overtaken Oxford in philosophy, law, politics, theology, maths, classics, anthropology and modern languages. In the 2009 Times Good University Guide Subject Rankings, it was ranked top (or joint top) in 34 out of the 42 subjects which it offers. But Cambridge has been ranked only 30th in the world and 3rd in the UK by the Mines ParisTech: Professional Ranking of World Universities based on the number of alumni holding CEO position in Fortune Global 500 companies. Student life Students' Union The Cambridge University Students' Union (CUSU) serves to represent all the students within the University which automatically become members upon arrival. It was founded in 1964 as the Students' Representative Council (SRC); the six most important positions in the Union are occupied by Sabbatical officers. However, turnout in recent elections has been low, with the 2014/15 president elected with votes in favour from only 7.5% of the whole student body. Sport Rowing is a particularly popular sport at Cambridge, and there are competitions between colleges, notably the bumps races, and against Oxford, the Boat Race. There are also Varsity matches against Oxford in many other sports, ranging from cricket and rugby, to chess and tiddlywinks. Athletes representing the University in certain sports are entitled to apply for a Cambridge Blue at the discretion of the Blues Committee, consisting of the captains of the thirteen most prestigious sports. There is also the self-described \"unashamedly elite\" Hawks' Club, which is for men only, whose membership is usually restricted to Cambridge Full Blues and Half Blues. The Ospreys are the equivalent female club. The University of Cambridge Sports Centre opened in August 2013. Phase 1 includes a 37x34m Sports Hall, a Fitness Suite, a Strength and Conditioning Room, a Multi-Purpose Room and Eton and Rugby Fives courts. Future developments will include squash courts, indoor and outdoor tennis courts and a swimming pool. The University also has an Athletics Track at Wilberforce Road, an Indoor Cricket School and Fenner's Cricket Ground. Societies Numerous student-run societies exist in order to encourage people who share a common passion or interest to periodically meet or discuss. As of 2010, there were 751 registered societies. In addition to these, individual colleges often promote their own societies and sports teams. Although technically independent from the University, the Cambridge Union Society serves as a focus for debating and public speaking, as the oldest free speech society in the world, and the largest in Cambridge. Drama societies notably include the Amateur Dramatic Club (ADC) and the comedy club Footlights, which are known for producing well-known show-business personalities. The Cambridge University Chamber Orchestra explores a range of programmes, from popular symphonies to lesser known works; membership of the orchestra is composed of students of the university. Newspapers and radio Student newspapers include the long-established Varsity, its younger rival The Cambridge Student, and news and culture magazine the Cambridge Globalist. The Globalist covers politics, culture, economics, science and technology. The Cambridge Globalist, subtitled 'An Undergraduate International Affairs Magazine', existed as an offshoot of the student newspaper Varsity for several years in the noughties and was in existence until 2007. At one point it had a print run of 11,000 and featured advertisers including the Financial Times. The Cambridge Globalist was refounded by Lucy Wark, Alasdair Phillips-Robins and Ravi Solanki in 2013. The new publication bears limited resemblance to its previous iteration. But the publication with by far the highest readership is The Tab, Cambridge's student tabloid. Together with colleagues from Anglia Ruskin University, students run a radio station, Cam FM, which provides members with an opportunity to produce and host weekly radio shows and promotes broadcast journalism, sports coverage, comedy and drama. JCR and MCR In addition to university-wide representation, students can benefit from their own college student unions, which are known as JCR (Junior Combination Room) for undergraduates and MCR (Middle Combination Room) for postgraduates. These serve as a link between college staff and members and consists of officers elected annually between the fellow students; individual JCR and MCRs also report to CUSU, which offers training courses for some of the most delicate positions within the body. Formal Halls and May Balls One of the most distinguishing aspects of student life at Cambridge is the possibility to take part in formal dinners at college. These are called Formal Hall and occur regularly during term time. Students sit down for a meal in their gowns, while Fellows eat separately on High Table: the beginning and end of the function is usually celebrated with a prayer. Special formals are organized for events such as Christmas or the Commemoration of Benefactors. After the exam period, May Week is held and it is customary to celebrate by attending May Balls. These are all-night long lavish parties held in the colleges where food and drinks are served and entertainment is provided. TIME magazine argues that some of the larger May Balls are among the best private parties in the world. Suicide Sunday, the first day of May Week, is a popular date for organizing garden parties. Notable alumni and academics Over the course of its history, a sizeable number of Cambridge University academics and alumni have become notable in their fields, both academic and in the wider world. Depending on criteria, affiliates of the University of Cambridge have won 90 Nobel prizes. Former undergraduates of the university have won a grand total of 61 Nobel prizes, 13 more than the undergraduates of any other university. Cambridge academics have also won 8 Fields Medals and 2 Abel Prizes, since the Abel award was first distributed in 2003. Mathematics and sciences Perhaps most of all, the university is renowned for a long and distinguished tradition in mathematics and the sciences. Among the most famous of Cambridge natural philosophers is Sir Isaac Newton, who spent the majority of his life at the university and conducted many of his now famous experiments within the grounds of Trinity College. Sir Francis Bacon, responsible for the development of the scientific method, entered the university when he was just twelve, and pioneering mathematicians John Dee and Brook Taylor soon followed. Other ground-breaking mathematicians to have studied at the university include G. H. Hardy, John Edensor Littlewood and Augustus De Morgan, three of the most renowned pure mathematicians in modern history; Sir Michael Atiyah, one of the most important mathematicians of the last half-century; Anil Kumar Gain, founder of Vidyasagar University and a Fellow of the Royal Statistical Society; William Oughtred, the inventor of the logarithmic scale; John Wallis, first to state the law of acceleration; Srinivasa Ramanujan, the self-taught genius who made incomparable contributions to mathematical analysis, number theory, infinite series and continued fractions; and, perhaps most importantly of all, James Clerk Maxwell, who is considered to have brought about the second great unification of physics (the first being accredited to Newton) with his classical electromagnetic theory. Mathematician Philippa Fawcett gained worldwide media coverage in 1890 as the person with the highest score in the Cambridge Mathematical Tripos exams, but as a woman, was unable to take the title of 'Senior Wrangler'. In biology, Charles Darwin, famous for developing the theory of natural selection, was an alumnus of Christ's College, though his education at the university was intended to allow him to become a clergyman. Subsequent Cambridge biologists include Francis Crick and James Watson, who worked out a model for the three-dimensional structure of DNA whilst working at the university's Cavendish Laboratory; fellow Cambridge graduates Maurice Wilkins and especially Rosalind Franklin produced key X-ray crystallography data, which was shared with Watson by Wilkins. Wilkins went on to help verify the proposed structure and win the Nobel Prize with Watson and Crick. Despite Cambridge's delay in admitting women to full degrees, Cambridge women were at the heart of scientific research throughout the 20th century. Pioneering biochemist Marjory Stephenson studied at Cambridge, as did plant physiologist Gabrielle Howard, social anthropologist Audrey Richards. Psycho-analyst Alix Strachey, who with her husband translated the works of Sigmund Freud, studied at Newnham College. Kavli Prize-winner Brenda Milner, co-discovery of specialized brain networks for memory and cognition, was also a graduate of Newnham College. Veterinary epidemiologist Sarah Cleaveland has worked to eliminate rabies in the Serengeti. More recently, Sir Ian Wilmut, the man who was responsible for the first cloning of a mammal with Dolly the Sheep in 1996, was a graduate student at Darwin College. Famous naturalist and broadcaster David Attenborough graduated from the university, while the ethologist Jane Goodall, the world's foremost expert on chimpanzees did a PhD in Ethology at Darwin College. Anthropologist Dame Alison Richard, former vice-chancellor of the university, is also a Newnham College graduate. The university can be considered the birthplace of the computer, with mathematician Charles Babbage having designed the world's first computing system as early as the mid-1800s. Alan Turing went on to devise what is essentially the basis for modern computing and Maurice Wilkes later created the first programmable computer. The webcam was also invented at Cambridge University, as a means for scientists to avoid interrupting their research and going all the way down to the laboratory dining room only to be disappointed by an empty coffee pot. Ernest Rutherford, generally regarded as the father of nuclear physics, spent much of his life at the university, where he worked closely with the likes of E. J. Williams and Niels Bohr, a major contributor to the understanding of the structure and function of the atom, J. J. Thomson, discoverer of the electron, Sir James Chadwick, discoverer of the neutron, and Sir John Cockcroft and Ernest Walton, the partnership responsible for first splitting the atom. J. Robert Oppenheimer, leader of the Manhattan Project that developed the atomic bomb, also studied at Cambridge under Rutherford and Thomson. Joan Curran devised the 'chaff' technique during the Second World War to disrupt radar on enemy planes. Astronomers Sir John Herschel and Sir Arthur Eddington both spent much of their careers at Cambridge, as did Paul Dirac, the discoverer of antimatter and one of the pioneers of Quantum Mechanics; Stephen Hawking, the founding father of the study of singularities and the university's long-serving Lucasian Professor of Mathematics until 2009; and Lord Martin Rees, the current Astronomer Royal and Master of Trinity College. John Polkinghorne, also a Cambridge mathematician prior to his entrance into the Anglican ministry, was knighted and received the Templeton Prize for his work reconciling science and religion. Other significant Cambridge scientists include Henry Cavendish, the discoverer of hydrogen; Frank Whittle, co-inventor of the jet engine; Lord Kelvin, who formulated the original Laws of Thermodynamics; William Fox Talbot, who invented the camera, Alfred North Whitehead, Einstein's major opponent; Sir Jagadish Chandra Bose, the man dubbed \"the father of radio science\"; Lord Rayleigh, one of the most pre-eminent physicists of the 20th century; Georges Lemaître, who first proposed a Big Bang Theory; and Frederick Sanger, the last man to win two Nobel prizes. Humanities, music and art In the humanities, Greek studies were inaugurated at Cambridge in the early sixteenth century by Desiderius Erasmus during the few years he held a professorship there; seminal contributions to the field were made by Richard Bentley and Richard Porson. John Chadwick was associated with Michael Ventris in the decipherment of Linear B. The eminent Latinist A. E. Housman taught at Cambridge but is more widely known as a poet. Simon Ockley made a significant contribution to Arabic Studies. Distinguished Cambridge academics in other fields include economists such as John Maynard Keynes, Thomas Malthus, Alfred Marshall, Milton Friedman, Joan Robinson, Piero Sraffa, and Amartya Sen, a former Master of Trinity College. Philosophers Sir Francis Bacon, Bertrand Russell, Ludwig Wittgenstein, Leo Strauss, George Santayana, G. E. M. Anscombe, Sir Karl Popper, Sir Bernard Williams, Sir Allama Muhammad Iqbal and G. E. Moore were all Cambridge scholars, as were historians such as Thomas Babington Macaulay, Frederic William Maitland, Lord Acton, Joseph Needham, E. H. Carr, Hugh Trevor-Roper, E. P. Thompson, Eric Hobsbawm, Niall Ferguson and Arthur M. Schlesinger, Jr, and famous lawyers such as Glanville Williams, Sir James Fitzjames Stephen, and Sir Edward Coke. Religious figures at the university have included Rowan Williams, former Archbishop of Canterbury and many of his predecessors; William Tyndale, the pioneer biblical translator; Thomas Cranmer, Hugh Latimer, and Nicholas Ridley, all Cambridge men, known as the \"Oxford martyrs\" from the place of their execution; Benjamin Whichcote and the Cambridge Platonists; William Paley, the Christian philosopher known primarily for formulating the teleological argument for the existence of God; William Wilberforce and Thomas Clarkson, largely responsible for the abolition of the slave trade; leading Evangelical churchman Charles Simeon; John William Colenso, the bishop of Natal who developed views on the interpretation of Scripture and relations with native peoples that seemed dangerously radical at the time; John Bainbridge Webster and David F. Ford, theologians of significant repute; and six winners of the Templeton Prize, the highest accolade for the study of religion since its foundation in 1972. Composers Ralph Vaughan Williams, Sir Charles Villiers Stanford, William Sterndale Bennett, Orlando Gibbons and, more recently, Alexander Goehr, Thomas Adès, John Rutter, Julian Anderson and Judith Weir were all at Cambridge. The university has also produced some of today's leading instrumentalists and conductors, including Colin Davis, John Eliot Gardiner, Roger Norrington, Trevor Pinnock, Andrew Manze, Richard Egarr, Mark Elder, Richard Hickox, Christopher Hogwood, Andrew Marriner, David Munrow, Simon Standage, Endellion Quartet and Fitzwilliam Quartet. Although known primarily for its choral music, the university has also produced members of contemporary bands such as Radiohead, Hot Chip, Procol Harum, Clean Bandit, songwriter and entertainer Jonathan King, Henry Cow, and the singer-songwriter Nick Drake. Artists Quentin Blake, Roger Fry and Julian Trevelyan also attended as undergraduates, as did sculptors Antony Gormley, Marc Quinn and Sir Anthony Caro, and photographers Antony Armstrong-Jones, Sir Cecil Beaton and Mick Rock. Literature Important writers to have studied at the university include the prominent Elizabethan dramatist Christopher Marlowe at Corpus Christi College, his fellow University Wits Thomas Nashe and Robert Greene, arguably the first professional authors in England, and John Fletcher, who collaborated with Shakespeare on The Two Noble Kinsmen, Henry VIII and the lost Cardenio and succeeded him as house playwright of The King's Men. Samuel Pepys matriculated in 1650, ten years before he began his diary, the original manuscripts of which are now housed in the Pepys Library at Magdalene College. Lawrence Sterne, whose novel Tristram Shandy is judged to have inspired many modern narrative devices and styles, was admitted in 1733. In the following century, the novelists W. M. Thackeray, best known for Vanity Fair, Charles Kingsley, author of Westward Ho! and Water Babies, and Samuel Butler, remembered for The Way of All Flesh and Erewhon, were all at Cambridge. Ghost story writer M. R. James served as provost of King's College from 1905 to 1918. Novelist Amy Levy was the first Jewish woman to attend the University. Modernist writers to have attended the university include E. M. Forster, Rosamond Lehmann, Vladimir Nabokov, Christopher Isherwood and Malcolm Lowry. Although not a student, Virginia Woolf wrote her essay A Room of One's Own while in residence at Newnham College. Playwright J. B. Priestley, physicist and novelist C. P. Snow and children's writer A. A. Milne were also among those who passed through the university in the early 20th century. They were followed by the postmodernists Patrick White, J. G. Ballard, and the early postcolonial writer E. R. Braithwaite. More recently, the university has educated the comedy writers Douglas Adams, Tom Sharpe and Howard Jacobson, the popular novelists A. S. Byatt, Sir Salman Rushdie, Nick Hornby, Zadie Smith, Robert Harris and Sebastian Faulks, the successful action writers Michael Crichton, David Gibbins and Jin Yong, and contemporary playwrights and screenwriters such as Julian Fellowes, Stephen Poliakoff, Michael Frayn and Sir Peter Shaffer. Cambridge poets include Edmund Spenser, author of The Faerie Queene, the Metaphysical poets Richard Davis, American poet, John Donne, George Herbert and Andrew Marvell, John Milton, renowned for his late epic Paradise Lost, the leading Restoration poet and playwright John Dryden, the pre-romantic Thomas Gray, best known his Elegy Written in a Country Churchyard, William Wordsworth and Samuel Taylor Coleridge, whose joint work Lyrical Ballads is often seen to mark the beginning of the Romantic movement, later Romantics such as Lord Byron and the postromantic Alfred, Lord Tennyson, classical scholar and lyric poet A. E. Housman, war poets Siegfried Sassoon and Rupert Brooke, modernist T. E. Hulme, confessional poets Ted Hughes, Sylvia Plath and John Berryman, and, more recently, Cecil Day-Lewis, Joseph Brodsky, Kathleen Raine and Geoffrey Hill. In all, at least nine of the Poets Laureate graduated from Cambridge. The university has also made a notable contribution to Literary Criticism, having produced, among others, F. R. Leavis, I. A. Richards, C. K. Ogden and William Empson, often collectively known as the Cambridge Critics, the important Marxists Raymond Williams, sometimes regarded as the founding father of Cultural Studies, and Terry Eagleton, author of Literary Theory: An Introduction, the most successful academic book ever published, the Aesthetician Harold Bloom, the New Historicist Stephen Greenblatt, and an extensive group of distinguished biographical writers such as Lytton Strachey, a central figure in the largely Cantabridgian Bloomsbury Group, Peter Ackroyd and Claire Tomalin. Actors and directors such as Sir Ian McKellen, Eleanor Bron, Miriam Margolyes, Sir Derek Jacobi, Sir Michael Redgrave, James Mason, Emma Thompson, Stephen Fry, Hugh Laurie, John Cleese, Eric Idle, Graham Chapman, Simon Russell Beale, Tilda Swinton, Thandie Newton, Rachel Weisz, Sacha Baron Cohen, Tom Hiddleston, Sara Mohr-Pietsch, Eddie Redmayne, Dan Stevens, Jamie Bamber, Lily Cole, David Mitchell, Robert Webb, Mel Giedroyc and Sue Perkins all studied at the university, as did recently acclaimed directors such as Mike Newell, Sam Mendes, Stephen Frears, Paul Greengrass, Chris Weitz and John Madden. Sports Athletes who are university graduates include more than 123 Olympic medalists; they won a total of 170 medals, including 80 gold. The legendary Chinese six-time world table tennis champion Deng Yaping; the sprinter and athletics hero Harold Abrahams; the inventors of the modern game of Football, Winton and Thring; and George Mallory, the famed mountaineer and possibly the first man ever to reach the summit of Mount Everest all attended Cambridge. Education Notable educationalists to have attended the university include the founders and early professors of Harvard University, including John Harvard himself; Emily Davies, founder of Girton College, the first residential higher education institution for women, and John Haden Badley, founder of the first mixed-sex school in England; and Anil Kumar Gain, prominent 20th century mathematician and founder of the Vidyasagar University in Bengal. Politics Cambridge also has a strong reputation in the fields of politics and governance, having educated: * 15 British Prime Ministers, including Robert Walpole, considered to be the first Prime Minister of Great Britain. * At least 30 foreign Heads of State/Government, including Presidents of India, Ireland, Zambia, South Korea, Uganda and Trinidad and Tobago; along with Prime Ministers of India, Burma, Pakistan, South Africa, New Zealand, Poland, Australia, France, Singapore, Sri Lanka, Malta, Thailand, Malaysia, and Jordan. * At least 9 monarchs, including Edward VII, George VI, King Peter II of Yugoslavia, Queen Margrethe II of Denmark and Queen Sofía of Spain. The university has also educated Charles, Prince of Wales and a large number of other royals. * 3 Signatories of the United States Declaration of Independence. * Oliver Cromwell, Lord Protector of England (1653–58). In literature and popular culture Throughout its history, the University has featured heavily in literature and artistic works by various authors. Here below are some notable examples. * In The Reeve's Tale from The Canterbury Tales by Geoffrey Chaucer, the two main characters are students at Soler Halle. It is believed that this refers to King's Hall, which is now part of Trinity College. * In The Prelude (1805 poem) by William Wordsworth, the entire third chapter is based on the poet's time at Cambridge. * In Memoriam A.H.H. (1849 poem) by Alfred, Lord Tennyson is a requiem written in memory of the poet's Cambridge friend Arthur Henry Hallam. The poem features numerous references to their time together at Trinity College, \"the reverend walls in which of old I wore the gown\". * In Doctor Thorne (1858 novel) by Anthony Trollope, Frank Gresham, heir to the near-bankrupt Gresham estate, is a Cambridge student. Despite his family's objections, he is determined to return to the University and study for a degree. * In Portraits of Places (1883 travel book), Henry James describes the college backs as \"the loveliest confusion of gothic windows and ancient trees, of grassy banks and mossy balustrades, of sun‐chequered avenues and groves, of lawns and gardens and terraces, of single arched bridges spanning the little stream, which ... looks as if it had been 'turned on' for ornamental purposes.\" * In the Sherlock Holmes series (1887–1927 collection of novels and short stories) by Arthur Conan Doyle, Holmes reveals that he first developed his methods of deduction while an undergraduate. The author Dorothy L. Sayers suggests that, given details in two of the Adventures, Holmes must have been at Cambridge rather than Oxford and that \"of all the Cambridge colleges, Sidney Sussex College perhaps offered the greatest number of advantages to a man in Holmes' position and, in default of more exact information, we may tentatively place him there\". * In Tess of the d'Urbervilles (1891 novel) by Thomas Hardy, Angel Clare rebels against his family's plans to have him sent to Cambridge and ordained as a minister of the Church of England. His older brothers are both Cambridge graduates and Cuthbert is the dean of a Cambridge college. * The Longest Journey (1907 novel) by E. M. Forster begins at Cambridge University. * In the Psmith series (1908–1923 collection of novels) by P. G. Wodehouse, both the title character and Mike, his closest friend, study at Cambridge University. * In Jacob's Room (1922 novel) by Virginia Woolf, the protagonist Jacob Flanders attends Cambridge. * Darkness at Pemberley (1932 novel) by T. H. White features St Bernard's College, a fictionalised version of Queens' College. * Glory (1932 novel) by Vladimir Nabokov is the story of an émigré student who escapes from Russia and is educated at Cambridge before returning to his native country. * Out of the Silent Planet (1938 novel) by C. S. Lewis begins at Cambridge University, where Elwin Ransom, the protagonist of The Space Trilogy, is Professor of Philology. The trilogy also features the University of Edgestow, a fictional institution which is essentially a third Oxbridge. * The Masters (1951 novel) and The Affair (1960 Novel) by C. P. Snow, both feature an unnamed fictional college, partly based on the author's own, Christ's. * The Millstone (1965 novel) by Margaret Drabble is the story of a young female Cambridge academic who becomes pregnant and is forced into a completely alien lifestyle. * In many novels and plays by Thomas Bernhard (written between 1970 and 2006), Cambridge (Geistesnest) is the refuge of a Geistesmensch escaping from Austria. * Maurice (1971 novel) by E. M. Forster is about the homosexual relationship of two Cambridge undergraduates. * Porterhouse Blue (1974 novel) and its sequel Grantchester Grind (1995 Novel) by Tom Sharpe both feature Porterhouse, a fictional Cambridge college. * Oxbridge Blues (1984 TV Drama) by Frederic Raphael features Cambridge University. * In Professional Foul (1977 play) by Tom Stoppard, the main character, Anderson, is Professor of Philosophy at Cambridge University. * In Shada (abandoned 1979 Doctor Who serial released on video in 1992) by Douglas Adams, much of the action takes place at the fictional St. Cedd's College, Cambridge. * Timescape (1980 novel) by Gregory Benford is the story of a group of scientists at the University of Cambridge and their attempts to warn the past about a series of global disasters that have left the world in a state of disarray. Benford's short story, Anomalies, is also set at Cambridge, where the main character, an amateur astronomer from Ely, meets the Master of Jesus College. * Chariots of Fire (1981 film) by Hugh Hudson is partly set at Cambridge between 1919 and 1924, when protagonist Harold Abrahams (played by Ben Cross) was a student there. * Floating Down to Camelot (1985 novel) by David Benedictus is set entirely at Cambridge University and was inspired by the author's time at Churchill College. * In Redback (1986 novel), Howard Jacobson creates the fictional Malapert College, drawing on his experiences at Downing College and Selwyn College. * Dirk Gently's Holistic Detective Agency (1987 Novel) by Douglas Adams contains considerable material recycled from the aborted Shada, therefore much of the action likewise takes place at St. Cedd's College, Cambridge. * The Matthew Bartholomew Chronicles (1990s novels) by Susanna Gregory, is a series of murder mysteries set in and around the university in medieval Cambridge. * In Stephen Fry's novels The Liar (1993) and Making History (1997), the main characters attend Cambridge University. * Wittgenstein's Poker (2001 biographical work) by David Edmonds recounts the celebrated confrontation between Sir Karl Popper and Ludwig Wittgenstein at Cambridge University's Moral Sciences Club. * Cambridge Spies (2003 TV drama) is about the famous Cambridge Five double agents who started their careers at Cambridge: Kim Philby, Guy Burgess, Donald Maclean and Anthony Blunt. * In Rock 'n Roll (2006 play) by Tom Stoppard, Cambridge University is a key setting. *The Newsroom character Mackenzie McHale attended Cambridge and was the President of the Cambridge Union Society. The West Wing character Will Bailey also attended Cambridge on a Marshall Scholarship and was the President of the Cambridge Union Society. * In The Theory of Everything (2014 film). Gallery File:TrinityCollegeCamGreatGate.jpg|The Great Gate of Trinity College File:Chapel of Corpus Christi College, Cambridge - 20100915.jpg|Corpus Christi College New Court File:Caius.JPG|Gonville and Caius College File:Pembroke College Cambridge.JPG|Pembroke College File:St Catharine's College, Cambridge (night).jpg|St Catharine's College File:Margaret Wileman Building, Hughes Hall.jpg|Hughes Hall File:Wolfson College, Cambridge (2).jpg|Wolfson College File:Stedmundscollege7.jpg|St Edmund's College Norfolk Building File:View towards East Range small3.jpg|Downing College East Range File:Cambridge Queens' Gatehouse.JPG|Queens' College Old Gatehouse File:ChristsCollege Gate.jpg|Christ's College Gatehouse File:PepysLibraryCambridge.jpg|The Pepys Library, Magdalene College File:Selwyn College Old Court Panorama from North-West corner.jpg|Selwyn College Old Court File:Jesus College Chapel, Cambridge - geograph.org.uk - 168873.jpg|Jesus College Chapel File:StJohnsCambridge Gatehouse02.jpg|St John's College Great gate File:TrinityHallCambridge.jpg|The entrance of Trinity Hall File:The Cavendish Building of Homerton College Cambridge, May 2011.jpg|The Cavendish Building of Homerton College, Cambridge File:River Cam running through Darwin College, Cambridge.JPG|Darwin College File:Sidney Sussex Chapel.jpg|The Chapel, Sidney Sussex College File:Cambridge University Judge Business School interior.jpg|Judge Business School interiortrinityWhich Cambridge College was attended by Anthony Blunt, Kim Philby and Guy Burgess?[TRINITY]
1rotterdamIn terms of tonnage of cargo handled, which is the world's largest port?[Rotterdam South, Rotterdam, South Holland, R'dam, Historisch Museum Rotterdam, UN/LOCODE:NLRTM, Rotterdam, Holland, Roterdam, Rotterdam, Netherlands, Rotterdam, Rotterdam, Zuid Holland]
2\"Take a Bow\" is a song recorded by Barbadian singer Rihanna for Good Girl Gone Bad: Reloaded (2008), the re-release of her third studio album Good Girl Gone Bad (2007). The song was written and produced by Tor Erik Hermansen, Mikkel Eriksen, and Shaffer Smith under their stage names StarGate and Ne-Yo. \"Take a Bow\" was released as the first single from the re-release and the fifth single overall from the two releases. It is an R&B song that contains elements of dance-pop. Critical reception of \"Take a Bow\" was mixed, with some critics praising the song's lyrics and powerful balladry impact, while others criticized the lack of originality with regard to StarGate's production. In the US, the song peaked at number one on the Billboard Hot 100 chart and became Rihanna's third song to do so. \"Take a Bow\" also peaked at number one on the US Hot R&B/Hip-Hop Songs chart and US Pop Songs chart, and has been certified quadruple platinum by the Recording Industry Association of America. The song reached number one in Canada, Denmark, Ireland, Slovakia and the United Kingdom, and attained top five positions in Australia, New Zealand and Norway. The song's accompanying music video was directed by Anthony Mandler and presents Rihanna as the female protagonist who leaves her boyfriend because of his infidelity. \"Take a Bow\" has been performed on \"AOL Music Sessions\" and was included in the set lists of the Good Girl Gone Bad Tour (2008–09), Last Girl on Earth (2010–11), Loud Tour (2011) and Diamonds World Tour (2013). Background and composition \"Take a Bow\" was written and produced by StarGate and Ne-Yo. The song premiered on February 14, 2008, on the KIIS-FM radio show On Air with Ryan Seacrest. \"Take a Bow\" was released as the fifth overall single from Good Girl Gone Bad, but the first from the re-release of the album, entitled Good Girl Gone Bad: Reloaded. \"Take a Bow\" was made available to purchase in media outlets, via Def Jam Recordings' website, on the same day as its radio premiere in the United States later being made available to download via iTunes on May 6, 2008. The song is written in the key of E major and is set in simple time with a metronome of 82 beats per minute. Rihanna's vocal range in the song spans from the low note of E3 to the high note of C♯5. Musically, the song draws influence from the musical genre of R&B and also incorporates elements of dance-pop, whilst lyrically, \"Take a Bow\" tells of how the female protagonist expresses disinterest in rekindling her relationship with an dishonorable and unfaithful ex-boyfriend. Critical reception \"Take a Bow\" received mixed reviews from music critics. Upon the song's release as an official single, Nick Levine of Digital Spy commented its choice for the promotion of Good Girl Gone Bad: Reloaded, writing that the singer could have chosen \"Breakin' Dishes\" which served as a promotional single for Good Girl Gone Bad and charted at number four on the US Hot Dance Club Songs chart in February 2008 —but had opted for \"Take a Bow\" due to it being new and more likely to find a receptive audience. Levine continued in his review to write that although the ballad succeeds in its mission of telling of a failed relationship, he noted that the song was not at the same level as the singer's previous single, \"Don't Stop the Music\" (2007). Levine cited that his reason for this was that \"'Take a Bow' does what it sets out to do very well, but it's an underwhelming follow-up to the dancefloor rush of 'Don't Stop The Music\". Levine also commented on the song with regard to the other new songs included on the re-release, \"Disturbia\" and \"If I Never See Your Face Again\" (a collaboration with Maroon 5), as part of his review of Good Girl Gone Bad: Reloaded, writing that \"Take a Bow\" is inferior to the former, but superior to the latter. Chart performance In the United States, the song leaped 52 positions from number 53 to number one on the US Billboard Hot 100 chart on May 14, 2008, with digital download sales of 267,000 copies, which prompted the song to debut at number one on the US Hot Digital Songs chart. With \"Take a Bow\" jumping fifty-two positions to number one, this marked the second largest leap to number one in the history of the chart as of May 2008, second only to Maroon 5s \"Makes Me Wonder\", which leaped from number 64 to number one in May 2007. Additionally, at the time of release, Rihanna held two of the top three opening week download tallies, with \"Take a Bow\" selling 267,000 copies, the lead single from Good Girl Gone Bad \"Umbrella\" selling 277,000 copies in May 2007, which held the record for having the largest opening digital sales tally, until Mariah Careys \"Touch My Body\" opened with sales of 286,000 copies in April 2008. The song became Rihanna's third number one single on the Hot 100, after \"SOS\" and \"Umbrella\". \"Take a Bow\" stayed on the Hot 100 chart for 27 weeks, and also peaked at number one on the US Hot R&B/Hip-Hop Songs, Mainstream Top 40 and Radio Songs charts, respectively. However, the song was less successful on the US Hot Dance Club Songs and Adult Contemporary charts, peaking at numbers 14 and 21, respectively. The song has been certified quadruple platinum by the Recording Industry Association of America and has sold 3 million copies in the United States as of June 2015. It also ranked at number 3 on Billboards Songs of Summer 2008. In Canada, the song leaped 69 positions from number 70 to number one on May 24, 2008, becoming the largest jump to number one in the history of the chart at the time. In Australia, \"Take a Bow\" debuted on the Australian Singles Chart at number 30 on May 15, 2008, and jumped to number 13 the following week. The song peaked at number three in its eighth week on the chart, after having spent four weeks fluctuating in the top ten. In total the song spent 11 weeks in the top ten and 22 weeks on the chart. \"Take a Bow\" has since been certified Platinum by the Australian Recording Industry Association, denoting shipments of over 70,000 copies. In New Zealand, the song debuted on the New Zealand Singles Chart at number four on May 5, 2008, and peaked at number two for five non-consecutive weeks. In total, the song spent 10 weeks inside the top five and 15 weeks in total on the chart. In the United Kingdom, \"Take a Bow\" debuted at number two on the UK Singles Chart on May 24, 2008, behind The Ting Tings \"That's Not My Name\". The following week, the two songs switched positions, with \"Take a Bow\" ascending to number one and \"That's Not My Name\" descending to number two; \"Take a Bow\" spent a total of two weeks atop the chart. On November 12, 2010, the song was certified Gold by the British Phonographic Industry, denoting shipments of over 400,000 copies. As of January 2016, it has sold over 499,900 copies in the UK. In Denmark, the song debuted at number 13 on the Danish Singles Chart on June 6, 2008, and peaked at number one in its third week. After fluctuating in the top ten for three weeks, the song ascended to number two in its seventh and eighth weeks, and went on to stay in the top ten for a further five weeks. \"Take a Bow\" spent 12 weeks in the top ten and 20 weeks on the chart in total. In Norway, the song debuted at number eight on the Norwegian Singles Chart and peaked at number five the following week. \"Take a Bow\" stayed in the top ten for four weeks and spent six weeks on the chart in total. In Austria, the song debuted at number 16 on the Austrian Singles Chart on June 6, 2008, and peaked at number six in its fourth week on the chart for six non-consecutive weeks. \"Take a Bow\" spent 10 weeks inside the top ten and a 25 weeks on the chart in total. In Switzerland, the song debuted at number 29 on the Swiss Singles Chart on May 18, 2008, and peaked at number seven for one week. \"Take a Bow\" spent a total of 29 weeks on the chart. Elsewhere in Europe, however, the song did not experience the same degree of success. \"Take a Bow\" peaked at numbers 10, 12 and 12 in The Netherlands, France and Sweden. Music video The music video was directed by Anthony Mandler, who had previously directed Rihanna videos for \"Hate That I Love You\" and \"Shut Up and Drive\". The video was shot in Venice, Los Angeles on April 3, 2008. The video begins with Rihanna standing in front of a black backdrop for the opening of the song. As the first verse starts, the scene is intercut with another of Rihanna looking out the window at her boyfriend and standing behind the front door as he approaches and asks to come in. As Rihanna walks way from the door singing the lyrics \"Don't tell me you're sorry cos you're not\", the viewer realizes that her boyfriend has perhaps done something wrong and been unfaithful. During the first chorus and second verse, Rihanna is shown in a different outfit, this time sitting in a silver Porsche in a garage. As Rihanna pulls out of the garage and onto the street, her ex-boyfriend walks alongside the car as she drives and begs her to forgive him; she pulls away. For the bridge, Rihanna is shown sitting on a bed as well as in front of the black backdrops as she reads a text message from her ex-boyfriend, who asks to meet her. During the last chorus, Rihanna appears in a different outfit and walks into a lounge, where she puts some clothes on a table and then sits on a sofa. As her ex-boyfriend walks in, the singer gets up and walks over to the clothes, where she withdraws some matches and strikes one, dropping it on what is made aware to be some of her ex-boyfriend's clothes. As the song comes to and end, Rihanna walks out of the room whilst he tries to put out the fire. Erika Brooks Adickman of Idolator commented that Rihanna had once again changed her hair style and was wearing a red jacket which looked similar to the one Michael Jacksons video \"Beat It\". Adickman continued to compare the content of the video to Beyoncé's \"Irreplaceable\", writing that it was her first video to contain a plot, but, in actuality, the videos of \"Unfaithful\" (2006) and \"Hate That I Love You\" (2007) both contain a plot. Live performances To promote Rihanna's fourth studio album, Rated R (2009) in the United Kingdom, Rihanna performed \"Take a Bow\" at the launch of the Nokia X6 smartphone at Brixton Academy in London. Other songs on the set list were \"Russian Roulette\" and \"Wait Your Turn\" from Rated R, and \"Don't Stop the Music\", \"Disturbia\", and with Jay-Z, \"Umbrella\" from Good Girl Gone Bad. During the promotion of Rated R, Rihanna also recorded video performances of her songs for \"AOL Music Sessions\"; these videos were made available to watch on AOL's website on February 23, 2010. The set included \"Take A Bow\", as well as \"Russian Roulette, \"Hard\", \"Rude Boy\" and a stripped down version of \"Disturbia\". \"Take a Bow\" has been included on four of Rihanna's arena tours: the Good Girl Gone Bad Tour, Last Girl on Earth, Loud Tour and Diamonds World Tour. The song was featured in the encore section of the Good Girl Gone Bad Tour, along with \"Umbrella\". For the Last Girl on Earth and Loud Tours, the song was featured as the last to be performed before the encore section. A minimal version of the song was also included on her Diamonds World Tour during the fourth act. Formats and track listings \"Take a Bow\" was released on CD and 12\" vinyl commercially and as \"Take a Bow: Remixes\" promotionally and digitally. Take a Bow ;International CD Single/Digital Download # \"Take a Bow\" (Album Version) – 3:46 # \"Don't Stop the Music\" (Solitaire's More Drama Remix) – 8:08 ;GR enhanced CD single # \"Take a Bow\" (Album Version) – 3:46 # \"Don't Stop the Music\" (Solitaire's More Drama Remix) – 8:08 # \"Take a Bow\" (Instrumental) – 3:46 # \"Take a Bow\" (Video) ;GR 12\" picture disc vinyl Side A & B # \"Take a Bow\" (Album Version) – 3:46 # \"Don't Stop the Music\" (Solitaire's More Drama Remix) – 8:08 Take a Bow: Remixes ;EU promo CD single (RIBOWCDX1) # \"Take a Bow\" (Seamus Haji & Paul Emanuel Radio Edit) – 3:56 # \"Take a Bow\" (Tony Moran & Warren Rigg Encore Radio Edit) – 4:02 # \"Take a Bow\" (Groove Junkies Moho Radio Edit) – 3:52 # \"Take a Bow\" (Subkulcha Radio Edit) – 4:21 # \"Take a Bow\" (Seamus Haji & Paul Emanuel Club) – 8:34 # \"Take a Bow\" (Tony Moran & Warren Rigg Encore Club) – 9:18 # \"Take a Bow\" (Groove Junkies Moho Club) – 7:21 # \"Take a Bow\" (Subkulcha Club) – 6:16 # \"Take a Bow\" (Groove Junkies Moho Dub) – 6:42 # \"Take a Bow\" (Groove Junkies Moho Dubstrumental) – 6:42 ;Digital CD Remixes # \"Take a Bow\" (Seamus Haji & Paul Emanuel Club Mix) – 8:34 # \"Take a Bow\" (Tony Moran & Warren Rigg's Encore Club Mix) – 9:18 # \"Take a Bow\" (Groove Junkies MoHo Club Mix) – 7:21 # \"Take a Bow\" (Subkulcha Club Mix) – 6:16 # \"Take a Bow\" (Groove Junkies Moho Dub) – 6:41 Credits and personnel Credits adapted from the liner notes of Good Girl Gone Bad:Reloaded. * Robyn \"Rihanna\" Fenty – Vocals * Mikkel S. Eriksen, Tor Erik Hermansen, Shaffer Smith – Songwriting * Stargate, Ne-Yo – Production * Mikkel S. Eriksen – Vocal production * Phil Tan – Mixing * Josh Houghkirk – Assistant Mixer * Stargate – Instrumentation Charts Weekly charts Year-end charts Sales and certifications Robyn Rihanna Fenty (; born February 20, 1988) is a Barbadian singer and songwriter. Born in Saint Michael and raised in Bridgetown, she first entered the music industry by recording demo tapes under the direction of record producer Evan Rogers in 2003. She ultimately signed a recording contract with Def Jam Recordings after auditioning for its then-president, hip-hop producer and rapper Jay Z. Rihanna's first two studio albums Music of the Sun (2005) and A Girl like Me (2006) charted on the top 10 of the U.S. Billboard 200 and respectively produced the singles \"Pon de Replay\" and \"SOS\". She assumed creative control of her third studio album Good Girl Gone Bad (2007) and became a household name following the release of its international successful lead single \"Umbrella\". She followed it with five Recording Industry Association of America (RIAA) platinum-certified albums, including the Grammy Award winning Unapologetic (2012) and her second Billboard 200 number-one album Anti (2016). Many of her songs rank among the world's best-selling singles of all time, including the singles \"Umbrella\", \"Take a Bow\", \"Disturbia\", \"Only Girl (In the World)\", \"S&M\", \"We Found Love\", \"Diamonds\", and \"Stay\" in which she is the lead artist, and her collaborations \"Live Your Life\" (with T.I.), \"Love the Way You Lie\" and \"The Monster\" (both with Eminem). With sales exceeding 200 million records worldwide, Rihanna is one of the best-selling artists of all time. Rihanna is the youngest and fastest solo artist to earn fourteen number-one singles on the Billboard Hot 100, and was named the Digital Songs Artist of the 2000s decade and the top Hot 100 artist of the 2010s decade by Billboard. Among numerous awards and accolades, Rihanna has won eight Grammy Awards, eight American Music Awards, 23 Billboard Music Awards, two BRIT Awards, and the inaugural Icon Award at the American Music Awards of 2013. Widely recognized for frequently reinventing her style and image, she received the Fashion Icon lifetime achievement award from the Council of Fashion Designers of America in 2014. Forbes ranked Rihanna the fourth most powerful celebrity of 2012, and was named one of Times \"100 Most Influential People in the World\" later that year. Early life Robyn Rihanna Fenty was born on February 20, 1988, in Saint Michael, Barbados. Her mother, Monica (Braithwaite), is a retired accountant of Black Guyanese background, and her father, Ronald Fenty, is a warehouse supervisor of Black Barbadian and Irish descent. Rihanna has two brothers, Rorrey and Rajad Fenty, and two half-sisters and a half-brother from her father's side, each born to different mothers from his previous relationships. She grew up in a three-bedroom bungalow in Bridgetown and sold clothes with her father in a stall on the street. Rihanna's childhood was deeply affected by her father's addiction to crack cocaine and alcohol which contributed to her parents' rocky marriage. As a child, she went through a lot of CT scans for the excruciating headaches she suffered: \"[The doctors] even thought it was a tumor, because it was that intense.\" By the time she was fourteen, Rihanna’s parents had divorced and her health began to improve. Rihanna grew up listening to reggae music and began singing at around the age of seven. She attended Charles F. Broome Memorial Primary School and Combermere High School, where she studied alongside future England cricketer Chris Jordan and future West Indies cricketer Kraigg Brathwaite. Rihanna was an army cadet in a sub-military programme; the singer-songwriter Shontelle was her drill sergeant. Although she initially wanted to graduate from high school, she chose to pursue a musical career instead. Career 2003–04: Career beginnings In 2003, Rihanna formed a musical trio with two of her classmates. She was discovered in her home country Barbados by American record producer Evan Rogers. Without a name or any material, the girl group managed to land an audition with Rogers who commented, \"The minute Rihanna walked into the room, it was like the other two girls didn't exist\". Rihanna went to Rogers' hotel room dressed in pink capris, pink shirt, and sneakers where she performed renditions of Destiny's Child's \"Emotion\" and Mariah Carey's \"Hero\". Impressed, Rogers scheduled a second meeting with her mother present, who then invited her to his hometown in the United States to record some demo tapes which could be sent to record labels. She recorded the demo over the next year intermittently, due to Rihanna only being able to record during school holidays. \"Pon de Replay\" and \"The Last Time\" were two tracks recorded for the demo tape, which were eventually included in her debut album Music of the Sun. In 2004, Rihanna won her high school talent show and the Miss Combermere school beauty pageant, where she was honored in the \"Best Gown\" and \"Most Photogenic\" categories. That same year, Rihanna was signed to Rogers' and Carl Sturken's production company, Syndicated Rhythm Productions, who assigned her a lawyer and manager, before the completed demo tape were distributed to various record labels around the world in late 2004. The demo was shipped out to Def Jam Recordings, where Jay Brown, an A&R executive at the record label, was one of the first to hear the demo. Brown played the demo tape for rapper Jay-Z, who had recently been appointed as president and Chief executive officer (CEO) of Def Jam. When Jay Z first heard the track \"Pon de Replay\", he felt the song was too big for her, saying \"when a song is that big, it's hard [for a new artist] to come back from. I don't sign songs, I sign artists\". Despite being skeptical, he invited Rihanna to audition for the label. In February 2005, Rihanna auditioned for Def Jam in New York, where Jay Z introduced her to music mogul Antonio \"L.A.\" Reid. At the audition, she sang Whitney Houston's cover of \"For the Love of You\" (1987), as well as the demo tracks \"Pon de Replay\" and \"The Last Time\". Jay-Z was absolutely certain about signing her after she performed her future hit single \"Pon de Replay\". His boss L.A. Reid was also impressed with her audition who then looked at Jay Z and told him not to let Rihanna leave the building until the contract was signed. Reid left it to Jay Z and his team to close the deal which resulted in a six-album record deal with Def Jam. She waited in Jay Z's office till three in the morning to get lawyers to draft up a contract because he wanted to prevent her from signing with another label. Looking back on the audition and meeting Jay-Z, Rihanna explained that \"when we first got there, I was shaking. I had never met a celebrity, and to have to audition for one and meet him at the same time ... I was hysterical. But the minute I went in the office, it was totally different. He was so welcoming; the environment was so warm and friendly. The jitters just went away immediately.\" Rihanna cancelled other meetings with record labels and relocated from Barbados to the United States to live with Rogers and his wife. During this time, Reid and Jay Z signed another young female artist named Teairra Mari and singer-songwriter Ne-Yo. With most of the attention on the other artist, the record label held a company showcase to introduce their newly signed artist, where Rihanna, Mari, and Ne-Yo were each performing. After the showcase, Beyoncé, who happened to be there with her husband Jay Z, was impressed and commented to L.A. Reid \"That Rihanna girl, she's a beast.\" 2005–06: Music of the Sun and A Girl like Me After signing with Def Jam, Jay Z and his team did the A&R for Rihanna's debut album and spent the next three months recording and completing her debut album. She worked with different producers to complete her debut studio album, primarily Rogers and his production partner Carl Sturken. With several songs to pick as a lead single, \"Pon de Replay\" was chosen because it seemed liked the best song suited for a summer release. In May 2005, her debut single, \"Pon de Replay\", was released which charted successfully worldwide, peaking in the top five in fifteen countries, including at number two on the U.S. Billboard Hot 100 chart and the UK Singles Chart. The song became a big club hit in the United States, peaking at number-one on the Billboard Dance Club Songs and was described as \"a poppy piece of dancehall reggae with slapping, syncopated beats recalling big-band jazz\" by Rolling Stone. That same month, she appeared on the track \"The One\" with rapper Memphis Bleek on his fourth studio album 534. Music of the Sun, was released in August 2005. It debuted at number ten on the Billboard 200 and received a gold certification from the Recording Industry Association of America (RIAA), denoting shipments of over 500,000 units. The album sold over two million copies worldwide. It received mixed reviews; Rolling Stone gave it two and a half out of five stars and described as lacking replay value, ingenuity, and rhythm. Sal Cinquemani of Slant Magazine described the album as a \"glut of teen R&B chanteuses\" and described her lead single as \"a dancehall-pop mixture that owes plenty of its sweat and shimmy to Beyoncé's \"Baby Boy\". She promoted Music of The Sun upon its release, performing at the 2005 MTV Video Music Awards pre-show and participating in the KIIS-FM Jingle Ball. A second single, \"If It's Lovin' that You Want\", was not as successful as its predecessor, but reached the top ten in Australia, Ireland, and New Zealand. Aside from her work in music, Rihanna made her acting debut in a cameo role in the straight-to-DVD film Bring It On: All or Nothing, released in August 2006. A month after the release of her debut album, Rihanna began working on her second studio album. A Girl like Me was released in April 2006. The album was a commercial success, charting in the top ten in thirteen countries. The album reached number one in Canada and number five in the United Kingdom and United States, where it sold 115,000 copies its first week. Its lead single, \"SOS\", was an international success, charting in the top five in eleven countries, including Canada, Germany, New Zealand, and the United Kingdom. The song reached number one on the US Billboard Hot 100 and in Australia, her first to reach this chart position. \"Unfaithful\", the album's second single, reached the top ten in eighteen countries, including number one in Canada and Switzerland. \"We Ride\" and \"Break It Off\", the latter featuring Sean Paul, were also released as singles. Following the release of the album, Rihanna embarked on her first headlining tour, the Rihanna: Live in Concert Tour. 2007–09: Good Girl Gone Bad and Rated R In early 2007, Rihanna began work on her third studio album Good Girl Gone Bad. She embraced a new musical direction through uptempo dance tracks produced by Timbaland, will.i.am and Sean Garrett. Released in May 2007, the album charted at number two in Australia and the US and topped the charts in multiple countries, including Brazil, Canada, Ireland, Japan, Russia and the UK. The album received the most positive critical reviews of her first three albums. The lead single, \"Umbrella\", topped the charts in thirteen countries and remained number one in the UK for ten consecutive weeks, the longest-running number one single since Wet Wet Wet's single \"Love Is All Around\" spent fifteen weeks at the top in 1994. It was Rihanna's first single to be named one of the best-selling singles worldwide, with sales of over 6.6 million copies. The songs \"Shut Up and Drive\", \"Hate That I Love You\" featuring Ne-Yo, and \"Don't Stop the Music\" were also released as singles. In support of the album, she began the Good Girl Gone Bad Tour in September 2007, with 80 shows across the US, Canada, and Europe. Rihanna was nominated for several 2008 Grammy Awards, winning Best Rap/Sung Collaboration for \"Umbrella\" alongside Jay-Z. Throughout 2008, Rihanna performed on the Glow in the Dark Tour alongside Kanye West, Lupe Fiasco, and N.E.R.D. Her third studio album's reissue, Good Girl Gone Bad: Reloaded, was released in June 2008 with three new songs: \"Disturbia\", \"Take a Bow\", and the Maroon 5 duet \"If I Never See Your Face Again\". All three were released as singles and charted highly, reaching peak positions worldwide. In August 2008, Rihanna and a host of other female singers, recorded the charity single \"Just Stand Up!\", the theme song to the anti-cancer campaign Stand Up to Cancer. \"Live Your Life\", a duet between T.I. and Rihanna, released that November, and topped the Billboard Hot 100. A remix album, Good Girl Gone Bad: The Remixes, was released in January 2009. Good Girl Gone Bad has sold over 2.8 million units in the United States alone, receiving a two-times-platinum certification from the RIAA. It is Rihanna's best-selling album in the country to date. The album has sold over seven million copies worldwide. By late 2008, Rihanna remained on the charts with her eighth single, \"Rehab\" and was named \"Diva of the Year\" by Entertainment Weekly for her \"newfound staying power\". In early 2009, Rihanna began working on her fourth studio album, Rated R. Meanwhile, she collaborated with Jay-Z and Kanye West on \"Run This Town\", which peaked at number two on the Billboard Hot 100, and reached the top ten in ten other countries. Rated R was released in November 2009 with Rolling Stone stating that Rihanna \"transformed her sound and made one of the best pop records of the year\". Rated R featured a darker and more foreboding tone than Rihanna's previous albums. Rated R debuted at number four on the US Billboard 200 chart, with first-week sales of 181,000 copies in the United States, giving Rihanna her highest first-week sales in the US at that time. The album was supported by six singles including \"Rude Boy\", which was the biggest worldwide success from the album, topping the US Billboard Hot 100 for six weeks and reaching top ten positions in twenty-two other countries. Rated R: Remixed was released in the spring of 2010 and featured ten tracks remixed by Chew Fu. To promote the album, Rihanna embarked on her second worldwide tour, the Last Girl on Earth Tour. At the 52nd Grammy Awards, \"Run This Town\" won Best Rap Song and Best Rap/Sung Collaboration. 2010–11: Loud and Talk That Talk In summer 2010, Rihanna collaborated with rapper Eminem on \"Love the Way You Lie\", which was a major worldwide success, reaching number one in over twenty countries. The song was Rihanna's seventh US number one of her career, making her the female artist with the fifth-most number ones in the chart's history. Reaching number two, the song became the biggest-selling song of 2010 in the UK, and the first of Rihanna's singles to sell over one million copies in the country. She also lent her vocals to \"All of the Lights\", a single from Kanye West's album, My Beautiful Dark Twisted Fantasy, alongside John Legend, The-Dream, Elly Jackson, Alicia Keys, Fergie, Kid Cudi, and Elton John. In October 2010, Rihanna switched managers, joining Jay-Z's Roc Nation Management. Loud, Rihanna's fifth studio album, was released in November 2010. Its lead single, \"Only Girl (In the World)\", reached number one in fifteen countries, including Australia, Canada, the United Kingdom, and the United States. The album's second single, \"What's My Name?\", featuring rapper Drake, also reached number one in the US and UK, making Rihanna the first female solo artist to have five number one singles on the UK Singles Chart in consecutive years. The song reached number one on the Billboard Hot 100 before \"Only Girl (In the World)\", the first time in the chart's history that an album's lead single reached number one after the second. The third single, \"S&M\", reached number one on the Hot 100 following the release of its official remix featuring Britney Spears, becoming her tenth number one single, which tied her with Janet Jackson for fourth place among female soloists who have topped the chart. With only four years, eleven months, and two weeks between her first and tenth number one on the chart, Rihanna set a record as the solo artist with the fastest accumulation of ten chart toppers. At the 53rd Grammy Awards, \"Only Girl (In the World)\" won the award for Best Dance Recording. \"Man Down\" and \"California King Bed\" were released as singles in May 2011 with moderate success. \"Cheers (Drink to That)\", which interpolates Avril Lavigne's 2002 single \"I'm with You\", was released as the sixth and final single from the album, reaching the top twenty in the UK and the top ten in the US. To promote the album, Rihanna embarked on her Loud Tour in June 2011, which sold out ten nights at the The O2 Arena in London, the most sold out shows for a female artist in the venue's history. The tour was the seventh highest grossing tour worldwide of 2011. The final three shows in London were filmed for Rihanna's second live video album, titled Loud Tour Live at the O2, which was released on December 18, 2012. Rihanna's sixth album, Talk That Talk, was released in November 2011. The album debuted at number three on the Billboard 200 with sales of 198,000 copies and number one in the UK, selling 163,000 copies. The lead single, \"We Found Love\", topped charts in twenty-seven countries worldwide, peaking in the top ten in thirty countries and breaking many records worldwide. It topped the Billboard Hot 100 for ten non-consecutive weeks, becoming Rihanna's longest-running number one single and the longest-running number one of 2011. The song was later named the 24th biggest hit of all time on the Billboard Hot 100. \"You Da One\" and the title track featuring Jay-Z were released as the second and third singles from the album to moderate success, the former reaching the top twenty in the UK and US. \"Where Have You Been\", the fifth single, successfully charted worldwide, reaching number five in the US and six in the UK. \"Cockiness (Love It)\" was released as the album's sixth and final single in a remixed form featuring rapper ASAP Rocky. 2012–14: Battleship and Unapologetic In early 2012, two collaborations featuring Rihanna were released: Coldplay's \"Princess of China\" from the album Mylo Xyloto and Drake's \"Take Care\" from his album of the same name. In February 2012, Rihanna won her third Grammy Award for Best Rap/Sung Collaboration at the 2012 Grammy Awards, and was voted the Best International Female Solo Artist at the 2012 BRIT Awards for the second consecutive year. March 2012 saw the simultaneous release of collaborations between Rihanna and Chris Brown: remixes of her song \"Birthday Cake\" and his \"Turn Up the Music\". The recordings received mainly negative responses due to the pair's history of domestic violence. In September 2012, \"We Found Love\" won the MTV Video Music Award for Video of the Year, making Rihanna the first woman to receive the accolade more than once. Rihanna performed at the 2012 NewNowNext Awards along with British singer Neon Hitch. Rihanna starred as Petty Officer (GM2) Cora Raikes in her first theatrical feature film Battleship, which was released on May 18, 2012. Loosely based on the game of the same name, both the film and Rihanna's performance received mixed-to-negative reviews; The New York Times said she was \"just fine in the rather generic role\". She received a Golden Raspberry Award for Worst Supporting Actress and, on a more positive note, a Teen Choice Award. She appeared in Katy Perry: Part of Me, a 3D autobiographical documentary-concert film about her friend Katy Perry. On August 19, 2012, Rihanna appeared in the first episode of the second season of Oprah Winfrey's American prime time television show Oprah's Next Chapter. The episode scored the second-highest ratings in the history of the Oprah Winfrey Network. Rihanna's seventh studio album, Unapologetic, was released in November 2012. In the United States, the album debuted at number one on the Billboard 200 with sales of 238,000, marking Rihanna's first number one album in the country. In addition, it was the best-selling debut week of her career, besting her fifth studio album Loud (2010). The album was Rihanna's third consecutive number one album in the United Kingdom and fifth in Switzerland. The lead single from the album, \"Diamonds\", reached number one in more than twenty countries worldwide, including on the US Billboard Hot 100, her twelfth number one on the chart which tied her with Madonna and The Supremes as the artists' with the fourth most number ones on the chart's history. The album's second single, \"Stay\", featuring Mikky Ekko, reached the top five in over twenty countries, including number three on the Billboard Hot 100. As promotion prior to the album's release, Rihanna embarked on the 777 Tour, a mini tour of seven shows in seven countries in seven days. A documentary DVD of the tour was later released. In February 2013 at the 55th Grammy Awards, Rihanna won her sixth Grammy Award, in the category Best Short Form Music Video for \"We Found Love\" (2011). Also that month, the Official Charts Company announced that Rihanna had sold 3,868,000 records in the past year in the UK alone, ranking at number one in the list of 2013 BRIT Awards artist nominees. Rihanna's fifth headlining concert tour, the Diamonds World Tour, began in March 2013 in support of Unapologetic. Rihanna appeared in the Seth Rogen and Evan Goldberg comedy film This Is the End, released in June 2013. That same month, American hip hop artist Wale released a remixed version of his single \"Bad\" featuring Rihanna. In October 2013, Eminem released his Rihanna-assisted single, \"The Monster\", the fourth release from his eighth studio album The Marshall Mathers LP 2 (2013). With the song entering the UK Singles Chart at number one, Rihanna joined Elvis Presley and The Beatles as just one of three acts to have scored a number one single each year over seven consecutive years in the chart's history. The song also peaked at number one on the Billboard Hot 100, which marked Rihanna's thirteenth chart topper, tying her with Michael Jackson for the third most number ones in the chart's 55-year history. Cher Lloyd performed the song on X-Factor. The song won them a Grammy Award for Best Rap/Sung Collaboration at the 57th Annual Grammy Awards. Rihanna appeared on Shakira's single, \"Can't Remember to Forget You\", which was released as the first single from Shakira's album on January 13, 2014. In May 2014, Rihanna left Def Jam to sign fully with Roc Nation, who had managed her career since October 2010. In 2013, Rihanna was originally to be featured on Pitbull's song \"Timber\", which later featured singer Kesha. 2015–present: Home and Anti News of Rihanna's work on her eighth studio album began in 2014. In January 2015, she released the single, \"FourFiveSeconds\", featuring Kanye West and Paul McCartney. Two more singles were released (\"Bitch Better Have My Money\" and \"American Oxygen\"), however the singles did not make the final track listing for her eight studio album. She also released a concept album based around the 3D animated film Home, which she starred in, alongside Jim Parsons, Steve Martin and Jennifer Lopez. \"Towards the Sun\" was released as the first single from the Home soundtrack on February 24. On July 1, 2015 the Recording Industry Association of America (RIAA) announced that Rihanna had surpassed more than 100 million Gold & Platinum song certifications. In doing so Rihanna has the most Digital Single Awards and is the first and only artist to surpass RIAA’s 100 million cumulative singles award threshold. On August 13, 2015, it was announced that Rihanna will be joining the ninth season of The Voice as the main advisor in October 2015. In October 2015 it was announced that Rihanna would have a major role in the upcoming Luc Besson film, Valerian and the City of a Thousand Planets, an adaptation of the comic book series Valérian and Laureline, which will begin filming in December 2015 and scheduled for a 2017 release. In late October 2015, it was announced that Rihanna had inked a $25 million contract with Samsung. The deal would see Rihanna promoting Samsung's Galaxy line of products whilst Samsung would sponsor the release of Anti and its supporting tour. The Anti World Tour was announced on November 23, 2015. The tour will start in February 2016, with Travis Scott supporting in North America, and The Weeknd and Big Sean supporting at selected European dates. Rihanna began her European tour in Amsterdam, Netherlands. On January 28, 2016, Rihanna released her eighth studio album Anti exclusively through streaming service Tidal. The following day, a deluxe version of the album, featuring three additional tracks, was also made available for streaming on Apple Music. After debuting at number twenty seven on the US Billboard 200, Anti went on to peak at number one becoming Rihanna's second number one and the eighth top ten album on the chart. The album was supported by the release of three singles including the lead single \"Work\" which features rapper Drake, and reached the number one spot on the Billboard Hot 100, and \"Needed Me\" which reached number seven. In the second quarter of 2016, Rihanna was featured on several singles. On April 29, 2016 Calvin Harris released \"This Is What You Came For\" which reached number three on the Billboard Hot 100 and number two on the UK single charts. Rihanna was also featured on Drakes \"Too Good\" from his album Views and Mike Will Made Its single, \"Nothing Is Promised\" which was released as the lead single from his debut album Ransom 2 on June 3, 2016. On June 27, 2016, Rihanna released \"Sledgehammer\", the lead single from the Star Trek Beyond soundtrack. Artistry Music and voice Rihanna possesses a mezzo-soprano vocal range of three octaves and two notes. While recording tracks for her third studio album, Good Girl Gone Bad (2007), Rihanna took vocal lessons from Ne-Yo. Speaking of the experience she stated, \"I've never had vocal training, so when I'm in the studio, he'll tell me how to breathe and stuff... He'll call out these big fancy words: 'OK, I want you to do staccato.' And I'm like, 'OK, I don't know what that is.'\" Her vocal performance on Loud (2010) received positive reviews from music critics. James Skinner from BBC praised Rihanna's vocals on the song \"Love the Way You Lie (Part II)\" and wrote that her voice is powerful and that \"it is Rihanna's vocal – at once commanding, soulful and vulnerable – that anchors the song, and Loud itself\". Andy Gill from The Independent feels that \"California King Bed\" features her best vocal performance. In a review of Unapologetic, Billboard magazine wrote, \"Diamonds finds Rihanna doing one of her throatiest, most impassioned vocals to date, on this inspirational pop ballad.\" Jon Caramanica of The New York Times stated, \"over the years, as her game face froze in place, her voice cured into a weapon of emotional chill and strategic indifference. It's decidedly unfriendly, made to give orders\". Rihanna's music has encompassed a broad range of genres; including dancehall, reggae and soca, as well as pop, R&B, dubstep, hip hop and electronic dance music. Some of her songs are also inspired through record sampling from other artists. Her musical career has been an experiment with new musical ideas and stated that she wants \"to make music that could be heard in parts of the world that I'd never been to\". Growing up in Barbados, she would listen to a lot of reggae, hip-hop, and soca music. When she moved to the United States in 2005, she was exposed to a lot of different types of music and became inspired to infuse a bit of rock music into her sound with the track \"Kisses Don't Lie\". At the time of her debut, she recorded songs that were inspired by her Caribbean roots and described her early sound as \"a fusion of reggae, hip-hop and R&B, with a little something different thrown in\". Her early dancehall roots can be found on her debut album, Music of the Sun (2005), and its follow-up, A Girl like Me (2006). She later experimented with pop, dubstep and rock music while shifting her musical style and image away from the Barbados island girl. Music of the Sun (2005) demonstrates the influence of Rihanna’s musical heritage of the Caribbean. Kelefa Sanneh of The New York Times complimented its combination of dancehall and reggae, who said, \"Dancehall reggae sometimes seems like a furiously insular form of music, but ... Rihanna is only the latest singer to discover how versatile the genre's spring-loaded electronic rhythms can be\". Her debut single, \"Pon de Replay\" features a dancehall-pop mixture that infuses a reggae style, while \"If It's Lovin' that You Want\" talks about a girl seducing a guy to be her boyfriend. Aiming for artistic growth, A Girl like Me (2006) expresses personal experiences that typical 18-year-old girls go through with ballads that were described as elegant and mature. Her third studio album Good Girl Gone Bad (2007) led Sal Cinquemani of Slant Magazine to write that Rihanna \"finally figured out that she's a dance artist and the majority of the album is uptempo dance-pop\" songs like \"Push Up On Me\" and \"Don't Stop the Music\". It represents a departure from the Caribbean sound of her previous albums, and is described as a turning point in her career. While the first half of the record shares a lot of 1980s pop influences with songs like \"Don't Stop the Music\" and \"Shut Up and Drive\", the second half retreats into standard R&B. Recorded after the assault by her then-boyfriend, Chris Brown, Rated R (2009) had a much darker tone and was filled with various emotions she experienced throughout 2009. In Loud (2010), Rihanna reflects on the fun and energetic vibe she had while recording the album. The album is a mixture of ballads, party anthems, and empowering love songs. Talk That Talk (2011) was similar to Rated R, as both contain hip hop, R&B, dancehall, and dubstep genres. Loud and Talk That Talk also saw her return to her dancehall roots, evident in the tracks like \"Man Down\" and \"Watch n' Learn\". She also branched out into house music with tracks like \"We Found Love\", \"Only Girl (In the World)\" and \"Complicated.\" Influences Rihanna has named Madonna as her idol and biggest influence. She said that she wanted to be the \"black Madonna\" and praised the singer for being able to constantly reinvent herself successfully throughout her career. \"I think that Madonna was a great inspiration for me, especially on my earlier work. If I had to examine her evolution through time, I think she reinvented her clothing style and music with success every single time. And at the same time remained a real force in entertainment in the whole world.\" Another major influence on Rihanna's music and career has been Mariah Carey, whose song \"Hero\" she performed when Rihanna was still a teenager at her high school talent show. She revealed that Carey's song \"Vision of Love\" \"was the song that made [her] want to do music\" and that \"everything Mariah did, [she] would try to do.\" She grew up watching videos of reggae legend Bob Marley on television because that's what they would play in the Caribbean. She stated, \"[h]e's one of my favorite artists of all time [...] he really paved the way for every other artist out of the Caribbean\". She built a shrine in her home dedicated to the reggae legend and has covered Marley's \"Is This Love\" and Bob Marley & The Wailers' \"Redemption Song\" during her concert tours. During her childhood, she would go around singing Whitney Houston songs and \"A Whole New World\" into her hairbrush so much that her neighbors started calling her \"Robyn Redbreast\". She also stated that one of the first songs she remembers falling in love with was Houston's version of \"I Will Always Love You\" and that it \"was really inspiring, and it made me develop a passion for music, so really, she’s partly responsible for me being here in this industry.\" Rihanna commented that Janet Jackson \"was one of the first female pop icons that I could relate to\" and that late R&B singer Aaliyah has a huge impact on her style and also complimented on the singers artistry as well. Watching Beyoncé on television with Destiny's Child also inspired Rihanna's musical career, who was chosen along with R&B recording artists Amerie and Teairra Marí, to give a tribute performance to the female group at the 2005 World Music Awards. Other musical influences and idols include Celine Dion, Alicia Keys, Prince[http://www.nme.com/blogs/nme-blogs/14-artists-that-wouldnt-be-the-same-without-prince 21 Artists Who Wouldn't Be The Same Without Prince] Fefe Dobson, and Brandy. Rihanna takes influence from the different types of music she discovered when she came to America and revealed that rock music was one of the first genres she fell in love with. She commented, \"as I grow older, I want to know more about music. I want to discover more types of music\". She cited Brandy's fourth studio album, Afrodisiac (2004), as her main inspiration for her third album, Good Girl Gone Bad (2007). In her early career, her music contained strong influences of Caribbean music, including reggae and dancehall. The music video of the song \"Rude Boy\" featured images inspired by her Caribbean roots. She commented that Marilyn Monroe and vintage clothing served as visual inspirations for the music videos for \"Hate That I Love You\" and \"Rehab\". Videos and stage Rihanna has worked with music video director Anthony Mandler on more than a dozen music videos, the first being \"Unfaithful\" (2006). \"We've done 16 videos together; they're not all tough, [...] Yeah, I mean, I'm known for the 'Disturbia's and the 'Russian Roulette's and things like that, but 'Only Girl (In the World)' is certainly an ethereal kind of empowering, beauty-filled video,\" Mandler said. Jocelyn Vena of MTV wrote, \"Rihanna, like Madonna, also has a tendency to make truly thought-provoking music videos that fit the songs they represent. Smattered in between glitzier, more glamorous clips, Madge and Ri want us to think about bigger issues\". Jon Bream of the Star Tribune commented \"[i]n the tradition of Madonna and Janet Jackson, Rihanna has become the video vixen of the '00s ... Rihanna has perfected the pout, the long-legged strut and trend-setting hairdos that keep women and men alike checking her out on YouTube.\" George Epaminondas of InStyle considers Rihanna's music videos to be \"cinematic\" due to her \"blend of lush island rhythms and swinging pop and ... mischievous sensuality.\" Tamar Anitai from MTV Buzzworthy listed \"Disturbia\" at number five on the \"Buzzworthy's Top 5 Most Paranoid Music Videos\" and said that \"Paranoia never looked so supernaturally sexy!\". Many of her music videos were shot as short films exploring issues such as love triangles, abuse, and substance abuse romance, including \"We Found Love\" and \"Man Down\". Her music video for \"Umbrella\" shows Rihanna's transition into adulthood and her newly adopted image. The \"dark, creepy\" scenes of \"Disturbia\" have been compared to Michael Jackson's Thriller. The video for \"Russian Roulette\" features Rihanna in a padded room playing a game of russian roulette with her partner. A scene of Rihanna being approached by a speeding car at night was compared to the altercation with Chris Brown. The Caribbean-inspired music video for \"Rude Boy\" was compared to rapper M.I.A.'s video \"Boyz\" by many critics for its colorful aesthetic similarities. In 2011, she released three controversial music videos about sadomasochism, rape, and domestic violence. \"Man Down\", which features Rihanna shooting a man in a train station, was criticized by the Parents Television Council. \"We Found Love\", which shows Rihanna and her love interest in a drug-filled unhealthy relationship, sparked criticism from the Rape Crisis Centre for its inappropriate message. But Charne Graham of the Houston Press defended the singer, asking, \"Why should Rihanna's music videos get everyone riled up when others' equally sexual and controversial videos are in rotation? [...] she just like[s] to make music videos that give us something to talk about.\" She is the first woman to pass two billion cumulative views on the music video website VEVO. As of September 2013, she has accumulated over four billion views on the site. Denis Armstrong of Canadian Online Explorer commented on her performance at the Ottawa Bluesfest, saying \"her show was a Disney-esque choreographed fantasy of non-stop hip-swiveling, sassy attitude and personal endearments and a string of funky, sugar-free hits.\" Her performance of \"Disturbia\" at the 2008 MTV Video Music Awards was ranked tenth best on the MTV Video Music Awards, according to a Billboard poll. Her revealing leather costumes during her Good Girl Gone Bad Tour were highly criticized by Malaysia's conservative Islamic party, who recommended that her concert tour should be banned. Whilst commenting on her third album's accompanying tour, The Times compared Rihanna's stage wardrobe styling to that of Janet Jackson and called her \"a vision of Ann Summers couture in thigh-high boots and a few scraps of black PVC.\" In the October 2011 issue of British Vogue, Rihanna said her performance outfits and appearances are all an act; \"[t]hat's not me. That's a part I play. You know, like it's a piece of art, with all these toys and textures to play with\". Image Public profile Known for reinventing her style and image, Rihanna's music and fashion sense are noted by the media. In 2009, New York magazine described Rihanna's early look as that of a cookie-cutter teen queen, noting she has the ability to shift looks dramatically and with great ease. Around the time of the release of her second studio album, A Girl like Me (2006), many critics felt that Rihanna's style, sound, and musical material were too similar to those of Beyoncé. In an interview with Look magazine, Rihanna spoke about comparisons to Beyonce: \"Beyoncé is a great artist and I feel honored to be mentioned in the same sentence, but we're different performers with different styles\". She revealed during Oprah's Next Chapter that Def Jam's pop-princess blueprint made her feel claustrophobic during her early years with the label. According to Rihanna, \"I felt like they were giving me a blueprint. [...] They had a brand, they had an idea of what they wanted me to be without figuring out who I was.\" With the release of her third album, Good Girl Gone Bad (2007), Rihanna dismissed her innocent image for an edgier look with a new hairstyle, which was inspired by Charlize Theron's bob cut in the science fiction thriller Æon Flux (2005). She followed the likes of recording artists Janet Jackson and Christina Aguilera who also shed their innocent image for an edgier look and sound. Nico Amarca of Highsnobiety magazine wrote \"over the course of her now 10-year career, [Rihanna] has undergone one of the most significant aesthetic metamorphoses the world has ever seen\". She has changed her personal appearance and fashion several times with different hairstyles since the release of her third album. She commented that as a child she \"used to watch her [mother] get dressed\" and that her love and admiration for fashion started with her mom. When putting together her own wardrobe she stated, \"It's become more about taking a risk ... I always look for the most interesting silhouette or something that's a little off.\" Jess Cartner-Morley of The Guardian wrote that \"Rihanna's wardrobe is the most talked-about, influential and dissected in pop right now\" and that whatever she wears \"is immediately reproduced on the high street, because it sells\". Despite being criticized for her revealing outfits, country singer Miranda Lambert admires Rihanna's fashion and style. \"I don't necessarily get inspired by the whole no-bra thing, but I love that you never know what she's going to wear. It always keeps you guessing, which makes her sassy and interesting.\" In an interview with Alexa Chung during Vogue Festival 2015, Balmain designer Olivier Rousteing praised Rihanna by stylistically comparing her to some of the biggest fashion icons in music history, such as Madonna, David Bowie, Michael Jackson, and Prince. Commenting on the cultural expectation for pop stars to be role models, she said \"[being a role model] became more of my job than I wanted it to be. But no, I just want to make music. That's it\". In a May 2013 interview with MTV, The Vagina Monologues writer and feminist Eve Ensler praised the singer, saying, \"I'm a huge Rihanna fan, I think she has a kind of agency over her sexuality and she's open about her sexuality, she has enormous grace and she's immensely talented.\" Appearance Described as one of the sexiest woman of her generation, she revealed that being a sex symbol is not a priority and that \"it's definitely flattering, but also uncomfortable.\" Emily Hewett from Metro wrote, \"Rihanna is quite possibly [the] most sexiest woman in the world. The 25-year-old songbird can grind like no other, pull off a provocative pose better than a Playboy pro.\" Her appearance has landed her on the cover of magazines such as Maxim, FHM, Rolling Stone and GQ. She has appeared in the top ten on Maxims Hot 100 list and on FHMs \"100 Sexiest Women in the World\" several times. In 2007, she was tagged Venus Breeze's \"Celebrity Legs of a Goddess\" by Gillette, and was ranked second on People magazine's list of \"10 Best Dressed Stars\" the following year. In 2009, Glamour ranked her at number 17 on the 50 Most Glamorous Women and Esquire named her the Sexiest Woman Alive of 2011. In December 2012, Rihanna became the first woman to be featured on the cover of GQ magazine's \"Men of the Year\" issue and ranked fifth on Complex list of \"100 Hottest Female Singers of All Time\". The following year, VH1 placed Rihanna second on their list of \"100 Sexiest Artists\". Rihanna is well known for having a wide collection of small tattoos around her body. The sixteen in total include two musical notes on the front of her ankle, a skull with a pink hair bow on the back of her ankle, a Pisces sign behind her right ear, a Sanskrit prayer going down her hip, a star in her left ear, the word \"love\" on her left middle finger, an Arabic phrase meaning \"Freedom in Christ\" on her ribcage area, a trail of stars going down the back of her neck, the phrase \"shhh...\" on her right index finger, the date 11.4.86 in Roman numerals on top of her left shoulder, a henna-style tribal dragon claw including hibiscus flowers inside her right hand/wrist, and a handgun under her right armpit. A gun tattoo was planned to be placed just below her shoulders but was ultimately located on her ribcage. In late 2009, Rihanna had the phrase, \"Never a failure, always a lesson\" inked onto her chest backwards as she wanted to be able to read it in the mirror; it is her \"motto in life for everything\". In mid-2010, the phrase \"rebelle fleur\" was tattooed onto the singer's neck. Legacy Rihanna's first albums established her as a \"Pop/R&B Princess\" by music critics; Nick Levine of Digital Spy described her third studio album Good Girl Gone Bad, as the closest thing to a Thriller that 2007/08 is likely to produce. Her single \"Umbrella\", famous for its \"ella ella\" hook, is considered by Rolling Stone to be one of the 500 Greatest Songs of All Time. Her 2011 single \"We Found Love\" was ranked by Billboard as the 24th biggest US Billboard Hot 100 hit of all time. The music video for the song won the Grammy for Best Short Form Music Video and MTV's Video of the Year. Time magazine included Rihanna on its 100 Most Influential People in 2012. Her collaboration with Eminem, \"Love the Way You Lie\", together with \"Umbrella\", \"Disturbia\", \"Only Girl (In the World)\", \"We Found Love\", and \"Diamonds\", are some of the best-selling singles of all time worldwide. In November 2013, Rihanna was given the \"Icon Award\" at the 2013 American Music Awards. On June 2, 2014, Rihanna was presented with Fashion Icon lifetime achievement award from Council of Fashion Designers of America (CFDA), a special prize reserved for \"an individual whose style has made a significant impact on popular culture on an international stage\". In August 2013, Rihanna was named the 15th biggest U.S. Billboard Hot 100 artist of all time, becoming the highest ranking newcomer on the list since the last time Billboard compiled all time artists in 2008. In the same month, Billboard ranked Rihanna the top Hot 100 artist of the 2010s decade. Rihanna's work has directly influenced a number of contemporary artists such as Little Mix, Selena Gomez, Justin Bieber, Ellie Goulding, Tegan and Sara, Jessie J, Cover Drive, Fifth Harmony, Demi Lovato, Alexandra Stan and Willow Smith. Rihanna has an honorary title of Ambassador for Culture and Youth in Barbados. Additionally, Rihanna has become a dominating figure in social media and internet streaming, ranking at number one on Forbes' 2012 list of Social Networking Superstars. In 2013, Rihanna was also named the most influential pop star in the United Kingdom by UK channel 4Music. Time magazine created a pop star ranking metric in 2014, measuring artist's number of hits, chart placement, and longevity since 1960. Rihanna was ranked second, only behind Mariah Carey. Achievements Rihanna has received numerous awards throughout her career such as eight Grammy Awards, twelve Billboard Music Awards, nine American Music Awards, four iHeartRadio Music Awards and more. Rihanna has sold over 200 million records worldwide, making her one of the best-selling music artists of all time. In the United States, Rihanna has sold over 10 million albums, while Nielsen SoundScan ranked her as the best-selling digital artist in the country, breaking a Guinness World Record for digital single sales of over 58 million as of 2012. On July 1, 2015 the Recording Industry Association of America (RIAA) announced that Rihanna had surpassed more than 100 million Gold & Platinum song certifications. In doing so Rihanna has the most digital single awards and is the first and only artist to surpass RIAA’s 100 million cumulative singles award threshold. In the United Kingdom, she has sold over 7 million albums making her 3rd best selling female this century. The singer has accumulated fourteen number one singles on the U.S. Billboard Hot 100 chart for the third most number ones in chart's history. She has been named the top Mainstream Top 40 chart artist of the past twenty years by Billboard; she ranks first with most entries (36), most top tens (23), and most number ones (10). As of March 2014, Rihanna has sold over 18 million singles and six million albums in the United Kingdom. She is the tenth best-selling and the second best-selling female singles artist in the country, only behind Madonna and is second only to The Beatles for the most million-selling singles in the UK of all time. Other ventures Endorsements Rihanna has ventured into other businesses and industries. In October 2005, Rihanna struck an endorsement deal (her first of many) with Secret Body Spray. In 2010, Rihanna featured in the Optus commercial, in conjunction with Optus supporting Rihanna's Last Girl on Earth Tour. The same year Rihanna also featured in the Kodak commercial along with rapper Pitbull. In October 2010, the singer released an eponymous book. The book, featured photos from Rihanna's Last Girl on Earth Tour and served as an accompaniment to her fourth studio album Rated R (2009). Rihanna's first fragrance, \"Reb'l Fleur\", was released on January 2011. The product became highly successful, according to Rolling Stone, Reb'l Fleur was a financial success and was expected to gross US$80 million at retail by the end of 2011. In 2011, Nivea celebrated its \"100 Years of Skincare\" festivities which featured several performances from Rihanna. Rihanna's song \"California King Bed\" was featured as a part of the \"100 Years of Skincare\" commercial campaign. Rihanna also became the face of Vita Coco in 2011. Rihanna's second fragrance, \"Rebelle\", was released in February 2012. The promotional campaign for Rebelle, was shot by director, Anthony Mandler, who also shot the promotional campaign for Reb'l Fleur. In November 2012, Rihanna released her third fragrance, \"Nude\". In 2013, the singer collaborated with MAC Cosmetics and released her own summer, fall and holiday lines of makeup called \"RiRi hearts MAC\". In July 2013, lager production company Budweiser announced that Rihanna had become a part of their global \"Made For Music\" campaign, also co-starring Jay-Z. A commercial video was released featuring the singer and song \"Right Now\". Rihanna's fourth women's fragrance, titled Rogue was released on September 14, 2013. The singer announces to release a men's version the following year. It was announced on August 1, 2014 that September 2014 will see the release Rihanna's first fragrance for men, \"Rogue Man\". Also in July 2015, she announced her latest fragrance, RiRi by Rihanna. The scent features notes of passion fruit extract, rum absolute, sparkling cassis, and Italian mandarin and arrive at retailers in September 2015. Business endeavours On March 30, 2015, it was announced that Rihanna is a co-owner, with various other music artists, in the music streaming service Tidal. The service specialises in lossless audio and high definition music videos. Jay Z acquired the parent company of Tidal, Aspiro, in the first quarter of 2015. Including Beyoncé and Jay-Z, sixteen artist stakeholders (such as Kanye West, Beyoncé, Madonna, Chris Martin, Nicki Minaj and more) co-own Tidal, with the majority owning a 3% equity stake. \"The challenge is to get everyone to respect music again, to recognize its value\", stated Jay-Z on the release of Tidal. In November 2015, Rihanna and Benoit Demouy launched a beauty and stylist agency named Fr8me. The business based in Los Angeles was set up in order to assist artists in booking commercials, editorial shoots, ad campaigns, and red-carpet appearances. Speaking on the venture Rihanna stated \"Hair, makeup and styling play an important role in creativity, I am very involved with that part of my process, so this agency was an organic thing for me to do.\" The roster includes Rihanna’s makeup artist Mylah Morales, wardrobe stylist Jason Bolden, hairstylist Patricia Morales, and Marcia Hamilton. In addition to Fr8me, Rihanna opened a photo agency called \"A Dog Ate My Homework\", which represents photographers Erik Asla and Deborah Anderson. Under her PUMA collection, she released the \"Puma Creepers\". Then in 2016, she released the PUMA Fenty Trainer, which premiered in red, white, and black, and then was scheduled to release in a grey \"Quarry\" colorway at midnight on June 14 via Packer Shoes; they sold for $180. The \"Fenty Trainers\" were available on June 15 in-store at both Packer Shoes locations. Fashion The singer's first fashion range, for Armani, became available in November 2011. Rihanna's 2010 song \"Skin\" was used in the Armani Jeans and Emporio Armani Underwear adds. Her first television program, Styled to Rock, premiered in the UK in August 2012 on Sky Living. In the ten-week series, Rihanna, Nicola Roberts, Lysa Cooper, and Henry Holland assist up-and-coming British designers with their clothing lines. In February 2013, Rihanna presented her first women's spring fashion collection at London Fashion Week for British street fashion brand River Island, collaborating with her personal stylist Adam Selman. They published two more collections for the brand, a summer edition released on May 25, 2013 and an autumn edition released on September 10, 2013. The fourth and last collection for River Island, the winter edition was released on November 7, 2013. Meanwhile, the US version of Styled to Rock premiered on October 25, 2013 on Bravo. In December 2013, it was announced that Rihanna is set to be the new face of the forthcoming spring/summer 2014 campaign — due to appear in magazines from January — of the French fashion house Balmain. On June 2, 2014, Rihanna \"will receive the Fashion Icon Award at the 2014 Council of Fashion Designers of America Fashion Awards\" at the Lincoln Center's Alice Tully Hall. In December 2014, it was announced that Rihanna would become the creative director of the fashion sportswear Puma, overseeing the brand’s women’s line which will include collaborations in apparel and footwear. In March 2015, it was announced that Rihanna was chosen as the new face of Dior; this makes her the first black woman to be the face of Dior. In July 2015, Rihanna announced that she would design multiple seasons of a signature women's collection of socks in a multi-year partnership with Stance. The first release included two limited-edition styles in a print called \"Murder Rih Wrote,\" with further designs to follow. In March 2016, Rihanna teamed up with Manolo Blahnik for an all denim shoe line. She has collaborated with luxury fashion Label Dior to create a line of futuristic sunglasses she debuted on instagram. It is simply called \"Rihanna. \" Mexican singer Becky G has stated Rihanna is one of style icons to Latina magazine. Philanthropy In 2006, she created her Believe Foundation to help terminally ill children. In 2007, Rihanna was named as one of the Cartier Love Charity Bracelet Ambassadors, with each celebrity representing a different global charity. To help raise awareness and combat HIV/AIDS, Rihanna and other public figures designed clothing for the February 2008 H&M Fashion Against AIDS line. In 2008, Rihanna performed a series of charity concerts entitled A Girl's Night Out to benefit the Believe Foundation. The concerts were made free for the public. Money from sponsors and advertisers were to be donated to provide medical supplies, school supplies and toys to children in need. In September 2008, Rihanna contributed to the song \"Just Stand Up!\" with fifteen other female artists, who shared the stage to perform the song live on September 5, 2008, during the \"Stand Up to Cancer\" television special. The proceeds from the single were given to the fundraiser. The television special helped raise $100 million for cancer research. Rihanna founded the Clara Lionel Foundation (CLF) in 2012, in honor of her grandparents, Clara and Lionel Braithwaite. Current programs include the Clara Braithwaite Center for Oncology and Nuclear Medicine at the Queen Elizabeth Hospital in Barbados, and education programs. The CLF host an annual Diamond Ball—charity fundraiser event. The inaugural event in 2014 raised over $2 million, with the second raising over $3 million. On February 12, 2012, Rihanna performed a benefit show at the House of Blues to raise money for the Children's Orthopaedic Center and The Mark Taper-Johnny Mercer Artists Program at Children's Hospital. In November 2012, Rihanna gave $100,000 to food bank donation for Hurricane Sandy, On January 3, 2014 Rihanna was part of the MAC Viva Glam campaign, which benefits women, men, and children living with HIV/AIDS. Personal life On February 8, 2009, Rihanna's scheduled performance at the 51st Annual Grammy Awards was cancelled. Reports surfaced that then-boyfriend, singer Chris Brown had beaten her. He was arrested on suspicion of making criminal threats. On March 5, 2009, Brown was charged with assault and making criminal threats. Due to a leaked photograph from the police department obtained by TMZ.com—which revealed that Rihanna had sustained visible injuries—an organization known as STOParazzi proposed \"Rihanna's Law\", which, if enacted, would \"deter employees of law enforcement agencies from releasing photos or information that exploits crime victims.\" Gil Kaufman of VH1 reported the \"nonstop coverage of the Rihanna/Brown case has brought up a number of issues regarding the privacy of alleged victims of domestic violence, including the decision by almost all major news outlets to divulge the identity of the victim—which is not typically done in domestic-violence cases\" and discussed the controversial distribution of the leaked photograph. Rihanna was subpoenaed to testify during a preliminary hearing in Los Angeles on June 22, 2009. On June 22, 2009, Brown pleaded guilty to felony assault. Brown received five years probation and was ordered to stay fifty yards (46 meters) away from Rihanna, unless at public events, which then would be reduced to ten yards (nine meters). In February 2011, at the request of Brown's lawyer and with Rihanna's consent, Judge Patricia Schnegg modified the restraining order to a \"level one order\", which allows the singers to appear at awards shows together in the future. From December 2009 to 2010, Rihanna dated Dodgers baseball player Matt Kemp. Canadian rapper Drake has also dated the singer. In a January 2013 interview with Rolling Stone, Rihanna confirmed that she had rekindled her relationship with Chris Brown, though he remained under probation for the 2009 domestic violence incident. The confirmation followed persistent media speculation throughout 2012 regarding the pair's reunion. In a May 2013 interview, Brown stated that he and Rihanna had broken up again. In 2015, Rihanna briefly dated Travis Scott, a rapper from Missouri City, Texas Rihanna has stated that she believes in God and that she focuses on obeying God and reading her Bible. She is a fan of Protestant charismatic minister Joyce Meyer. In 2015, Rihanna told Harper's Bazaar that her faith in God has helped her throughout her career. During her performance at the NCAA March Madness Music Festival, Rihanna expressed her disagreement with Indiana's Religious Freedom Restoration Act that allows companies and individuals to use their religious beliefs as protection, in case of being accused of discrimination against LGBT people. Rihanna along with numerous other high profile celebrities featured in an online video entitled \"23 Ways You Could Be Killed If You Are Black in America\". The video was released in partnership with the We Are Here Movement and called for action against police brutality. According to the New York Post, Rihanna filed a lawsuit against Peter Gunis and the firm Berdon LLP for $35 million but settled out of court for more than $10 million. Forbes began reporting on Rihanna's earnings in 2012, calculating that she earned $53 million between May 2011 and May 2012, for her music, tour, and endorsements. In 2013, Rihanna came in at number 13 on the list with a total earning of $43 million due to endorsements such as vita coco. In 2015 Rihanna earned $26 million, which resulted in her net worth rising to $160 million by 2016. In July 2016, Forbes magazine placed Rihanna at number 13 on their list of highest paid celebrities, earning $75 million between 2015 and 2016. Rihanna currently lives in Manhattan, New York City and owns a penthouse there that is worth $14 million. Discography * Music of the Sun (2005) * A Girl like Me (2006) * Good Girl Gone Bad (2007) * Rated R (2009) * Loud (2010) * Talk That Talk (2011) * Unapologetic (2012) * Anti (2016) Filmography * Bring It On: All or Nothing (2006) * Battleship (2012) * Coldplay Live 2012 (2012) * Katy Perry: Part of Me (2012) * This Is the End (2013) * Annie (2014) * Home (2015) * Bates Motel (2017) * Valerian and the City of a Thousand Planets (2017) Tours * Rihanna: Live in Concert Tour (2006) * Good Girl Gone Bad Tour (2007–09) * Last Girl on Earth Tour (2010–11) * Loud Tour (2011) * Diamonds World Tour (2013) * The Monster Tour (with Eminem) (2014) * Anti World Tour (2016)rihannaWhich singer had a 2008 number one hit with the song 'Take A Bow'?[RihRih, Rianna, Robyn R. Fenty, Emergency room (song), Whipping My Hair, Ihanna, Westbury Road Entertainment, Rihanna (singer), Emergency Room (Rihanna song), James Joint, Emergency Room (Featuring Akon), Diamonds tour, Rihanna in popular culture, RiRi, Robyn Fenty, Rihanna Fenty, Whippin' My Hair, Whipping My Hair (Rihanna Song), RIHANNA, Rihana, Riri, Rihanna, Renown (Rihanna album), Rihannah, Coffret 4 CD, Rihanna: World Tour 2013, Robyn Rihanna, Robyn Rihanna Fenty]
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "dqcviRfJw-3M" }, "source": [ "Great 🙂 - that's the structure that we wanted! Looking at the examples should also make it clear how large the context of the model can be! Also note that some samples can have an empty context. For those samples it is impossible to extract the correct answer from the context since the context is empty. \n", "\n", "We filter out those data samples for which we can use the convenient `.filter()` function of `datasets` again." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "j0vSycFKxc0O", "outputId": "c852efba-6cf5-4e04-8d91-1233e12e1a67" }, "source": [ "validation_dataset = validation_dataset.filter(lambda x: len(x[\"context\"]) > 0)\n", "# check out how many samples are left\n", "validation_dataset" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Loading cached processed dataset at /root/.cache/huggingface/datasets/trivia_qa/rc/1.1.0/c1b17d80bfdd2f43b13e4b5741847f704115379ecebcb21e0917d2e8ddaa1ff1/cache-499a8202c2805ef0.arrow\n" ], "name": "stderr" }, { "output_type": "execute_result", "data": { "text/plain": [ "Dataset({\n", " features: ['context', 'norm_target', 'question', 'targets'],\n", " num_rows: 16504\n", "})" ] }, "metadata": { "tags": [] }, "execution_count": 26 } ] }, { "cell_type": "markdown", "metadata": { "id": "IMh8EvQoxsrF" }, "source": [ "Looks like more or less half of our examples have no context and are now filtered out. Let's think about the evaluation on *BigBird* now. \n", "\n", "*BigBird* is able to process inputs of up to a length of **4096** tokens, has a vocab size of *ca.* 50K tokens, and makes use of the [**sentencepiece**](https://github.com/google/sentencepiece) tokenizer. \n", "\n", "Given this information, we can assume that a single token of *BigBird*'s vocabulary represents roughly 4 characters on average. \n", "\n", "To better understand how important it is to take the full context into account, we create a second `\"shorter\"` dataset. This `short_validation_dataset` only consists of data samples that do **not** exceed 4 * 4096 characters, which corresponds *ca.* to *BigBird*'s maximum length of 4096 tokens.\n", "\n", "Again we can apply the convenient `.filter()` function." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4ebRyrOmy4br", "outputId": "5758fbe4-e657-47ae-e08f-a9dc137b6ecc" }, "source": [ "short_validation_dataset = validation_dataset.filter(lambda x: (len(x['question']) + len(x['context'])) < 4 * 4096)\n", "short_validation_dataset\n" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Loading cached processed dataset at /root/.cache/huggingface/datasets/trivia_qa/rc/1.1.0/c1b17d80bfdd2f43b13e4b5741847f704115379ecebcb21e0917d2e8ddaa1ff1/cache-742ab8ff9f852b82.arrow\n" ], "name": "stderr" }, { "output_type": "execute_result", "data": { "text/plain": [ "Dataset({\n", " features: ['context', 'norm_target', 'question', 'targets'],\n", " num_rows: 4159\n", "})" ] }, "metadata": { "tags": [] }, "execution_count": 27 } ] }, { "cell_type": "markdown", "metadata": { "id": "m5MyFEZqzwZ-" }, "source": [ "Interesting! We can see that only 4159 of the 16504 examples have less than $4 \\times 4096 = 16384$ characters...\n", "\n", "Most examples seem to have a very long context which will have to be cut to BigBird's maximum length when evaluating the full dataset.\n", "\n", "Great, we have now preprocessed the complete dataset `validation_dataset` and a shorter version `short_validation_dataset` that only consists of data samples $< 4096$ tokens." ] }, { "cell_type": "markdown", "metadata": { "id": "WDPrsXOTV6R2" }, "source": [ "### Evaluation\n", "\n", "It's time to evaluate *BigBird* on *TriviaQA* 🚀. `BigBirdTokenizer` requires the `sentencepiece` library, so let's install this first." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wNYKpdyRChkE", "outputId": "21d2bbce-dbe3-4887-9a01-8a8258d5b9ae" }, "source": [ "!pip install sentencepiece" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (0.1.95)\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "gK8J_AvFKTcc" }, "source": [ "Let's import the pretrained `BigBirdForQuestionAnswering` model. For more details on `BigBirdForQuestionAnswering`, see [TODO: here]()." ] }, { "cell_type": "code", "metadata": { "id": "ecjNtnAuKYo8" }, "source": [ "from transformers import BigBirdTokenizer, BigBirdForQuestionAnswering\n", "\n", "tokenizer = BigBirdTokenizer.from_pretrained(\"google/bigbird-base-trivia-itc\")\n", "model = BigBirdForQuestionAnswering.from_pretrained(\"google/bigbird-base-trivia-itc\").to(\"cuda\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "MfCi-h_xCqQ1" }, "source": [ "\n", "Next, we will write the evaluation function. Google's official evaluation scripts for *TriviaQA* includes many improvements to make sure that:\n", "\n", "- multiple aliases of **both** the model's prediction **and** the label are created. This way it can be assured that a correct prediction that only differs in format to the label is indeed classified as being correct. For this, we will write an `expand_to_aliases` function which will normalise the targets and prediction.\n", "- the model returns a *non-empty* answer. This can be achieved by using the `top_k` scores returned by model to filter out scores which would lead to an emtpy answer.\n", "\n", "Let's first define the `expand_to_aliases` function." ] }, { "cell_type": "code", "metadata": { "id": "umi9J1DBxTyC" }, "source": [ "PUNCTUATION_SET_TO_EXCLUDE = set(''.join(['‘', '’', '´', '`', '.', ',', '-', '\"']))\n", "\n", "def get_sub_answers(answers, begin=0, end=None):\n", " return [\" \".join(x.split(\" \")[begin:end]) for x in answers if len(x.split(\" \")) > 1]\n", "\n", "def expand_to_aliases(given_answers, make_sub_answers=False):\n", " if make_sub_answers:\n", " # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word\n", " # *e.g.* if the correct answer contains a prefix such as \"the\", or \"a\"\n", " given_answers = given_answers + get_sub_answers(given_answers, begin=1) + get_sub_answers(given_answers, end=-1)\n", " answers = []\n", " for answer in given_answers:\n", " alias = answer.replace('_', ' ').lower()\n", " alias = ''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias)\n", " answers.append(' '.join(alias.split()).strip())\n", " return set(answers)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ceT6f51qxfnt" }, "source": [ "Here we use a RoBERTa-like model for question answering which means that the answer is not generated auto-regressively by the model, but rather the model has to extract the correct answer from the context. It does so by predicted the start index of the answer as well as the end index and therefore returns two logit vectors at evaluation:\n", "\n", "1. `start_scores` of size $\\text{batch_size} \\times \\text{sequence_length}$ and\n", "2. `end_scores` of size $\\text{batch_size} \\times \\text{sequence_length}$, with\n", "\n", "$\\text{sequence_length}$ being the sum of the question length and the context length. Each value in `start_scores` and `end_scores` is therefore proportional to the probability that probability that its corresponding index is the start (*resp.* end) index of the answer. The answer can then be computed as the subarray of the tokenized input question + input context: \n", "\n", "```python\n", "predicted_answer = tokenized_question_and_context[best_start_index: best_end_index + 1]\n", "``` \n", "\n", "Now instead of just taking the most likely start and end index, we can compute the $\\text{top_k}^2$ best start and end index combinations and pick the most likely combination that is **valid**. We define a **valid** combination as one where the start index is smaller than the end index and where $\\text{end_index} - \\text{start_index} < \\text{max_size}$.\n", "\n", "Alright quite some theory here - let's write up the function." ] }, { "cell_type": "code", "metadata": { "id": "oIUReLlh9tG8" }, "source": [ "def get_best_valid_start_end_idx(start_scores, end_scores, top_k=1, max_size=100):\n", " best_start_scores, best_start_idx = torch.topk(start_scores, top_k)\n", " best_end_scores, best_end_idx = torch.topk(end_scores, top_k)\n", "\n", " widths = best_end_idx[:, None] - best_start_idx[None, :]\n", " mask = torch.logical_or(widths < 0, widths > max_size)\n", " scores = (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)\n", " best_score = torch.argmax(scores).item()\n", "\n", " return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "8sXUJq8710qb" }, "source": [ "Alright, finally we can write the evaluation function. First, we tokenize the `question` and `context` and make sure that the maximum length does not exceed the model's `max_position_embeddings` of 4096 tokens.\n", "Then, we pass the concatenated question and context token IDs to the model to retrieve the `start_scores` and `end_scores`.\n", "\n", "As explained earlier, using just the most likely start and end idx could lead to invalid answers, so we apply the `get_best_valid_start_end_idx(...)` function to retrieve the best *valid* start and end token ids.\n", "Given those ids, we can then retrieve the actual predicted answer string.\n", "Finally, we create normalized aliases for both the predicted answer and the labels. If there is an overlap between all \"extended\" predictions and labels then we have a match." ] }, { "cell_type": "code", "metadata": { "id": "sHgcdBt41gby" }, "source": [ "def evaluate(example):\n", " # encode question and context so that they are seperated by a tokenizer.sep_token and cut at max_length\n", " encoding = tokenizer(example[\"question\"], example[\"context\"], return_tensors=\"pt\", max_length=4096, padding=\"max_length\", truncation=True)\n", " input_ids = encoding.input_ids.to(\"cuda\")\n", "\n", " with torch.no_grad():\n", " start_scores, end_scores = model(input_ids=input_ids).to_tuple()\n", "\n", " start_score, end_score = get_best_valid_start_end_idx(start_scores[0], end_scores[0], top_k=8, max_size=16)\n", "\n", " # Let's convert the input ids back to actual tokens \n", " all_tokens = tokenizer.convert_ids_to_tokens(encoding[\"input_ids\"][0].tolist())\n", " answer_tokens = all_tokens[start_score: end_score + 1]\n", "\n", " example[\"output\"] = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))\n", " #.replace('\"', '') # remove space prepending space token and remove unnecessary '\"'\n", "\n", " answers = expand_to_aliases(example[\"targets\"], make_sub_answers=True)\n", " predictions = expand_to_aliases([example[\"output\"]])\n", "\n", " # if there is a common element, it's a match\n", " example[\"match\"] = len(list(answers & predictions)) > 0\n", "\n", " return example\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "bh65UMh8WP2I" }, "source": [ "To begin with, we are interested in the performance of the model on short and long samples.\n", "Let's evaluate the model on tnhe `short_validation_dataset`. \n", "\n", "**Note**: This function is expected to run for *ca.* 1h if the full dataset is used ⌛" ] }, { "cell_type": "code", "metadata": { "id": "R2REN9qH4HR2", "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "cc720864436943c98de25e071a26843c", "d65d4adf92ee43b6873859c52471b3ea", "901ab7e5922e47db8f473997a2ee7fd4", "6d45edb0e3d546109ca744a3562bac3b", "03004e584c9b4db9b773b20e6fe78599", "070532e66488475d9aadf9efcb47e3ab", "a32d5c0b77834c9fbbe8510f7a57076e", "361369f3b92c442c94771ff402615deb" ] }, "outputId": "e80943bc-d77b-479c-d416-3991452c9f8a" }, "source": [ "results_short = short_validation_dataset.map(evaluate)" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cc720864436943c98de25e071a26843c", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=4159.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "aqhB3HJYdM66" }, "source": [ "Alright, let's see the result. We report all results as \"Exact Match\" which is just the ration of correctly answered questions to all questions." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fUtmgk45zhSH", "outputId": "0ab17d18-be92-416d-d4fe-4a9e414c2a8b" }, "source": [ "print(\"Exact Match (EM): {:.2f}\".format(100 * sum(results_short['match'])/len(results_short)))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Exact Match (EM): 81.29\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "CRTa6oMsd4E_" }, "source": [ "105/127 = 82.68 => pretty good 🔥. Let's take a look at the wrong examples.\n", "For this we first filter out the correct examples and then print them out." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "79d481bcbcfd43a2bb5c39eb4140743a", "876b55959a93432aa77385e4fae1e379", "e53d7c66c9604602bcc86bd778c91e09", "970e796e9a2446d89b9e97f6c746dbe7", "b5eace5e12ee426fb81af105c06f72f7", "1359df9df1ec4b91bcd949bb2b1e63ec", "0e8825648a26469cba13ed9718d7c996", "6c52d6e7d02c4999bad041d8193955a4", "ea13db5691df461c898634f2c758966f", "ba1d2ca622e148a9a0d18283dfc83cdf", "52259114148845b1ba0d23c0d8a1153c", "74da55cdcfd54ea9a5810f978037b8c5", "383cf6b55e6542658232d31b03ff628a", "1494ccad801848eab7e2077e8d6799ed", "6c99a3b5d05c4e3e8c3b33e9dfef631f", "e96056a7caa54cf8b84c87e1fa1b96b5" ] }, "id": "EnEaQ6eZd1r9", "outputId": "d0626b65-d57c-43b2-bbde-e483da4d983b" }, "source": [ "wrong_results = results_short.filter(lambda x: x['match'] is False)\n", "print(f\"\\nWrong examples: \")\n", "print_out = wrong_results.map(lambda x, i: print(f\"{i} - Output: {x['output']} - Target: {x['norm_target']}\"), with_indices=True)" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "79d481bcbcfd43a2bb5c39eb4140743a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=5.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "\n", "Wrong examples: \n", "0 - Output: Mainland - Target: shetland\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ea13db5691df461c898634f2c758966f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=778.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "0 - Output: Mainland - Target: shetland\n", "1 - Output: - Target: film making\n", "2 - Output: tartar sauce - Target: puttanesca\n", "3 - Output: collapsible - Target: baby buggy\n", "4 - Output: five - Target: 3\n", "5 - Output: Martin Austin Ruane - Target: martin ruane\n", "6 - Output: Kipps: The Story of a Simple Soul - Target: kipps\n", "7 - Output: - Target: corset\n", "8 - Output: 102 - Target: 8\n", "9 - Output: heart failure - Target: high blood pressure\n", "10 - Output: Potatoes - Target: onions\n", "11 - Output: citric acid - Target: tartaric acid\n", "12 - Output: Johnny Hooker - Target: henry gondorf\n", "13 - Output: Roald Amundsen - Target: edmond hillary\n", "14 - Output: Sebastian - Target: jasper\n", "15 - Output: George III - Target: george\n", "16 - Output: 1938 - Target: 13\n", "17 - Output: Gary Lewis - Target: gary lewis and playboys\n", "18 - Output: collapsible - Target: baby buggy\n", "19 - Output: - Target: florence henderson\n", "20 - Output: - Target: presley\n", "21 - Output: Belle Elmore - Target: his wife\n", "22 - Output: - Target: chauvinism\n", "23 - Output: in blood - Target: muscle tissue\n", "24 - Output: . The ovary (From, literally \"egg\" or \"nut\") - Target: corpus luteum\n", "25 - Output: double nelson - Target: nelson\n", "26 - Output: 11 - Target: 11 years and 302 days\n", "27 - Output: He - Target: nick berry\n", "28 - Output: Hugh Grant - Target: stephen bing\n", "29 - Output: Cliff Richard - Target: beatles\n", "30 - Output: Stuart - Target: house of dunkeld\n", "31 - Output: Mickey Murphy - Target: windy miller\n", "32 - Output: Get Well Soon - Target: steptoe and son\n", "33 - Output: cereal - Target: frosties\n", "34 - Output: Putney - Target: sw19\n", "35 - Output: pink - Target: purple\n", "36 - Output: diamond - Target: 1st\n", "37 - Output: Nicole Kidman - Target: jodie foster\n", "38 - Output: Lesley-Anne Down - Target: wayne sleep\n", "39 - Output: microcar - Target: car\n", "40 - Output: Argentina - Target: italy\n", "41 - Output: read - Target: george w bush\n", "42 - Output: 35 years - Target: 35 years of age\n", "43 - Output: Manchester - Target: birmingham\n", "44 - Output: orange - Target: green\n", "45 - Output: Hotel - Target: charlie\n", "46 - Output: 112 - Target: 20\n", "47 - Output: busby - Target: lamb\n", "48 - Output: 120 - Target: 720\n", "49 - Output: 3000 - Target: 150\n", "50 - Output: [SEP] - Target: k\n", "51 - Output: Rice pudding - Target: custard\n", "52 - Output: graves - Target: buried alive\n", "53 - Output: 110 metres hurdles - Target: long jump\n", "54 - Output: - Target: doe\n", "55 - Output: St. Clement's - Target: st martins\n", "56 - Output: 1985 - Target: 1885\n", "57 - Output: backgammon - Target: bridge\n", "58 - Output: eyelid - Target: eye\n", "59 - Output: graves - Target: buried alive\n", "60 - Output: - Target: may\n", "61 - Output: Chalice - Target: grail\n", "62 - Output: - Target: eyas\n", "63 - Output: 1963 Great Train Robbery - Target: great train robbery\n", "64 - Output: [SEP] - Target: 12\n", "65 - Output: sturgeon - Target: fish\n", "66 - Output: Robert - Target: robert robot\n", "67 - Output: Hertfordshire - Target: staffordshire\n", "68 - Output: - Target: commissioner\n", "69 - Output: carpet munching - Target: tipping velvet\n", "70 - Output: Cashew - Target: pecan\n", "71 - Output: Cyndi Lauper - Target: roy orbison\n", "72 - Output: Totopoly - Target: scoop\n", "73 - Output: [SEP] - Target: janet and john\n", "74 - Output: [SEP] - Target: honolulu\n", "75 - Output: - Target: brighton\n", "76 - Output: wife - Target: husband\n", "77 - Output: Airport - Target: armageddon\n", "78 - Output: Dyeing - Target: dye\n", "79 - Output: Money Saving - Target: finance\n", "80 - Output: starting the Great Fire of London - Target: starting great fire\n", "81 - Output: sacred offerings - Target: priesthood\n", "82 - Output: Brassica - Target: mustard\n", "83 - Output: transvestite - Target: transgender\n", "84 - Output: Otto - Target: hawley\n", "85 - Output: Mr. Men and Little Miss - Target: mr men books\n", "86 - Output: Martin Austin Ruane - Target: martin ruane\n", "87 - Output: Rolling Stones - Target: jumpin jack flash\n", "88 - Output: Kipps: The Story of a Simple Soul - Target: kipps\n", "89 - Output: Eddie Crane - Target: eddie\n", "90 - Output: Mensa Mensa - Target: mensa\n", "91 - Output: - Target: great pyramid of giza\n", "92 - Output: [SEP] - Target: bull nose\n", "93 - Output: - Target: first day cover\n", "94 - Output: - Target: tripe\n", "95 - Output: Atlantic Ocean - Target: canary islands\n", "96 - Output: - Target: silverfish\n", "97 - Output: sewing - Target: macrame\n", "98 - Output: - Target: paul gauguin\n", "99 - Output: - Target: abstract\n", "100 - Output: status zero - Target: not in education employment\n", "101 - Output: Peter Jackson - Target: monaghan\n", "102 - Output: Pete and Dud - Target: peter cook and dudley moore\n", "103 - Output: Prime Minister - Target: chancellor of exchequer\n", "104 - Output: Francis Dashwood - Target: hellfire club\n", "105 - Output: Janet Hodgson - Target: princess anne\n", "106 - Output: [SEP] - Target: eighteen\n", "107 - Output: Isola del Giglio - Target: giglio\n", "108 - Output: Sardinia - Target: sicily\n", "109 - Output: - Target: set square\n", "110 - Output: Jimmy Robertson - Target: frank saul\n", "111 - Output: Carry On Follow That Camel - Target: carry on nurse\n", "112 - Output: [SEP] - Target: st ives\n", "113 - Output: Teddy Sheringham - Target: norman tebbit\n", "114 - Output: Port of Tokyo - Target: yokohama\n", "115 - Output: food packaging - Target: packaging\n", "116 - Output: 12 Years a Slave - Target: argo\n", "117 - Output: - Target: loch ness monster\n", "118 - Output: tree\" and gramma \"drawing\") is a tree - Target: tree\n", "119 - Output: robe - Target: burial shroud\n", "120 - Output: - Target: idli\n", "121 - Output: Eros - Target: anteros\n", "122 - Output: Lastminute - Target: lastminute com\n", "123 - Output: human botulism - Target: botulism\n", "124 - Output: British Children's Laureate - Target: children s laureate\n", "125 - Output: lug nut - Target: lug\n", "126 - Output: firearm - Target: gun\n", "127 - Output: milligram - Target: pico\n", "128 - Output: - Target: hard\n", "129 - Output: lives - Target: social environment\n", "130 - Output: million - Target: 4 billion\n", "131 - Output: - Target: alphabetical\n", "132 - Output: - Target: highball\n", "133 - Output: airliners - Target: executive jets\n", "134 - Output: rainforest - Target: island\n", "135 - Output: bucket seats - Target: seats\n", "136 - Output: [SEP] - Target: dim sum\n", "137 - Output: Hershey's - Target: hershey\n", "138 - Output: [SEP] - Target: america\n", "139 - Output: acoustic guitar - Target: guitar\n", "140 - Output: hippie counterculture - Target: summer of love\n", "141 - Output: Polyuria - Target: diabetes\n", "142 - Output: neck - Target: face\n", "143 - Output: [SEP] - Target: third\n", "144 - Output: water - Target: two thirds\n", "145 - Output: hips - Target: buttocks\n", "146 - Output: saffron - Target: woad\n", "147 - Output: Gorilla - Target: fairtrade\n", "148 - Output: kelvin - Target: celsius\n", "149 - Output: bathtub curve - Target: bathtub\n", "150 - Output: [SEP] - Target: inertia\n", "151 - Output: fish - Target: pike\n", "152 - Output: - Target: quaver\n", "153 - Output: 6 - Target: 5\n", "154 - Output: Little Jack Horner - Target: jack\n", "155 - Output: ship - Target: on ship\n", "156 - Output: [SEP] - Target: pearl\n", "157 - Output: Video Killed The Radio Star - Target: video killed radio star by buggles\n", "158 - Output: [SEP] - Target: statue of liberty\n", "159 - Output: eclipsing - Target: eclipse\n", "160 - Output: Tom Cruise - Target: cruise\n", "161 - Output: - Target: in hills\n", "162 - Output: Fairmont Hairpin - Target: portier\n", "163 - Output: 1 May - Target: first monday in september\n", "164 - Output: - Target: robert kiprono cheruiyot\n", "165 - Output: launching remains into space - Target: first space burial\n", "166 - Output: 112 - Target: 911\n", "167 - Output: [SEP] - Target: antediluvian\n", "168 - Output: paper - Target: book\n", "169 - Output: Shall We Dance - Target: they can t take that away from me\n", "170 - Output: - Target: india\n", "171 - Output: quilts - Target: fabrics\n", "172 - Output: Lars Porsena - Target: etruscan\n", "173 - Output: Woman in Red - Target: lady in red\n", "174 - Output: Prince of Wales - Target: princess beatrice of york\n", "175 - Output: birds - Target: dinosaurs\n", "176 - Output: - Target: eminem\n", "177 - Output: 1000 - Target: 100\n", "178 - Output: The Yellow Book - Target: book of fifty drawings\n", "179 - Output: Rolling Stones - Target: jumpin jack flash\n", "180 - Output: ice cream - Target: cake\n", "181 - Output: Conjunction - Target: phrase\n", "182 - Output: 1618 or 1619 - Target: 1388\n", "183 - Output: - Target: robin van persie\n", "184 - Output: Erato - Target: euterpe\n", "185 - Output: Up for Grabs - Target: removalists\n", "186 - Output: police drama - Target: detective\n", "187 - Output: - Target: lungs\n", "188 - Output: Who Framed Roger Rabbit - Target: mrs henderson presents\n", "189 - Output: music - Target: recorder\n", "190 - Output: - Target: shearers\n", "191 - Output: filmmaking - Target: film\n", "192 - Output: blue - Target: grey\n", "193 - Output: cigarette - Target: tobacco or alcohol\n", "194 - Output: Cumberland sausage - Target: cumberland\n", "195 - Output: - Target: film making\n", "196 - Output: John F Kennedy - Target: teddy roosevelt\n", "197 - Output: hopeful expectation - Target: optimism\n", "198 - Output: Kazakhstan - Target: ethiopia\n", "199 - Output: [SEP] - Target: belfast\n", "200 - Output: Dublin - Target: london\n", "201 - Output: - Target: andorra\n", "202 - Output: coal miners - Target: coal mining\n", "203 - Output: Parson Weems - Target: george washington\n", "204 - Output: - Target: pumpkin\n", "205 - Output: [SEP] - Target: japan\n", "206 - Output: - Target: corset\n", "207 - Output: - Target: ghost\n", "208 - Output: Oscar Mayer - Target: oscar\n", "209 - Output: - Target: capitol hill\n", "210 - Output: Annals of Internal Medicine - Target: new england journal of medicine\n", "211 - Output: high-fructose corn syrup - Target: sugar\n", "212 - Output: Little Johnny Flynn - Target: little tommy stout\n", "213 - Output: Gresham's - Target: sir thomas gresham\n", "214 - Output: Passer - Target: passeridae\n", "215 - Output: mouth - Target: cheek\n", "216 - Output: - Target: vulpine\n", "217 - Output: Goodbye to Berlin - Target: cabaret\n", "218 - Output: cricket - Target: australian rules football\n", "219 - Output: Jezebel - Target: naboth\n", "220 - Output: housefly - Target: fly\n", "221 - Output: lamb - Target: rack of lamb\n", "222 - Output: 102 - Target: 8\n", "223 - Output: Cabinet Office Briefing - Target: cabinet\n", "224 - Output: ) - Target: cyprus\n", "225 - Output: 2011 - Target: 2001\n", "226 - Output: air traffic controllers - Target: air traffic control\n", "227 - Output: Chief Whip of the Government - Target: government chief whip\n", "228 - Output: ear hat - Target: hat\n", "229 - Output: Marmara - Target: black sea\n", "230 - Output: back of the neck - Target: neck\n", "231 - Output: Queen's Greatest Hits - Target: bridge over troubled water\n", "232 - Output: . - Target: mikhail baryshnikov\n", "233 - Output: 1994 - Target: 1998\n", "234 - Output: Albert Einstein - Target: bart simpson\n", "235 - Output: DMC-12 - Target: de lorean\n", "236 - Output: Dr Mopp - Target: lord belborough\n", "237 - Output: bowling legend Dick Weber, is the Ten-pin bowling - Target: ten pin bowling\n", "238 - Output: getting married - Target: marriage\n", "239 - Output: - Target: mugger\n", "240 - Output: Wombles - Target: right said fred\n", "241 - Output: [SEP] - Target: 1\n", "242 - Output: beef - Target: sesame seeds\n", "243 - Output: Sandown Park - Target: sandown\n", "244 - Output: Herbert Kitchener - Target: sir herbert kitchener\n", "245 - Output: accountant - Target: teacher\n", "246 - Output: [SEP] - Target: meerkovo\n", "247 - Output: [SEP] - Target: ormolu\n", "248 - Output: Yuvraj Singh - Target: herschelle gibbs\n", "249 - Output: steam locomotives - Target: wheel arrangements\n", "250 - Output: Port wine - Target: port\n", "251 - Output: They Shoot Horses, Don't They?, - Target: they shoot horses don t they\n", "252 - Output: St Helens R.F.C. - Target: st helens\n", "253 - Output: Puff - Target: puff magic dragon\n", "254 - Output: Mornay sauce - Target: mornay\n", "255 - Output: - Target: corinth\n", "256 - Output: sea sickness - Target: travel sickness\n", "257 - Output: Westwall - Target: hindenberg line\n", "258 - Output: Michel Roux - Target: michel albert roux\n", "259 - Output: javelin - Target: mile\n", "260 - Output: Al Gore - Target: jimmy carter\n", "261 - Output: Cato Street Conspiracy - Target: cato street\n", "262 - Output: ohm - Target: watts\n", "263 - Output: bluebottle fly - Target: bluebottle\n", "264 - Output: heart failure - Target: high blood pressure\n", "265 - Output: Herald - Target: herald of free enterprise\n", "266 - Output: - Target: m\n", "267 - Output: protein - Target: carbohydrates\n", "268 - Output: - Target: york\n", "269 - Output: Venturer - Target: explorer scouts\n", "270 - Output: blackcurrant - Target: cassis\n", "271 - Output: 9 - Target: 30\n", "272 - Output: Agnolo di Cosimo - Target: alessandro allori\n", "273 - Output: Dalgarven - Target: alloway\n", "274 - Output: throat - Target: tonsils\n", "275 - Output: USA - Target: colombia\n", "276 - Output: giraffid - Target: giraffes\n", "277 - Output: vodka - Target: tequila\n", "278 - Output: Equus - Target: amadeus\n", "279 - Output: Potatoes - Target: onions\n", "280 - Output: Hythe - Target: colchester\n", "281 - Output: Jokers - Target: death wish\n", "282 - Output: rose absolute - Target: attar\n", "283 - Output: Rab C. Nesbitt - Target: para handy\n", "284 - Output: Sue Magnier - Target: queen\n", "285 - Output: nerve - Target: nerve cell cluster\n", "286 - Output: Quahog - Target: clam\n", "287 - Output: Californium - Target: berkelium\n", "288 - Output: Nailsmith - Target: tanner\n", "289 - Output: Young Rascals - Target: wilson pickett\n", "290 - Output: motorcycle speedway - Target: speedway\n", "291 - Output: - Target: je suis un rock star\n", "292 - Output: Madame Bovary - Target: emma\n", "293 - Output: Leonard Nimoy - Target: mr spock\n", "294 - Output: eight - Target: six\n", "295 - Output: [SEP] - Target: merry widow\n", "296 - Output: Titus - Target: galba otho vitellius\n", "297 - Output: Seville - Target: florence\n", "298 - Output: Mary Ann Evans, who wrote under the pen-name George Eliot - Target: george eliot\n", "299 - Output: St. Simon - Target: saint john\n", "300 - Output: Aidan Turner - Target: robin ellis\n", "301 - Output: - Target: ian duncan smith\n", "302 - Output: poster design - Target: poster designs\n", "303 - Output: - Target: red eye gravy\n", "304 - Output: Mount Everest - Target: summit of mount everest\n", "305 - Output: Liechtenstein - Target: vatican city\n", "306 - Output: well - Target: long names\n", "307 - Output: spiral - Target: tuning fork\n", "308 - Output: - Target: brian glover\n", "309 - Output: pfennigs - Target: pfennig\n", "310 - Output: [SEP] - Target: perth\n", "311 - Output: Shellfish - Target: molluscs\n", "312 - Output: [SEP] - Target: inch\n", "313 - Output: Jake - Target: paul\n", "314 - Output: Cameroon - Target: zambia\n", "315 - Output: Nurding - Target: nurdin\n", "316 - Output: collapsible - Target: baby buggy\n", "317 - Output: architecture prizes; it is often referred to as the Nobel Prize of architecture - Target: architecture\n", "318 - Output: [SEP] - Target: hand\n", "319 - Output: The Cat in the Hat - Target: so i married axe murderer\n", "320 - Output: worm - Target: blackhead\n", "321 - Output: Carry On Follow That Camel - Target: carry on up khyber\n", "322 - Output: York Racecourse - Target: york\n", "323 - Output: [SEP] - Target: rock bye baby\n", "324 - Output: Helios - Target: apollo\n", "325 - Output: [SEP] - Target: herd\n", "326 - Output: three - Target: twelve\n", "327 - Output: rampant - Target: volant\n", "328 - Output: Keith Houchen - Target: gary mabbutt\n", "329 - Output: Just Walkin' in the Rain - Target: come on eileen\n", "330 - Output: William Pitt the Younger - Target: 3rd earl of bute\n", "331 - Output: Winnie Mae - Target: winnie mae\n", "332 - Output: citric acid - Target: tartaric acid\n", "333 - Output: Institute of Chartered Accountants - Target: law society\n", "334 - Output: Wizard - Target: hotspur\n", "335 - Output: Liechtenstein - Target: andorra\n", "336 - Output: yttria - Target: ytterby\n", "337 - Output: Royal London Hospital - Target: st bartholomew s\n", "338 - Output: Gary Puckett - Target: puckett\n", "339 - Output: Trams - Target: train\n", "340 - Output: Oxytocin - Target: endorphins\n", "341 - Output: Ouse and Trent - Target: river hull\n", "342 - Output: Surrey - Target: kent\n", "343 - Output: Bangladesh - Target: russia\n", "344 - Output: Russell Thompkins, Jr. - Target: russell thompkins\n", "345 - Output: - Target: ankle\n", "346 - Output: Norfolk Island - Target: botany bay\n", "347 - Output: Barings - Target: morgan grenfell\n", "348 - Output: - Target: eva herzigova\n", "349 - Output: Spice Girls - Target: travis\n", "350 - Output: Dad's Army - Target: j wentworth b\n", "351 - Output: Jack London - Target: london\n", "352 - Output: Kirk Douglas - Target: douglas\n", "353 - Output: Robert Duvall - Target: sutherland\n", "354 - Output: Johnny Hooker - Target: henry gondorf\n", "355 - Output: duck - Target: chicken\n", "356 - Output: Spain - Target: morocco\n", "357 - Output: high jump - Target: long jump\n", "358 - Output: ), - Target: i wanna tell you story\n", "359 - Output: Eurythmics - Target: annie lennox\n", "360 - Output: Science - Target: science and nature\n", "361 - Output: The Big Chill - Target: sleeping with enemy\n", "362 - Output: - Target: outbreak of foot and mouth disease\n", "363 - Output: Park Royal - Target: cockfosters\n", "364 - Output: Violent Storm - Target: storm\n", "365 - Output: Dennis Zager and Rick Evans - Target: zager evans\n", "366 - Output: Claude Snudge - Target: snudge\n", "367 - Output: [SEP] - Target: japan\n", "368 - Output: Cairngorms - Target: cairngorms national park\n", "369 - Output: Charlie Brown - Target: woodstock\n", "370 - Output: Hogna - Target: lycos\n", "371 - Output: Cooling - Target: carburettor\n", "372 - Output: Refrigerator - Target: fridge\n", "373 - Output: New Year - Target: december\n", "374 - Output: once every two weeks - Target: weekly\n", "375 - Output: toad - Target: frog\n", "376 - Output: Tailoring Savile Row's reputation is built on bespoke tailoring - Target: tailoring\n", "377 - Output: back of the head - Target: neck\n", "378 - Output: - Target: idaho montana and wyoming\n", "379 - Output: Napoleon - Target: adam smith\n", "380 - Output: Rocky and Bullwinkle Show - Target: dudley do right\n", "381 - Output: head - Target: head and neck\n", "382 - Output: Loyal Order of Water Buffaloes - Target: loyal order of water buffalo\n", "383 - Output: antelope - Target: buffalo deer antelope\n", "384 - Output: eight-ball - Target: nine ball\n", "385 - Output: mesosphere - Target: troposphere\n", "386 - Output: automobile doors - Target: car door\n", "387 - Output: insurance - Target: credit reporting agencies\n", "388 - Output: Mars - Target: space\n", "389 - Output: sesame seed bun - Target: two all beef patties special sauce lettuce cheese pickles onions on sesame seed bun\n", "390 - Output: Adam's Apple Martini - Target: appletini\n", "391 - Output: Roald Amundsen - Target: edmond hillary\n", "392 - Output: Sebastian - Target: jasper\n", "393 - Output: George III - Target: george\n", "394 - Output: 1938 - Target: 13\n", "395 - Output: Gary Lewis - Target: gary lewis and playboys\n", "396 - Output: collapsible - Target: baby buggy\n", "397 - Output: - Target: florence henderson\n", "398 - Output: - Target: presley\n", "399 - Output: Belle Elmore - Target: his wife\n", "400 - Output: - Target: chauvinism\n", "401 - Output: in blood - Target: muscle tissue\n", "402 - Output: . The ovary (From, literally \"egg\" or \"nut\") - Target: corpus luteum\n", "403 - Output: double nelson - Target: nelson\n", "404 - Output: five - Target: 3\n", "405 - Output: 11 - Target: 11 years and 302 days\n", "406 - Output: He - Target: nick berry\n", "407 - Output: Hugh Grant - Target: stephen bing\n", "408 - Output: Stuart - Target: house of dunkeld\n", "409 - Output: [SEP] - Target: earl and countess of wessex\n", "410 - Output: Mickey Murphy - Target: windy miller\n", "411 - Output: Get Well Soon - Target: steptoe and son\n", "412 - Output: cereal - Target: frosties\n", "413 - Output: Putney - Target: sw19\n", "414 - Output: pink - Target: purple\n", "415 - Output: diamond - Target: 1st\n", "416 - Output: Nicole Kidman - Target: jodie foster\n", "417 - Output: Lesley-Anne Down - Target: wayne sleep\n", "418 - Output: microcar - Target: car\n", "419 - Output: read - Target: george w bush\n", "420 - Output: 35 years - Target: 35 years of age\n", "421 - Output: Manchester - Target: birmingham\n", "422 - Output: orange - Target: green\n", "423 - Output: Hotel - Target: charlie\n", "424 - Output: 112 - Target: 20\n", "425 - Output: busby - Target: lamb\n", "426 - Output: 120 - Target: 720\n", "427 - Output: - Target: scapula shoulder blade\n", "428 - Output: [SEP] - Target: k\n", "429 - Output: Rice pudding - Target: custard\n", "430 - Output: graves - Target: buried alive\n", "431 - Output: 110 metres hurdles - Target: long jump\n", "432 - Output: - Target: doe\n", "433 - Output: St. Clement's - Target: st martins\n", "434 - Output: 1985 - Target: 1885\n", "435 - Output: backgammon - Target: bridge\n", "436 - Output: eyelid - Target: eye\n", "437 - Output: graves - Target: buried alive\n", "438 - Output: - Target: may\n", "439 - Output: Chalice - Target: grail\n", "440 - Output: - Target: eyas\n", "441 - Output: 1963 Great Train Robbery - Target: great train robbery\n", "442 - Output: [SEP] - Target: 12\n", "443 - Output: sturgeon - Target: fish\n", "444 - Output: Robert - Target: robert robot\n", "445 - Output: Hertfordshire - Target: staffordshire\n", "446 - Output: - Target: commissioner\n", "447 - Output: carpet munching - Target: tipping velvet\n", "448 - Output: Cashew - Target: pecan\n", "449 - Output: Cyndi Lauper - Target: roy orbison\n", "450 - Output: Totopoly - Target: scoop\n", "451 - Output: [SEP] - Target: mohs scale of hardness\n", "452 - Output: [SEP] - Target: janet and john\n", "453 - Output: [SEP] - Target: honolulu\n", "454 - Output: - Target: brighton\n", "455 - Output: wife - Target: husband\n", "456 - Output: Airport - Target: armageddon\n", "457 - Output: Money Saving - Target: finance\n", "458 - Output: starting the Great Fire of London - Target: starting great fire\n", "459 - Output: sacred offerings - Target: priesthood\n", "460 - Output: Brassica - Target: mustard\n", "461 - Output: transvestite - Target: transgender\n", "462 - Output: Otto - Target: hawley\n", "463 - Output: Mr. Men and Little Miss - Target: mr men books\n", "464 - Output: Martin Austin Ruane - Target: martin ruane\n", "465 - Output: Rolling Stones - Target: jumpin jack flash\n", "466 - Output: Kipps: The Story of a Simple Soul - Target: kipps\n", "467 - Output: Eddie Crane - Target: eddie\n", "468 - Output: Mensa Mensa - Target: mensa\n", "469 - Output: - Target: great pyramid of giza\n", "470 - Output: [SEP] - Target: bull nose\n", "471 - Output: - Target: first day cover\n", "472 - Output: - Target: tripe\n", "473 - Output: [SEP] - Target: shrub\n", "474 - Output: Atlantic Ocean - Target: canary islands\n", "475 - Output: - Target: silverfish\n", "476 - Output: sewing - Target: macrame\n", "477 - Output: - Target: paul gauguin\n", "478 - Output: - Target: abstract\n", "479 - Output: status zero - Target: not in education employment\n", "480 - Output: Ted Turner - Target: carl sagan\n", "481 - Output: Peter Jackson - Target: monaghan\n", "482 - Output: Pete and Dud - Target: peter cook and dudley moore\n", "483 - Output: Prime Minister - Target: chancellor of exchequer\n", "484 - Output: Francis Dashwood - Target: hellfire club\n", "485 - Output: Janet Hodgson - Target: princess anne\n", "486 - Output: Isola del Giglio - Target: giglio\n", "487 - Output: Sardinia - Target: sicily\n", "488 - Output: - Target: set square\n", "489 - Output: Jimmy Robertson - Target: frank saul\n", "490 - Output: Carry On Follow That Camel - Target: carry on nurse\n", "491 - Output: [SEP] - Target: st ives\n", "492 - Output: Teddy Sheringham - Target: norman tebbit\n", "493 - Output: Port of Tokyo - Target: yokohama\n", "494 - Output: food packaging - Target: packaging\n", "495 - Output: 12 Years a Slave - Target: argo\n", "496 - Output: - Target: loch ness monster\n", "497 - Output: tree\" and gramma \"drawing\") is a tree - Target: tree\n", "498 - Output: robe - Target: burial shroud\n", "499 - Output: - Target: idli\n", "500 - Output: Eros - Target: anteros\n", "501 - Output: human botulism - Target: botulism\n", "502 - Output: British Children's Laureate - Target: children s laureate\n", "503 - Output: lug nut - Target: lug\n", "504 - Output: firearm - Target: gun\n", "505 - Output: milligram - Target: pico\n", "506 - Output: - Target: hard\n", "507 - Output: lives - Target: social environment\n", "508 - Output: million - Target: 4 billion\n", "509 - Output: [SEP] - Target: lava\n", "510 - Output: - Target: alphabetical\n", "511 - Output: - Target: highball\n", "512 - Output: airliners - Target: executive jets\n", "513 - Output: rainforest - Target: island\n", "514 - Output: bucket seats - Target: seats\n", "515 - Output: [SEP] - Target: dim sum\n", "516 - Output: Champions League - Target: european footballer of year\n", "517 - Output: Hershey's - Target: hershey\n", "518 - Output: [SEP] - Target: america\n", "519 - Output: acoustic guitar - Target: guitar\n", "520 - Output: hippie counterculture - Target: summer of love\n", "521 - Output: Polyuria - Target: diabetes\n", "522 - Output: USA - Target: canada\n", "523 - Output: neck - Target: face\n", "524 - Output: [SEP] - Target: third\n", "525 - Output: water - Target: two thirds\n", "526 - Output: hips - Target: buttocks\n", "527 - Output: saffron - Target: woad\n", "528 - Output: Gorilla - Target: fairtrade\n", "529 - Output: kelvin - Target: celsius\n", "530 - Output: bathtub curve - Target: bathtub\n", "531 - Output: [SEP] - Target: inertia\n", "532 - Output: fish - Target: pike\n", "533 - Output: - Target: quaver\n", "534 - Output: 6 - Target: 5\n", "535 - Output: Little Jack Horner - Target: jack\n", "536 - Output: ship - Target: on ship\n", "537 - Output: [SEP] - Target: pearl\n", "538 - Output: Video Killed The Radio Star - Target: video killed radio star by buggles\n", "539 - Output: [SEP] - Target: elijah\n", "540 - Output: [SEP] - Target: statue of liberty\n", "541 - Output: eclipsing - Target: eclipse\n", "542 - Output: Tom Cruise - Target: cruise\n", "543 - Output: - Target: in hills\n", "544 - Output: Fairmont Hairpin - Target: portier\n", "545 - Output: 1 May - Target: first monday in september\n", "546 - Output: - Target: robert kiprono cheruiyot\n", "547 - Output: launching remains into space - Target: first space burial\n", "548 - Output: gymnastics - Target: boxing\n", "549 - Output: Queen Mary - Target: freedom of seas\n", "550 - Output: 112 - Target: 911\n", "551 - Output: [SEP] - Target: antediluvian\n", "552 - Output: paper - Target: book\n", "553 - Output: Shall We Dance - Target: they can t take that away from me\n", "554 - Output: - Target: india\n", "555 - Output: quilts - Target: fabrics\n", "556 - Output: Lars Porsena - Target: etruscan\n", "557 - Output: Woman in Red - Target: lady in red\n", "558 - Output: Prince of Wales - Target: princess beatrice of york\n", "559 - Output: birds - Target: dinosaurs\n", "560 - Output: - Target: eminem\n", "561 - Output: 1000 - Target: 100\n", "562 - Output: The Yellow Book - Target: book of fifty drawings\n", "563 - Output: Rolling Stones - Target: jumpin jack flash\n", "564 - Output: ice cream - Target: cake\n", "565 - Output: Conjunction - Target: phrase\n", "566 - Output: 1618 or 1619 - Target: 1388\n", "567 - Output: - Target: robin van persie\n", "568 - Output: Erato - Target: euterpe\n", "569 - Output: Up for Grabs - Target: removalists\n", "570 - Output: police drama - Target: detective\n", "571 - Output: - Target: lungs\n", "572 - Output: Who Framed Roger Rabbit - Target: mrs henderson presents\n", "573 - Output: It - Target: tide\n", "574 - Output: music - Target: recorder\n", "575 - Output: - Target: shearers\n", "576 - Output: filmmaking - Target: film\n", "577 - Output: blue - Target: grey\n", "578 - Output: cigarette - Target: tobacco or alcohol\n", "579 - Output: Cumberland sausage - Target: cumberland\n", "580 - Output: John F Kennedy - Target: teddy roosevelt\n", "581 - Output: hopeful expectation - Target: optimism\n", "582 - Output: Kazakhstan - Target: ethiopia\n", "583 - Output: [SEP] - Target: belfast\n", "584 - Output: Dublin - Target: london\n", "585 - Output: - Target: andorra\n", "586 - Output: coal miners - Target: coal mining\n", "587 - Output: - Target: pumpkin\n", "588 - Output: [SEP] - Target: japan\n", "589 - Output: - Target: corset\n", "590 - Output: - Target: ghost\n", "591 - Output: Oscar Mayer - Target: oscar\n", "592 - Output: - Target: capitol hill\n", "593 - Output: Annals of Internal Medicine - Target: new england journal of medicine\n", "594 - Output: high-fructose corn syrup - Target: sugar\n", "595 - Output: Little Johnny Flynn - Target: little tommy stout\n", "596 - Output: Gresham's - Target: sir thomas gresham\n", "597 - Output: Passer - Target: passeridae\n", "598 - Output: mouth - Target: cheek\n", "599 - Output: - Target: vulpine\n", "600 - Output: [SEP] - Target: mexico\n", "601 - Output: Goodbye to Berlin - Target: cabaret\n", "602 - Output: cricket - Target: australian rules football\n", "603 - Output: Jezebel - Target: naboth\n", "604 - Output: housefly - Target: fly\n", "605 - Output: lamb - Target: rack of lamb\n", "606 - Output: 102 - Target: 8\n", "607 - Output: Cabinet Office Briefing - Target: cabinet\n", "608 - Output: ) - Target: cyprus\n", "609 - Output: 2011 - Target: 2001\n", "610 - Output: air traffic controllers - Target: air traffic control\n", "611 - Output: Chief Whip of the Government - Target: government chief whip\n", "612 - Output: ear hat - Target: hat\n", "613 - Output: Marmara - Target: black sea\n", "614 - Output: back of the neck - Target: neck\n", "615 - Output: Queen's Greatest Hits - Target: bridge over troubled water\n", "616 - Output: . - Target: mikhail baryshnikov\n", "617 - Output: 1994 - Target: 1998\n", "618 - Output: DMC-12 - Target: de lorean\n", "619 - Output: Dr Mopp - Target: lord belborough\n", "620 - Output: bowling legend Dick Weber, is the Ten-pin bowling - Target: ten pin bowling\n", "621 - Output: Britannia - Target: ship\n", "622 - Output: Conspicuous Gallantry Cross - Target: conspicuous gallantry\n", "623 - Output: - Target: mugger\n", "624 - Output: Wombles - Target: right said fred\n", "625 - Output: [SEP] - Target: 1\n", "626 - Output: beef - Target: sesame seeds\n", "627 - Output: Sandown Park - Target: sandown\n", "628 - Output: Herbert Kitchener - Target: sir herbert kitchener\n", "629 - Output: accountant - Target: teacher\n", "630 - Output: [SEP] - Target: meerkovo\n", "631 - Output: [SEP] - Target: ormolu\n", "632 - Output: Yuvraj Singh - Target: herschelle gibbs\n", "633 - Output: steam locomotives - Target: wheel arrangements\n", "634 - Output: Port wine - Target: port\n", "635 - Output: They Shoot Horses, Don't They?, - Target: they shoot horses don t they\n", "636 - Output: St Helens R.F.C. - Target: st helens\n", "637 - Output: Puff - Target: puff magic dragon\n", "638 - Output: Mornay sauce - Target: mornay\n", "639 - Output: - Target: corinth\n", "640 - Output: sea sickness - Target: travel sickness\n", "641 - Output: Westwall - Target: hindenberg line\n", "642 - Output: Michel Roux - Target: michel albert roux\n", "643 - Output: javelin - Target: mile\n", "644 - Output: Al Gore - Target: jimmy carter\n", "645 - Output: Cato Street Conspiracy - Target: cato street\n", "646 - Output: ohm - Target: watts\n", "647 - Output: bluebottle fly - Target: bluebottle\n", "648 - Output: heart failure - Target: high blood pressure\n", "649 - Output: eyes - Target: being stared at\n", "650 - Output: Herald - Target: herald of free enterprise\n", "651 - Output: - Target: m\n", "652 - Output: protein - Target: carbohydrates\n", "653 - Output: - Target: york\n", "654 - Output: blackcurrant - Target: cassis\n", "655 - Output: Agnolo di Cosimo - Target: alessandro allori\n", "656 - Output: Dalgarven - Target: alloway\n", "657 - Output: throat - Target: tonsils\n", "658 - Output: USA - Target: colombia\n", "659 - Output: giraffid - Target: giraffes\n", "660 - Output: vodka - Target: tequila\n", "661 - Output: Equus - Target: amadeus\n", "662 - Output: Potatoes - Target: onions\n", "663 - Output: Hythe - Target: colchester\n", "664 - Output: Jokers - Target: death wish\n", "665 - Output: rose absolute - Target: attar\n", "666 - Output: Rab C. Nesbitt - Target: para handy\n", "667 - Output: Sue Magnier - Target: queen\n", "668 - Output: nerve - Target: nerve cell cluster\n", "669 - Output: Quahog - Target: clam\n", "670 - Output: Californium - Target: berkelium\n", "671 - Output: Nailsmith - Target: tanner\n", "672 - Output: Young Rascals - Target: wilson pickett\n", "673 - Output: motorcycle speedway - Target: speedway\n", "674 - Output: - Target: je suis un rock star\n", "675 - Output: Madame Bovary - Target: emma\n", "676 - Output: Leonard Nimoy - Target: mr spock\n", "677 - Output: 8 - Target: six\n", "678 - Output: [SEP] - Target: merry widow\n", "679 - Output: Titus - Target: galba otho vitellius\n", "680 - Output: Seville - Target: florence\n", "681 - Output: nuts - Target: chocolate\n", "682 - Output: Mary Ann Evans, who wrote under the pen-name George Eliot - Target: george eliot\n", "683 - Output: Aidan Turner - Target: robin ellis\n", "684 - Output: - Target: ian duncan smith\n", "685 - Output: poster design - Target: poster designs\n", "686 - Output: - Target: red eye gravy\n", "687 - Output: Mount Everest - Target: summit of mount everest\n", "688 - Output: Liechtenstein - Target: vatican city\n", "689 - Output: well - Target: long names\n", "690 - Output: spiral - Target: tuning fork\n", "691 - Output: - Target: brian glover\n", "692 - Output: pfennigs - Target: pfennig\n", "693 - Output: [SEP] - Target: perth\n", "694 - Output: Shellfish - Target: molluscs\n", "695 - Output: St Anne - Target: oh god our help in ages past\n", "696 - Output: - Target: only thing we have to fear is fear itself\n", "697 - Output: [SEP] - Target: inch\n", "698 - Output: Jake - Target: paul\n", "699 - Output: Cameroon - Target: zambia\n", "700 - Output: Nurding - Target: nurdin\n", "701 - Output: collapsible - Target: baby buggy\n", "702 - Output: architecture prizes; it is often referred to as the Nobel Prize of architecture - Target: architecture\n", "703 - Output: [SEP] - Target: hand\n", "704 - Output: Loch Tay - Target: loch ness\n", "705 - Output: grapefruit - Target: orange\n", "706 - Output: The Cat in the Hat - Target: so i married axe murderer\n", "707 - Output: worm - Target: blackhead\n", "708 - Output: blood relatives - Target: related by blood\n", "709 - Output: Carry On Follow That Camel - Target: carry on up khyber\n", "710 - Output: York Racecourse - Target: york\n", "711 - Output: [SEP] - Target: rock bye baby\n", "712 - Output: Helios - Target: apollo\n", "713 - Output: [SEP] - Target: herd\n", "714 - Output: three - Target: twelve\n", "715 - Output: Asher - Target: barretts\n", "716 - Output: Keith Houchen - Target: gary mabbutt\n", "717 - Output: Just Walkin' in the Rain - Target: come on eileen\n", "718 - Output: William Pitt the Younger - Target: 3rd earl of bute\n", "719 - Output: Winnie Mae - Target: winnie mae\n", "720 - Output: citric acid - Target: tartaric acid\n", "721 - Output: Institute of Chartered Accountants - Target: law society\n", "722 - Output: Wizard - Target: hotspur\n", "723 - Output: Liechtenstein - Target: andorra\n", "724 - Output: yttria - Target: ytterby\n", "725 - Output: Royal London Hospital - Target: st bartholomew s\n", "726 - Output: Gary Puckett - Target: puckett\n", "727 - Output: Ouse and Trent - Target: river hull\n", "728 - Output: Surrey - Target: kent\n", "729 - Output: Bangladesh - Target: russia\n", "730 - Output: Russell Thompkins, Jr. - Target: russell thompkins\n", "731 - Output: - Target: ankle\n", "732 - Output: Norfolk Island - Target: botany bay\n", "733 - Output: Barings - Target: morgan grenfell\n", "734 - Output: - Target: eva herzigova\n", "735 - Output: Spice Girls - Target: travis\n", "736 - Output: Jack London - Target: london\n", "737 - Output: Kirk Douglas - Target: douglas\n", "738 - Output: Robert Duvall - Target: sutherland\n", "739 - Output: Johnny Hooker - Target: henry gondorf\n", "740 - Output: duck - Target: chicken\n", "741 - Output: Cat Stevens - Target: p p arnold\n", "742 - Output: Spain - Target: morocco\n", "743 - Output: high jump - Target: long jump\n", "744 - Output: ), - Target: i wanna tell you story\n", "745 - Output: Eurythmics - Target: annie lennox\n", "746 - Output: Science - Target: science and nature\n", "747 - Output: Ave Maria - Target: memory\n", "748 - Output: The Big Chill - Target: sleeping with enemy\n", "749 - Output: - Target: outbreak of foot and mouth disease\n", "750 - Output: Park Royal - Target: cockfosters\n", "751 - Output: Violent Storm - Target: storm\n", "752 - Output: Dennis Zager and Rick Evans - Target: zager evans\n", "753 - Output: Claude Snudge - Target: snudge\n", "754 - Output: [SEP] - Target: japan\n", "755 - Output: Cairngorms - Target: cairngorms national park\n", "756 - Output: Charlie Brown - Target: woodstock\n", "757 - Output: Hogna - Target: lycos\n", "758 - Output: Cooling - Target: carburettor\n", "759 - Output: Refrigerator - Target: fridge\n", "760 - Output: New Year - Target: december\n", "761 - Output: once every two weeks - Target: weekly\n", "762 - Output: toad - Target: frog\n", "763 - Output: Tailoring Savile Row's reputation is built on bespoke tailoring - Target: tailoring\n", "764 - Output: back of the head - Target: neck\n", "765 - Output: Vitamin D - Target: calcium\n", "766 - Output: - Target: idaho montana and wyoming\n", "767 - Output: Napoleon - Target: adam smith\n", "768 - Output: Rocky and Bullwinkle Show - Target: dudley do right\n", "769 - Output: head - Target: head and neck\n", "770 - Output: Loyal Order of Water Buffaloes - Target: loyal order of water buffalo\n", "771 - Output: antelope - Target: buffalo deer antelope\n", "772 - Output: eight-ball - Target: nine ball\n", "773 - Output: mesosphere - Target: troposphere\n", "774 - Output: Ferrari - Target: 55\n", "775 - Output: Mars - Target: space\n", "776 - Output: sesame seed bun - Target: two all beef patties special sauce lettuce cheese pickles onions on sesame seed bun\n", "777 - Output: Adam's Apple Martini - Target: appletini\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "GWbgnWdXeIuh" }, "source": [ "Ok! Except for the wrong answer no. 15: *George III* vs. *george*, where the model arguably gave a correct answer, incorrectly rated model predictions are in fact very much wrong." ] }, { "cell_type": "markdown", "metadata": { "id": "ULyIPLZIbtIP" }, "source": [ "We also want to evaluate the `BigBird` on the full validation set. Let's run it.\n", "\n", "**Note**: This function is expected to run for ca. 4h if the full dataset is used ⌛⌛⌛⌛. In this case, you should make sure that your training doesn't stop due to inactivity. A simple hack to prevent this is to paste the following code into the console of this tab (right mouse click -> inspect -> Console tab and insert code)." ] }, { "cell_type": "markdown", "metadata": { "id": "53DdowTejHTz" }, "source": [ "```javascript\n", "function ConnectButton(){\n", " console.log(\"Connect pushed\"); \n", " document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n", "}\n", "setInterval(ConnectButton,60000);\n", "```" ] }, { "cell_type": "code", "metadata": { "id": "jWNtljj3Wr9h", "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "23ec81dff9674abf9804afc23bfbdde5", "c09af03f6d4e44c58ec499865001cc57", "c7b3a9abcc9e4016ab78a0395f99b9bf", "527da5d0fbac4cf98b9b7e63383ff8f5", "27841f3ff58241908d990b85574549e8", "4014c978e2aa49a7be79e66543cda2b6", "090bc0c0306446c5ad1afb6cd7407ca8", "b538cdad607e4c79804663874ed831ae" ] }, "outputId": "bb4e4845-6154-487c-e551-8eaa09ffbfbe" }, "source": [ "results = validation_dataset.map(evaluate)" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "23ec81dff9674abf9804afc23bfbdde5", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=16504.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "FqcT8AEGezGx" }, "source": [ "Finally, let's print out the score." ] }, { "cell_type": "code", "metadata": { "id": "wyDYG4YDXFV7", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "1c803380-8a40-45ea-b810-de21895ccf3f" }, "source": [ "print(\"Exact Match (EM): {:.2f}\".format(100 * sum(results['match'])/len(results)))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Exact Match (EM): 65.78\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "e80MqFyP4JIj" }, "source": [ "Here, we now see a degradation, which is expected since the added data samples all have a longer context than the model can handle. It is still a very good score though.\n", "\n", "**Note**: *BigBird* reached a new SOTA on TriviaQA - see Table 2 in [paper](https://arxiv.org/pdf/2007.14062.pdf). Running the notebook on the full validation set should give you an Exact Match score (EM) of **65.78**. \n", "This score is significantly lower than the reported EM of **75.77** and F-score of **79.5** because the [official evaluation script](https://github.com/tensorflow/models/tree/f540d472d5cdaa701615e7ceb138ef66f3da2770/official/nlp/projects/triviaqa) contain more improvements, such as splitting long contexts, etc..." ] }, { "cell_type": "markdown", "metadata": { "id": "suv6-Rb5Xe9X" }, "source": [ "🤗 🤗 **Finish** 🤗🤗" ] } ] } ================================================ FILE: Fine_Tune_MMS_on_Common_Voice.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "accelerator": "GPU", "colab": { "provenance": [], "machine_shape": "hm", "gpuType": "A100", "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "dc1fcf154e644222bbb1f2868d08272c": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [ "IPY_MODEL_86f3a81ec04747f6af24e22cd94c525c", "IPY_MODEL_80bc662cbd1548e6ab60d581d66d2ce3", "IPY_MODEL_75a0a961aba74754b5826e7d3659fa74", "IPY_MODEL_972a4b32a2ea4d6e82c10eaf8ad96c6e", "IPY_MODEL_ffceedd4d73f4ab1bcb8d0a07fcd55f5" ], "layout": "IPY_MODEL_7a736546b5f647e69734209de14c12f3" } }, "86f3a81ec04747f6af24e22cd94c525c": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_00f1f3e91d4047d5becbac73949d7b11", "placeholder": "​", "style": "IPY_MODEL_d91e490b4de24f1c9d613eec0595b475", "value": "

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Adapter Models for Multi-Lingual ASR**\n", "\n", "***New (06/2023)***: *This blog post is strongly inspired by \"Fine-tuning XLS-R on Multi-Lingual ASR\" https://huggingface.co/blog/fine-tune-xlsr-wav2vec2* and can be seen as an improved version of it." ] }, { "cell_type": "markdown", "metadata": { "id": "V7YOT2mnUiea" }, "source": [ "**Wav2Vec2** is a pretrained model for Automatic Speech Recognition (ASR) and was released in [September 2020](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by *Alexei Baevski, Michael Auli, and Alex Conneau*. Soon after the superior performance of Wav2Vec2 was demonstrated on one of the most popular English datasets for ASR, called [LibriSpeech](https://huggingface.co/datasets/librispeech_asr), *Facebook AI* presented a multi-lingual version of Wav2Vec2, called [XLSR](https://arxiv.org/abs/2006.13979) and [XLS-R](https://ai.facebook.com/blog/-xlm-r-state-of-the-art-cross-lingual-understanding-through-self-supervision/). XLSR stands for *cross-lingual speech representations* and refers to model's ability to learn speech representations that are useful across multiple languages.\n", "\n", "MetaAI's most recent release, the [**Massive Multilingual Speech (MMS)**](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) by *Vineel Pratap, Andros Tjandra, Bowen Shi, et al.* takes multi-lingual speech representations to a new level. Over 1000 spoken languages can be identified, transcribed and generated with the [ASR, LID and TTS checkpoints that were released](https://huggingface.co/models?other=mms).\n", "\n", "In this blog post, we show how MMS's Adapter training achieves astonishingly low word error rates after just 10-20 minutes of fine-tuning.\n", "\n", "For low-resource languages, we **strongly** recommend using MMS' Adapter training as opposed to fine-tuning the whole model as is done in [\"Fine-tuning XLS-R on Multi-Lingual ASR\"](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2).\n", "\n", "In our experiments, MMS' Adapter training is both more memory efficient, more robust and yields better performance for low-resource languages. For medium to high resource languages it can still be advantegous to fine-tune the whole checkpoint instead of using Adapter layers though.\n", "\n", "![wav2vec2_structure](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/mms_map.png)\n" ] }, { "cell_type": "markdown", "source": [ "## **Preserving the world's language diversity**\n", "\n", "According to https://www.ethnologue.com/ around 3000, or 40% of all \"living\" languages, are endangered due to fewer and fewer native speakers.\n", "This trend will only continue in an increasingly globalized world.\n", "\n", "**MMS** is capable of transcribing many languages which are endangered, such as *Ari* or *Kaivi*. In the future, MMS can play a vital role in keeping languages alive by helping the remaining speakers to create written records and communicating in their native tongue.\n", "\n", "To adapt to 1000+ different vocabularies, **MMS** uses of Adapters - a training method where only a small fraction of model weights are trained.\n", "\n", "Adapter layers act like linguistic bridges, enabling the model to leverage knowledge from one language when deciphering another.\n", "\n", "## **Fine-tuning MMS**\n", "\n", "**MMS** unsupervised checkpoints were pre-trained on more than **half a million** hour of audio in over **1400** languages ranging from 300 million to one billion parameters.\n", "\n", "You can find the pretrained-only checkpoints on the 🤗 Hub:\n", "\n", "- [**`mms-300m`**](https://huggingface.co/facebook/mms-300m)\n", "- [**`mms-1b`**](https://huggingface.co/facebook/mms-1b)\n", "\n", "Similar to [BERT's masked language modeling objective](http://jalammar.github.io/illustrated-bert/), MMS learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network during self-supervised pre-training (*i.e.* diagram on the left below).\n", "\n", "For Automatic Speech Recognitino (ASR), the pretrained `MMS-1B` checkpoint was further fine-tuned in supervised fasion on 1000+ languages with a joint vocabulary output layer. As a final step, the joint vocabulary output layer was then thrown away and **only** ca. 2.5M adapter weights are trained on specific languages. The adapter weights hereby include small linear projection layers for each attention block as well as a language-specific vocabulary output layer.\n", "\n", "**MMS**'s released three checkpoints fine-tuned for speech recognition (ASR) that have 102, 1107, and 1162 adapter weights respectively (one for each language):\n", "\n", "- [**`mms-1b-fl102`**](https://huggingface.co/facebook/mms-1b-fl102)\n", "- [**`mms-1b-l1107`**](https://huggingface.co/facebook/mms-1b-1107)\n", "- [**`mms-1b-all`**](https://huggingface.co/facebook/mms-1b-all)\n", "\n", "You can see that the base models are saved (as usual) as a [`model.safetensors` file](https://huggingface.co/facebook/mms-1b-all/blob/main/model.safetensors), but in addition these repositories have many adapter weights stored in the repository, *e.g.* under the name [`adapter.fra.safetensors`](https://huggingface.co/facebook/mms-1b-all/blob/main/adapter.fra.safetensors) for French.\n", "\n", "The Hugging Face docs explain very well how such checkpoints can be used for inference [here](https://huggingface.co/docs/transformers/main/en/model_doc/mms#loading), so in this blog post we will instead focus on learning how we can efficiently train highly performant adapter models based on any of the released ASR checkpoints." ], "metadata": { "id": "VWe4pIR8lRrg" } }, { "cell_type": "markdown", "source": [ "## Training adaptive weights\n", "\n", "In machine learning, [adapters](https://arxiv.org/pdf/1902.00751.pdf) are a method used to fine-tune pre-trained models while keeping the original model parameters unchanged. They do this by inserting small, trainable modules, called adapter layers, between the pre-existing layers of the model, which then adapt the model to a specific task without requiring extensive retraining.\n", "\n", "Adapters have a long history in speech recognition and especially **speaker recognition**. In speaker recognition, adapters have been effectively used to tweak pre-existing models to recognize individual speaker idiosyncrasies, as highlighted in [Gales and Woodland's (1996)](https://www.isca-speech.org/archive_v0/archive_papers/icslp_1996/i96_1832.pdf) and [Miao et al.'s (2014)](https://www.cs.cmu.edu/~ymiao/pub/tasl_sat.pdf) work. This approach not only greatly reduces computational requirements compared to full model, but also allows for better and more flexible speaker-specific adjustments.\n", "\n", "The work done in **MMS** leverages this idea of adapters for speech recognition across different languages. Adapter weights a fine-tuned to grasp unique phonetic and grammatical traits of each target language. Thereby, MMS enables a single large base model (*e.g.*, the [**`mms-1b-all`**](https://huggingface.co/facebook/mms-1b-all) checkpoint) and 1000+ small adapter layers (2.5M weights each for **`mms-1b-all`**) to comprehend and transcribe multiple languages. This dramatically reduces the computational demand of developing distinct models for each language.\n", "\n", "Great! Now that we understood the motivation and theory, let's look into fine-tuning a couple adapter weights for **`mms-1b-all`** 🔥" ], "metadata": { "id": "miNvI1EdCbtf" } }, { "cell_type": "markdown", "metadata": { "id": "nT_QrfWtsxIz" }, "source": [ "## Notebook Setup" ] }, { "cell_type": "markdown", "metadata": { "id": "kruqixOYHaIo" }, "source": [ "As done previously in the [\"Fine-tuning XLS-R on Multi-Lingual ASR\"](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) blog post, we fine-tune the model on the low resource ASR dataset of [Common Voice](https://huggingface.co/datasets/common_voice) that contains only *ca.* 4h of validated training data." ] }, { "cell_type": "markdown", "metadata": { "id": "Gx9OdDYrCtQ1" }, "source": [ "Just like Wav2Vec2 or XLS-R, MMS is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems, such as ASR and handwriting recognition.\n", "\n", "I highly recommend reading the well-written blog post [*Sequence Modeling with CTC (2017)*](https://distill.pub/2017/ctc/) by Awni Hannun." ] }, { "cell_type": "markdown", "metadata": { "id": "wcHuXIaWyHZU" }, "source": [ "First, let's try to get a good GPU in our colab! With Google Colab's free version it's sadly becoming much harder to get access to a good GPU. With Google Colab Pro, however, one should easily get either a V100 or P100 GPU." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YELVqGxMxnbG", "outputId": "6889c80f-dddc-41d6-fcb2-10526bb2ef09" }, "source": [ "gpu_info = !nvidia-smi\n", "gpu_info = '\\n'.join(gpu_info)\n", "if gpu_info.find('failed') >= 0:\n", " print('Not connected to a GPU')\n", "else:\n", " print(gpu_info)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Tue Jun 20 10:37:02 2023 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 NVIDIA A100-SXM... Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 35C P0 50W / 400W | 9375MiB / 40960MiB | 0% Default |\n", "| | | Disabled |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "+-----------------------------------------------------------------------------+\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "e335hPmdtASZ" }, "source": [ "Before we start, let's install `datasets` and `transformers`. Also, we need the `torchaudio` to load audio files and `jiwer` to evaluate our fine-tuned model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric ${}^1$." ] }, { "cell_type": "code", "metadata": { "id": "c8eh87Hoee5d" }, "source": [ "%%capture\n", "!pip install datasets[audio]\n", "!pip install evaluate\n", "!pip install git+https://github.com/huggingface/transformers.git\n", "!pip install jiwer\n", "!pip install accelerate" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0xxt_LwxDQlO" }, "source": [ "We strongly suggest to upload your training checkpoints directly to the [🤗 Hub](https://huggingface.co/) while training. The [🤗 Hub](https://huggingface.co/) has integrated version control so you can be sure that no model checkpoint is getting lost during training.\n", "\n", "To do so you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!)" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 303, "referenced_widgets": [ "dc1fcf154e644222bbb1f2868d08272c", "86f3a81ec04747f6af24e22cd94c525c", "80bc662cbd1548e6ab60d581d66d2ce3", "75a0a961aba74754b5826e7d3659fa74", "972a4b32a2ea4d6e82c10eaf8ad96c6e", "ffceedd4d73f4ab1bcb8d0a07fcd55f5", "7a736546b5f647e69734209de14c12f3", "00f1f3e91d4047d5becbac73949d7b11", "d91e490b4de24f1c9d613eec0595b475", "baafc9e12fe84b7297585eddc790de0b", "448560e481c74e838ae0e03ea2e44bfa", "99d297a8de2342209757bc2f9efd67f0", "d4e7e0a2985f40b5955471511bff6cd9", "a9381691740c436fa9ed0c5cae8ce3ab", "742e31402fa145e7888a89bbc558e6c6", "d290d079190d4ef39a0c22537f7b7d98", "4465ddd01211409087923a8431d00529" ] }, "id": "mlMSH3T3EazV", "outputId": "37cc045f-4235-4dfc-c0c9-6d7b650909cd" }, "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
" ], "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sentence
0\"Anlaşma Lefkoşa'da imzalandı.\"
1Grup olarak özel ilgi görmeyi hakediyorlar.
2Makedonya ile olan ilişkileriniz ne durumda?
3Kurbanlar anısına sokaklara çiçekler bırakıldı.
4Geçen yılsa bu rakam beş yüz on milyon avro idi.
5Proje yaklaşık altı yüz yeni iş yaratacak.
6\"\"\"Yaşamak istiyorum!\"\" Rukavina'nın mektubunun ilk satırıydı.\"
7\"Kararın Kosova'daki yansımaları ne oldu?\"
8Bu bana çok sık olmaz.
9Fakat artık bu durum değişti.
" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "fowcOllGNNju" }, "source": [ "Alright! The transcriptions look fairly clean. Having translated the transcribed sentences, it seems that the language corresponds more to written-out text than noisy dialogue. This makes sense considering that [Common Voice](https://huggingface.co/datasets/common_voice) is a crowd-sourced read speech corpus." ] }, { "cell_type": "markdown", "metadata": { "id": "vq7OR50LN49m" }, "source": [ "We can see that the transcriptions contain some special characters, such as `,.?!;:`. Without a language model, it is much harder to classify speech chunks to such special characters because they don't really correspond to a characteristic sound unit. *E.g.*, the letter `\"s\"` has a more or less clear sound, whereas the special character `\".\"` does not.\n", "Also in order to understand the meaning of a speech signal, it is usually not necessary to include special characters in the transcription.\n", "\n", "Let's simply remove all characters that don't contribute to the meaning of a word and cannot really be represented by an acoustic sound and normalize the text." ] }, { "cell_type": "code", "metadata": { "id": "svKzVJ_hQGK6" }, "source": [ "import re\n", "chars_to_remove_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\']'\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n", " return batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XIHocAuTQbBR", "outputId": "9fcc53da-1473-4be4-f2f0-4ff52622af08" }, "source": [ "common_voice_train = common_voice_train.map(remove_special_characters)\n", "common_voice_test = common_voice_test.map(remove_special_characters)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice_6_1/tr/6.1.0/f4d7854c466f5bd4908988dbd39044ec4fc634d89e0515ab0c51715c0127ffe3/cache-bfc6c84ab17df069.arrow\n", "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice_6_1/tr/6.1.0/f4d7854c466f5bd4908988dbd39044ec4fc634d89e0515ab0c51715c0127ffe3/cache-1ed99f80bee30e93.arrow\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "TxnVS9gIhIma" }, "source": [ "Let's look at the processed text labels again." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "RBDRAAYxRE6n", "outputId": "75ccbdcf-3ccd-44ee-c4f5-d6ee7dd94ba0" }, "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sentence
0seçmen katılımı yüzde seksen civarında gerçekleşti
1i̇yi vakit geçirdik
2fakat belgradda herkes durumu böyle görmüyor
3en az altmış kişi hayatını kaybetti
4fikir herkes tarafından hoş karşılanmadı
5ancak zorluklar da yok değil
6polis varlığı barizdi
7i̇lk fırın altı aydır atıl durumdaydı
8bosnalı sırp polisi de destek verdi
9temyiz süreci şu anda devam ediyor
" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "jwfaptH5RJwA" }, "source": [ "Good! This looks better. We have removed most special characters from transcriptions and normalized them to lower-case only.\n", "\n", "Before finalizing the pre-processing, it is always advantageous to consult a native speaker of the target language to see whether the text can be further simplified.\n", "For this blog post, [Merve](https://twitter.com/mervenoyann) was kind enough to take a quick look and noted that \"hatted\" characters - like `â` - aren't really used anymore in Turkish and can be replaced by their \"un-hatted\" equivalent, *e.g.* `a`.\n", "\n", "This means that we should replace a sentence like `\"yargı sistemi hâlâ sağlıksız\"` to `\"yargı sistemi hala sağlıksız\"`.\n", "\n", "Let's write another short mapping function to further simplify the text labels. Remember - the simler the text labels, the easier it is for the model to learn to predict those labels.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "aZcrz6z7lgGm" }, "source": [ "def replace_hatted_characters(batch):\n", " batch[\"sentence\"] = re.sub('[â]', 'a', batch[\"sentence\"])\n", " batch[\"sentence\"] = re.sub('[î]', 'i', batch[\"sentence\"])\n", " batch[\"sentence\"] = re.sub('[ô]', 'o', batch[\"sentence\"])\n", " batch[\"sentence\"] = re.sub('[û]', 'u', batch[\"sentence\"])\n", " return batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ieGhhND5mSwI", "outputId": "401e6078-88b2-4976-fd26-d6dc4d4714e6" }, "source": [ "common_voice_train = common_voice_train.map(replace_hatted_characters)\n", "common_voice_test = common_voice_test.map(replace_hatted_characters)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice_6_1/tr/6.1.0/f4d7854c466f5bd4908988dbd39044ec4fc634d89e0515ab0c51715c0127ffe3/cache-b9fea5150b6c7ed4.arrow\n", "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice_6_1/tr/6.1.0/f4d7854c466f5bd4908988dbd39044ec4fc634d89e0515ab0c51715c0127ffe3/cache-99bbff57d42b7c68.arrow\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "3ORHDb2Th2TW" }, "source": [ "In CTC, it is common to classify speech chunks into letters, so we will do the same here.\n", "Let's extract all distinct letters of the training and test data and build our vocabulary from this set of letters.\n", "\n", "We write a mapping function that concatenates all transcriptions into one long transcription and then transforms the string into a set of chars.\n", "It is important to pass the argument `batched=True` to the `map(...)` function so that the mapping function has access to all transcriptions at once." ] }, { "cell_type": "code", "metadata": { "id": "LwCshNbbeRZR" }, "source": [ "def extract_all_chars(batch):\n", " all_text = \" \".join(batch[\"sentence\"])\n", " vocab = list(set(all_text))\n", " return {\"vocab\": [vocab], \"all_text\": [all_text]}" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "f2437e254bc54bdba99398e39270b585", "490f1214fa9f4b0fb2c64beef8d06873", "2057b17679074e6d8a323793d541f19b", "694d682671614dcc9512eacd16545a88", "bf66c808457a4d7e94f8aedfa6f34419", "d5a55e695a5145b4a3505641185da31f", "238154bf4ce344f99c4c725f6246021e", "5929a62f57f44d569df7cfc4f4cf336f", "129aed3d7c7145a68522c8fac05e7642", "c1041b33745148e2be6876182006e7a9", "1cec89b123cf45dd95734b1789e807f3", "62a74ea78b6f439f964ee1da7385b8c1", "69022d62f63e411d97495c3661c35a97", "449a942ee2564cbc812919c110bde334", "942bd2ec5de84e868741f063496370a3", "afd123eb1bee4bbca7f8fd1501d16319", "660548079daf41e69a18a68652b7bec2", "af6e3179d038470784b39c52d943665e", "f84bab301eb44087817728ff5c4d4935", "cdb3e901611c4c559f56cb1845db4a01", "397079cb4e7f453b9cd0ed6a036cfc68", "5b5c663ada5e42a6a1a4238a4c996a89" ] }, "id": "_m6uUjjcfbjH", "outputId": "b22437be-0067-449c-b0be-00bbea2216f9" }, "source": [ "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n", "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/3478 [00:00/wav2vec2-large-mms-1b-tr-colab`\n" ] }, { "cell_type": "markdown", "metadata": { "id": "mYcIiR2FQ96i" }, "source": [ "### Create `Wav2Vec2FeatureExtractor`" ] }, { "cell_type": "markdown", "metadata": { "id": "Y6mDEyW719rx" }, "source": [ "Speech is a continuous signal and to be treated by computers, it first has to be discretized, which is usually called **sampling**. The sampling rate hereby plays an important role in that it defines how many data points of the speech signal are measured per second. Therefore, sampling with a higher sampling rate results in a better approximation of the *real* speech signal but also necessitates more values per second.\n", "\n", "A pretrained checkpoint expects its input data to have been sampled more or less from the same distribution as the data it was trained on. The same speech signals sampled at two different rates have a very different distribution, *e.g.*, doubling the sampling rate results in data points being twice as long. Thus,\n", "before fine-tuning a pretrained checkpoint of an ASR model, it is crucial to verify that the sampling rate of the data that was used to pretrain the model matches the sampling rate of the dataset used to fine-tune the model.\n", "\n", "MMS was pre-trained at a sampling rate of 16kHz. Common Voice, in its original form, has a sampling rate of 48kHz, thus we will have to downsample the fine-tuning data to 16kHz in the following.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "KuUbPW7oV-B5" }, "source": [ "A `Wav2Vec2FeatureExtractor` object requires the following parameters to be instantiated:\n", "\n", "- `feature_size`: Speech models take a sequence of feature vectors as an input. While the length of this sequence obviously varies, the feature size should not. In the case of Wav2Vec2, the feature size is 1 because the model was trained on the raw speech signal ${}^2$.\n", "- `sampling_rate`: The sampling rate at which the model is trained on.\n", "- `padding_value`: For batched inference, shorter inputs need to be padded with a specific value\n", "- `do_normalize`: Whether the input should be *zero-mean-unit-variance* normalized or not. Usually, speech models perform better when normalizing the input\n", "- `return_attention_mask`: Whether the model should make use of an `attention_mask` for batched inference. In general, XLS-R models checkpoints should **always** use the `attention_mask`." ] }, { "cell_type": "code", "metadata": { "id": "kAR0-2KLkopp" }, "source": [ "from transformers import Wav2Vec2FeatureExtractor\n", "\n", "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qUETetgqYC3W" }, "source": [ "Great, MMS's feature extraction pipeline is thereby fully defined!\n", "\n", "For improved user-friendliness, the feature extractor and tokenizer are *wrapped* into a single `Wav2Vec2Processor` class so that one only needs a `model` and `processor` object." ] }, { "cell_type": "code", "metadata": { "id": "KYZtoW-tlZgl" }, "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DrKnYuvDIoOO" }, "source": [ "Next, we can prepare the dataset." ] }, { "cell_type": "markdown", "metadata": { "id": "YFmShnl7RE35" }, "source": [ "### Preprocess Data\n", "\n", "So far, we have not looked at the actual values of the speech signal but just the transcription. In addition to `sentence`, our datasets include two more column names `path` and `audio`. `path` states the absolute path of the audio file. Let's take a look.\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "TTCS7W6XJ9BG", "outputId": "03131bfa-4b5e-4563-d59d-217b1340d513" }, "source": [ "common_voice_train[0][\"path\"]" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'/root/.cache/huggingface/datasets/downloads/extracted/71ba9bd154da9d8c769b736301417178729d2b87b9e00cda59f6450f742ed778/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_17346025.mp3'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 96 } ] }, { "cell_type": "markdown", "metadata": { "id": "T6ndIjHGFp0W" }, "source": [ "MMS expects the input in the format of a 1-dimensional array of 16 kHz. This means that the audio file has to be loaded and resampled.\n", "\n", " Thankfully, `datasets` does this automatically by calling the other column `audio`. Let try it out." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qj_z5Zc3GAs9", "outputId": "0e8d38b8-f199-4788-9461-eb437e444ea9" }, "source": [ "common_voice_train[0][\"audio\"]" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'path': '/root/.cache/huggingface/datasets/downloads/extracted/71ba9bd154da9d8c769b736301417178729d2b87b9e00cda59f6450f742ed778/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_17346025.mp3',\n", " 'array': array([ 0.00000000e+00, -2.98378618e-13, -1.59835903e-13, ...,\n", " -2.01663317e-12, -1.87991593e-12, -1.17969588e-12]),\n", " 'sampling_rate': 48000}" ] }, "metadata": {}, "execution_count": 97 } ] }, { "cell_type": "markdown", "metadata": { "id": "WUUTgI1bGHW-" }, "source": [ "In the example above we can see that the audio data is loaded with a sampling rate of 48kHz whereas 16kHz are expected by the model. We can set the audio feature to the correct sampling rate by making use of [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column):" ] }, { "cell_type": "code", "metadata": { "id": "rrv65aj7G95i" }, "source": [ "common_voice_train = common_voice_train.cast_column(\"audio\", Audio(sampling_rate=16_000))\n", "common_voice_test = common_voice_test.cast_column(\"audio\", Audio(sampling_rate=16_000))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "PcnO4x-NGBEi" }, "source": [ "Let's take a look at `\"audio\"` again." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aKtkc1o_HWHC", "outputId": "657a2e48-b269-4dbc-b071-78f53f759c2b" }, "source": [ "common_voice_train[0][\"audio\"]" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'path': '/root/.cache/huggingface/datasets/downloads/extracted/71ba9bd154da9d8c769b736301417178729d2b87b9e00cda59f6450f742ed778/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_17346025.mp3',\n", " 'array': array([ 9.09494702e-13, -6.13908924e-12, -1.09139364e-11, ...,\n", " 1.81898940e-12, 4.54747351e-13, 3.63797881e-12]),\n", " 'sampling_rate': 16000}" ] }, "metadata": {}, "execution_count": 99 } ] }, { "cell_type": "markdown", "metadata": { "id": "SOckzFd4Mbzq" }, "source": [ "This seemed to have worked! Let's listen to a couple of audio files to better understand the dataset and verify that the audio was correctly loaded.\n", "\n", "**Note**: *You can click the following cell a couple of times to listen to different speech samples.*" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 93 }, "id": "dueM6U7Ev0OA", "outputId": "aab18683-295c-40bf-ae1e-8d7398779bc6" }, "source": [ "import IPython.display as ipd\n", "import numpy as np\n", "import random\n", "\n", "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(common_voice_train[rand_int][\"sentence\"])\n", "ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "fabrikanın yedi yüz kişiye iş sağlaması bekleniyor\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {}, "execution_count": 100 } ] }, { "cell_type": "markdown", "metadata": { "id": "gY8m3vARHYTa" }, "source": [ "It seems like the data is now correctly loaded and resampled." ] }, { "cell_type": "markdown", "metadata": { "id": "1MaL9J2dNVtG" }, "source": [ "It can be heard, that the speakers change along with their speaking rate, accent, and background environment, etc. Overall, the recordings sound acceptably clear though, which is to be expected from a crowd-sourced read speech corpus.\n", "\n", "Let's do a final check that the data is correctly prepared, by printing the shape of the speech input, its transcription, and the corresponding sampling rate.\n", "\n", "**Note**: *You can click the following cell a couple of times to verify multiple samples.*" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Po2g7YPuRTx", "outputId": "05dd189c-1f8c-4b34-c777-8b716452607b" }, "source": [ "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n", "print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n", "print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Target text: bu çok açıktı\n", "Input array shape: (30720,)\n", "Sampling rate: 16000\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "M9teZcSwOBJ4" }, "source": [ "Good! Everything looks fine - the data is a 1-dimensional array, the sampling rate always corresponds to 16kHz, and the target text is normalized." ] }, { "cell_type": "markdown", "metadata": { "id": "k3Pbn5WvOYZF" }, "source": [ "Finally, we can leverage `Wav2Vec2Processor` to process the data to the format expected by `Wav2Vec2ForCTC` for training. To do so let's make use of Dataset's [`map(...)`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=map#datasets.DatasetDict.map) function.\n", "\n", "First, we load and resample the audio data, simply by calling `batch[\"audio\"]`.\n", "Second, we extract the `input_values` from the loaded audio file. In our case, the `Wav2Vec2Processor` only normalizes the data. For other speech models, however, this step can include more complex feature extraction, such as [Log-Mel feature extraction](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum).\n", "Third, we encode the transcriptions to label ids." ] }, { "cell_type": "code", "metadata": { "id": "eJY7I0XAwe9p" }, "source": [ "def prepare_dataset(batch):\n", " audio = batch[\"audio\"]\n", " batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n", " batch[\"input_length\"] = len(batch[\"input_values\"])\n", "\n", " batch[\"labels\"] = processor(text=batch[\"sentence\"]).input_ids\n", " return batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "q6Pg_WR3OGAP" }, "source": [ "Let's apply the data preparation function to all examples." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-np9xYK-wl8q", "outputId": "8dae5929-659a-446f-98b4-37d6ec14f408" }, "source": [ "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names)\n", "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice_6_1/tr/6.1.0/f4d7854c466f5bd4908988dbd39044ec4fc634d89e0515ab0c51715c0127ffe3/cache-685475104fc5c53a.arrow\n", "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice_6_1/tr/6.1.0/f4d7854c466f5bd4908988dbd39044ec4fc634d89e0515ab0c51715c0127ffe3/cache-46887d370a180f5a.arrow\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "nKcEWHvKI1by" }, "source": [ "**Note**: `datasets` automatically takes care of audio loading and resampling. If you wish to implement your own costumized data loading/sampling, feel free to just make use of the `\"path\"` column instead and disregard the `\"audio\"` column." ] }, { "cell_type": "markdown", "metadata": { "id": "1ZWDCCKqwcfS" }, "source": [ "Awesome, now we are ready to start training!" ] }, { "cell_type": "markdown", "metadata": { "id": "gYlQkKVoRUos" }, "source": [ "## Training\n", "\n", "The data is processed so that we are ready to start setting up the training pipeline. We will make use of 🤗's [Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer) for which we essentially need to do the following:\n", "\n", "- Define a data collator. In contrast to most NLP models, MMS has a much larger input length than output length. *E.g.*, a sample of input length 50000 has an output length of no more than 100. Given the large input sizes, it is much more efficient to pad the training batches dynamically meaning that all training samples should only be padded to the longest sample in their batch and not the overall longest sample. Therefore, fine-tuning MMS requires a special padding data collator, which we will define below\n", "\n", "- Evaluation metric. During training, the model should be evaluated on the word error rate. We should define a `compute_metrics` function accordingly\n", "\n", "- Load a pretrained checkpoint. We need to load a pretrained checkpoint and configure it correctly for training.\n", "\n", "- Define the training configuration.\n", "\n", "After having fine-tuned the model, we will correctly evaluate it on the test data and verify that it has indeed learned to correctly transcribe speech." ] }, { "cell_type": "markdown", "metadata": { "id": "Slk403unUS91" }, "source": [ "### Set-up Trainer\n", "\n", "Let's start by defining the data collator. The code for the data collator was copied from [this example](https://github.com/huggingface/transformers/blob/7e61d56a45c19284cfda0cee8995fb552f6b1f4e/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L219).\n", "\n", "Without going into too many details, in contrast to the common data collators, this data collator treats the `input_values` and `labels` differently and thus applies to separate padding functions on them (again making use of MMS processor's context manager). This is necessary because in speech input and output are of different modalities meaning that they should not be treated by the same padding function.\n", "Analogous to the common data collators, the padding tokens in the labels with `-100` so that those tokens are **not** taken into account when computing the loss." ] }, { "cell_type": "code", "metadata": { "id": "tborvC9hx88e" }, "source": [ "import torch\n", "\n", "from dataclasses import dataclass, field\n", "from typing import Any, Dict, List, Optional, Union\n", "\n", "@dataclass\n", "class DataCollatorCTCWithPadding:\n", " \"\"\"\n", " Data collator that will dynamically pad the inputs received.\n", " Args:\n", " processor (:class:`~transformers.Wav2Vec2Processor`)\n", " The processor used for proccessing the data.\n", " padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n", " Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n", " among:\n", " * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n", " sequence if provided).\n", " * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n", " maximum acceptable input length for the model if that argument is not provided.\n", " * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n", " different lengths).\n", " \"\"\"\n", "\n", " processor: Wav2Vec2Processor\n", " padding: Union[bool, str] = True\n", "\n", " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n", " # split inputs and labels since they have to be of different lenghts and need\n", " # different padding methods\n", " input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", "\n", " batch = self.processor.pad(\n", " input_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", " labels_batch = self.processor.pad(\n", " labels=label_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " # replace padding with -100 to ignore loss correctly\n", " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", "\n", " batch[\"labels\"] = labels\n", "\n", " return batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "lbQf5GuZyQ4_" }, "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xO-Zdj-5cxXp" }, "source": [ "Next, the evaluation metric is defined. As mentioned earlier, the\n", "predominant metric in ASR is the word error rate (WER), hence we will use it in this notebook as well." ] }, { "cell_type": "code", "metadata": { "id": "9Xsux2gmyXso" }, "source": [ "from evaluate import load\n", "\n", "wer_metric = load(\"wer\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "E1qZU5p-deqB" }, "source": [ "The model will return a sequence of logit vectors:\n", "$\\mathbf{y}_1, \\ldots, \\mathbf{y}_m$ with $\\mathbf{y}_1 = f_{\\theta}(x_1, \\ldots, x_n)[0]$ and $n >> m$.\n", "\n", "A logit vector $\\mathbf{y}_1$ contains the log-odds for each word in the vocabulary we defined earlier, thus $\\text{len}(\\mathbf{y}_i) =$ `config.vocab_size`. We are interested in the most likely prediction of the model and thus take the `argmax(...)` of the logits. Also, we transform the encoded labels back to the original string by replacing `-100` with the `pad_token_id` and decoding the ids while making sure that consecutive tokens are **not** grouped to the same token in CTC style ${}^1$." ] }, { "cell_type": "code", "metadata": { "id": "1XZ-kjweyTy_" }, "source": [ "def compute_metrics(pred):\n", " pred_logits = pred.predictions\n", " pred_ids = np.argmax(pred_logits, axis=-1)\n", "\n", " pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n", "\n", " pred_str = processor.batch_decode(pred_ids)\n", " # we do not want to group tokens when computing the metrics\n", " label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n", "\n", " wer = wer_metric.compute(predictions=pred_str, references=label_str)\n", "\n", " return {\"wer\": wer}" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Xmgrx4bRwLIH" }, "source": [ "Now, we can load the pretrained checkpoint of [**`mms-all-1b`**](https://huggingface.co/facebook/mms-1b-all). The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of `Wav2Vec2ForCTC` also CTC's *blank token* ${}^2$.\n", "\n", "Since, we're only training a small subset of weights, the model is not prone to overfitting. Therefore, we make sure to disable all dropout layers.\n", "\n", "**Note**: When using this notebook to train MMS on another language of Common Voice those hyper-parameter settings might not work very well. Feel free to adapt those depending on your use case." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e7cqAWIayn6w", "outputId": "560c5566-7411-4d0a-ea58-f394c39e1f59" }, "source": [ "from transformers import Wav2Vec2ForCTC\n", "\n", "model = Wav2Vec2ForCTC.from_pretrained(\n", " \"facebook/mms-1b-all\",\n", " attention_dropout=0.0,\n", " hidden_dropout=0.0,\n", " feat_proj_dropout=0.0,\n", " layerdrop=0.0,\n", " ctc_loss_reduction=\"mean\",\n", " pad_token_id=processor.tokenizer.pad_token_id,\n", " vocab_size=len(processor.tokenizer),\n", " ignore_mismatched_sizes=True,\n", ")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/mms-1b-all and are newly initialized because the shapes did not match:\n", "- lm_head.bias: found shape torch.Size([154]) in the checkpoint and torch.Size([39]) in the model instantiated\n", "- lm_head.weight: found shape torch.Size([154, 1280]) in the checkpoint and torch.Size([39, 1280]) in the model instantiated\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "1DwR3XLSzGDD" }, "source": [ "**Note**: It is expected that some weights are newly initialized. Those weights correspond to the newly initilaized vocabulary output layer.\n", "\n", "We now want to make sure that only the adapter weights will be trained and that the rest of the model stays frozen.\n", "\n", "First, we re-initialize all the adapter weights which can be done with the handy `init_adapter_layers` method. It is also possible to not re-initilize the adapter weights and continue fine-tuning, but in this case one should make sure to load fitting adapter weights via the [`load_adapter(...)` method](https://huggingface.co/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC.load_adapter) before training. Often the vocabulary still will not match the custom training data very well though, so it's usually easier to just re-initialize all adapter layers so that they can be easily fine-tuned." ] }, { "cell_type": "code", "source": [ "model.init_adapter_layers()" ], "metadata": { "id": "3LcbD4PGP6CG" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Next, we freeze all weights, **but** the adapter layers." ], "metadata": { "id": "xl8Y-njRQgrs" } }, { "cell_type": "code", "metadata": { "id": "oGI8zObtZ3V0" }, "source": [ "model.freeze_base_model()\n", "\n", "adapter_weights = model._get_adapters()\n", "for param in adapter_weights.values():\n", " param.requires_grad = True" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "lD4aGhQM0K-D" }, "source": [ "In a final step, we define all parameters related to training.\n", "To give more explanation on some of the parameters:\n", "- `group_by_length` makes training more efficient by grouping training samples of similar input length into one batch. This can significantly speed up training time by heavily reducing the overall number of useless padding tokens that are passed through the model\n", "- `learning_rate` was chosen to be 1e-3 which is a common default value for training with Adam. Other learning rates might work equally well.\n", "\n", "For more explanations on other parameters, one can take a look at the [docs](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer#trainingarguments).\n", "\n", " To save GPU memory, we enable PyTorch's [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html) and also set the loss reduction to \"*mean*\".\n", "\n", " MMS adapter fine-tuning converges extremely fast to very good performance, so even for a dataset as small as 4h we will only train for 4 epochs.\n", "\n", "During training, a checkpoint will be uploaded asynchronously to the hub every 200 training steps. It allows you to also play around with the demo widget even while your model is still training.\n", "\n", "**Note**: If one does not want to upload the model checkpoints to the hub, simply set `push_to_hub=False`." ] }, { "cell_type": "code", "source": [ "from transformers import TrainingArguments\n", "\n", "training_args = TrainingArguments(\n", " output_dir=repo_name,\n", " group_by_length=True,\n", " per_device_train_batch_size=32,\n", " evaluation_strategy=\"steps\",\n", " num_train_epochs=4,\n", " gradient_checkpointing=True,\n", " fp16=True,\n", " save_steps=200,\n", " eval_steps=100,\n", " logging_steps=100,\n", " learning_rate=1e-3,\n", " warmup_steps=100,\n", " save_total_limit=2,\n", " push_to_hub=True,\n", ")" ], "metadata": { "id": "Yg0mnd1lR7SC" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "OsW-WZcL1ZtN" }, "source": [ "Now, all instances can be passed to Trainer and we are ready to start training!" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rY7vBmFCPFgC", "outputId": "4881d763-b89a-4331-dceb-8f5e1f03e818" }, "source": [ "from transformers import Trainer\n", "\n", "trainer = Trainer(\n", " model=model,\n", " data_collator=data_collator,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " train_dataset=common_voice_train,\n", " eval_dataset=common_voice_test,\n", " tokenizer=processor.feature_extractor,\n", ")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/content/wav2vec2-large-mms-1b-turkish-colab-test is already a clone of https://huggingface.co/patrickvonplaten/wav2vec2-large-mms-1b-turkish-colab-test. Make sure you pull the latest changes with `repo.git_pull()`.\n", "WARNING:huggingface_hub.repository:/content/wav2vec2-large-mms-1b-turkish-colab-test is already a clone of https://huggingface.co/patrickvonplaten/wav2vec2-large-mms-1b-turkish-colab-test. Make sure you pull the latest changes with `repo.git_pull()`.\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "UoXBx1JAA0DX" }, "source": [ "\n", "\n", "---\n", "\n", "${}^1$ To allow models to become independent of the speaker rate, in CTC, consecutive tokens that are identical are simply grouped as a single token. However, the encoded labels should not be grouped when decoding since they don't correspond to the predicted tokens of the model, which is why the `group_tokens=False` parameter has to be passed. If we wouldn't pass this parameter a word like `\"hello\"` would incorrectly be encoded, and decoded as `\"helo\"`.\n", "\n", "${}^2$ The blank token allows the model to predict a word, such as `\"hello\"` by forcing it to insert the blank token between the two l's. A CTC-conform prediction of `\"hello\"` of our model would be `[PAD] [PAD] \"h\" \"e\" \"e\" \"l\" \"l\" [PAD] \"l\" \"o\" \"o\" [PAD]`." ] }, { "cell_type": "markdown", "metadata": { "id": "rpvZHM1xReIW" }, "source": [ "### Training" ] }, { "cell_type": "markdown", "metadata": { "id": "j-3oKSzZ1hGq" }, "source": [ "Training should take less than 30 minutes depending on the GPU allocated to this notebook.\n", "\n", "In case you want to use this google colab to fine-tune your model, you should make sure that your training doesn't stop due to inactivity. A simple hack to prevent this is to paste the following code into the console of this tab (*right mouse click -> inspect -> Console tab and insert code*)." ] }, { "cell_type": "markdown", "metadata": { "id": "VYYAvgkW4P0m" }, "source": [ "```javascript\n", "function ConnectButton(){\n", " console.log(\"Connect pushed\");\n", " document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click()\n", "}\n", "setInterval(ConnectButton,60000);\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "7bGgLV2r0yvZ" }, "source": [ "Cool, let's start training!" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 325 }, "id": "9fRr9TG5pGBl", "outputId": "a11a4423-8a9b-4b67-e696-6bfc12ec562a" }, "source": [ "trainer.train()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "Several commits (2) will be pushed upstream.\n", "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=436, training_loss=1.2575483672115781, metrics={'train_runtime': 1428.6751, 'train_samples_per_second': 9.738, 'train_steps_per_second': 0.305, 'total_flos': 5.380615123975965e+18, 'train_loss': 1.2575483672115781, 'epoch': 4.0})" ] }, "metadata": {}, "execution_count": 113 } ] }, { "cell_type": "markdown", "metadata": { "id": "a9q4mgMZplr_" }, "source": [ "The training loss and validation WER go down nicely.\n", "\n", "We see that fine-tuning adapter layers of `mms-1b-all` for just 100 steps outperforms fine-tuning the whole `xls-r-300m` checkpoint as shown [here](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2#training-1) already by a large margin.\n", "\n", "From the [official paper](https://scontent-cdg4-3.xx.fbcdn.net/v/t39.8562-6/348827959_6967534189927933_6819186233244071998_n.pdf?_nc_cat=104&ccb=1-7&_nc_sid=ad8a9d&_nc_ohc=fSo3qQ7uxr0AX8EWnWl&_nc_ht=scontent-cdg4-3.xx&oh=00_AfBL34K0MAAPb0CgnthjbHfiB6pSnnwbn5esj9DZVPvyoA&oe=6495E802) and this quick comparison it becomes clear that `mms-1b-all` has a much higher capability of transfering knowledge to a low-resource language and should be preferred over `xls-r-300m`. In addition, training is also more memory-efficient as only a small subset of layers are trained." ] }, { "cell_type": "markdown", "metadata": { "id": "4Ya7WEy0pd13" }, "source": [ "The adapter weights will be uploaded as part of the model checkpoint, but we also want to make sure to save them seperately so that they can easily be off- and onloaded.\n", "\n", "Let's save all the adapter layers into the training output dir so that it can be correctly uploaded to the Hub." ] }, { "cell_type": "code", "source": [ "from safetensors.torch import save_file as safe_save_file\n", "from transformers.models.wav2vec2.modeling_wav2vec2 import WAV2VEC2_ADAPTER_SAFE_FILE\n", "import os\n", "\n", "adapter_file = WAV2VEC2_ADAPTER_SAFE_FILE.format(target_lang)\n", "adapter_file = os.path.join(training_args.output_dir, adapter_file)\n", "\n", "safe_save_file(model._get_adapters(), adapter_file, metadata={\"format\": \"pt\"})" ], "metadata": { "id": "cMI_Q7DGdIgY" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Finally, you can upload the result of the training to the 🤗 Hub." ], "metadata": { "id": "uRgIquiPdkKv" } }, { "cell_type": "code", "metadata": { "colab": { "referenced_widgets": [ "a63c6bdb005b4ecda20f865e0eacfdf9", "04506028109c4adb8d4aea1d4acc77dd", "ad11c6489fcb4677aa434298a36dbd8d", "d2968a0d879240a99fa5490a23436a88", "79cb3df85e764311895076f9c5e454c6", "981e78eb6acc47a6b90a4ae7d15654ce", "0e2d47edeb8d4d66b037be888ee0ad4b", "4b6d078c2e6a45bcbca61c9d0234f384", "eb29399bee424f509e6f7513ab397e1b", "d200af835bef4af49994548af692b541", "8fe5fe7705e64e65b8a3a8d9e49da032", "82662fc18f304e48bbdc1021712c3965", "67399a1143e9438cbacfebc1b32e68b6", "e7cd61fd768349bea17e8d039b439e4f", "b79567f2aab84840993fbde44b754b95", "5cdeb21099064c57b884f1d7d0ffedce", "905bdcfaebe94073a976f1a82f130887", "b73f10eb52364707ac2bd0bc429d4b5c", "b69a7d01a42c4df0b57544342a1e3a60", "15ea67ff9ee94888b48ed96c225a3f0d", "9548b725391e458ba4020bed9bdc2510", "a7a978cb024049d8adf20810ee68c21f", "fc7007fc48dd456aa2442cba95c7712c", "4da6336005e64defae4f6a03c397f5f6", "26eb2429d1ae4cbba60323432acbfeb3", "adba957b802148ff813c13edf72bccda", "a51f6d57477a488597e86bc3f72c5dc9", "df659fc73a3e45c581c59cdfccb7ed0e", "10a00c6579914a4da467cec1ba99897b", "1854f015adeb40458270ca43f1e212d6", "b8ecbfbfdc74475cb9f68b317314c49d", "5fb1c87079394be18f84fa2b3e76170f", "0b85e8ad1bfe41c888d604336e5c87e7" ], "base_uri": "https://localhost:8080/", "height": 409 }, "id": "ArG1Thf6NBWm", "outputId": "7c2d3d24-cb02-4c2d-9440-40aba9976168" }, "source": [ "trainer.push_to_hub()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Several commits (2) will be pushed upstream.\n", "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n", "The progress bars may be unreliable.\n", "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Upload file pytorch_model.bin: 0%| | 1.00/3.59G [00:00 main\n", "\n", "WARNING:huggingface_hub.repository:To https://huggingface.co/patrickvonplaten/wav2vec2-large-mms-1b-turkish-colab-test\n", " 3208dbd..c8740b6 main -> main\n", "\n", "To https://huggingface.co/patrickvonplaten/wav2vec2-large-mms-1b-turkish-colab-test\n", " c8740b6..ad298a9 main -> main\n", "\n", "WARNING:huggingface_hub.repository:To https://huggingface.co/patrickvonplaten/wav2vec2-large-mms-1b-turkish-colab-test\n", " c8740b6..ad298a9 main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-large-mms-1b-turkish-colab-test/commit/c8740b6541e54660b7bafac0631d7eb4aac42bea'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 115 } ] }, { "cell_type": "markdown", "source": [ "One of the main advantage of adapter weights training is that the \"base\" model which makes up roughly 99% of the model weights is kept unchanged and only a small [2.5M adapter checkpoint](https://huggingface.co/patrickvonplaten/wav2vec2-large-mms-1b-turkish-colab/blob/main/adapter.tur.safetensors) has to be shared in order to use the trained checkpoint.\n", "\n", "This makes it extremely simply to train additional adapter layers and add them to your repository.\n", "\n", "You can do some very easily by simply re-running this script and changing the language you would like to train on to a different one, *e.g.* `swe` for Swedish. In addition, you should make sure that the vocabulary does not get completely overwritten but that the new language vocabulary is **appended** to the existing one as stated above in the commented out cells.\n", "\n", "To demonstrate how different adapter layers can be loaded, I have trained and uploaded also an adapter layer for Swedish under the iso language code `swe` as you can see [here](https://huggingface.co/patrickvonplaten/wav2vec2-large-mms-1b-turkish-colab/blob/main/adapter.swe.safetensors)" ], "metadata": { "id": "zh1j3PdQeEN2" } }, { "cell_type": "markdown", "source": [ "You can load the fine-tuned checkpoint as usual by using `from_pretrained(...)`, but you should make sure to also add a `target_lang=\"\"` to the method so that the correct adapter is loaded. Also should you set the target language correctly for your tokenizer.\n", "\n", "Let's see how we can load the Turkish checkpoint first." ], "metadata": { "id": "K397I_Xsd594" } }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 273, "referenced_widgets": [ "d38842686eef43c5a80a63e19e4f6b53", "2a2e8024b2b148b1af0a0c391f329c7b", "3e37daa37b024011882591d40072862e", "f93f079a10054058993a0b2c1fd1d5c1", "998b14776c8743679925f0c730f0a3fe", "0c07e3f2b997461c885ae8450aa7f5af", "06e0374fc57e42c2bf0f527fa08d5222", "60ee3e3de33b41baae523b65331ec141", "8983fd0d3faf4947a45e3547a37cb5fe", "85d05224871e4678bcadff8a0db72a14", "c235c474db1d4200a29dfd6a41a19c6b", "acf6b1de3eac409bbeae38841ec2d243", "c3eb6c0dc7f9450db28ea841c6e51c2b", "41fce02b63614fed98ee6b71c5b274ff", "8e3944ece7944eb59d06ebeeae9b56ee", "028dc099b86d42cb83e0f8f9683a6c4e", "a038781f661343ec9aed0f61706e4955", "9dd5fcc275da489f80a50ec0dbf1fdae", "80920707ff4047dd9a215cd4b862e16c", "e6b10dcb9aeb485989570470041bdd82", "90015c877f954389998441b54bd20e58", "99ba230e28c34ebf9f0f7a3f4f1d089e", "1cfd672b91b849f6bd1426287a7cb319", "7ab60aa001d14e12a89f5f9c122fb88d", "ba89b77ea8f24dd1ae408e9a9e2e1a8d", "044b79115ea341b0af88f12501ec6138", "f78a920a2ce74ee184bb1b441cf4b3b6", "6bbfa21e7144456ba898279688736fde", "8e04cce84f7447ec8d8caf9b2552427c", "bef8bb793bd34b588df850243cfdc95a", "0eadce6d4c914aa18ab5e043ff94da34", "9b69ee2632af45eba4f200ca06159601", "c157f607adfb4f25907d211fc11200d4", "bc00e1e8309443b193ffd95079d0ea6e", "d699efb6dddb4ee4aeb349c0c4aa237f", "ae2ac08d635e4e2782165d5f7b32f183", "003af1b2b04544459f1951fd0488370b", "7e2a4e01fcd641cea9b9d93470ec8282", "00d2b3836e7949b4ad3b050c32eb0738", "8e6807a19d6446f2828ebc662629eb21", "177bb65e35044b4582cc2fcdf3bca19d", "e0ec81e0aa134c85a40c2232792dc617", "0ce87145e3524feeacd3de83c66eb38f", "e33a9da1b85b47bb9be4d25ad65e6ab9", "db4d2ddf4489467394d3b131be703b5d", "a3d2f6838cef457d94854bb1e18ab571", "a606ed39f8584ad381a584cb65f9f8f9", "858ac62413584449a9ff35cbc3f3d8b4", "dff7935debc14087a740e0833558c8c2", "2853b574bbeb40a39e74aeb56d8f9f27", "30c880fc4ab7455a9384203cecab0fb6", "6cab7ade3d0548ffa03c36c6fe4a2d5f", "01d896fc1d244aafb129e68e014e7762", "059a69eb08924256a42cc372f4497213", "cd5724a6ad584baba16b94679156fbd1", "c3b3756e14e447a18dbb0528374ebc96", "48e4fb0952ec43bd9337a6057805767e", "46465720f427494889eebd413f8f4fc7", "2fb38ddfc0b24b0a98391c21efa9036e", "e81854bdbb0f4d8998285b218cccd87f", "0d57ad2ae83a40a9a1ddac461b2775ee", "67d809cfc2454d0e9309bf7e5ac10a81", "f4edaa65746c40a194163185efa466c7", "940f163cf1e04e6a974aaa4c6ed43603", "8ea872d0b08e467ebe80086b5cb2c8de", "067c06ceb69a4bc8b72bc7a0b2395053", "186dadf9246c4dc3aa452d5db33bef41", "2551934919ba4db78604b3685254eb54", "37944cb373524bda98bb8cc263d26309", "b1305a9a78204f88b151c5ea2ace1abb", "3dca929e8c7b4a71b1402ad854d2c1ff", "f167790ce2834c0db5d3ee8b39172f42", "0d9b8d5711674635a5327f432c75aefe", "f3be09e9ac5646828c5a9dff827546f4", "f4e002cfce574fea83e73e4d8f88605f", "79e1afd0f7e54a8c89850714f3f13983", "9a5b0f01442048f4b18eb69246db3016", "994831202c704932bf4acf8694735fa1", "1600987291d54e4889ff88d28475868e", "d0404bcc64614ba19944fac983dd508e", "3312bec6dec34542b9a0ba1c535328a8", "af7c658e987d498f84f9f5c95dcccbe9", "0a613ebc2bdb42d9a0df3ff1911923cd", "51b2d45c1b34454dbbaf03cda807cd2f", "19c845278b0d40d4b843a872dcbef351", "09436b852a324e8887c33d749e053bef", "2975db4983b64361922693b9a79db800", "134cbeb172d2436a97509466ba2f5dfc" ] }, "id": "R351I9IQp_9D", "outputId": "97e9ec64-b9a4-44cd-8227-23e21de32afc" }, "source": [ "model_id = \"patrickvonplaten/wav2vec2-large-mms-1b-turkish-colab\"\n", "\n", "model = Wav2Vec2ForCTC.from_pretrained(model_id, target_lang=\"tur\").to(\"cuda\")\n", "processor = Wav2Vec2Processor.from_pretrained(model_id)\n", "\n", "processor.tokenizer.set_target_lang(\"tur\")" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)lve/main/config.json: 0%| | 0.00/2.05k [00:00\nHugging Face\n
\nThe AI community building the future\n
\nImmediately click login after typing your password or it might be stored in plain text in this notebook file.\n

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2020](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by Alexei Baevski, Michael Auli, and Alex Conneau. Soon after the superior performance of Wav2Vec2 was demonstrated on the English ASR dataset LibriSpeech, *Facebook AI* presented XLSR-Wav2Vec2 (click [here](https://arxiv.org/abs/2006.13979)). XLSR stands for *cross-lingual speech representations* and refers to XLSR-Wav2Vec2's ability to learn speech representations that are useful across multiple languages.\n", "\n", "Similar to Wav2Vec2, XLSR-Wav2Vec2 learns powerful speech representations from hundreds of thousands of hours of speech in more than 50 languages of unlabeled speech. Similar, to [BERT's masked language modeling](http://jalammar.github.io/illustrated-bert/), the model learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network.\n", "\n", "![wav2vec2_structure](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xlsr_wav2vec2.png)\n", "\n", "The authors show for the first time that massively pretraining an ASR model on cross-lingual unlabeled speech data, followed by language-specific fine-tuning on very little labeled data achieves state-of-the-art results. See Table 1-5 of the official [paper](https://arxiv.org/pdf/2006.13979.pdf)." ] }, { "cell_type": "markdown", "metadata": { "id": "nT_QrfWtsxIz" }, "source": [ "In this notebook, we will give an in-detail explanation of how XLSR-Wav2Vec2's pretrained checkpoint can be fine-tuned on a low-resource ASR dataset of any language. Note that in this notebook, we will fine-tune XLSR-Wav2Vec2 without making use of a language model. It is much simpler and more efficient to use XLSR-Wav2Vec2 without a language model, but better results can be achieved by including a language model. \n", "\n", "For demonstration purposes, we fine-tune the [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the low resource Turkish ASR dataset of [Common Voice](https://huggingface.co/datasets/common_voice) that contains just ~6h of validated training data." ] }, { "cell_type": "markdown", "metadata": { "id": "Gx9OdDYrCtQ1" }, "source": [ "XLSR-Wav2Vec2 is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems and mainly in Automatic Speech Recognition and handwriting recognition. \n", "\n", "I highly recommend reading the blog post [Sequence Modeling with CTC (2017)](https://distill.pub/2017/ctc/) very well-written blog post by Awni Hannun." ] }, { "cell_type": "markdown", "metadata": { "id": "wcHuXIaWyHZU" }, "source": [ "First, let's try to get a good GPU in our colab! With Google Colab's free version it's sadly becoming much harder to get access to a good GPU. With Google Colab Pro, one has a much easier time getting access to a V100 or P100 GPU however." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YELVqGxMxnbG", "outputId": "bdf41fd9-2742-4f62-b095-49f6575f1bad" }, "source": [ "gpu_info = !nvidia-smi\n", "gpu_info = '\\n'.join(gpu_info)\n", "if gpu_info.find('failed') >= 0:\n", " print('Not connected to a GPU')\n", "else:\n", " print(gpu_info)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Tue Oct 19 14:28:52 2021 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 470.74 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla V100-SXM2... Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 41C P0 52W / 300W | 0MiB / 16160MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "e335hPmdtASZ" }, "source": [ "Before we start, let's install both `datasets` and `transformers` from master. Also, we need the `torchaudio` and `librosa` package to load audio files and the `jiwer` to evaluate our fine-tuned model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric ${}^1$." ] }, { "cell_type": "code", "metadata": { "id": "c8eh87Hoee5d" }, "source": [ "%%capture\n", "!pip install datasets==1.13.3\n", "!pip install transformers==4.11.3\n", "!pip install huggingface_hub==0.0.19\n", "!pip install torchaudio\n", "!pip install librosa\n", "!pip install jiwer" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0xxt_LwxDQlO" }, "source": [ "Next we strongly suggest to upload your training checkpoints directly to the [🤗 Hub](https://huggingface.co/) while training. The [🤗 Hub](https://huggingface.co/) has integrated version control so you can be sure that no model checkpoint is getting lost during training. \n", "\n", "To do so you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!)" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 239, "referenced_widgets": [ "c6ffed91adc84ae49321c949e200bcf2", "cc23f1bd2a2949af8372e20d5ea21ee5", "8122df22a7214caba24e9ec17ef6c8a8", "74cc3407b05847f8aacbe7b4a8ed14a0", "58c3213ea02c48e78b0568d27b8d73d8", "12ee910317f6417999177008352f543a", "8974c4c781da4baf909283c668f0f939", "e9770051957a4ef1b5397f16f74efe0a", "f835a80efad84de7a4c61916180ecf5e", "de9a6f7a243f4743ace22cb19a022e8f", "2befaa23df75481a9c73d8b3ab6c2893", "7d9ee4275e2641d0a61d4ef6c304b1e5", "26c4ee5be42249128209ea3412185ea5", "76badcb568f04481a12116d1bde833eb", "3a5a9a7e7c644610be5bc4e41c71e823", "bd91b234ff3f46608af6e33549b66216" ] }, "id": "mlMSH3T3EazV", "outputId": "90c4c865-ac25-4649-a434-633b18d3d302" }, "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n", "\u001b[1m\u001b[31mAuthenticated through git-crendential store but this isn't the helper defined on your machine.\n", "You will have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal to set it as the default\n", "\n", "git config --global credential.helper store\u001b[0m\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "ujdZ2TxhElk6" }, "source": [ "\n", "Then you need to install Git-LFS to upload your model checkpoints:" ] }, { "cell_type": "code", "metadata": { "id": "WcR-d83OEkqb" }, "source": [ "%%capture\n", "!apt install git-lfs" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Mn9swf6EQ9Vd" }, "source": [ "\n", "\n", "\n", "---\n", "\n", "${}^1$ In the [paper](https://arxiv.org/pdf/2006.13979.pdf), the model was evaluated using the phoneme error rate (PER), but by far the most common metric in ASR is the word error rate (WER). To keep this notebook as general as possible we decided to evaluate the model using WER." ] }, { "cell_type": "markdown", "metadata": { "id": "0mW-C1Nt-j7k" }, "source": [ "## Prepare Data, Tokenizer, Feature Extractor" ] }, { "cell_type": "markdown", "metadata": { "id": "BeBosnY9BH3e" }, "source": [ "ASR models transcribe speech to text, which means that we both need a feature extractor that processes the speech signal to the model's input format, *e.g.* a feature vector, and a tokenizer that processes the model's output format to text. \n", "\n", "In 🤗 Transformers, the XLSR-Wav2Vec2 model is thus accompanied by both a tokenizer, called [Wav2Vec2CTCTokenizer](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#wav2vec2ctctokenizer), and a feature extractor, called [Wav2Vec2FeatureExtractor](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#wav2vec2featureextractor).\n", "\n", "Let's start by creating the tokenizer responsible for decoding the model's predictions." ] }, { "cell_type": "markdown", "metadata": { "id": "sEXEWEJGQPqD" }, "source": [ "### Create Wav2Vec2CTCTokenizer" ] }, { "cell_type": "markdown", "metadata": { "id": "tWmMikuNEKl_" }, "source": [ "The [pretrained Wav2Vec2 checkpoint]( ) maps the speech signal to a sequence of context representations as illustrated in the figure above. A fine-tuned XLSR-Wav2Vec2 checkpoint needs to map this sequence of context representations to its corresponding transcription so that a linear layer has to be added on top of the transformer block (shown in yellow). This linear layer is used to classifies each context representation to a token class analogous how, *e.g.*, after pretraining a linear layer is added on top of BERT's embeddings for further classification - *cf.* with *'BERT'* section of this [blog post](https://huggingface.co/blog/warm-starting-encoder-decoder).\n", "\n", "The output size of this layer corresponds to the number of tokens in the vocabulary, which does **not** depend on XLSR-Wav2Vec2's pretraining task, but only on the labeled dataset used for fine-tuning. So in the first step, we will take a look at Common Voice and define a vocabulary based on the dataset's transcriptions." ] }, { "cell_type": "markdown", "metadata": { "id": "idBczw8mWzgt" }, "source": [ "First, let's go to [Common Voice](https://commonvoice.mozilla.org/en/datasets) and pick a language to fine-tune XLSR-Wav2Vec2 on. For this notebook, we will use Turkish. \n", "\n", "For each language-specific dataset, you can find a language code corresponding to your chosen language. On [Common Voice](https://commonvoice.mozilla.org/en/datasets), look for the field \"Version\". The language code then corresponds to the prefix before the underscore. For Turkish, *e.g.* the language code is `\"tr\"`.\n", "\n", "Great, now we can use 🤗 Datasets' simple API to download the data. The dataset name will be `\"common_voice\"`, the config name corresponds to the language code - `\"tr\"` in our case." ] }, { "cell_type": "markdown", "metadata": { "id": "bee4g9rpLxll" }, "source": [ "Common Voice has many different splits including `invalidated`, which refers to data that was not rated as \"clean enough\" to be considered useful. In this notebook, we will only make use of the splits `\"train\"`, `\"validation\"` and `\"test\"`. \n", "\n", "Because the Turkish dataset is so small, we will merge both the validation and training data into a training dataset and simply use the test data for validation." ] }, { "cell_type": "code", "metadata": { "id": "2MMXcWFFgCXU", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "38fef82d-cce6-4ffa-c9c1-213258689bc5" }, "source": [ "from datasets import load_dataset, load_metric, Audio\n", "\n", "common_voice_train = load_dataset(\"common_voice\", \"tr\", split=\"train+validation\")\n", "common_voice_test = load_dataset(\"common_voice\", \"tr\", split=\"test\")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Reusing dataset common_voice (/root/.cache/huggingface/datasets/common_voice/tr/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9)\n", "Reusing dataset common_voice (/root/.cache/huggingface/datasets/common_voice/tr/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9)\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "ri5y5N_HMANq" }, "source": [ "Many ASR datasets only provide the target text, `'sentence'` for each audio array `'audio'` and file `'path'`. Common Voice actually provides much more information about each audio file, such as the `'accent'`, etc. However, we want to keep the notebook as general as possible, so that we will only consider the transcribed text for fine-tuning.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "kbyq6lDgQc2a" }, "source": [ "common_voice_train = common_voice_train.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])\n", "common_voice_test = common_voice_test.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Go9Hq4e4NDT9" }, "source": [ "Let's write a short function to display some random samples of the dataset and run it a couple of times to get a feeling for the transcriptions." ] }, { "cell_type": "code", "metadata": { "id": "72737oog2F6U" }, "source": [ "from datasets import ClassLabel\n", "import random\n", "import pandas as pd\n", "from IPython.display import display, HTML\n", "\n", "def show_random_elements(dataset, num_examples=10):\n", " assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n", " picks = []\n", " for _ in range(num_examples):\n", " pick = random.randint(0, len(dataset)-1)\n", " while pick in picks:\n", " pick = random.randint(0, len(dataset)-1)\n", " picks.append(pick)\n", " \n", " df = pd.DataFrame(dataset[picks])\n", " display(HTML(df.to_html()))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "K_JUmf3G3b9S", "outputId": "15314e16-e69c-4b85-899f-bc37873d13a8" }, "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sentence
0Birkaç saat sonra, Belçika da aynı yolu izledi.
1Veliler öfkeliydi.
2Şimdi Ankara yeniden denemeye hazır.
3Görüşmelerde Kosova konusuna da değinildi.
4\"Arnavutluk'ta ihracat zayıf.\"
5Kredilerin büyük bölümü özel şirketlere verildi.
6Ancak iki taraf da konuşma yapmadı.
7Yeni anayasanın onaylamasına çalışılacak.
8Uyulmadığı takdirde ülke cezalandırılacak.
9İlk dersler Eylül ayında başlayacak.
" ], "text/plain": [ "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "fowcOllGNNju" }, "source": [ "Alright! The transcriptions look fairly clean. Having translated the transcribed sentences, it seems that the language corresponds more to written-out text than noisy dialogue. This makes sense considering that [Common Voice](https://huggingface.co/datasets/common_voice) is a crowd-sourced read speech corpus." ] }, { "cell_type": "markdown", "metadata": { "id": "vq7OR50LN49m" }, "source": [ "We can see that the transcriptions contain some special characters, such as `,.?!;:`. Without a language model, it is much harder to classify speech chunks to such special characters because they don't really correspond to a characteristic sound unit. *E.g.*, the letter `\"s\"` has a more or less clear sound, whereas the special character `\".\"` does not.\n", "Also in order to understand the meaning of a speech signal, it is usually not necessary to include special characters in the transcription.\n", "\n", "In addition, we normalize the text to only have lower case letters and append a word separator token at the end." ] }, { "cell_type": "code", "metadata": { "id": "svKzVJ_hQGK6" }, "source": [ "import re\n", "chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�]'\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower() + \" \"\n", " return batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XIHocAuTQbBR", "outputId": "ca356868-9f46-481c-fd8e-b671f2b4e085" }, "source": [ "common_voice_train = common_voice_train.map(remove_special_characters)\n", "common_voice_test = common_voice_test.map(remove_special_characters)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Loading cached processed dataset at /root/.cache/huggingface/datasets/common_voice/tr/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9/cache-f2c5b6ba7aabc67e.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/common_voice/tr/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9/cache-914c2131e8aabe6e.arrow\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "RBDRAAYxRE6n", "outputId": "5441554f-992b-4e23-ab69-4baaf0fd7aa1" }, "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sentence
0diğer partiler gereken baraja ulaşamadılar
1yatırımlar üç virgül iki milyon avroyu buldu
2muhalefet bu sava inanmıyor
3bende mücadele edeceğime bu yolu seçtim
4ancak jurciç teklifi reddetmişti
5operasyon olaysız geçti
6birkaç günde öğrenmek için çok fazla şey vardı
7bu kış ziyaretine gittim
8ancak yetkililer nuh deyip peygamber demiyorlar
9ve tabii ki banyoda kaçak var
" ], "text/plain": [ "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "jwfaptH5RJwA" }, "source": [ "Good! This looks better. We have removed most special characters from transcriptions and normalized them to lower-case only.\n", "\n", "In CTC, it is common to classify speech chunks into letters, so we will do the same here. \n", "Let's extract all distinct letters of the training and test data and build our vocabulary from this set of letters.\n", "\n", "We write a mapping function that concatenates all transcriptions into one long transcription and then transforms the string into a set of chars. \n", "It is important to pass the argument `batched=True` to the `map(...)` function so that the mapping function has access to all transcriptions at once." ] }, { "cell_type": "code", "metadata": { "id": "LwCshNbbeRZR" }, "source": [ "def extract_all_chars(batch):\n", " all_text = \" \".join(batch[\"sentence\"])\n", " vocab = list(set(all_text))\n", " return {\"vocab\": [vocab], \"all_text\": [all_text]}" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "03f7dcf5bbd04aeb9cd288a3b0705ce7", "c9ade09ec7af48008746b62f550167e2", "bd842d20bbbf4436af94251738284b03", "828246baa633416394aa51a5fd606d02", "6273bf4ab09e42468e225ae58665341a", "ce374b92cea14810a9947003e46df4b2", "f3938c3edbf1462f87f4ae506d108af0", "a3b316759ad345109004632ecb8e3445", "9f40d2ec77e2418894cffbd7279232fb", "b46d2dfa6abd4b8fa722f39297bea660", "773a150288b64328b07245f300bc7c65", "8fb3ba2619e249168fcabf6e909511c6", "e419dd1f4351438297118613544ab0a9", "aeb2d51f3e1b482cb419a5175078aa0a", "7e3715da0b23459db9d4bed7602921de", "2c01996c3f24431fa0ebd4c782cd1079", "9e908680e75c4eac8d7e33749983e945", "aa5363c3405e473a91c4ae1ef7c8c034", "b2c0ebf5e0df4a12b6bbcb5d98e39496", "6798c8248f0e47dfb61c984875818a1a", "128f2ed071bf4b278670e40338c777dd", "5cdba9fd99a543cdb82bd2344b851e97" ] }, "id": "_m6uUjjcfbjH", "outputId": "19f01219-9007-4a1b-d4b8-17e8942f5ab1" }, "source": [ "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n", "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "03f7dcf5bbd04aeb9cd288a3b0705ce7", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00 main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab/commit/18eefaa60924e6e5b6412e80449517ad39567fe7'" ] }, "metadata": {}, "execution_count": 60 } ] }, { "cell_type": "markdown", "metadata": { "id": "SwQM8lH_GGuP" }, "source": [ "Great, you can see the just created repository under `https://huggingface.co//wav2vec2-large-xlsr-turkish-demo-colab`" ] }, { "cell_type": "markdown", "metadata": { "id": "mYcIiR2FQ96i" }, "source": [ "### Create XLSR-Wav2Vec2 Feature Extractor" ] }, { "cell_type": "markdown", "metadata": { "id": "Y6mDEyW719rx" }, "source": [ "Speech is a continuous signal and to be treated by computers, it first has to be discretized, which is usually called **sampling**. The sampling rate hereby plays an important role in that it defines how many data points of the speech signal are measured per second. Therefore, sampling with a higher sampling rate results in a better approximation of the *real* speech signal but also necessitates more values per second.\n", "\n", "A pretrained checkpoint expects its input data to have been sampled more or less from the same distribution as the data it was trained on. The same speech signals sampled at two different rates have a very different distribution, *e.g.*, doubling the sampling rate results in data points being twice as long. Thus, \n", "before fine-tuning a pretrained checkpoint of an ASR model, it is crucial to verify that the sampling rate of the data that was used to pretrain the model matches the sampling rate of the dataset used to fine-tune the model.\n", "\n", "XLSR-Wav2Vec2 was pretrained on the audio data of [Babel](https://huggingface.co/datasets/librispeech_asr), \n", "[Multilingual LibriSpeech (MLS)](https://ai.facebook.com/blog/a-new-open-data-set-for-multilingual-speech-research/), and [Common Voice](https://huggingface.co/datasets/common_voice). Most of those datasets were sampled at 16kHz, so that Common Voice, sampled at 48kHz, has to be downsampled to 16kHz for training. Therefore, we will have to downsample our fine-tuning data to 16kHz in the following.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "KuUbPW7oV-B5" }, "source": [ "A XLSR-Wav2Vec2 feature extractor object requires the following parameters to be instantiated:\n", "\n", "- `feature_size`: Speech models take a sequence of feature vectors as an input. While the length of this sequence obviously varies, the feature size should not. In the case of Wav2Vec2, the feature size is 1 because the model was trained on the raw speech signal ${}^2$.\n", "- `sampling_rate`: The sampling rate at which the model is trained on.\n", "- `padding_value`: For batched inference, shorter inputs need to be padded with a specific value\n", "- `do_normalize`: Whether the input should be *zero-mean-unit-variance* normalized or not. Usually, speech models perform better when normalizing the input\n", "- `return_attention_mask`: Whether the model should make use of an `attention_mask` for batched inference. In general, XLSR-Wav2Vec2 models should **always** make use of the `attention_mask`." ] }, { "cell_type": "code", "metadata": { "id": "kAR0-2KLkopp" }, "source": [ "from transformers import Wav2Vec2FeatureExtractor\n", "\n", "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qUETetgqYC3W" }, "source": [ "Great, XLSR-Wav2Vec2's feature extraction pipeline is thereby fully defined!\n", "\n", "To make the usage of XLSR-Wav2Vec2 as user-friendly as possible, the feature extractor and tokenizer are *wrapped* into a single `Wav2Vec2Processor` class so that one only needs a `model` and `processor` object." ] }, { "cell_type": "code", "metadata": { "id": "KYZtoW-tlZgl" }, "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DrKnYuvDIoOO" }, "source": [ "Next, we can prepare the dataset." ] }, { "cell_type": "markdown", "metadata": { "id": "YFmShnl7RE35" }, "source": [ "### Preprocess Data\n", "\n", "So far, we have not looked at the actual values of the speech signal but just the transcription. In addition to `sentence`, our datasets include two more column names `path` and `audio`. `path` states the absolute path of the audio file. Let's take a look.\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "TTCS7W6XJ9BG", "outputId": "d1621033-53af-4ff8-fe37-2a35c8ed381d" }, "source": [ "common_voice_train[0][\"path\"]" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3'" ] }, "metadata": {}, "execution_count": 61 } ] }, { "cell_type": "markdown", "metadata": { "id": "T6ndIjHGFp0W" }, "source": [ "`XLSR-Wav2Vec2` expects the input in the format of a 1-dimensional array of 16 kHz. This means that the audio file has to be loaded and resampled.\n", "\n", " Thankfully, `datasets` does this automatically by calling the other column `audio`. Let try it out. " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qj_z5Zc3GAs9", "outputId": "1e8abb8e-5869-40f0-96f3-00f579f7bd01" }, "source": [ "common_voice_train[0][\"audio\"]" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " -8.8930130e-05, -3.8027763e-05, -2.9146671e-05], dtype=float32),\n", " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3',\n", " 'sampling_rate': 48000}" ] }, "metadata": {}, "execution_count": 62 } ] }, { "cell_type": "markdown", "metadata": { "id": "WUUTgI1bGHW-" }, "source": [ "Great, we can see that the audio file has automatically been loaded. This is thanks to the new [`\"Audio\"` feature](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=audio#datasets.Audio) introduced in `datasets == 4.13.3`, which loads and resamples audio files on-the-fly upon calling.\n", "\n", "In the example above we can see that the audio data is loaded with a sampling rate of 48kHz whereas 16kHz are expected by the model. We can set the audio feature to the correct sampling rate by making use of [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column):" ] }, { "cell_type": "code", "metadata": { "id": "rrv65aj7G95i" }, "source": [ "common_voice_train = common_voice_train.cast_column(\"audio\", Audio(sampling_rate=16_000))\n", "common_voice_test = common_voice_test.cast_column(\"audio\", Audio(sampling_rate=16_000))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "PcnO4x-NGBEi" }, "source": [ "Let's take a look at `\"audio\"` again." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aKtkc1o_HWHC", "outputId": "c8d48e24-6128-42ec-a5ec-76082b3f144b" }, "source": [ "common_voice_train[0][\"audio\"]" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " -7.4556941e-05, -1.4621433e-05, -5.7861507e-05], dtype=float32),\n", " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3',\n", " 'sampling_rate': 16000}" ] }, "metadata": {}, "execution_count": 64 } ] }, { "cell_type": "markdown", "metadata": { "id": "SOckzFd4Mbzq" }, "source": [ "This seemed to have worked! Let's listen to a couple of audio files to better understand the dataset and verify that the audio was correctly loaded. \n", "\n", "**Note**: *You can click the following cell a couple of times to listen to different speech samples.*" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 92 }, "id": "dueM6U7Ev0OA", "outputId": "d7d99650-868e-4207-c6ed-320c9f9e6cd7" }, "source": [ "import IPython.display as ipd\n", "import numpy as np\n", "import random\n", "\n", "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(common_voice_train[rand_int][\"sentence\"])\n", "ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "seçimlerde önemli bir olay bildirilmedi \n" ] }, { "output_type": "execute_result", "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "execution_count": 65 } ] }, { "cell_type": "markdown", "metadata": { "id": "gY8m3vARHYTa" }, "source": [ "It seems like the data is now correctly loaded and resampled. " ] }, { "cell_type": "markdown", "metadata": { "id": "1MaL9J2dNVtG" }, "source": [ "It can be heard, that the speakers change along with their speaking rate, accent, and background environment, etc. Overall, the recordings sound acceptably clear though, which is to be expected from a crowd-sourced read speech corpus.\n", "\n", "Let's do a final check that the data is correctly prepared, by printing the shape of the speech input, its transcription, and the corresponding sampling rate.\n", "\n", "**Note**: *You can click the following cell a couple of times to verify multiple samples.*" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Po2g7YPuRTx", "outputId": "70e03c0f-73ba-497b-89c0-c72ed663abd7" }, "source": [ "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n", "print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n", "print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Target text: çok güzel \n", "Input array shape: (34560,)\n", "Sampling rate: 16000\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "M9teZcSwOBJ4" }, "source": [ "Good! Everything looks fine - the data is a 1-dimensional array, the sampling rate always corresponds to 16kHz, and the target text is normalized." ] }, { "cell_type": "markdown", "metadata": { "id": "k3Pbn5WvOYZF" }, "source": [ "Finally, we can leverage `Wav2Vec2Processor` to process the data to the format expected by `Wav2Vec2ForCTC` for training. To do so let's make use of Dataset's [`map(...)`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=map#datasets.DatasetDict.map) function.\n", "\n", "First, we load and resample the audio data, simply by calling `batch[\"audio\"]`.\n", "Second, we extract the `input_values` from the loaded audio file. In our case, the `Wav2Vec2Processor` only normalizes the data. For other speech models, however, this step can include more complex feature extraction, such as [Log-Mel feature extraction](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). \n", "Third, we encode the transcriptions to label ids.\n", "\n", "**Note**: This mapping function is a good example of how the `Wav2Vec2Processor` class should be used. In \"normal\" context, calling `processor(...)` is redirected to `Wav2Vec2FeatureExtractor`'s call method. When wrapping the processor into the `as_target_processor` context, however, the same method is redirected to `Wav2Vec2CTCTokenizer`'s call method.\n", "For more information please check the [docs](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#transformers.Wav2Vec2Processor.__call__)." ] }, { "cell_type": "code", "metadata": { "id": "eJY7I0XAwe9p" }, "source": [ "def prepare_dataset(batch):\n", " audio = batch[\"audio\"]\n", "\n", " # batched output is \"un-batched\"\n", " batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n", " batch[\"input_length\"] = len(batch[\"input_values\"])\n", " \n", " with processor.as_target_processor():\n", " batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n", " return batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "q6Pg_WR3OGAP" }, "source": [ "Let's apply the data preparation function to all examples." ] }, { "cell_type": "code", "metadata": { "id": "-np9xYK-wl8q", "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "5dd141a840a74072906812bd073a6c82", "04e663f750fa410ba2003e73ae0643f9", "6e24afccb40343d4805f60799efab2a9", "a67171d7dad4401486e70168b3e8244d", "52d6e74d6942442d9ce0a2ac221b02f9", "fbfd1a2cf9574d5a909d28f1f980c8f6", "080c6d8c84d54a00afdfe7254a55f38e", "917011a6d5f041879516e8b260133e97", "f348ca022a0e48018b0cbc0966f5fdcf", "cd4e3a1441da4d6ab52ce1ea624c375e", "9965d7df621c4e23a3704eea9bfbe1d8" ] }, "outputId": "e71a13d3-f359-43ea-94da-7c9e5ef93cfd" }, "source": [ "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names)\n", "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5dd141a840a74072906812bd073a6c82", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/3478 [00:00=\n", " 7.5 (Volta).\n", " \"\"\"\n", "\n", " processor: Wav2Vec2Processor\n", " padding: Union[bool, str] = True\n", " max_length: Optional[int] = None\n", " max_length_labels: Optional[int] = None\n", " pad_to_multiple_of: Optional[int] = None\n", " pad_to_multiple_of_labels: Optional[int] = None\n", "\n", " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n", " # split inputs and labels since they have to be of different lenghts and need\n", " # different padding methods\n", " input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", "\n", " batch = self.processor.pad(\n", " input_features,\n", " padding=self.padding,\n", " max_length=self.max_length,\n", " pad_to_multiple_of=self.pad_to_multiple_of,\n", " return_tensors=\"pt\",\n", " )\n", " with self.processor.as_target_processor():\n", " labels_batch = self.processor.pad(\n", " label_features,\n", " padding=self.padding,\n", " max_length=self.max_length_labels,\n", " pad_to_multiple_of=self.pad_to_multiple_of_labels,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " # replace padding with -100 to ignore loss correctly\n", " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", "\n", " batch[\"labels\"] = labels\n", "\n", " return batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "lbQf5GuZyQ4_" }, "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xO-Zdj-5cxXp" }, "source": [ "Next, the evaluation metric is defined. As mentioned earlier, the \n", "predominant metric in ASR is the word error rate (WER), hence we will use it in this notebook as well." ] }, { "cell_type": "code", "metadata": { "id": "9Xsux2gmyXso" }, "source": [ "wer_metric = load_metric(\"wer\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "E1qZU5p-deqB" }, "source": [ "The model will return a sequence of logit vectors:\n", "$\\mathbf{y}_1, \\ldots, \\mathbf{y}_m$ with $\\mathbf{y}_1 = f_{\\theta}(x_1, \\ldots, x_n)[0]$ and $n >> m$.\n", "\n", "A logit vector $\\mathbf{y}_1$ contains the log-odds for each word in the vocabulary we defined earlier, thus $\\text{len}(\\mathbf{y}_i) =$ `config.vocab_size`. We are interested in the most likely prediction of the model and thus take the `argmax(...)` of the logits. Also, we transform the encoded labels back to the original string by replacing `-100` with the `pad_token_id` and decoding the ids while making sure that consecutive tokens are **not** grouped to the same token in CTC style ${}^1$." ] }, { "cell_type": "code", "metadata": { "id": "1XZ-kjweyTy_" }, "source": [ "def compute_metrics(pred):\n", " pred_logits = pred.predictions\n", " pred_ids = np.argmax(pred_logits, axis=-1)\n", "\n", " pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n", "\n", " pred_str = processor.batch_decode(pred_ids)\n", " # we do not want to group tokens when computing the metrics\n", " label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n", "\n", " wer = wer_metric.compute(predictions=pred_str, references=label_str)\n", "\n", " return {\"wer\": wer}" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Xmgrx4bRwLIH" }, "source": [ "Now, we can load the pretrained `XLSR-Wav2Vec2` checkpoint. The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of `Wav2Vec2ForCTC` also CTC's *blank token* ${}^2$. To save GPU memory, we enable PyTorch's [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html) and also set the loss reduction to \"*mean*\".\n", "\n", "Because the dataset is quite small (~6h of training data) and because Common Voice is quite noisy, fine-tuning Facebook's [wav2vec2-large-xlsr-53 checkpoint](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) seems to require some hyper-parameter tuning. Therefore, I had to play around a bit with different values for dropout, [SpecAugment](https://arxiv.org/abs/1904.08779)'s masking dropout rate, layer dropout, and the learning rate until training seemed to be stable enough. \n", "\n", "**Note**: When using this notebook to train XLSR-Wav2Vec2 on another language of Common Voice those hyper-parameter settings might not work very well. Feel free to adapt those depending on your use case. " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e7cqAWIayn6w", "outputId": "b348aec6-72f0-448f-c9ba-ae93c6f92b3a" }, "source": [ "from transformers import Wav2Vec2ForCTC\n", "\n", "model = Wav2Vec2ForCTC.from_pretrained(\n", " \"facebook/wav2vec2-large-xlsr-53\", \n", " attention_dropout=0.1,\n", " hidden_dropout=0.1,\n", " feat_proj_dropout=0.0,\n", " mask_time_prob=0.05,\n", " layerdrop=0.1,\n", " ctc_loss_reduction=\"mean\", \n", " pad_token_id=processor.tokenizer.pad_token_id,\n", " vocab_size=len(processor.tokenizer)\n", ")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "loading configuration file https://huggingface.co/facebook/wav2vec2-large-xlsr-53/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/8508c73cd595eb416a1d517b90762416c0bc6cfbef529578079aeae4d8c14336.7581ed2ee0c677f1e933180df51bd1a668c4a2b6d5fd1297d32069373dac097c\n", "Model config Wav2Vec2Config {\n", " \"activation_dropout\": 0.0,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 768,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": true,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"mean\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": true,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_dropout\": 0.0,\n", " \"feat_extract_norm\": \"layer\",\n", " \"feat_proj_dropout\": 0.0,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 4096,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.1,\n", " \"mask_channel_length\": 10,\n", " \"mask_channel_min_space\": 1,\n", " \"mask_channel_other\": 0.0,\n", " \"mask_channel_prob\": 0.0,\n", " \"mask_channel_selection\": \"static\",\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_space\": 1,\n", " \"mask_time_other\": 0.0,\n", " \"mask_time_prob\": 0.05,\n", " \"mask_time_selection\": \"static\",\n", " \"model_type\": \"wav2vec2\",\n", " \"num_attention_heads\": 16,\n", " \"num_codevector_groups\": 2,\n", " \"num_codevectors_per_group\": 320,\n", " \"num_conv_pos_embedding_groups\": 16,\n", " \"num_conv_pos_embeddings\": 128,\n", " \"num_feat_extract_layers\": 7,\n", " \"num_hidden_layers\": 24,\n", " \"num_negatives\": 100,\n", " \"pad_token_id\": 39,\n", " \"proj_codevector_dim\": 768,\n", " \"transformers_version\": \"4.11.3\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 40\n", "}\n", "\n", "loading weights file https://huggingface.co/facebook/wav2vec2-large-xlsr-53/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/5d2a20b45a1689a376ec4a6282b9d9be42f931cdf8daf07c3668ba1070a059d9.622b46163a38532eae8ac5423b0481dfc0b9ea401af488b5141772bdff889079\n", "Some weights of the model checkpoint at facebook/wav2vec2-large-xlsr-53 were not used when initializing Wav2Vec2ForCTC: ['project_q.bias', 'project_hid.weight', 'project_hid.bias', 'quantizer.weight_proj.bias', 'quantizer.weight_proj.weight', 'quantizer.codevectors', 'project_q.weight']\n", "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-large-xlsr-53 and are newly initialized: ['lm_head.weight', 'lm_head.bias']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "1DwR3XLSzGDD" }, "source": [ "The first component of XLSR-Wav2Vec2 consists of a stack of CNN layers that are used to extract acoustically meaningful - but contextually independent - features from the raw speech signal. This part of the model has already been sufficiently trained during pretraining and as stated in the [paper](https://arxiv.org/pdf/2006.13979.pdf) does not need to be fine-tuned anymore. \n", "Thus, we can set the `requires_grad` to `False` for all parameters of the *feature extraction* part." ] }, { "cell_type": "code", "metadata": { "id": "oGI8zObtZ3V0" }, "source": [ "model.freeze_feature_extractor()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "lD4aGhQM0K-D" }, "source": [ "In a final step, we define all parameters related to training. \n", "To give more explanation on some of the parameters:\n", "- `group_by_length` makes training more efficient by grouping training samples of similar input length into one batch. This can significantly speed up training time by heavily reducing the overall number of useless padding tokens that are passed through the model\n", "- `learning_rate` and `weight_decay` were heuristically tuned until fine-tuning has become stable. Note that those parameters strongly depend on the Common Voice dataset and might be suboptimal for other speech datasets.\n", "\n", "For more explanations on other parameters, one can take a look at the [docs](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer#trainingarguments).\n", "\n", "During training, a checkpoint will be uploaded asynchronously to the hub every 400 training steps. It allows you to also play around with the demo widget even while your model is still training.\n", "\n", "**Note**: If one does not want to upload the model checkpoints to the hub, simply set `push_to_hub=False`." ] }, { "cell_type": "code", "metadata": { "id": "KbeKSV7uzGPP", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "29a5d3f3-f78c-4105-cdd6-0119f147f37f" }, "source": [ "from transformers import TrainingArguments\n", "\n", "training_args = TrainingArguments(\n", " output_dir=repo_name,\n", " group_by_length=True,\n", " per_device_train_batch_size=16,\n", " gradient_accumulation_steps=2,\n", " evaluation_strategy=\"steps\",\n", " num_train_epochs=30,\n", " gradient_checkpointing=True,\n", " fp16=True,\n", " save_steps=400,\n", " eval_steps=400,\n", " logging_steps=400,\n", " learning_rate=3e-4,\n", " warmup_steps=500,\n", " save_total_limit=2,\n", " push_to_hub=True,\n", ")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "PyTorch: setting up devices\n", "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "OsW-WZcL1ZtN" }, "source": [ "Now, all instances can be passed to Trainer and we are ready to start training!" ] }, { "cell_type": "code", "metadata": { "id": "rY7vBmFCPFgC", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "4e695362-d016-48ec-cf41-ad19fc036b24" }, "source": [ "from transformers import Trainer\n", "\n", "trainer = Trainer(\n", " model=model,\n", " data_collator=data_collator,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " train_dataset=common_voice_train,\n", " eval_dataset=common_voice_test,\n", " tokenizer=processor.feature_extractor,\n", ")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/content/wav2vec2-large-xlsr-turkish-demo-colab is already a clone of https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab. Make sure you pull the latest changes with `repo.git_pull()`.\n", "Using amp fp16 backend\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "UoXBx1JAA0DX" }, "source": [ "\n", "\n", "---\n", "\n", "${}^1$ To allow models to become independent of the speaker rate, in CTC, consecutive tokens that are identical are simply grouped as a single token. However, the encoded labels should not be grouped when decoding since they don't correspond to the predicted tokens of the model, which is why the `group_tokens=False` parameter has to be passed. If we wouldn't pass this parameter a word like `\"hello\"` would incorrectly be encoded, and decoded as `\"helo\"`.\n", "\n", "${}^2$ The blank token allows the model to predict a word, such as `\"hello\"` by forcing it to insert the blank token between the two l's. A CTC-conform prediction of `\"hello\"` of our model would be `[PAD] [PAD] \"h\" \"e\" \"e\" \"l\" \"l\" [PAD] \"l\" \"o\" \"o\" [PAD]`." ] }, { "cell_type": "markdown", "metadata": { "id": "rpvZHM1xReIW" }, "source": [ "### Training" ] }, { "cell_type": "markdown", "metadata": { "id": "j-3oKSzZ1hGq" }, "source": [ "Training will take multiple hours depending on the GPU allocated to this notebook. While the trained model yields somewhat satisfying results on *Common Voice*'s test data of Turkish, it is by no means an optimally fine-tuned model. The purpose of this notebook is to demonstrate how XLSR-Wav2Vec2's [checkpoint](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) can be fine-tuned on a low-resource ASR dataset.\n", "\n", "In case you want to use this google colab to fine-tune your model, you should make sure that your training doesn't stop due to inactivity. A simple hack to prevent this is to paste the following code into the console of this tab (*right mouse click -> inspect -> Console tab and insert code*)." ] }, { "cell_type": "markdown", "metadata": { "id": "VYYAvgkW4P0m" }, "source": [ "```javascript\n", "function ConnectButton(){\n", " console.log(\"Connect pushed\"); \n", " document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n", "}\n", "setInterval(ConnectButton,60000);\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "7bGgLV2r0yvZ" }, "source": [ "Depending on what GPU was allocated to your google colab it might be possible that you are seeing an `\"out-of-memory\"` error here. In this case, it's probably best to reduce `per_device_train_batch_size` to 8 or even less and increase [`gradient_accumulation`](https://huggingface.co/transformers/master/main_classes/trainer.html#trainingarguments)." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "9fRr9TG5pGBl", "outputId": "900eab68-7219-4c6c-88f6-8ff278e51376" }, "source": [ "trainer.train()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running training *****\n", " Num examples = 3027\n", " Num Epochs = 30\n", " Instantaneous batch size per device = 16\n", " Total train batch size (w. parallel, distributed & accumulation) = 32\n", " Gradient Accumulation steps = 2\n", " Total optimization steps = 2850\n", "/usr/local/lib/python3.7/dist-packages/transformers/trainer.py:1357: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_grad_norm_; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior.\n", " args.max_grad_norm,\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "
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" ], "text/plain": [ "" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/preprocessor_config.json\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800/preprocessor_config.json\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2400\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2400/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2800\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2800/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000] due to args.save_total_limit\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=2850, training_loss=0.9370169477295457, metrics={'train_runtime': 6267.5878, 'train_samples_per_second': 14.489, 'train_steps_per_second': 0.455, 'total_flos': 1.0303791368179046e+19, 'train_loss': 0.9370169477295457, 'epoch': 30.0})" ] }, "metadata": {}, "execution_count": 80 } ] }, { "cell_type": "markdown", "metadata": { "id": "a9q4mgMZplr_" }, "source": [ "The training loss and validation WER go down nicely." ] }, { "cell_type": "markdown", "metadata": { "id": "4Ya7WEy0pd13" }, "source": [ "You can now upload the result of the training to the Hub, just execute this instruction:" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 351, "referenced_widgets": [ "4014d89119054c25ac0d1938ac4e2f8a", "c8db6791b1e64c62b6df2f098738eaa4", "fa08a1c6fc6f49038a878076ae2809c5", "f8e74bf4449f4d55bfc52d765ba44882", "115ae9ce4fcd4ff5b5c18648e034c446", "953293c28fa14e71a8e354ea1b844b75", "e84826abaf754217b98672dfd41d35fc", "d506cfa68fef41b6933a71d6d00b7b1e", "946ad0cbe0eb4ee3af73d2b1de3ea95a", "7a16d09a448d47938a4c016a54522ae3", "5738d171b6744ae2a991751a6bc2d850", "5ac1a0aaf868461ca207a09079ca6c53", "5e9e02a9b0bd473081cafbd5bf128507", "2ea0e8ecacb74670b9f9a3455e496eda", "a89587d630fa44bab17fed5a58ebb68b", "7ca6e27713b54a03b5bd52e626f33fc7", "222eef3cbb084b3d8a5ffe8068060340", "cb4652f2b0974680b9f224ef1ef97ddb", "678ac926ffa64e4482f0461aecbf4484", "b46ceb3a5c0d4f4eb7d222f1639c88f5", "2896391751074c528768de57d29d807e", "de70b5deb8894bf8ab9f75a79a228f8d" ] }, "id": "ArG1Thf6NBWm", "outputId": "cac62ad7-d950-43b6-e6cb-d5d76a721f38" }, "source": [ "trainer.push_to_hub()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/preprocessor_config.json\n", "Several commits (2) will be pushed upstream.\n", "The progress bars may be unreliable.\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4014d89119054c25ac0d1938ac4e2f8a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.36k/1.18G [00:00 main\n", "\n", "Dropping the following result as it does not have all the necessary field:\n", "{'dataset': {'name': 'common_voice', 'type': 'common_voice', 'args': 'tr'}}\n", "To https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab\n", " f642252..38e6fca main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab/commit/f64225259534e44179de0f715b96ada1a621551f'" ] }, "metadata": {}, "execution_count": 81 } ] }, { "cell_type": "markdown", "metadata": { "id": "RHIVc44_fY2N" }, "source": [ "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier \"your-username/the-name-you-picked\" so for instance:" ] }, { "cell_type": "markdown", "metadata": { "id": "5lWWIKyBpx1h" }, "source": [ "```python\n", "from transformers import AutoModelForCTC, Wav2Vec2Processor\n", "\n", "model = AutoModelForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab\")\n", "processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab\")\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "L8b8Qkoy3KyS" }, "source": [ "### Evaluation\n", "\n", "As a final check, let's load the model and verify that it indeed has learned to transcribe Turkish speech.\n", "\n", "Let's first load the pretrained checkpoint." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R351I9IQp_9D", "outputId": "337e06a5-99ce-4fe2-8c8d-55d32ef854d3" }, "source": [ "model = Wav2Vec2ForCTC.from_pretrained(repo_name).to(\"cuda\")\n", "processor = Wav2Vec2Processor.from_pretrained(repo_name)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "loading configuration file wav2vec2-large-xlsr-turkish-demo-colab/config.json\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"facebook/wav2vec2-large-xlsr-53\",\n", " \"activation_dropout\": 0.0,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForCTC\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 768,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": true,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"mean\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": true,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_dropout\": 0.0,\n", " \"feat_extract_norm\": \"layer\",\n", " \"feat_proj_dropout\": 0.0,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 4096,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.1,\n", " \"mask_channel_length\": 10,\n", " \"mask_channel_min_space\": 1,\n", " \"mask_channel_other\": 0.0,\n", " \"mask_channel_prob\": 0.0,\n", " \"mask_channel_selection\": \"static\",\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_space\": 1,\n", " \"mask_time_other\": 0.0,\n", " \"mask_time_prob\": 0.05,\n", " \"mask_time_selection\": \"static\",\n", " \"model_type\": \"wav2vec2\",\n", " \"num_attention_heads\": 16,\n", " \"num_codevector_groups\": 2,\n", " \"num_codevectors_per_group\": 320,\n", " \"num_conv_pos_embedding_groups\": 16,\n", " \"num_conv_pos_embeddings\": 128,\n", " \"num_feat_extract_layers\": 7,\n", " \"num_hidden_layers\": 24,\n", " \"num_negatives\": 100,\n", " \"pad_token_id\": 39,\n", " \"proj_codevector_dim\": 768,\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.11.3\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 40\n", "}\n", "\n", "loading weights file wav2vec2-large-xlsr-turkish-demo-colab/pytorch_model.bin\n", "All model checkpoint weights were used when initializing Wav2Vec2ForCTC.\n", "\n", "All the weights of Wav2Vec2ForCTC were initialized from the model checkpoint at wav2vec2-large-xlsr-turkish-demo-colab.\n", "If your task is similar to the task the model of the checkpoint was trained on, you can already use Wav2Vec2ForCTC for predictions without further training.\n", "loading feature extractor configuration file wav2vec2-large-xlsr-turkish-demo-colab/preprocessor_config.json\n", "Feature extractor Wav2Vec2FeatureExtractor {\n", " \"do_normalize\": true,\n", " \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n", " \"feature_size\": 1,\n", " \"padding_side\": \"right\",\n", " \"padding_value\": 0.0,\n", " \"return_attention_mask\": true,\n", " \"sampling_rate\": 16000\n", "}\n", "\n", "Didn't find file wav2vec2-large-xlsr-turkish-demo-colab/added_tokens.json. We won't load it.\n", "Didn't find file wav2vec2-large-xlsr-turkish-demo-colab/tokenizer.json. We won't load it.\n", "loading file wav2vec2-large-xlsr-turkish-demo-colab/vocab.json\n", "loading file wav2vec2-large-xlsr-turkish-demo-colab/tokenizer_config.json\n", "loading file None\n", "loading file wav2vec2-large-xlsr-turkish-demo-colab/special_tokens_map.json\n", "loading file None\n", "Adding to the vocabulary\n", "Adding to the vocabulary\n", "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "jD7TZ1YS3S_K" }, "source": [ "\n", "Now, we will just take the first example of the test set, run it through the model and take the `argmax(...)` of the logits to retrieve the predicted token ids." ] }, { "cell_type": "code", "metadata": { "id": "pax07TnL3WZn" }, "source": [ "input_dict = processor(common_voice_test[0][\"input_values\"], return_tensors=\"pt\", padding=True)\n", "\n", "logits = model(input_dict.input_values.to(\"cuda\")).logits\n", "\n", "pred_ids = torch.argmax(logits, dim=-1)[0]" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "7nkzSQu53Zs2" }, "source": [ "We adapted `common_voice_test` quite a bit so that the dataset instance does not contain the original sentence label anymore. Thus, we re-use the original dataset to get the label of the first example." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 91, "referenced_widgets": [ "14524fb280554ded8192b574e28c8864", "2fab019129fb40a892d2c81921231b8d", "8f5c6d6ea9154f47bc6071ac617a129b", "16d5efe4505c4cf9ad506f727f13ca3f", "b795a95bb02f4b9ea529ae9739e93216", "06e41d172ee64c8b83de13d9a57deb4e", "11a8b77920ac467f8cdbf24e57c18ef2", "39dbd14c0a3141039f0fe0ce0b2e9520", "070317210be940dcb8dd861a7c121ad4", "0313c6db28c84419aad9452323d1446e", "8b7c4d629a254fd1b6a9b7efd277a55f", "9f9438fbb97646bd9b37536f10a26ac3", "fa6e51fa1e89407ba6f276cbc44ab35f", "cf37561bde0f4d4797a05e06b30743dc", "baad24eb45224cd69a7b382c0096c8f1", "01641b1aa2374948853900e6088c4552", "ff9ab3382892452390e1a41fc8960a0f", "0aa4bf638b3547cdae22a5b5b5ab6598", "572cff3a23d7488caf46c35294078aaf", "9c310528cbe443f187316acf14000933", "9eec34d32cbb49a4af0d751ae6bfb185", "9537f0e638a847b6bcf1c29a6cb4f4aa", "581cfd540e4b4830ac2b1087b9e710db", "c6d4e6e19094431588f909ae2f140850", "0b2b6ea6c3144dc0b34070087a6a5ba1", "47f78fc7015a49efbab403dc0cd1e35b", "1d0a998d573c43c4babddb70c4a150f2", "0628b76767e44db0a940ffc37c28c6a6", "4e22bd96a345484a8f47bd7315db9614", "173759aa9d564e96b2517aab6e0f4cdb", "32ef0510fa05479f88d62d7231b044c7", "d92d71486cd84d81bb652f9cb3f83db5", "c6f5f2fcdfbc4b6693d5266c0787a8d0", "6208513b50d448369cda35ef4853adda", "240dc9dd037747fe9afd33f1b9d1706e", "c61e9a7fc607432aaa7b2fef83c07ee6", "e2e2d0e26c114f58ba80b95c14ffa78f", "3bb2711756c34d0aaa2e6c14e1271187", "e306a97a6aa742649afeb81767cbaec4", "01069fae49114fc5a648bb89dce06acd", "0b28824d18734d41add4833bbf2cb666", "0e157aa426bb438eb134695ae4402aec", "23d1789486af45108a6c1ea040be1bee", "57ad71c6dd75478da3881f314a41b077", "8542e9ff4902459fb168a07d70383260", "85dc18d2cf7d4c4ea29b90f2769a8624", "6277391b4480457197a8813d1be29546", "190a1262f4064fe7b7b518b0873fe626", "6354db6cfb2f450ebc3ef6a526c24907", "f615d17b4f204894861cc07caaf6bada", "d1caaa7b3cea4d29b39d7a6b818f52f0", "5199568a4233490fa6a1d599db27ee6b", "a774d27705b148d4bb9017e59b03614e", "bac51043837b468b901d489721786f45", "adc546a919e244449014ff3fcf7cc87a" ] }, "id": "fe2AE-2xqKHx", "outputId": "ec85db5e-687f-4cad-bd70-07650519da3f" }, "source": [ "common_voice_test_transcription = load_dataset(\"common_voice\", \"tr\", data_dir=\"./cv-corpus-6.1-2020-12-11\", split=\"test\")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Using custom data configuration tr-ad9f7b76efa9f3a0\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Downloading and preparing dataset common_voice/tr (download: 592.09 MiB, generated: 2.89 MiB, post-processed: Unknown size, total: 594.98 MiB) to /root/.cache/huggingface/datasets/common_voice/tr-ad9f7b76efa9f3a0/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9...\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "14524fb280554ded8192b574e28c8864", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9f9438fbb97646bd9b37536f10a26ac3", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "581cfd540e4b4830ac2b1087b9e710db", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6208513b50d448369cda35ef4853adda", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8542e9ff4902459fb168a07d70383260", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Dataset common_voice downloaded and prepared to /root/.cache/huggingface/datasets/common_voice/tr-ad9f7b76efa9f3a0/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9. Subsequent calls will reuse this data.\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "epu8kCQZ3h70" }, "source": [ "\n", "Finally, we can decode the example." ] }, { "cell_type": "code", "metadata": { "id": "K4xWqmk_qMn0", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "8ebd95cb-e61e-4115-8aec-5fd562dccd00" }, "source": [ "print(\"Prediction:\")\n", "print(processor.decode(pred_ids))\n", "\n", "print(\"\\nReference:\")\n", "print(common_voice_test_transcription[0][\"sentence\"].lower())" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Prediction:\n", "hata kütük şeyler için bir büyük biz şeyleri koğaluyor ve yene küçük şeyler için bir bir bizi i̇ncilkiyoruz\n", "\n", "Reference:\n", "hayatta küçük şeyleri kovalıyor ve yine küçük şeyler için birbirimizi incitiyoruz.\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "HwhyoMml3oOT" }, "source": [ "Alright! The transcription can definitely be recognized from our prediction, but it is far from being perfect. Training the model a bit longer, spending more time on the data preprocessing, and especially using a language model for decoding would certainly improve the model's overall performance.\n", "\n", "For a demonstration model on a low-resource language, the results are acceptable, however 🤗." ] } ] } ================================================ FILE: Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_🤗_Transformers.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Fine-Tune XLSR-Wav2Vec2 on Turkish ASR with 🤗 Transformers.ipynb", "provenance": [], "collapsed_sections": [], "machine_shape": "hm", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "ff427572296c4f0cb7ecf132b3b328d0": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_view_name": "HBoxView", "_dom_classes": [], "_model_name": "HBoxModel", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_view_count": null, "_view_module_version": "1.5.0", "box_style": "", "layout": "IPY_MODEL_bdeb8617f3a3430086bce2e6800a6d1f", "_model_module": "@jupyter-widgets/controls", "children": [ "IPY_MODEL_71c9a60955bd43afbce1b5cbf650983a", "IPY_MODEL_6625650047ad432bbd3e00c156ec6d53", "IPY_MODEL_7c0d44c9c02f44598c1b40e98cb11fa2" ] } }, "bdeb8617f3a3430086bce2e6800a6d1f": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_view_name": "LayoutView", "grid_template_rows": null, "right": null, "justify_content": null, "_view_module": "@jupyter-widgets/base", "overflow": null, "_model_module_version": "1.2.0", "_view_count": null, "flex_flow": null, "width": null, "min_width": null, "border": null, "align_items": null, "bottom": null, "_model_module": "@jupyter-widgets/base", "top": null, "grid_column": null, "overflow_y": null, "overflow_x": null, "grid_auto_flow": null, "grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } }, "71c9a60955bd43afbce1b5cbf650983a": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_view_name": "HTMLView", "style": "IPY_MODEL_eac571e0f18041049e24a795222ea281", "_dom_classes": [], "description": "", "_model_name": "HTMLModel", "placeholder": "​", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "value": "Downloading: ", "_view_count": null, "_view_module_version": "1.5.0", "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", "layout": "IPY_MODEL_4dd188e7a95e43caa220ceeed7e3dd84" } }, "6625650047ad432bbd3e00c156ec6d53": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_view_name": "ProgressView", "style": "IPY_MODEL_56ff7e6788b34e0e8e1f5f9a80138d47", "_dom_classes": [], "description": "", "_model_name": "FloatProgressModel", "bar_style": "success", "max": 4444, "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "value": 4444, "_view_count": null, "_view_module_version": "1.5.0", "orientation": "horizontal", "min": 0, "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", "layout": "IPY_MODEL_32969fbb1c4d460fa51f447648564e24" } }, "7c0d44c9c02f44598c1b40e98cb11fa2": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_view_name": "HTMLView", "style": "IPY_MODEL_89496fe85e1242178c47c7d2bb6c3fdf", "_dom_classes": [], "description": "", "_model_name": "HTMLModel", "placeholder": "​", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "value": " 22.5k/? 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"grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "LBSYoWbi-45k" }, "source": [ "# **Fine-tuning XLSR-Wav2Vec2 for Multi-Lingual ASR with 🤗 Transformers**" ] }, { "cell_type": "markdown", "metadata": { "id": "V7YOT2mnUiea" }, "source": [ "Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in [September 2020](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by Alexei Baevski, Michael Auli, and Alex Conneau. Soon after the superior performance of Wav2Vec2 was demonstrated on the English ASR dataset LibriSpeech, *Facebook AI* presented XLSR-Wav2Vec2 (click [here](https://arxiv.org/abs/2006.13979)). XLSR stands for *cross-lingual speech representations* and refers to XLSR-Wav2Vec2's ability to learn speech representations that are useful across multiple languages.\n", "\n", "Similar to Wav2Vec2, XLSR-Wav2Vec2 learns powerful speech representations from hundreds of thousands of hours of speech in more than 50 languages of unlabeled speech. Similar, to [BERT's masked language modeling](http://jalammar.github.io/illustrated-bert/), the model learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network.\n", "\n", "![wav2vec2_structure](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xlsr_wav2vec2.png)\n", "\n", "The authors show for the first time that massively pretraining an ASR model on cross-lingual unlabeled speech data, followed by language-specific fine-tuning on very little labeled data achieves state-of-the-art results. See Table 1-5 of the official [paper](https://arxiv.org/pdf/2006.13979.pdf)." ] }, { "cell_type": "markdown", "metadata": { "id": "nT_QrfWtsxIz" }, "source": [ "In this notebook, we will give an in-detail explanation of how XLSR-Wav2Vec2's pretrained checkpoint can be fine-tuned on a low-resource ASR dataset of any language. Note that in this notebook, we will fine-tune XLSR-Wav2Vec2 without making use of a language model. It is much simpler and more efficient to use XLSR-Wav2Vec2 without a language model, but better results can be achieved by including a language model. \n", "\n", "For demonstration purposes, we fine-tune the [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the low resource Turkish ASR dataset of [Common Voice](https://huggingface.co/datasets/common_voice) that contains just ~6h of validated training data." ] }, { "cell_type": "markdown", "metadata": { "id": "Gx9OdDYrCtQ1" }, "source": [ "XLSR-Wav2Vec2 is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems and mainly in Automatic Speech Recognition and handwriting recognition. \n", "\n", "I highly recommend reading the blog post [Sequence Modeling with CTC (2017)](https://distill.pub/2017/ctc/) very well-written blog post by Awni Hannun." ] }, { "cell_type": "markdown", "metadata": { "id": "e335hPmdtASZ" }, "source": [ "Before we start, let's install both `datasets` and `transformers` from master. Also, we need the `torchaudio` and `librosa` package to load audio files and the `jiwer` to evaluate our fine-tuned model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric ${}^1$." ] }, { "cell_type": "code", "metadata": { "id": "c8eh87Hoee5d" }, "source": [ "%%capture\n", "!pip install datasets==1.13.3\n", "!pip install transformers==4.11.3\n", "!pip install torchaudio\n", "!pip install librosa\n", "!pip install jiwer" ], "execution_count": 1, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Mn9swf6EQ9Vd" }, "source": [ "\n", "\n", "\n", "---\n", "\n", "${}^1$ In the [paper](https://arxiv.org/pdf/2006.13979.pdf), the model was evaluated using the phoneme error rate (PER), but by far the most common metric in ASR is the word error rate (WER). To keep this notebook as general as possible we decided to evaluate the model using WER." ] }, { "cell_type": "markdown", "metadata": { "id": "0mW-C1Nt-j7k" }, "source": [ "## Prepare Data, Tokenizer, Feature Extractor" ] }, { "cell_type": "markdown", "metadata": { "id": "BeBosnY9BH3e" }, "source": [ "ASR models transcribe speech to text, which means that we both need a feature extractor that processes the speech signal to the model's input format, *e.g.* a feature vector, and a tokenizer that processes the model's output format to text. \n", "\n", "In 🤗 Transformers, the XLSR-Wav2Vec2 model is thus accompanied by both a tokenizer, called [Wav2Vec2CTCTokenizer](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#wav2vec2ctctokenizer), and a feature extractor, called [Wav2Vec2FeatureExtractor](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#wav2vec2featureextractor).\n", "\n", "Let's start by creating the tokenizer responsible for decoding the model's predictions." ] }, { "cell_type": "markdown", "metadata": { "id": "sEXEWEJGQPqD" }, "source": [ "### Create Wav2Vec2CTCTokenizer" ] }, { "cell_type": "markdown", "metadata": { "id": "tWmMikuNEKl_" }, "source": [ "The [pretrained Wav2Vec2 checkpoint]( ) maps the speech signal to a sequence of context representations as illustrated in the figure above. A fine-tuned XLSR-Wav2Vec2 checkpoint needs to map this sequence of context representations to its corresponding transcription so that a linear layer has to be added on top of the transformer block (shown in yellow). This linear layer is used to classifies each context representation to a token class analogous how, *e.g.*, after pretraining a linear layer is added on top of BERT's embeddings for further classification - *cf.* with *'BERT'* section of this [blog post](https://huggingface.co/blog/warm-starting-encoder-decoder).\n", "\n", "The output size of this layer corresponds to the number of tokens in the vocabulary, which does **not** depend on XLSR-Wav2Vec2's pretraining task, but only on the labeled dataset used for fine-tuning. So in the first step, we will take a look at Common Voice and define a vocabulary based on the dataset's transcriptions." ] }, { "cell_type": "markdown", "metadata": { "id": "idBczw8mWzgt" }, "source": [ "First, let's go to [Common Voice](https://commonvoice.mozilla.org/en/datasets) and pick a language to fine-tune XLSR-Wav2Vec2 on. For this notebook, we will use Turkish. \n", "\n", "For each language-specific dataset, you can find a language code corresponding to your chosen language. On [Common Voice](https://commonvoice.mozilla.org/en/datasets), look for the field \"Version\". The language code then corresponds to the prefix before the underscore. For Turkish, *e.g.* the language code is `\"tr\"`.\n", "\n", "Great, now we can use 🤗 Datasets' simple API to download the data. The dataset name will be `\"common_voice\"`, the config name corresponds to the language code - `\"tr\"` in our case." ] }, { "cell_type": "markdown", "metadata": { "id": "bee4g9rpLxll" }, "source": [ "Common Voice has many different splits including `invalidated`, which refers to data that was not rated as \"clean enough\" to be considered useful. In this notebook, we will only make use of the splits `\"train\"`, `\"validation\"` and `\"test\"`. \n", "\n", "Because the Turkish dataset is so small, we will merge both the validation and training data into a training dataset and simply use the test data for validation." ] }, { "cell_type": "code", "metadata": { "id": "2MMXcWFFgCXU", "colab": { "base_uri": "https://localhost:8080/", "height": 187, "referenced_widgets": [ "ff427572296c4f0cb7ecf132b3b328d0", "bdeb8617f3a3430086bce2e6800a6d1f", "71c9a60955bd43afbce1b5cbf650983a", "6625650047ad432bbd3e00c156ec6d53", "7c0d44c9c02f44598c1b40e98cb11fa2", "eac571e0f18041049e24a795222ea281", "4dd188e7a95e43caa220ceeed7e3dd84", "56ff7e6788b34e0e8e1f5f9a80138d47", "32969fbb1c4d460fa51f447648564e24", "89496fe85e1242178c47c7d2bb6c3fdf", "1a402131429441f5afdb7cb5e5c1b5b3", "a2594d257b464b079ad2ace9417c8cd5", "b34ea734eaf34aa0ab5bed7003aa5bd0", "ed6fa346d630492684ec90f0641efa79", "8e3939e4dfd049b689b678686888d008", "c68bb05ceefd4681906e181422e6c53c", "9528f059b480402eabf0aa96c2bd55f4", "cadc0747ecae441e8e8204119796d770", "5d40503b6d28484bb19b9c8a4c898992", "8190e713c0c444f5962fe05884f2c83f", "e1e3f9342aa8486085b55ae68caec5d2", "60a22f6e6ff4423d841fac1339c14734", "ebc644518d2f41c498f32294e6b22f7c", "6d2b3f02e64b4956b2903d696aad4d9a", "df39620f438e4e0d8e9d392927a0e843", "ca0d44f8fad74fd6b944ef5a637dd60f", "b4588e66e85e4fafa7d7ae844ce25dc4", "8458dc3acedb4197aad0deada58cfd25", "d8eefbfbe4764744a002bbb1682cbc88", "61f4330bf7284d73bb12d1100051c8e5", "e0ef591bcd7643a3818d7d5bf2432ae0", "93ab612c7b554e97bf2a522fb783d49c", "9f20df6b18d246339840c37853938c99", "eefa50e77b184b33802135f7480dd2f2", "06ad49ffacf741e2b519af87b755154d", "f5b256d9939e4c769efdd1c9e04d4b54", "f27521d5489d4b3093cd61f2286ca9b2", "5b53fb1bff7940cf9358d9291970d838", "1e6dc5a87c574b478c1e28df7eecadc2", "b91af4f88ec04658908782651f24e730", "7e2db9e4089e4635b24fceaf1724746c", "7630a6d7835e4a7490fb63f29d906429", "8d479f1afbcd4aa6aa3a816f6b4c42c3", "1e720c6683054082a4e17960f19534bd", "6b07c506e16643b1999a3d226b05cf9c", "b6e49bc1deaf4d28816eebbdb109d3bb", "df26bfe066094d9190f14574b34fd996", "3f5f1e14b56f44fa82530418fa593c9c", "7069ed63eafa485eb01492683bb866a9", "24540f306f60473fa28d2fc1d4e9bea9", "a59a855daf914a1b8512ed00cd9c52aa", "d0d70e6bd574436d83ebc6137ce410ba", "dc78b909ec6e4bcd9a79f2f49b3813a5", "6bdb560651fc444885625e8c95aca8a9", "485200f20fbe4184bd71c94fa42ae14e", "965ece753c1f44b58556b550bd210e82", "cd4b8ce147ba417999b894259218bd28", "07a09089af6f4010a5e2ce7ad22e2b06", "76e4bab6217a40edbad61fc031d1bf47", "ae0ba46be2ad485db2d9d7505e1c3f60", "07a27546c3634ff08e02d2cbdd651973", "9648b7ef6a0341dda4ce47a7b60cada0", "79aa1fcdd7b74c8b848a3a30d478ee46", "9e42e0cac1b24a52a8ecd4bbe125cd6e", "362fbd00a94042ec86af1f9305e34b3b", "da7bc14c770443f6ba6e2b816f87305e", "d8b043ef46ec4f909c9964352357e8d9", "356251b037154bb68a1f9e25c4ea6d24", "a26a78afc0504dc9b0194d6c4944e779", "d78788c2e47443ac95d4c246e5aabe63", "7019d14a7d344c958218150060747a6d", "5d02eb5db6814c8b9f0b7684216f805d", "672a41068a1b40289f10008d19f3bed2", "3389ed8a1e054719b70f992a1066a281", "31d72e7602c34353a612f467dc13981b", "0faefadcd55a4a6ea87f22762463c114", "29bb142cd6e443cf86f89caeaeb1d8dd", "dc40046e47b64d6d9ea4616ad5b71923", "46ebcdffe80241fd905eefe7cd08fa06", "4ac2d5f578304b86b9ae5e7946522079", "6e282338983f44f98db6c158a0760211", "9c19e508fec44f6a8cfd4a4891189316", "f9d499bb16b449238148e09ac122c0d2", "257e16813da44cc9a11947d1e37d8dcb", "daa560dfbd1240f5909796222fb14318", "2b7c85a597434e268f9934660a07c699", "178b7aeb7a074c559ff102088fc7a7bc", "01b2476817374433bcd8dcbd224afeac" ] }, "outputId": "2db75f10-d2f3-4fb9-b793-621c325df384" }, "source": [ "from datasets import load_dataset, load_metric, Audio\n", "\n", "common_voice_train = load_dataset(\"common_voice\", \"tr\", split=\"train+validation\")\n", "common_voice_test = load_dataset(\"common_voice\", \"tr\", split=\"test\")" ], "execution_count": 2, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ff427572296c4f0cb7ecf132b3b328d0", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/4.44k [00:00\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " Bir vergi politikası, nasıl bir yapıda ve ne düzeyde vergi gelirleri hedeflemelidir?\n", " \n", " \n", " 1\n", " Bal!\n", " \n", " \n", " 2\n", " İlk BM barış gücü grubu bölgeye geldi.\n", " \n", " \n", " 3\n", " Onay, iki gün süren tartışmalar sonrasında geldi.\n", " \n", " \n", " 4\n", " Bu da fiyatarda düşüşe yol açacak.\n", " \n", " \n", " 5\n", " \"Lajiç'in eşi de iş ihtiyacını vurguluyor.\"\n", " \n", " \n", " 6\n", " Çantaları taşıyamayacakları kadar ağırdı.\n", " \n", " \n", " 7\n", " Kısıtlı kaynaklar tedavi kalitesini de etkiliyor.\n", " \n", " \n", " 8\n", " Ancak Yunanlılar işini biliyordu.\n", " \n", " \n", " 9\n", " Kosova mutfağı kara iklimiyle son derece uyumlu.\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "fowcOllGNNju" }, "source": [ "Alright! The transcriptions look fairly clean. Having translated the transcribed sentences, it seems that the language corresponds more to written-out text than noisy dialogue. This makes sense considering that [Common Voice](https://huggingface.co/datasets/common_voice) is a crowd-sourced read speech corpus." ] }, { "cell_type": "markdown", "metadata": { "id": "vq7OR50LN49m" }, "source": [ "We can see that the transcriptions contain some special characters, such as `,.?!;:`. Without a language model, it is much harder to classify speech chunks to such special characters because they don't really correspond to a characteristic sound unit. *E.g.*, the letter `\"s\"` has a more or less clear sound, whereas the special character `\".\"` does not.\n", "Also in order to understand the meaning of a speech signal, it is usually not necessary to include special characters in the transcription.\n", "\n", "In addition, we normalize the text to only have lower case letters and append a word separator token at the end." ] }, { "cell_type": "code", "metadata": { "id": "svKzVJ_hQGK6" }, "source": [ "import re\n", "chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�]'\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower() + \" \"\n", " return batch" ], "execution_count": 6, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "a0f7ed4f7bda499e858c3a8b96d192a0", "78f6784d8eab4c3e984b99111576d1d7", "928ff839ec5046cba19d0ebc40da3b57", "2e95594f957c4ac8858a7a9f787f5ea5", "2bcd25eabbed41619641be3b29886d3b", "a82fb3080da4462880e75c800b06ab09", "65b6a575f675428196297acfde14a5a5", "bf7acf0d4ef141c8943ea0fb05bb4f8d", "8fbd5e817554465ea8506b79f5b7ef85", "78b073f8f13243de9ef794f83322935c", "74caa9c93e4b4c7cb559eb6ee89b6470", "d988fac0b5054ec5b64f0070ea0d37cd", "ea8d9171170247d39da281e3d655c896", "1b1d3e58c8e14ac988f7cec9eb9d799a", "9c89ef746c5740248a332d204320a223", "592b37febf02417cb48aea3334406622", "3767f46b20684691b306ba0924aa1816", "2dcf0ff59fe94487acfa828c77e50ff4", "69214b1d8c494a588d40ae8b74fb2762", "ec5f93ed7ec249f5a87ad99cb1faabe9", "024ca8b44da84b2bbd15b169f8edb895", "ea9da49dbceb446698479023e436badd" ] }, "id": "XIHocAuTQbBR", "outputId": "15623228-c648-41e6-c498-ef08840d9fd8" }, "source": [ "common_voice_train = common_voice_train.map(remove_special_characters)\n", "common_voice_test = common_voice_test.map(remove_special_characters)" ], "execution_count": 7, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a0f7ed4f7bda499e858c3a8b96d192a0", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/3478 [00:00\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " drugclan aynı fikirde değil\n", " \n", " \n", " 1\n", " i̇ktidara geldiğinde tamamen farklı olacak\n", " \n", " \n", " 2\n", " tutuklanan doktorların sayısı her hafta artıyor\n", " \n", " \n", " 3\n", " anıtın yeri henüz belirlenmiş değil\n", " \n", " \n", " 4\n", " proje iki bin on yılında tamamlanacak\n", " \n", " \n", " 5\n", " i̇lk fırın altı aydır atıl durumdaydı\n", " \n", " \n", " 6\n", " dimitrov isim önerisi getirilmediğini doğruladı\n", " \n", " \n", " 7\n", " reform süreçlerinin hızından memnun musunuz\n", " \n", " \n", " 8\n", " etrafımda bir sürü yerli insan var\n", " \n", " \n", " 9\n", " bu deliği de tıkadılar\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "jwfaptH5RJwA" }, "source": [ "Good! This looks better. We have removed most special characters from transcriptions and normalized them to lower-case only.\n", "\n", "In CTC, it is common to classify speech chunks into letters, so we will do the same here. \n", "Let's extract all distinct letters of the training and test data and build our vocabulary from this set of letters.\n", "\n", "We write a mapping function that concatenates all transcriptions into one long transcription and then transforms the string into a set of chars. \n", "It is important to pass the argument `batched=True` to the `map(...)` function so that the mapping function has access to all transcriptions at once." ] }, { "cell_type": "code", "metadata": { "id": "LwCshNbbeRZR" }, "source": [ "def extract_all_chars(batch):\n", " all_text = \" \".join(batch[\"sentence\"])\n", " vocab = list(set(all_text))\n", " return {\"vocab\": [vocab], \"all_text\": [all_text]}" ], "execution_count": 9, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "c8aea32c58df4a3d9ca4fd45f6095811", "e69e0b954a3246658453c263eabceca8", "69541581fafc4ef487adfc0b6bd0bd10", "40108feb76854d4484e83cf337f19a7a", "f2fd2acebb7c4f13b207b09a2013238e", "a38a3445076940ccb54d568edd59fe3a", "dd3ee56120ed475c85df62f5e2d187e6", "bab2d3c1e603423290ffca4531805378", "db4f2261fe1a49528a9b93191ea32865", "e38fa996f996440fb8344a29af696e09", "711f9962574b418f85ed408c9c96b460", "10afc812df7e404dbb5f71e40146c33f", "e635a84d73d34650b0f62b16036cc514", "30d6443f0d2f42b48179030e0602a473", "91f99428ab524bb08f72116a8be72554", "37cd3369ce1c478c92d169519c6bae96", "03faf57e7cdd41658dd0fd949ab44ab4", "5bbc649fda0540ca9332907f866819e2", "9eef8d8ac2f440ce953bef054799962f", "4eba4feaf5324e0996cec7727dd5e089", "39b929d0554f46258816215ab02cdbc2", "a961777137074d0b9f676cfcb28eaaa2" ] }, "id": "_m6uUjjcfbjH", "outputId": "5318bb8b-71fa-4217-dd23-4b2004a65d1e" }, "source": [ "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n", "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)" ], "execution_count": 10, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c8aea32c58df4a3d9ca4fd45f6095811", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00\n", " \n", " Your browser does not support the audio element.\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "execution_count": 25 } ] }, { "cell_type": "markdown", "metadata": { "id": "gY8m3vARHYTa" }, "source": [ "Great, it seems like the data is now correctly loaded and resampled. " ] }, { "cell_type": "markdown", "metadata": { "id": "1MaL9J2dNVtG" }, "source": [ "It can be heard, that the speakers change along with their speaking rate, accent, and background environment, etc. Overall, the recordings sound acceptably clear though, which is to be expected from a crowd-sourced read speech corpus.\n", "\n", "Let's do a final check that the data is correctly prepared, by printing the shape of the speech input, its transcription, and the corresponding sampling rate.\n", "\n", "**Note**: *You can click the following cell a couple of times to verify multiple samples.*" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Po2g7YPuRTx", "outputId": "ed01e899-1a15-4f4e-a435-c196071f4c82" }, "source": [ "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n", "print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n", "print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])" ], "execution_count": 26, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Target text: rusya ülkeye dünya örgütünde destek vermişti \n", "Input array shape: (79872,)\n", "Sampling rate: 16000\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "M9teZcSwOBJ4" }, "source": [ "Good! Everything looks fine - the data is a 1-dimensional array, the sampling rate always corresponds to 16kHz, and the target text is normalized." ] }, { "cell_type": "markdown", "metadata": { "id": "k3Pbn5WvOYZF" }, "source": [ "Finally, we can leverage `Wav2Vec2Processor` to process the data to the format expected by `Wav2Vec2ForCTC` for training. We will again make use of the `map(...)` function.\n", "\n", "First, we load and resample the audio data, simply by calling `batch[\"audio\"]`.\n", "Second, we extract the `input_values` from the loaded audio file. In our case, this includes only normalization, but for other speech models, this step could correspond to extracting, *e.g.* [Log-Mel features](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). \n", "Third, we encode the transcriptions to label ids.\n", "\n", "**Note**: This mapping function is a good example of how the `Wav2Vec2Processor` class should be used. In \"normal\" context, calling `processor(...)` is redirected to `Wav2Vec2FeatureExtractor`'s call method. When wrapping the processor into the `as_target_processor` context, however, the same method is redirected to `Wav2Vec2CTCTokenizer`'s call method.\n", "For more information please check the [docs](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#transformers.Wav2Vec2Processor.__call__)." ] }, { "cell_type": "code", "metadata": { "id": "eJY7I0XAwe9p" }, "source": [ "def prepare_dataset(batch):\n", " audio = batch[\"audio\"]\n", "\n", " # batched output is \"un-batched\"\n", " batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n", " \n", " with processor.as_target_processor():\n", " batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n", " return batch" ], "execution_count": 28, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "q6Pg_WR3OGAP" }, "source": [ "Let's apply the data preparation function to all examples." ] }, { "cell_type": "code", "metadata": { "id": "-np9xYK-wl8q" }, "source": [ "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names, num_proc=4)\n", "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names, num_proc=4)" ], "execution_count": 29, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "nKcEWHvKI1by" }, "source": [ "**Note**: Currently `datasets` make use of [`torchaudio`](https://pytorch.org/audio/stable/index.html) and [`librosa`](https://librosa.org/doc/latest/index.html) for audio loading and resampling. If you wish to implement your own costumized data loading/sampling, feel free to just make use of the `\"path\"` column instead and disregard the `\"audio\"` column." ] }, { "cell_type": "markdown", "metadata": { "id": "gYlQkKVoRUos" }, "source": [ "## Training\n", "\n", "The data is processed so that we are ready to start setting up the training pipeline. We will make use of 🤗's [Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer) for which we essentially need to do the following:\n", "\n", "- Define a data collator. In contrast to most NLP models, XLSR-Wav2Vec2 has a much larger input length than output length. *E.g.*, a sample of input length 50000 has an output length of no more than 100. Given the large input sizes, it is much more efficient to pad the training batches dynamically meaning that all training samples should only be padded to the longest sample in their batch and not the overall longest sample. Therefore, fine-tuning XLSR-Wav2Vec2 requires a special padding data collator, which we will define below\n", "\n", "- Evaluation metric. During training, the model should be evaluated on the word error rate. We should define a `compute_metrics` function accordingly\n", "\n", "- Load a pretrained checkpoint. We need to load a pretrained checkpoint and configure it correctly for training.\n", "\n", "- Define the training configuration.\n", "\n", "After having fine-tuned the model, we will correctly evaluate it on the test data and verify that it has indeed learned to correctly transcribe speech." ] }, { "cell_type": "markdown", "metadata": { "id": "Slk403unUS91" }, "source": [ "### Set-up Trainer\n", "\n", "Let's start by defining the data collator. The code for the data collator was copied from [this example](https://github.com/huggingface/transformers/blob/9a06b6b11bdfc42eea08fa91d0c737d1863c99e3/examples/research_projects/wav2vec2/run_asr.py#L81).\n", "\n", "Without going into too many details, in contrast to the common data collators, this data collator treats the `input_values` and `labels` differently and thus applies to separate padding functions on them (again making use of XLSR-Wav2Vec2's context manager). This is necessary because in speech input and output are of different modalities meaning that they should not be treated by the same padding function.\n", "Analogous to the common data collators, the padding tokens in the labels with `-100` so that those tokens are **not** taken into account when computing the loss." ] }, { "cell_type": "code", "metadata": { "id": "tborvC9hx88e" }, "source": [ "import torch\n", "\n", "from dataclasses import dataclass, field\n", "from typing import Any, Dict, List, Optional, Union\n", "\n", "@dataclass\n", "class DataCollatorCTCWithPadding:\n", " \"\"\"\n", " Data collator that will dynamically pad the inputs received.\n", " Args:\n", " processor (:class:`~transformers.Wav2Vec2Processor`)\n", " The processor used for proccessing the data.\n", " padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n", " Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n", " among:\n", " * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n", " sequence if provided).\n", " * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n", " maximum acceptable input length for the model if that argument is not provided.\n", " * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n", " different lengths).\n", " max_length (:obj:`int`, `optional`):\n", " Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).\n", " max_length_labels (:obj:`int`, `optional`):\n", " Maximum length of the ``labels`` returned list and optionally padding length (see above).\n", " pad_to_multiple_of (:obj:`int`, `optional`):\n", " If set will pad the sequence to a multiple of the provided value.\n", " This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=\n", " 7.5 (Volta).\n", " \"\"\"\n", "\n", " processor: Wav2Vec2Processor\n", " padding: Union[bool, str] = True\n", " max_length: Optional[int] = None\n", " max_length_labels: Optional[int] = None\n", " pad_to_multiple_of: Optional[int] = None\n", " pad_to_multiple_of_labels: Optional[int] = None\n", "\n", " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n", " # split inputs and labels since they have to be of different lenghts and need\n", " # different padding methods\n", " input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", "\n", " batch = self.processor.pad(\n", " input_features,\n", " padding=self.padding,\n", " max_length=self.max_length,\n", " pad_to_multiple_of=self.pad_to_multiple_of,\n", " return_tensors=\"pt\",\n", " )\n", " with self.processor.as_target_processor():\n", " labels_batch = self.processor.pad(\n", " label_features,\n", " padding=self.padding,\n", " max_length=self.max_length_labels,\n", " pad_to_multiple_of=self.pad_to_multiple_of_labels,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " # replace padding with -100 to ignore loss correctly\n", " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", "\n", " batch[\"labels\"] = labels\n", "\n", " return batch" ], "execution_count": 30, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "lbQf5GuZyQ4_" }, "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ], "execution_count": 31, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xO-Zdj-5cxXp" }, "source": [ "Next, the evaluation metric is defined. As mentioned earlier, the \n", "predominant metric in ASR is the word error rate (WER), hence we will use it in this notebook as well." ] }, { "cell_type": "code", "metadata": { "id": "9Xsux2gmyXso", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "91b9939e3da0414ca210bb60f0649741", "86df11b90a2c41bcb013e48be3aa6ce1", "77eb2d2ed73947d29b96ac194f84cd72", "9934081e12ae48da8340661a95c63f32", "90e16472877749e4b2e1c9f9b998483f", "f933157cee4b4333bc1bd54f91d31ffe", "930a797df100485fad9752132bf0e3c7", "839531fbecb94e23a9e0ec7f1b045c69", "71946fe0f704479aa3feee1da6938bbf", "7f97be3a313b45229e25fbae81d28b95", "1a05d11b10434baba0aa9b042fc3d20d" ] }, "outputId": "1d1caa4e-f73b-431b-bb1c-227bca1e631d" }, "source": [ "wer_metric = load_metric(\"wer\")" ], "execution_count": 32, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "91b9939e3da0414ca210bb60f0649741", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.95k [00:00> m$.\n", "\n", "A logit vector $\\mathbf{y}_1$ contains the log-odds for each word in the vocabulary we defined earlier, thus $\\text{len}(\\mathbf{y}_i) =$ `config.vocab_size`. We are interested in the most likely prediction of the model and thus take the `argmax(...)` of the logits. Also, we transform the encoded labels back to the original string by replacing `-100` with the `pad_token_id` and decoding the ids while making sure that consecutive tokens are **not** grouped to the same token in CTC style ${}^1$." ] }, { "cell_type": "code", "metadata": { "id": "1XZ-kjweyTy_" }, "source": [ "def compute_metrics(pred):\n", " pred_logits = pred.predictions\n", " pred_ids = np.argmax(pred_logits, axis=-1)\n", "\n", " pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n", "\n", " pred_str = processor.batch_decode(pred_ids)\n", " # we do not want to group tokens when computing the metrics\n", " label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n", "\n", " wer = wer_metric.compute(predictions=pred_str, references=label_str)\n", "\n", " return {\"wer\": wer}" ], "execution_count": 33, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Xmgrx4bRwLIH" }, "source": [ "Now, we can load the pretrained `XLSR-Wav2Vec2` checkpoint. The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of `Wav2Vec2ForCTC` also CTC's *blank token* ${}^2$. To save GPU memory, we enable PyTorch's [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html) and also set the loss reduction to \"*mean*\".\n", "\n", "Because the dataset is quite small (~6h of training data) and because Common Voice is quite noisy, fine-tuning Facebook's [wav2vec2-large-xlsr-53 checkpoint](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) seems to require some hyper-parameter tuning. Therefore, I had to play around a bit with different values for dropout, [SpecAugment](https://arxiv.org/abs/1904.08779)'s masking dropout rate, layer dropout, and the learning rate until training seemed to be stable enough. \n", "\n", "**Note**: When using this notebook to train XLSR-Wav2Vec2 on another language of Common Voice those hyper-parameter settings might not work very well. Feel free to adapt those depending on your use case. " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 191, "referenced_widgets": [ "eaab09a8d1344ef495857150772774d0", "4b3c69af59024d12a226f96ecf4c1160", "e2e22b49f414495e9d65071aa212cf52", "2e1a49e41a2d47768b6d6ad590e99a3e", "657bb2b45ac04c27bbab0be67463ed9c", "fbf7b312dc094c1a81665b180bfa7bc1", "d694f70b57ad419ca7735357299a1c28", "f64233294f004b97b306995e0b1e09d5", "451266e4e47d4f15bb94ebb2ff8ffb89", "605f57cda2214d2fabf09310d07e22e2", "746d4e57cd50455a90770c6e28d8cf23", "edc14234a3a04fc99b264a80d6f07da8", "dea77ede594540009e2f266cb00a4c06", "101840d8f4b94eca9fbf6fd9971aa56c", "d8190a73f9274c62957e0b8ff684294a", "89c8e6eea2424d4ca1c302017f927f67", "97e8f3359f994b85b2773a4d72478856", "7eb9cdff79434d35a46270b7ea0b86ce", "49f24f5f9b5340b499572ebd55b1a058", "985844f3740a4333b9d1b08aa9f95265", "01cb8d98df0d44dda0f1aaf3deca8e85", "7bdcba7e382e4517a0641b4025e69b2f" ] }, "id": "e7cqAWIayn6w", "outputId": "7805bbf0-3796-49a7-8295-15445641c5f8" }, "source": [ "from transformers import Wav2Vec2ForCTC\n", "\n", "model = Wav2Vec2ForCTC.from_pretrained(\n", " \"facebook/wav2vec2-large-xlsr-53\", \n", " attention_dropout=0.1,\n", " hidden_dropout=0.1,\n", " feat_proj_dropout=0.0,\n", " mask_time_prob=0.05,\n", " layerdrop=0.1,\n", " ctc_loss_reduction=\"mean\", \n", " pad_token_id=processor.tokenizer.pad_token_id,\n", " vocab_size=len(processor.tokenizer)\n", ")" ], "execution_count": 34, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "eaab09a8d1344ef495857150772774d0", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.73k [00:00 inspect -> Console tab and insert code*)." ] }, { "cell_type": "markdown", "metadata": { "id": "VYYAvgkW4P0m" }, "source": [ "```javascript\n", "function ConnectButton(){\n", " console.log(\"Connect pushed\"); \n", " document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n", "}\n", "setInterval(ConnectButton,60000);\n", "```" ] }, { "cell_type": "code", "metadata": { "id": "_UEjJqGsQw24", "colab": { "base_uri": "https://localhost:8080/", "height": 975 }, "outputId": "6eba6566-615a-4081-964a-fb44a2cf8081" }, "source": [ "trainer.train()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "***** Running training *****\n", " Num examples = 3478\n", " Num Epochs = 30\n", " Instantaneous batch size per device = 16\n", " Total train batch size (w. parallel, distributed & accumulation) = 32\n", " Gradient Accumulation steps = 2\n", " Total optimization steps = 3270\n", "/usr/local/lib/python3.7/dist-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\n", "To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.)\n", " return torch.floor_divide(self, other)\n", "/usr/local/lib/python3.7/dist-packages/transformers/trainer.py:1357: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_grad_norm_; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior.\n", " args.max_grad_norm,\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "

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" ], "text/plain": [ "" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to ./wav2vec2-large-xlsr-turkish-demo/checkpoint-100\n", "Configuration saved in ./wav2vec2-large-xlsr-turkish-demo/checkpoint-100/config.json\n", "Model weights saved in ./wav2vec2-large-xlsr-turkish-demo/checkpoint-100/pytorch_model.bin\n", "Configuration saved in ./wav2vec2-large-xlsr-turkish-demo/checkpoint-100/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to ./wav2vec2-large-xlsr-turkish-demo/checkpoint-200\n", "Configuration saved in ./wav2vec2-large-xlsr-turkish-demo/checkpoint-200/config.json\n", "Model weights saved in ./wav2vec2-large-xlsr-turkish-demo/checkpoint-200/pytorch_model.bin\n", "Configuration saved in ./wav2vec2-large-xlsr-turkish-demo/checkpoint-200/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to ./wav2vec2-large-xlsr-turkish-demo/checkpoint-300\n", "Configuration saved in ./wav2vec2-large-xlsr-turkish-demo/checkpoint-300/config.json\n", "Model weights saved in ./wav2vec2-large-xlsr-turkish-demo/checkpoint-300/pytorch_model.bin\n", "Configuration saved in ./wav2vec2-large-xlsr-turkish-demo/checkpoint-300/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo/checkpoint-100] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to ./wav2vec2-large-xlsr-turkish-demo/checkpoint-400\n", "Configuration saved in ./wav2vec2-large-xlsr-turkish-demo/checkpoint-400/config.json\n", "Model weights saved in ./wav2vec2-large-xlsr-turkish-demo/checkpoint-400/pytorch_model.bin\n", "Configuration saved in ./wav2vec2-large-xlsr-turkish-demo/checkpoint-400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo/checkpoint-200] due to args.save_total_limit\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "RHIVc44_fY2N" }, "source": [ "The training and validation loss go down nicely. The WER starts improving only at a later stage. Because this notebook is just for demonstration purposes, we can stop here.\n", "\n", "To fine-tune larger models on larger datasets using CTC loss, one should take a look at the official speech-recognition examples [here](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition#connectionist-temporal-classification-without-language-model-ctc-wo-lm) 🤗." ] } ] } ================================================ FILE: Fine_Tune_XLS_R_on_Common_Voice.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "accelerator": "GPU", "colab": { "name": "Copy of Fine-Tune XLS-R on Common Voice.ipynb", "provenance": [], "collapsed_sections": [], "toc_visible": true, "machine_shape": "hm", "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "b896566369e3403091e357af18648740": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [ "IPY_MODEL_26bf8a70fe5449a99ca1b06430b2fc8f", "IPY_MODEL_dd5d9b3f70ee4a0dabe18e87ab38145c", "IPY_MODEL_2d4eb3e5acc348948299c506db83ca90" ], "layout": "IPY_MODEL_384228b409cb4bb0bbd77ef259b9a568" } }, "26bf8a70fe5449a99ca1b06430b2fc8f": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_0c01a04a8a924740af301c5c53a51315", "placeholder": "​", "style": "IPY_MODEL_15f8964842df434ca0dee00d7dc1bd9a", "value": "

\nHugging Face\n
\nThe AI community building the future\n
\nImmediately click login after typing your password or it might be stored in plain text in this notebook file.\n
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"_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "LBSYoWbi-45k" }, "source": [ "# **Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers**\n", "\n", "***New (11/2021)***: *This blog post has been updated to feature XLSR's successor, called [XLS-R](https://huggingface.co/models?other=xls_r)*." ] }, { "cell_type": "markdown", "metadata": { "id": "V7YOT2mnUiea" }, "source": [ "**Wav2Vec2** is a pretrained model for Automatic Speech Recognition (ASR) and was released in [September 2020](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by *Alexei Baevski, Michael Auli, and Alex Conneau*. Soon after the superior performance of Wav2Vec2 was demonstrated on one of the most popular English datasets for ASR, called [LibriSpeech](https://huggingface.co/datasets/librispeech_asr), *Facebook AI* presented a multi-lingual version of Wav2Vec2, called [XLSR](https://arxiv.org/abs/2006.13979). XLSR stands for *cross-lingual speech representations* and refers to model's ability to learn speech representations that are useful across multiple languages.\n", "\n", "XLSR's successor, simply called **XLS-R** (refering to the [*''XLM-R*](https://ai.facebook.com/blog/-xlm-r-state-of-the-art-cross-lingual-understanding-through-self-supervision/) *for Speech''*), was released in [November 2021](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) by *Arun Babu, Changhan Wang, Andros Tjandra, et al.* XLS-R used almost **half a million** hours of audio data in 128 languages for self-supervised pre-training and comes in sizes ranging from 300 milion up to **two billion** parameters. You can find the pretrained checkpoints on the 🤗 Hub:\n", "\n", "- [**Wav2Vec2-XLS-R-300M**](https://huggingface.co/facebook/wav2vec2-xls-r-300m)\n", "- [**Wav2Vec2-XLS-R-1B**](https://huggingface.co/facebook/wav2vec2-xls-r-1b)\n", "- [**Wav2Vec2-XLS-R-2B**](https://huggingface.co/facebook/wav2vec2-xls-r-2b)\n", "\n", "Similar to [BERT's masked language modeling objective](http://jalammar.github.io/illustrated-bert/), XLS-R learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network during self-supervised pre-training (*i.e.* diagram on the left below). \n", "\n", "For fine-tuning, a single linear layer is added on top of the pre-trained network to train the model on labeled data of audio downstream tasks such as speech recognition, speech translation and audio classification (*i.e.* diagram on the right below).\n", "\n", "![wav2vec2_structure](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xls_r.png)\n", "\n", "XLS-R shows impressive improvements over previous state-of-the-art results on both speech recognition, speech translation and speaker/language identification, *cf.* with Table 3-6, Table 7-10, and Table 11-12 respectively of the official [paper](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages)." ] }, { "cell_type": "markdown", "metadata": { "id": "nT_QrfWtsxIz" }, "source": [ "## Notebook Setup" ] }, { "cell_type": "markdown", "metadata": { "id": "kruqixOYHaIo" }, "source": [ "\n", "In this notebook, we will give an in-detail explanation of how XLS-R - more specifically the pre-trained checkpoint [**Wav2Vec2-XLS-R-300M**](https://huggingface.co/facebook/wav2vec2-xls-r-300m) - can be fine-tuned for ASR. \n", "\n", "For demonstration purposes, we fine-tune the model on the low resource ASR dataset of [Common Voice](https://huggingface.co/datasets/common_voice) that contains only *ca.* 4h of validated training data." ] }, { "cell_type": "markdown", "metadata": { "id": "Gx9OdDYrCtQ1" }, "source": [ "XLS-R is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems, such as ASR and handwriting recognition. \n", "\n", "I highly recommend reading the well-written blog post [*Sequence Modeling with CTC (2017)*](https://distill.pub/2017/ctc/) by Awni Hannun." ] }, { "cell_type": "markdown", "metadata": { "id": "wcHuXIaWyHZU" }, "source": [ "First, let's try to get a good GPU in our colab! With Google Colab's free version it's sadly becoming much harder to get access to a good GPU. With Google Colab Pro, however, one should easily get either a V100 or P100 GPU." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YELVqGxMxnbG", "outputId": "1ab7eb67-409d-4371-b99e-7eb1171cb5fb" }, "source": [ "gpu_info = !nvidia-smi\n", "gpu_info = '\\n'.join(gpu_info)\n", "if gpu_info.find('failed') >= 0:\n", " print('Not connected to a GPU')\n", "else:\n", " print(gpu_info)" ], "execution_count": null, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fri Nov 12 14:31:29 2021 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 495.44 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 41C P0 26W / 250W | 0MiB / 16280MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "e335hPmdtASZ" }, "source": [ "Before we start, let's install `datasets` and `transformers`. Also, we need the `torchaudio` to load audio files and `jiwer` to evaluate our fine-tuned model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric ${}^1$." ] }, { "cell_type": "code", "metadata": { "id": "c8eh87Hoee5d" }, "source": [ "%%capture\n", "!pip install datasets==1.18.3\n", "!pip install transformers==4.11.3\n", "!pip install torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n", "!pip install jiwer" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0xxt_LwxDQlO" }, "source": [ "We strongly suggest to upload your training checkpoints directly to the [🤗 Hub](https://huggingface.co/) while training. The [🤗 Hub](https://huggingface.co/) has integrated version control so you can be sure that no model checkpoint is getting lost during training. \n", "\n", "To do so you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!)" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 239, "referenced_widgets": [ "b896566369e3403091e357af18648740", "26bf8a70fe5449a99ca1b06430b2fc8f", "dd5d9b3f70ee4a0dabe18e87ab38145c", "2d4eb3e5acc348948299c506db83ca90", "384228b409cb4bb0bbd77ef259b9a568", "0c01a04a8a924740af301c5c53a51315", "15f8964842df434ca0dee00d7dc1bd9a", "49f98244a8634be9b2305bb87731e765", "a28d3e82e04646b79c89f93cf40ee596", "4c3e043e600641538a479df6ad389784", "d11d94912b26401a860c80eba767907a", "6dd7aa6b17b34d18ba46d834ffd559bc", "4bf1f9d3031e4877aa6ef704e9a227d7", "bfb6a1e47f6e4f74af4db7e71227ef3e", "8541f8f949ea49b7a1335aaf31ca4f81", "c237b5686d3e48aeb226c87aa703f63d" ] }, "id": "mlMSH3T3EazV", "outputId": "cecdacd4-9e12-4878-d5ae-fd2eeeb7d355" }, "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "execution_count": null, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b896566369e3403091e357af18648740", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"
\\n\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " Etkinliğe dünyadan otuzdan fazla sanatçı katıldı.\n", " \n", " \n", " 1\n", " İnşaat iki bin dokuz yılında başlayacak.\n", " \n", " \n", " 2\n", " Lükse yer kalmıyor.\n", " \n", " \n", " 3\n", " Gümrük idaresi beş yüz yetmiş iki virgül iki milyon avro topladı.\n", " \n", " \n", " 4\n", " Pahor bu hareketi eleştirdi.\n", " \n", " \n", " 5\n", " İki tane vardı.\n", " \n", " \n", " 6\n", " Mali sorunlara rağmen, bunu tekrar başardı.\n", " \n", " \n", " 7\n", " Ancak yakında bu durum değişebilir.\n", " \n", " \n", " 8\n", " Bu teklifleri hangi koşullarda kabul edersiniz?\n", " \n", " \n", " 9\n", " Fakat bu yaz, bunların bir kısmı adadan ayrıldı.\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ] }, { "cell_type": "markdown", "metadata": { "id": "fowcOllGNNju" }, "source": [ "Alright! The transcriptions look fairly clean. Having translated the transcribed sentences, it seems that the language corresponds more to written-out text than noisy dialogue. This makes sense considering that [Common Voice](https://huggingface.co/datasets/common_voice) is a crowd-sourced read speech corpus." ] }, { "cell_type": "markdown", "metadata": { "id": "vq7OR50LN49m" }, "source": [ "We can see that the transcriptions contain some special characters, such as `,.?!;:`. Without a language model, it is much harder to classify speech chunks to such special characters because they don't really correspond to a characteristic sound unit. *E.g.*, the letter `\"s\"` has a more or less clear sound, whereas the special character `\".\"` does not.\n", "Also in order to understand the meaning of a speech signal, it is usually not necessary to include special characters in the transcription.\n", "\n", "Let's simply remove all characters that don't contribute to the meaning of a word and cannot really be represented by an acoustic sound and normalize the text." ] }, { "cell_type": "code", "metadata": { "id": "svKzVJ_hQGK6" }, "source": [ "import re\n", "chars_to_remove_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\']'\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n", " return batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XIHocAuTQbBR", "outputId": "32b09453-6f60-42c4-e417-4e87edad58dd" }, "source": [ "common_voice_train = common_voice_train.map(remove_special_characters)\n", "common_voice_test = common_voice_test.map(remove_special_characters)" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading cached processed dataset at /root/.cache/huggingface/datasets/common_voice/tr/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9/cache-2963c9a6418469ef.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/common_voice/tr/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9/cache-a3aa7587e4d2fd25.arrow\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "TxnVS9gIhIma" }, "source": [ "Let's look at the processed text labels again." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "RBDRAAYxRE6n", "outputId": "5bbca4d5-57b3-48ca-e591-b5feeab0db3d" }, "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))" ], "execution_count": null, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sentence
0ama sonra pazarlık başlıyordu
1diğerleri de hep birlikte işi bırakabilir
2tüm bölge liderleri başsağlığı dilediler
3onurun gözlerindeki büyü
4i̇lkiyse bin dokuz yüz elli dokuz yılında meydana gelmişti
5selin kontrol edilememesi ihtimali hakkındaki fikri sorulan cumhurbaşkanı allah korusun dedi
6bunlar iyi işlemesi gereken ufak çarklar
7halkın yaklaşık yüzde otuz üçü oy kullanmayacak
8güzel alternatif hedefler aramadığını belirtti
9kamuoyu denktaşa karşı
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ] }, { "cell_type": "markdown", "metadata": { "id": "jwfaptH5RJwA" }, "source": [ "Good! This looks better. We have removed most special characters from transcriptions and normalized them to lower-case only.\n", "\n", "Before finalizing the pre-processing, it is always advantageous to consult a native speaker of the target language to see whether the text can be further simplified. \n", "For this blog post, [Merve](https://twitter.com/mervenoyann) was kind enough to take a quick look and noted that \"hatted\" characters - like `â` - aren't really used anymore in Turkish and can be replaced by their \"un-hatted\" equivalent, *e.g.* `a`. \n", "\n", "This means that we should replace a sentence like `\"yargı sistemi hâlâ sağlıksız\"` to `\"yargı sistemi hala sağlıksız\"`.\n", "\n", "Let's write another short mapping function to further simplify the text labels. Remember - the simler the text labels, the easier it is for the model to learn to predict those labels.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "aZcrz6z7lgGm" }, "source": [ "def replace_hatted_characters(batch):\n", " batch[\"sentence\"] = re.sub('[â]', 'a', batch[\"sentence\"])\n", " batch[\"sentence\"] = re.sub('[î]', 'i', batch[\"sentence\"])\n", " batch[\"sentence\"] = re.sub('[ô]', 'o', batch[\"sentence\"])\n", " batch[\"sentence\"] = re.sub('[û]', 'u', batch[\"sentence\"])\n", " return batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ieGhhND5mSwI", "outputId": "2f5cee33-a98a-45ae-a8b4-0f10d97f4d15" }, "source": [ "common_voice_train = common_voice_train.map(replace_hatted_characters)\n", "common_voice_test = common_voice_test.map(replace_hatted_characters)" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading cached processed dataset at /root/.cache/huggingface/datasets/common_voice/tr/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9/cache-42d8bb33221ffcb6.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/common_voice/tr/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9/cache-7feed192f520d0a3.arrow\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "3ORHDb2Th2TW" }, "source": [ "In CTC, it is common to classify speech chunks into letters, so we will do the same here. \n", "Let's extract all distinct letters of the training and test data and build our vocabulary from this set of letters.\n", "\n", "We write a mapping function that concatenates all transcriptions into one long transcription and then transforms the string into a set of chars. \n", "It is important to pass the argument `batched=True` to the `map(...)` function so that the mapping function has access to all transcriptions at once." ] }, { "cell_type": "code", "metadata": { "id": "LwCshNbbeRZR" }, "source": [ "def extract_all_chars(batch):\n", " all_text = \" \".join(batch[\"sentence\"])\n", " vocab = list(set(all_text))\n", " return {\"vocab\": [vocab], \"all_text\": [all_text]}" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "e78238c96d6a4152bf40f5d7a81e5495", "75b26f4a95d24e9d8939d1f7b376eb51", "f58af0561fc242238cfd0163f0ad5e5f", "a1419cc920324f62be58d6e48a82e275", "6ec3c80319fb45f3b6f7715049c9bce0", "02519344818c40d0b961c5ea210a5c62", "e575e46791b24f449b8afcb773e46dcc", "5cff6b2dd6944c59b8c2f205db5f49bf", "5ba2a38c71e64253a4fd39cf7d3b3326", "bc11f833cc9c441b9a58d69ea792fa9c", "4f81e460906a428f9f74a0aa469ba63f", "093927de0d86406290365759e88fd4e7", "f6e777f3a1684a7da3efc63281553b5e", "609781a3f55d4d3dbf0b3640972755fa", "8783792878a64402981a416989924348", "92e295e353624110880cff6835b9e119", "db7a9c27eabb4ba391bfb9d819d7a36e", "fd2ba2bf4b2946078a0761b773626b64", "a5777207c788498fb4ac4900af91d7cf", "890ff54dc35e460b9108e2690966fafe", "cc70741031664fa89bfe8e8e672c5b2b", "46c8681bd36f419495d33b06c96311ed" ] }, "id": "_m6uUjjcfbjH", "outputId": "67fa96dd-2df9-4393-f551-4d599c42efff" }, "source": [ "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n", "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)" ], "execution_count": null, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e78238c96d6a4152bf40f5d7a81e5495", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1 [00:00 main\n", "\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-turkish-colab/commit/56ebe744b5acf5853233e4425bd914b938c4f013'" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "metadata": { "id": "SwQM8lH_GGuP" }, "source": [ "Great, you can see the just created repository under `https://huggingface.co//wav2vec2-large-xls-r-300m-tr-colab`" ] }, { "cell_type": "markdown", "metadata": { "id": "mYcIiR2FQ96i" }, "source": [ "### Create `Wav2Vec2FeatureExtractor`" ] }, { "cell_type": "markdown", "metadata": { "id": "Y6mDEyW719rx" }, "source": [ "Speech is a continuous signal and to be treated by computers, it first has to be discretized, which is usually called **sampling**. The sampling rate hereby plays an important role in that it defines how many data points of the speech signal are measured per second. Therefore, sampling with a higher sampling rate results in a better approximation of the *real* speech signal but also necessitates more values per second.\n", "\n", "A pretrained checkpoint expects its input data to have been sampled more or less from the same distribution as the data it was trained on. The same speech signals sampled at two different rates have a very different distribution, *e.g.*, doubling the sampling rate results in data points being twice as long. Thus, \n", "before fine-tuning a pretrained checkpoint of an ASR model, it is crucial to verify that the sampling rate of the data that was used to pretrain the model matches the sampling rate of the dataset used to fine-tune the model.\n", "\n", "XLS-R was pretrained on audio data of [Babel](http://www.reading.ac.uk/AcaDepts/ll/speechlab/babel/r), \n", "[Multilingual LibriSpeech (MLS)](https://huggingface.co/datasets/multilingual_librispeech), [Common Voice](https://huggingface.co/datasets/common_voice), [VoxPopuli](https://arxiv.org/abs/2101.00390), and [VoxLingua107](https://arxiv.org/abs/2011.12998) at a sampling rate of 16kHz. Common Voice, in its original form, has a sampling rate of 48kHz, thus we will have to downsample the fine-tuning data to 16kHz in the following.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "KuUbPW7oV-B5" }, "source": [ "A `Wav2Vec2FeatureExtractor` object requires the following parameters to be instantiated:\n", "\n", "- `feature_size`: Speech models take a sequence of feature vectors as an input. While the length of this sequence obviously varies, the feature size should not. In the case of Wav2Vec2, the feature size is 1 because the model was trained on the raw speech signal ${}^2$.\n", "- `sampling_rate`: The sampling rate at which the model is trained on.\n", "- `padding_value`: For batched inference, shorter inputs need to be padded with a specific value\n", "- `do_normalize`: Whether the input should be *zero-mean-unit-variance* normalized or not. Usually, speech models perform better when normalizing the input\n", "- `return_attention_mask`: Whether the model should make use of an `attention_mask` for batched inference. In general, XLS-R models checkpoints should **always** use the `attention_mask`." ] }, { "cell_type": "code", "metadata": { "id": "kAR0-2KLkopp" }, "source": [ "from transformers import Wav2Vec2FeatureExtractor\n", "\n", "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qUETetgqYC3W" }, "source": [ "Great, XLS-R's feature extraction pipeline is thereby fully defined!\n", "\n", "For improved user-friendliness, the feature extractor and tokenizer are *wrapped* into a single `Wav2Vec2Processor` class so that one only needs a `model` and `processor` object." ] }, { "cell_type": "code", "metadata": { "id": "KYZtoW-tlZgl" }, "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DrKnYuvDIoOO" }, "source": [ "Next, we can prepare the dataset." ] }, { "cell_type": "markdown", "metadata": { "id": "YFmShnl7RE35" }, "source": [ "### Preprocess Data\n", "\n", "So far, we have not looked at the actual values of the speech signal but just the transcription. In addition to `sentence`, our datasets include two more column names `path` and `audio`. `path` states the absolute path of the audio file. Let's take a look.\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "id": "TTCS7W6XJ9BG", "outputId": "d4b375f8-a536-4be9-b995-578fd321acfe" }, "source": [ "common_voice_train[0][\"path\"]" ], "execution_count": null, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3'" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "metadata": { "id": "T6ndIjHGFp0W" }, "source": [ "XLS-R expects the input in the format of a 1-dimensional array of 16 kHz. This means that the audio file has to be loaded and resampled.\n", "\n", " Thankfully, `datasets` does this automatically by calling the other column `audio`. Let try it out. " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qj_z5Zc3GAs9", "outputId": "6b0dca9c-aadc-493f-f754-b01d0f35fe28" }, "source": [ "common_voice_train[0][\"audio\"]" ], "execution_count": null, "outputs": [ { "data": { "text/plain": [ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " -8.8930130e-05, -3.8027763e-05, -2.9146671e-05], dtype=float32),\n", " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3',\n", " 'sampling_rate': 48000}" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "metadata": { "id": "WUUTgI1bGHW-" }, "source": [ "Great, we can see that the audio file has automatically been loaded. This is thanks to the new [`\"Audio\"` feature](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=audio#datasets.Audio) introduced in `datasets == 4.13.3`, which loads and resamples audio files on-the-fly upon calling.\n", "\n", "In the example above we can see that the audio data is loaded with a sampling rate of 48kHz whereas 16kHz are expected by the model. We can set the audio feature to the correct sampling rate by making use of [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column):" ] }, { "cell_type": "code", "metadata": { "id": "rrv65aj7G95i" }, "source": [ "common_voice_train = common_voice_train.cast_column(\"audio\", Audio(sampling_rate=16_000))\n", "common_voice_test = common_voice_test.cast_column(\"audio\", Audio(sampling_rate=16_000))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "PcnO4x-NGBEi" }, "source": [ "Let's take a look at `\"audio\"` again." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aKtkc1o_HWHC", "outputId": "c2c1375e-4812-4112-d843-9e3da6dd327d" }, "source": [ "common_voice_train[0][\"audio\"]" ], "execution_count": null, "outputs": [ { "data": { "text/plain": [ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " -7.4556941e-05, -1.4621433e-05, -5.7861507e-05], dtype=float32),\n", " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3',\n", " 'sampling_rate': 16000}" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "metadata": { "id": "SOckzFd4Mbzq" }, "source": [ "This seemed to have worked! Let's listen to a couple of audio files to better understand the dataset and verify that the audio was correctly loaded. \n", "\n", "**Note**: *You can click the following cell a couple of times to listen to different speech samples.*" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 92 }, "id": "dueM6U7Ev0OA", "outputId": "1a771d0a-085d-441b-cc1b-f05438425ce4" }, "source": [ "import IPython.display as ipd\n", "import numpy as np\n", "import random\n", "\n", "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(common_voice_train[rand_int][\"sentence\"])\n", "ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)" ], "execution_count": null, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "yapabileceklerine inanıyorum\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "metadata": { "id": "gY8m3vARHYTa" }, "source": [ "It seems like the data is now correctly loaded and resampled. " ] }, { "cell_type": "markdown", "metadata": { "id": "1MaL9J2dNVtG" }, "source": [ "It can be heard, that the speakers change along with their speaking rate, accent, and background environment, etc. Overall, the recordings sound acceptably clear though, which is to be expected from a crowd-sourced read speech corpus.\n", "\n", "Let's do a final check that the data is correctly prepared, by printing the shape of the speech input, its transcription, and the corresponding sampling rate.\n", "\n", "**Note**: *You can click the following cell a couple of times to verify multiple samples.*" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Po2g7YPuRTx", "outputId": "63478ae9-2927-4ec1-c13c-41fb3754e18e" }, "source": [ "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n", "print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n", "print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])" ], "execution_count": null, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target text: ülke olimpiyatları yirmi ikinci sırada tamamladı\n", "Input array shape: (74880,)\n", "Sampling rate: 16000\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "M9teZcSwOBJ4" }, "source": [ "Good! Everything looks fine - the data is a 1-dimensional array, the sampling rate always corresponds to 16kHz, and the target text is normalized." ] }, { "cell_type": "markdown", "metadata": { "id": "k3Pbn5WvOYZF" }, "source": [ "Finally, we can leverage `Wav2Vec2Processor` to process the data to the format expected by `Wav2Vec2ForCTC` for training. To do so let's make use of Dataset's [`map(...)`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=map#datasets.DatasetDict.map) function.\n", "\n", "First, we load and resample the audio data, simply by calling `batch[\"audio\"]`.\n", "Second, we extract the `input_values` from the loaded audio file. In our case, the `Wav2Vec2Processor` only normalizes the data. For other speech models, however, this step can include more complex feature extraction, such as [Log-Mel feature extraction](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). \n", "Third, we encode the transcriptions to label ids.\n", "\n", "**Note**: This mapping function is a good example of how the `Wav2Vec2Processor` class should be used. In \"normal\" context, calling `processor(...)` is redirected to `Wav2Vec2FeatureExtractor`'s call method. When wrapping the processor into the `as_target_processor` context, however, the same method is redirected to `Wav2Vec2CTCTokenizer`'s call method.\n", "For more information please check the [docs](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#transformers.Wav2Vec2Processor.__call__)." ] }, { "cell_type": "code", "metadata": { "id": "eJY7I0XAwe9p" }, "source": [ "def prepare_dataset(batch):\n", " audio = batch[\"audio\"]\n", "\n", " # batched output is \"un-batched\"\n", " batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n", " batch[\"input_length\"] = len(batch[\"input_values\"])\n", " \n", " with processor.as_target_processor():\n", " batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n", " return batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "q6Pg_WR3OGAP" }, "source": [ "Let's apply the data preparation function to all examples." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "012b09b271a842d0a7f1cd1260654c82", "6a40faf77bf445308534e7acada6bf0d", "a62ff1ee03dc47c9834b43720df991ea", "03d00b443bfd445d9286f64fd8556031", "557063bf861648f7aefacf71aa53ea9b", "21af0f39c0ce40a5846b980f98481940", "f6e0ea791e5a44abb5d5891bb3e254ad", "6323e35581be4b338a904266a5e85005", "6da7072e7f634a3aa2927fca1bcf38c0", "716dc4ab4fe74abab49f3c0f009357f5", "8a669878b8354867944e577860910001", "3aa317d2b42e4674b38dafa87b5599c5", "0a7c2124936345febe50288a476a12a6", "bdcf9380af5d4601b9b2de84b06ecb40", "b9c5532ffabb44078de8a6f76a4b8be6", "7732bb1317f940faa455f992f4fd2b26", "f28c64f34f9540b6b6bdc765c84e2307", "02016dcc44994c15b69e7f2c4dd0d9c4", "bbfc45e0cf5644f6bc24118fa9c6c098", "eca458b185ae4570b1ec1e77389c871e", "fe156fa95ee9410daff8f7c9a8b0d3a3", "a5e2e1fe3ab94cc6ae1a73cc1ab6f9a8" ] }, "id": "-np9xYK-wl8q", "outputId": "00d6940a-a7bf-4128-896b-76bc289e5b7f" }, "source": [ "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names)\n", "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)" ], "execution_count": null, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "012b09b271a842d0a7f1cd1260654c82", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3478 [00:00 Dict[str, torch.Tensor]:\n", " # split inputs and labels since they have to be of different lenghts and need\n", " # different padding methods\n", " input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", "\n", " batch = self.processor.pad(\n", " input_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", " with self.processor.as_target_processor():\n", " labels_batch = self.processor.pad(\n", " label_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " # replace padding with -100 to ignore loss correctly\n", " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", "\n", " batch[\"labels\"] = labels\n", "\n", " return batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "lbQf5GuZyQ4_" }, "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xO-Zdj-5cxXp" }, "source": [ "Next, the evaluation metric is defined. As mentioned earlier, the \n", "predominant metric in ASR is the word error rate (WER), hence we will use it in this notebook as well." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "7c81059d35534b799623afe872095cac", "379128a308ab43ee9e785e234bb94049", "ff8e746cf8ef4f988617b8d4c0dd3c9c", "0204a7bc668746a68f6e5e5632753c1c", "84361b363998428a87b9e50b6625cca9", "c0919f3c8d594911815ce63273f7fe51", "69f2a9e473534148b96b2e9bd639a087", "5cd8c74ba2e845d498afdc1c25009767", "3f0c587f7b764d96837c8e16254aab12", "d3b5f5a0d35642e0b606ac63c38f88fa", "7ce84a97d72848c0967f732734afdd68" ] }, "id": "9Xsux2gmyXso", "outputId": "18ceeb9e-1a0d-4ee8-f511-a12ad3608bf1" }, "source": [ "wer_metric = load_metric(\"wer\")" ], "execution_count": null, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7c81059d35534b799623afe872095cac", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/1.95k [00:00> m$.\n", "\n", "A logit vector $\\mathbf{y}_1$ contains the log-odds for each word in the vocabulary we defined earlier, thus $\\text{len}(\\mathbf{y}_i) =$ `config.vocab_size`. We are interested in the most likely prediction of the model and thus take the `argmax(...)` of the logits. Also, we transform the encoded labels back to the original string by replacing `-100` with the `pad_token_id` and decoding the ids while making sure that consecutive tokens are **not** grouped to the same token in CTC style ${}^1$." ] }, { "cell_type": "code", "metadata": { "id": "1XZ-kjweyTy_" }, "source": [ "def compute_metrics(pred):\n", " pred_logits = pred.predictions\n", " pred_ids = np.argmax(pred_logits, axis=-1)\n", "\n", " pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n", "\n", " pred_str = processor.batch_decode(pred_ids)\n", " # we do not want to group tokens when computing the metrics\n", " label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n", "\n", " wer = wer_metric.compute(predictions=pred_str, references=label_str)\n", "\n", " return {\"wer\": wer}" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Xmgrx4bRwLIH" }, "source": [ "Now, we can load the pretrained checkpoint of [Wav2Vec2-XLS-R-300M](https://huggingface.co/facebook/wav2vec2-xls-r-300m). The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of `Wav2Vec2ForCTC` also CTC's *blank token* ${}^2$. To save GPU memory, we enable PyTorch's [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html) and also set the loss reduction to \"*mean*\".\n", "\n", "Because the dataset is quite small (~6h of training data) and because Common Voice is quite noisy, fine-tuning Facebook's [wav2vec2-xls-r-300m checkpoint](https://huggingface.co/facebook/wav2vec2-xls-r-300m) seems to require some hyper-parameter tuning. Therefore, I had to play around a bit with different values for dropout, [SpecAugment](https://arxiv.org/abs/1904.08779)'s masking dropout rate, layer dropout, and the learning rate until training seemed to be stable enough. \n", "\n", "**Note**: When using this notebook to train XLS-R on another language of Common Voice those hyper-parameter settings might not work very well. Feel free to adapt those depending on your use case. " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 191, "referenced_widgets": [ "1ca82f9af3ae423096661a1ffd8a165f", "ce429683f48349c083e5fc3253a897d7", "acc35dfdfca542828d1bb02cbde15dfe", "aa92ff120d9c4dd68ae941d7a1f33a81", "731a283141f642b9ab5bf9f7053381d2", "4246da00789f44e2abce952906bde6f1", "059588e34a88476eb6bbc1860ddc27ef", "6ae3b3139e6641fc9e62ae5744349add", "92c30a7c6102443cad2f6f03211c32e0", "ad95ae421231492e814238bde2ecb551", "136f973d9623421eb38e09c73c6b6810", "367c85905a024248bce6c620b822b2ee", "7ea310732a56432da1a095a8d30edc1c", "2d2b8b8d5774494aa73d872e9a4c10d9", "f4af6a0756e241e49e5d325f1bdb18b4", "2b7e52a9be904663ae74226691de6ebd", "771530ea22364f1ea8963e2d542fcf49", "1cdac9cda666466ca1e09f32661d40ed", "3ec9917890f4452883bcaeac444d056c", "e9531fcd48b74929bfdb45cd3f3315e7", "349c9e8b2a8846c1bff5a03d2a28066a", "de1e5c31eacf4fa59cfa51926354acca" ] }, "id": "e7cqAWIayn6w", "outputId": "3e6cebea-78ef-45df-87b0-f63b78ba9644" }, "source": [ "from transformers import Wav2Vec2ForCTC\n", "\n", "model = Wav2Vec2ForCTC.from_pretrained(\n", " \"facebook/wav2vec2-xls-r-300m\", \n", " attention_dropout=0.0,\n", " hidden_dropout=0.0,\n", " feat_proj_dropout=0.0,\n", " mask_time_prob=0.05,\n", " layerdrop=0.0,\n", " ctc_loss_reduction=\"mean\", \n", " pad_token_id=processor.tokenizer.pad_token_id,\n", " vocab_size=len(processor.tokenizer),\n", ")" ], "execution_count": null, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1ca82f9af3ae423096661a1ffd8a165f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/1.53k [00:00 inspect -> Console tab and insert code*)." ] }, { "cell_type": "markdown", "metadata": { "id": "VYYAvgkW4P0m" }, "source": [ "```javascript\n", "function ConnectButton(){\n", " console.log(\"Connect pushed\"); \n", " document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n", "}\n", "setInterval(ConnectButton,60000);\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "7bGgLV2r0yvZ" }, "source": [ "Depending on what GPU was allocated to your google colab it might be possible that you are seeing an `\"out-of-memory\"` error here. In this case, it's probably best to reduce `per_device_train_batch_size` to 8 or even less and increase [`gradient_accumulation`](https://huggingface.co/transformers/master/main_classes/trainer.html#trainingarguments)." ] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true, "base_uri": "https://localhost:8080/", "height": 311 }, "id": "9fRr9TG5pGBl", "outputId": "122ea040-7b24-452a-c7d4-7e72e1c46973" }, "source": [ "trainer.train()" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running training *****\n", " Num examples = 3478\n", " Num Epochs = 30\n", " Instantaneous batch size per device = 16\n", " Total train batch size (w. parallel, distributed & accumulation) = 32\n", " Gradient Accumulation steps = 2\n", " Total optimization steps = 3270\n", "/usr/local/lib/python3.7/dist-packages/transformers/feature_extraction_utils.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:201.)\n", " tensor = as_tensor(value)\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n" ] }, { "data": { "text/html": [ "\n", "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/preprocessor_config.json\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800/preprocessor_config.json\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2800\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-3200\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-3200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-3200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-3200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] }, { "data": { "text/plain": [ "TrainOutput(global_step=3270, training_loss=0.5767540113641582, metrics={'train_runtime': 16918.4457, 'train_samples_per_second': 6.167, 'train_steps_per_second': 0.193, 'total_flos': 1.2873093995838829e+19, 'train_loss': 0.5767540113641582, 'epoch': 30.0})" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "metadata": { "id": "a9q4mgMZplr_" }, "source": [ "The training loss and validation WER go down nicely." ] }, { "cell_type": "markdown", "metadata": { "id": "4Ya7WEy0pd13" }, "source": [ "You can now upload the result of the training to the 🤗 Hub, just execute this instruction:" ] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true, "referenced_widgets": [ "2d2f71d2c70d466cb9a0d3317fb3095e", "2e894a5b95cb489db8b27c6617fd9533" ] }, "id": "ArG1Thf6NBWm", "outputId": "62ef1c3d-786c-4e25-f9c5-4020e71aa298" }, "source": [ "trainer.push_to_hub()" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/preprocessor_config.json\n", "Several commits (2) will be pushed upstream.\n", "The progress bars may be unreliable.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2d2f71d2c70d466cb9a0d3317fb3095e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.35k/1.18G [00:00 main\n", "\n", "Dropping the following result as it does not have all the necessary field:\n", "{'dataset': {'name': 'common_voice', 'type': 'common_voice', 'args': 'tr'}}\n", "To https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-turkish-colab\n", " fe76946..5f0d67b main -> main\n", "\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-turkish-colab/commit/fe769461e4e2fb9534740e6c278a0cfabf268474'" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "metadata": { "id": "RHIVc44_fY2N" }, "source": [ "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier \"your-username/the-name-you-picked\" so for instance:" ] }, { "cell_type": "markdown", "metadata": { "id": "5lWWIKyBpx1h" }, "source": [ "```python\n", "from transformers import AutoModelForCTC, Wav2Vec2Processor\n", "\n", "model = AutoModelForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-large-xls-r-300m-tr-colab\")\n", "processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-large-xls-r-300m-tr-colab\")\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "pmi1cX0fRBit" }, "source": [ "For more examples of how XLS-R can be fine-tuned, please take a look at the [official speech recognition examples](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition#examples)." ] }, { "cell_type": "markdown", "metadata": { "id": "L8b8Qkoy3KyS" }, "source": [ "### Evaluation\n", "\n", "As a final check, let's load the model and verify that it indeed has learned to transcribe Turkish speech.\n", "\n", "Let's first load the pretrained checkpoint." ] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "R351I9IQp_9D", "outputId": "f2a2ee99-7db6-4962-e140-0107054102d3" }, "source": [ "model = Wav2Vec2ForCTC.from_pretrained(repo_name).to(\"cuda\")\n", "processor = Wav2Vec2Processor.from_pretrained(repo_name)" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "loading configuration file wav2vec2-large-xls-r-300m-turkish-colab/config.json\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"facebook/wav2vec2-xls-r-300m\",\n", " \"activation_dropout\": 0.0,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForCTC\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 768,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": true,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"mean\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": true,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_dropout\": 0.0,\n", " \"feat_extract_norm\": \"layer\",\n", " \"feat_proj_dropout\": 0.0,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.0,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 4096,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.0,\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_prob\": 0.05,\n", " \"model_type\": \"wav2vec2\",\n", " \"num_attention_heads\": 16,\n", " \"num_codevector_groups\": 2,\n", " \"num_codevectors_per_group\": 320,\n", " \"num_conv_pos_embedding_groups\": 16,\n", " \"num_conv_pos_embeddings\": 128,\n", " \"num_feat_extract_layers\": 7,\n", " \"num_hidden_layers\": 24,\n", " \"num_negatives\": 100,\n", " \"pad_token_id\": 36,\n", " \"proj_codevector_dim\": 768,\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.11.3\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 39\n", "}\n", "\n", "loading weights file wav2vec2-large-xls-r-300m-turkish-colab/pytorch_model.bin\n", "All model checkpoint weights were used when initializing Wav2Vec2ForCTC.\n", "\n", "All the weights of Wav2Vec2ForCTC were initialized from the model checkpoint at wav2vec2-large-xls-r-300m-turkish-colab.\n", "If your task is similar to the task the model of the checkpoint was trained on, you can already use Wav2Vec2ForCTC for predictions without further training.\n", "loading feature extractor configuration file wav2vec2-large-xls-r-300m-turkish-colab/preprocessor_config.json\n", "Feature extractor Wav2Vec2FeatureExtractor {\n", " \"do_normalize\": true,\n", " \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n", " \"feature_size\": 1,\n", " \"padding_side\": \"right\",\n", " \"padding_value\": 0.0,\n", " \"return_attention_mask\": true,\n", " \"sampling_rate\": 16000\n", "}\n", "\n", "Didn't find file wav2vec2-large-xls-r-300m-turkish-colab/tokenizer.json. We won't load it.\n", "loading file wav2vec2-large-xls-r-300m-turkish-colab/vocab.json\n", "loading file wav2vec2-large-xls-r-300m-turkish-colab/tokenizer_config.json\n", "loading file wav2vec2-large-xls-r-300m-turkish-colab/added_tokens.json\n", "loading file wav2vec2-large-xls-r-300m-turkish-colab/special_tokens_map.json\n", "loading file None\n", "Adding to the vocabulary\n", "Adding to the vocabulary\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "jD7TZ1YS3S_K" }, "source": [ "\n", "Now, we will just take the first example of the test set, run it through the model and take the `argmax(...)` of the logits to retrieve the predicted token ids." ] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "pax07TnL3WZn", "outputId": "867787ff-0cb7-41e9-f926-96f7b53e7134" }, "source": [ "input_dict = processor(common_voice_test[0][\"input_values\"], return_tensors=\"pt\", padding=True)\n", "\n", "logits = model(input_dict.input_values.to(\"cuda\")).logits\n", "\n", "pred_ids = torch.argmax(logits, dim=-1)[0]" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "7nkzSQu53Zs2" }, "source": [ "We adapted `common_voice_test` quite a bit so that the dataset instance does not contain the original sentence label anymore. Thus, we re-use the original dataset to get the label of the first example." ] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true, "referenced_widgets": [ "54097a6c744849128d7cc3da8aad6609", "b8827ecbec9e44f09216f62c1ee12840", "8728bb32478240b7abe576375b01e640", "19b8a530352a423794395979e6415bea", "5fc8ec04870e4bb7b63b20844c355ab6" ] }, "id": "fe2AE-2xqKHx", "outputId": "1d8321b3-4f41-4d71-e74e-f33f32a7b261" }, "source": [ "common_voice_test_transcription = load_dataset(\"common_voice\", \"tr\", data_dir=\"./cv-corpus-6.1-2020-12-11\", split=\"test\")" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using custom data configuration tr-ad9f7b76efa9f3a0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading and preparing dataset common_voice/tr (download: 592.09 MiB, generated: 2.89 MiB, post-processed: Unknown size, total: 594.98 MiB) to /root/.cache/huggingface/datasets/common_voice/tr-ad9f7b76efa9f3a0/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "54097a6c744849128d7cc3da8aad6609", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b8827ecbec9e44f09216f62c1ee12840", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8728bb32478240b7abe576375b01e640", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "19b8a530352a423794395979e6415bea", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5fc8ec04870e4bb7b63b20844c355ab6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Dataset common_voice downloaded and prepared to /root/.cache/huggingface/datasets/common_voice/tr-ad9f7b76efa9f3a0/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9. Subsequent calls will reuse this data.\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "epu8kCQZ3h70" }, "source": [ "\n", "Finally, we can decode the example." ] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "K4xWqmk_qMn0", "outputId": "d9e40b3c-f02a-48a1-d081-6d7e8b37dcaf" }, "source": [ "print(\"Prediction:\")\n", "print(processor.decode(pred_ids))\n", "\n", "print(\"\\nReference:\")\n", "print(common_voice_test_transcription[0][\"sentence\"].lower())" ], "execution_count": null, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prediction:\n", "ha ta küçük şeyleri için bir büyük biş şeylir koğoluyor ve yeneküçük şeyler için bir birmizi incilkiyoruz\n", "\n", "Reference:\n", "hayatta küçük şeyleri kovalıyor ve yine küçük şeyler için birbirimizi incitiyoruz.\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "HwhyoMml3oOT" }, "source": [ "Alright! The transcription can definitely be recognized from our prediction, but it is not perfect yet. Training the model a bit longer, spending more time on the data preprocessing, and especially using a language model for decoding would certainly improve the model's overall performance.\n", "\n", "For a demonstration model on a low-resource language, the results are quite acceptable however 🤗." ] } ] } ================================================ FILE: Fine_tune_Longformer_Encoder_Decoder_(LED)_for_Summarization_on_pubmed.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Fine-tune Longformer Encoder-Decoder (LED) for Summarization on pubmed", "provenance": [], "collapsed_sections": [], "toc_visible": true, "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "a761423f9acc4483b21cc3d8214c593a": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "state": { "_view_name": "HBoxView", "_dom_classes": [], "_model_name": "HBoxModel", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_view_count": null, "_view_module_version": "1.5.0", "box_style": "", "layout": "IPY_MODEL_3f1c827867694804b6346774fc9932c7", "_model_module": "@jupyter-widgets/controls", "children": [ "IPY_MODEL_8468fd4225f54ab48d497992152141b8", "IPY_MODEL_c530cb86b4fb4f4884ecec2c6a8434da" ] } }, "3f1c827867694804b6346774fc9932c7": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { "_view_name": "LayoutView", "grid_template_rows": null, "right": null, "justify_content": null, "_view_module": "@jupyter-widgets/base", "overflow": null, "_model_module_version": "1.2.0", "_view_count": null, "flex_flow": null, "width": null, "min_width": null, "border": null, "align_items": null, "bottom": null, "_model_module": "@jupyter-widgets/base", "top": null, "grid_column": null, "overflow_y": null, "overflow_x": null, "grid_auto_flow": null, "grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } }, "8468fd4225f54ab48d497992152141b8": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "state": { "_view_name": "ProgressView", "style": "IPY_MODEL_ea28145a437047e79c067d942d028ac5", "_dom_classes": [], "description": "Downloading: ", "_model_name": "FloatProgressModel", "bar_style": "success", "max": 2069, "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "value": 2069, "_view_count": null, "_view_module_version": "1.5.0", "orientation": "horizontal", "min": 0, "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", "layout": "IPY_MODEL_97ce9a52c93e454da2494c1ccf2dc9f5" } }, "c530cb86b4fb4f4884ecec2c6a8434da": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "state": { "_view_name": "HTMLView", "style": "IPY_MODEL_4cf41e9c03bf465b8e670dbb08850564", "_dom_classes": [], "description": "", "_model_name": "HTMLModel", "placeholder": "​", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "value": " 5.41k/? 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Peters, Arman Cohan.\n", "\n", "In this notebook we will finetune *LED* for Summarization on [Pubmed](https://huggingface.co/datasets/viewer/?dataset=scientific_papers). *Pubmed* is a long-range summarization dataset, which makes it a good candidate for LED. LED will be finetuned up to an input length of 8K tokens on a single GPU.\n", "\n", "We will leverage 🤗`Seq2SeqTrainer`, gradient checkpointing and as usual 🤗`datasets`." ] }, { "cell_type": "markdown", "metadata": { "id": "0B19PhgrCHM1" }, "source": [ "First, let's try to get a GPU with at least 15GB RAM." ] }, { "cell_type": "code", "metadata": { "id": "4JnoCUoL-2Jz" }, "source": [ "# crash colab to get more RAM\n", "# !kill -9 -1" ], "execution_count": 1, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "tZ11Va-qCp96" }, "source": [ "To check that we are having enough RAM we can run the following command.\n", "If the randomely allocated GPU is too small, the above cells can be run \n", "to crash the notebook hoping to get a better GPU." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "o9IkphgF-90-", "outputId": "5ae51b46-a992-472d-aa64-1c554a10f0ce" }, "source": [ "!nvidia-smi" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "text": [ "Wed Jan 13 15:19:30 2021 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 460.27.04 Driver Version: 418.67 CUDA Version: 10.1 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 42C P8 9W / 70W | 0MiB / 15079MiB | 0% Default |\n", "| | | ERR! |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "eHSLE5TdC7RL" }, "source": [ "Next, we install 🤗Transformers, 🤗Datasets, and `rouge_score`.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "Pzh-JNaUK3Y_" }, "source": [ "%%capture\n", "!pip install datasets==1.2.1\n", "!pip install transformers==4.2.0\n", "!pip install rouge_score" ], "execution_count": 3, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0u9twLMqYEzT" }, "source": [ "Let's start by loading and preprocessing the dataset.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "ODLQ8MUJfmi4" }, "source": [ "from datasets import load_dataset, load_metric" ], "execution_count": 4, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "hEZ20fa9DP4W" }, "source": [ "Next, we download the pubmed train and validation dataset ([click to see on 🤗Datasets Hub](https://huggingface.co/datasets/scientific_papers)). This can take a couple of minutes **☕** ." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 273, "referenced_widgets": [ "a761423f9acc4483b21cc3d8214c593a", "3f1c827867694804b6346774fc9932c7", "8468fd4225f54ab48d497992152141b8", "c530cb86b4fb4f4884ecec2c6a8434da", "ea28145a437047e79c067d942d028ac5", "97ce9a52c93e454da2494c1ccf2dc9f5", "4cf41e9c03bf465b8e670dbb08850564", "287fa63dab8a41a4b373b1e6077bbd47", "17a427d9848449a7aa3a7e0506ca5fa2", "170a08f3e1ca4771b04774349a1f443b", "3d744422a2c848e4bd486b01e738b410", "abed08be27754f19b48107c149f70f27", "97688910d60540b7a976759389821709", "98578d98e2d4431b810e9a831dd0931c", "7a1077c7757d4e4db648b14741882aed", "b95c2c19b13042ad98791dd9ee506141", "8404d2ad47384fbe994ed1ddab94f15f", "8495b12f621b4a7ca6fc6c5a863d7745", "4803e76764c04e778b41fbb570af11ee", "acfa869e8b024d4086f460362b7ee85f", "6980acf7341c4205a7420eb93126c933", "3e8465b9912a464985b8c8f955a21124", 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"be878b520cb94e57910aadb92da6ff83", "70c5e99d985c41ac9b300f3850636ae4", "26218c0ae8ba4193bc898487e3051d57", "7e858fbfcdf747ceb8fbfbbb5f905ddd", "2d76254a7ff04345ae2fb8986b5be738", "6d23af871f384e5681b8a57a18086878", "2fbbac1762104103b87f4f5494a0cbbf" ] }, "id": "bR9A9aaMci2i", "outputId": "77cfb2ee-a745-4da0-a6da-510792c5830c" }, "source": [ "train_dataset = load_dataset(\"scientific_papers\", \"pubmed\", split=\"train\")" ], "execution_count": 5, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a761423f9acc4483b21cc3d8214c593a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=2069.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "17a427d9848449a7aa3a7e0506ca5fa2", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1231.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "Downloading and preparing dataset scientific_papers/pubmed (download: 4.20 GiB, generated: 2.33 GiB, post-processed: Unknown size, total: 6.53 GiB) to /root/.cache/huggingface/datasets/scientific_papers/pubmed/1.1.1/043e40ed208b8a66ee9e8228c86874946c99d2fc6155a1daee685795851cfdfc...\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8404d2ad47384fbe994ed1ddab94f15f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=3624420843.0, style=ProgressStyle(descr…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "579e6a383fbc44f5b73a67d4f2631c1a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=880225504.0, style=ProgressStyle(descri…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "68a84f43e6bb453ebfeda6f725764282", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\r" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "abc729d423b5453b9d806e32cc227bee", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\r" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a91be53861104cf4a56f213726ca245f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\rDataset scientific_papers downloaded and prepared to /root/.cache/huggingface/datasets/scientific_papers/pubmed/1.1.1/043e40ed208b8a66ee9e8228c86874946c99d2fc6155a1daee685795851cfdfc. Subsequent calls will reuse this data.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6GRz0rksYb3h", "outputId": "5c816879-bfb2-4c26-b674-d90209075baa" }, "source": [ "val_dataset = load_dataset(\"scientific_papers\", \"pubmed\", split=\"validation\")" ], "execution_count": 6, "outputs": [ { "output_type": "stream", "text": [ "Reusing dataset scientific_papers (/root/.cache/huggingface/datasets/scientific_papers/pubmed/1.1.1/043e40ed208b8a66ee9e8228c86874946c99d2fc6155a1daee685795851cfdfc)\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "TWM_4AChFnpz" }, "source": [ "It's always a good idea to take a look at some data samples. Let's do that here." ] }, { "cell_type": "code", "metadata": { "id": "eaz72rzWGBAK" }, "source": [ "import datasets\n", "import random\n", "import pandas as pd\n", "from IPython.display import display, HTML\n", "\n", "def show_random_elements(dataset, num_examples=4):\n", " assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n", " picks = []\n", " for _ in range(num_examples):\n", " pick = random.randint(0, len(dataset)-1)\n", " while pick in picks:\n", " pick = random.randint(0, len(dataset)-1)\n", " picks.append(pick)\n", " \n", " df = pd.DataFrame(dataset[picks])\n", " for column, typ in dataset.features.items():\n", " if isinstance(typ, datasets.ClassLabel):\n", " df[column] = df[column].transform(lambda i: typ.names[i])\n", " display(HTML(df.to_html()))" ], "execution_count": 7, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "lrwXZ_LBGLwt" }, "source": [ "We can see that the input data is the `article` - a scientific report and the target data is the `abstract` - a concise summary of the report." ] }, { "cell_type": "markdown", "metadata": { "id": "OkDoz13UB3gY" }, "source": [ "Cool! Having downloaded the dataset, let's tokenize it.\n", "We'll import the convenient `AutoTokenizer` class." ] }, { "cell_type": "code", "metadata": { "id": "R-gvC8jiYtS_" }, "source": [ "from transformers import AutoTokenizer" ], "execution_count": 8, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Gst7xqXECbRc" }, "source": [ " and load the tokenizer" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 167, "referenced_widgets": [ "e144d3363abd4681b6e905a0626c2501", "82a6c46956b5496b87e0549c8e245fd5", "61f0caa33bc74688aefb8b8a5707ac8b", "493a9509d5de408f8e880b106c88432d", "dbc485492c48406ab1bf117173e61b24", "94dbe51b3b4b45b08621a17d6bfe4a37", "17ff9d36e1974c059a22019b3f40d9c1", "f02a0fb9638f4d049254770e8be88521", "df316b67c6954c629f6b2eee676d93e2", "ce9a347785ea419999a2e650af5845a8", "5550f8890fa6487b86a5ef31921ee4cf", "a6b62239822d49f798b7787b33a0a2d0", "b367af1ef6644eca8f5cc3f21fc518a1", "c5b51c4c7e7d4f88a35049fc847f7988", "5526ec3192e144d3bede17b1f81bf485", "e273b8e54be14ff7bee0bd396113b958", "3139b95760c74c29bcc92ca41202d57d", "979cb037f2314ee98536ba508cdfd519", "304215762bd34c2182943134f237f11e", "2a96fe53653c4500839fa7d95c22df53", "eda05f8fb1ff405183ee8ed699940c3f", "bb940485306f4e1fa3344e0d732c7958", "d5518da9acbe48b0b0b8dd7cad83ba18", "11996effc06c425c81fd839d7df62a24" ] }, "id": "jpUr9QeebZ-n", "outputId": "7a186700-0fa5-4fe5-9ed5-11a428472b02" }, "source": [ "tokenizer = AutoTokenizer.from_pretrained(\"allenai/led-base-16384\")" ], "execution_count": 9, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e144d3363abd4681b6e905a0626c2501", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1092.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "df316b67c6954c629f6b2eee676d93e2", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=898822.0, style=ProgressStyle(descripti…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3139b95760c74c29bcc92ca41202d57d", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "ZhQeQg3oCcL-" }, "source": [ "Note that for the sake of this notebook, we finetune the \"smaller\" LED checkpoint [\"allenai/led-base-16384\"](https://huggingface.co/allenai/led-base-16384). Better performance can however be attained by finetuning [\"allenai/led-large-16384\"](https://huggingface.co/allenai/led-large-16384) at the cost of a higher required GPU RAM." ] }, { "cell_type": "markdown", "metadata": { "id": "f-GnxlXnDK7g" }, "source": [ "Pubmed's input data has a median token length of 2715 with the 90%-ile token length being 6101. The output data has a media token length of 171 with the 90%-ile token length being 352.${}^1$. \n", "\n", "Thus, we set the maximum input length to 8192 and the maximum output length to 512 to ensure that the model can attend to almost all input tokens is able to generate up to a large enough number of output tokens.\n", "\n", "In this notebook, we are only able to train on `batch_size=2` to prevent out-of-memory errors.\n", "\n", "---\n", "${}^1$ The data is taken from page 11 of [Big Bird: Transformers for Longer Sequences](https://arxiv.org/pdf/2007.14062.pdf).\n" ] }, { "cell_type": "code", "metadata": { "id": "Nbb24Uh-Y4xO" }, "source": [ "max_input_length = 8192\n", "max_output_length = 512\n", "batch_size = 2" ], "execution_count": 10, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "WN_SAv1JE40f" }, "source": [ "Now, let's write down the input data processing function that will be used to map each data sample to the correct model format.\n", "As explained earlier `article` represents here our input data and `abstract` is the target data. The datasamples are thus tokenized up to the respective maximum lengths of 8192 and 512.\n", "\n", "In addition to the usual `attention_mask`, LED can make use of an additional `global_attention_mask` defining which input tokens are attended globally and which are attended only locally, just as it's the case of [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). For more information on Longformer's self-attention, please take a look at the corresponding [docs](https://huggingface.co/transformers/model_doc/longformer.html#longformer-self-attention). For summarization, we follow recommendations of the [paper](https://arxiv.org/abs/2004.05150) and use global attention only for the very first token. Finally, we make sure that no loss is computed on padded tokens by setting their index to `-100`." ] }, { "cell_type": "code", "metadata": { "id": "lEcAaZhNY8ge" }, "source": [ "def process_data_to_model_inputs(batch):\n", " # tokenize the inputs and labels\n", " inputs = tokenizer(\n", " batch[\"article\"],\n", " padding=\"max_length\",\n", " truncation=True,\n", " max_length=max_input_length,\n", " )\n", " outputs = tokenizer(\n", " batch[\"abstract\"],\n", " padding=\"max_length\",\n", " truncation=True,\n", " max_length=max_output_length,\n", " )\n", "\n", " batch[\"input_ids\"] = inputs.input_ids\n", " batch[\"attention_mask\"] = inputs.attention_mask\n", "\n", " # create 0 global_attention_mask lists\n", " batch[\"global_attention_mask\"] = len(batch[\"input_ids\"]) * [\n", " [0 for _ in range(len(batch[\"input_ids\"][0]))]\n", " ]\n", "\n", " # since above lists are references, the following line changes the 0 index for all samples\n", " batch[\"global_attention_mask\"][0][0] = 1\n", " batch[\"labels\"] = outputs.input_ids\n", "\n", " # We have to make sure that the PAD token is ignored\n", " batch[\"labels\"] = [\n", " [-100 if token == tokenizer.pad_token_id else token for token in labels]\n", " for labels in batch[\"labels\"]\n", " ]\n", "\n", " return batch" ], "execution_count": 11, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "iyseRmC5IcXj" }, "source": [ "For the sake of this notebook, we will reduce the training and validation data \n", "to a dummy dataset of sizes 250 and 25 respectively. For a full training run, those lines should be commented out." ] }, { "cell_type": "code", "metadata": { "id": "xMAkJfjCZKeE" }, "source": [ "train_dataset = train_dataset.select(range(250))\n", "val_dataset = val_dataset.select(range(25))" ], "execution_count": 12, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "fNzZZACtIoCD" }, "source": [ "Great, having defined the mapping function, let's preprocess the training data" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "7684f65e86af40cb88f22cc34bf82963", "974512d3845c411ba527c011bbdbf6a5", "46495529a4394871b50511b6513c5654", "19cfa8f1d6c2449ab942090dd64b7e0b", "467150bb943749ad9d518246762868c3", "b17d022c09374414ae1ac8370d0c221d", "94ad8e56979741a5940dd6cd98f7460f", "6a4b5884f5cc4769a183e80a12ac4e92" ] }, "id": "8RClMjZOZCPO", "outputId": "4e9e472a-dd78-4757-dff0-ce51d689d463" }, "source": [ "train_dataset = train_dataset.map(\n", " process_data_to_model_inputs,\n", " batched=True,\n", " batch_size=batch_size,\n", " remove_columns=[\"article\", \"abstract\", \"section_names\"],\n", ")" ], "execution_count": 13, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7684f65e86af40cb88f22cc34bf82963", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=125.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "RUtRFmgVIuHG" }, "source": [ "and validation data" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "0465057a949248e0af90bd7cdf7d1c67", "437e38bd314b493fa68e55749d072709", "8b3a79527164496092e819f61ff01e54", "00d396ad20ee499a8e88a2adfef487c6", "4cf36b319afc48f8a3c316b158b137ce", "9014018e64b349a98ec3d466d6bdb8b1", "5b55b85c16cd453b873afd5ef3ab03bc", "ecba352678f14ac1869669337dcfb0b9" ] }, "id": "Gy_9DCocZD5G", "outputId": "43398423-85f3-4f1f-83f4-ac0a27ad09aa" }, "source": [ "val_dataset = val_dataset.map(\n", " process_data_to_model_inputs,\n", " batched=True,\n", " batch_size=batch_size,\n", " remove_columns=[\"article\", \"abstract\", \"section_names\"],\n", ")" ], "execution_count": 14, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0465057a949248e0af90bd7cdf7d1c67", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=13.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "evRRitZaIw9N" }, "source": [ "Finally, the datasets should be converted into the PyTorch format as follows." ] }, { "cell_type": "code", "metadata": { "id": "ci2QYHCMZiNO" }, "source": [ "train_dataset.set_format(\n", " type=\"torch\",\n", " columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\n", ")\n", "val_dataset.set_format(\n", " type=\"torch\",\n", " columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\n", ")" ], "execution_count": 15, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "dI-QHywgI6Hb" }, "source": [ "Alright, we're almost ready to start training. Let's load the model via the `AutoModelForSeq2SeqLM` class." ] }, { "cell_type": "code", "metadata": { "id": "SZf_8QXJacIc" }, "source": [ "from transformers import AutoModelForSeq2SeqLM" ], "execution_count": 16, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "hNJQlLDKJHIK" }, "source": [ "We've decided to stick to the smaller model `\"allenai/led-base-16384\"` for the sake of this notebook. In addition, we directly enable gradient checkpointing and disable the caching mechanism to save memory." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "ec5383f4926d417d921c27e47528ab48", "3611c7805a1d48fdadbf77e021e4b572", "e8d558bd275541cb93a3d7c097d4c57e", "c444b8e61ebc4bfb8ec28891183a3876", "03b3203b76c9472787938ea4dc2e4765", "2aa3807c9a9342819f0ad0dcebab7ced", "d5e8c7d5bf264358a7fcc5a822178492", "039416f036f04b2ca864c871664f3f97" ] }, "id": "R-UfEo0Zadpl", "outputId": "8092c192-bd80-4069-f186-78baa24ca897" }, "source": [ "led = AutoModelForSeq2SeqLM.from_pretrained(\"allenai/led-base-16384\", gradient_checkpointing=True, use_cache=False)" ], "execution_count": 17, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ec5383f4926d417d921c27e47528ab48", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=647693783.0, style=ProgressStyle(descri…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "kQOaX6eRJXkM" }, "source": [ "During training, we want to evaluate the model on Rouge, the most common metric used in summarization, to make sure the model is indeed improving during training. For this, we set fitting generation parameters. We'll use beam search with a small beam of just 2 to save memory. Also, we force the model to generate at least 100 tokens, but no more than 512. In addition, some other generation parameters are set that have been found helpful for generation. For more information on those parameters, please take a look at the [docs](https://huggingface.co/transformers/main_classes/model.html?highlight=generate#transformers.generation_utils.GenerationMixin.generate)." ] }, { "cell_type": "code", "metadata": { "id": "kPnNi_tWaklV" }, "source": [ "# set generate hyperparameters\n", "led.config.num_beams = 2\n", "led.config.max_length = 512\n", "led.config.min_length = 100\n", "led.config.length_penalty = 2.0\n", "led.config.early_stopping = True\n", "led.config.no_repeat_ngram_size = 3" ], "execution_count": 18, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "p7FTiwHrKSBA" }, "source": [ "Next, we also have to define the function the will compute the `\"rouge\"` score during evalution.\n", "\n", "Let's load the `\"rouge\"` metric from 🤗datasets and define the `compute_metrics(...)` function." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "71c7024b47564fc891a1e15346c57536", "78a7b7c13e54414185e6ac7c37e75a5f", "9a27aa37ec714295b9e26d07633c9fad", "c274f0a8891540478d3226795b06714b", "95c90dd799bd4cf99e3bfa0bbd22d7a6", "333b3fe7f011498787ed34dd33e72516", "ba304c76d53849fea17168bb9e589106", "3cc7793d7bec40658fa957a16c62f6e4" ] }, "id": "S5_rc9wUaBop", "outputId": "d71ab9a1-9520-4ac1-83b9-6b240e49f67a" }, "source": [ "rouge = load_metric(\"rouge\")" ], "execution_count": 19, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "71c7024b47564fc891a1e15346c57536", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1955.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "wsytOIWVKqFz" }, "source": [ "The compute metrics function expects the generation output, called `pred.predictions` as well as the gold label, called `pred.label_ids`.\n", "\n", "Those tokens are decoded and consequently, the rouge score can be computed." ] }, { "cell_type": "code", "metadata": { "id": "1ahmf7mcZzU7" }, "source": [ "def compute_metrics(pred):\n", " labels_ids = pred.label_ids\n", " pred_ids = pred.predictions\n", "\n", " pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n", " labels_ids[labels_ids == -100] = tokenizer.pad_token_id\n", " label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\n", "\n", " rouge_output = rouge.compute(\n", " predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"]\n", " )[\"rouge2\"].mid\n", "\n", " return {\n", " \"rouge2_precision\": round(rouge_output.precision, 4),\n", " \"rouge2_recall\": round(rouge_output.recall, 4),\n", " \"rouge2_fmeasure\": round(rouge_output.fmeasure, 4),\n", " }" ], "execution_count": 20, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "rrX2gBnYLAna" }, "source": [ "Now, we're ready to start training. Let's import the `Seq2SeqTrainer` and `Seq2SeqTrainingArguments`." ] }, { "cell_type": "code", "metadata": { "id": "Gq7CajIWaUo5" }, "source": [ "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments" ], "execution_count": 21, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ga-fCOB4LI4W" }, "source": [ "In contrast to the usual `Trainer`, the `Seq2SeqTrainer` makes it possible to use the `generate()` function during evaluation. This should be enabled with `predict_with_generate=True`. Because our GPU RAM is limited, we make use of gradient accumulation by setting `gradient_accumulation_steps=4` to have an effective `batch_size` of 2 * 4 = 8.\n", "\n", "Other training arguments can be read upon in the [docs](https://huggingface.co/transformers/main_classes/trainer.html?highlight=trainingarguments#transformers.TrainingArguments)." ] }, { "cell_type": "code", "metadata": { "id": "mMehwI4QZqB_" }, "source": [ "# enable fp16 apex training\n", "training_args = Seq2SeqTrainingArguments(\n", " predict_with_generate=True,\n", " evaluation_strategy=\"steps\",\n", " per_device_train_batch_size=batch_size,\n", " per_device_eval_batch_size=batch_size,\n", " fp16=True,\n", " output_dir=\"./\",\n", " logging_steps=5,\n", " eval_steps=10,\n", " save_steps=10,\n", " save_total_limit=2,\n", " gradient_accumulation_steps=4,\n", " num_train_epochs=1,\n", ")" ], "execution_count": 22, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "JZCY-C5mLzXY" }, "source": [ "The training arguments, along with the model, tokenizer, datasets and the `compute_metrics` function can then be passed to the `Seq2SeqTrainer`" ] }, { "cell_type": "code", "metadata": { "id": "6WPyTYO_JfHW" }, "source": [ "trainer = Seq2SeqTrainer(\n", " model=led,\n", " tokenizer=tokenizer,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " train_dataset=train_dataset,\n", " eval_dataset=val_dataset,\n", ")" ], "execution_count": 23, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xZxSe_afL9TH" }, "source": [ "and we can start training. This will take about ~35min." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 511 }, "id": "g4zkCpeQa2NN", "outputId": "451ec352-67a0-447c-bfc9-090c35b35a43" }, "source": [ "trainer.train()" ], "execution_count": 24, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n", " return torch.tensor(x, **format_kwargs)\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "text/html": [ "\n", "

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\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgen_tokens\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mngram_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ] }, { "cell_type": "markdown", "metadata": { "id": "gs1gYYb1MJOr" }, "source": [ "This completes the fine-tuning tutorial for LED. This training script with some small changes was used to train [this](https://huggingface.co/patrickvonplaten/led-large-16384-pubmed) checkpoint, called `\" patrickvonplaten/led-large-16384-pubmed\"` on a single GPU for ca. 3 days. Evaluating `\" patrickvonplaten/led-large-16384-pubmed\"` on Pubmed's test data gives a Rouge-2 score of **19.33** which is around 1 Rouge-2 point below SOTA performance on Pubmed.\n", "\n", "In the Appendix below, the condensed training and evaluation scripts that were used locally to finetune `\" patrickvonplaten/led-large-16384-pubmed\"` are attached." ] }, { "cell_type": "markdown", "metadata": { "id": "g_t9sjHha5T8" }, "source": [ "# **Appendix**" ] }, { "cell_type": "markdown", "metadata": { "id": "Uv7edHocbDCD" }, "source": [ "## Training" ] }, { "cell_type": "code", "metadata": { "id": "9U3vpJxnOJiH" }, "source": [ "#!/usr/bin/env python3\n", "from datasets import load_dataset, load_metric\n", "from transformers import (\n", " Seq2SeqTrainer,\n", " Seq2SeqTrainingArguments,\n", " AutoTokenizer,\n", " AutoModelForSeq2SeqLM,\n", ")\n", "\n", "# load rouge\n", "rouge = load_metric(\"rouge\")\n", "\n", "# load pubmed\n", "pubmed_train = load_dataset(\"scientific_papers\", \"pubmed\", ignore_verifications=True, split=\"train\")\n", "pubmed_val = load_dataset(\"scientific_papers\", \"pubmed\", ignore_verifications=True, split=\"validation[:10%]\")\n", "\n", "# comment out following lines for a test run\n", "# pubmed_train = pubmed_train.select(range(32))\n", "# pubmed_val = pubmed_val.select(range(32))\n", "\n", "# load tokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(\"allenai/led-large-16384\")\n", "\n", "\n", "# max encoder length is 8192 for PubMed\n", "encoder_max_length = 8192\n", "decoder_max_length = 512\n", "batch_size = 2\n", "\n", "\n", "def process_data_to_model_inputs(batch):\n", " # tokenize the inputs and labels\n", " inputs = tokenizer(\n", " batch[\"article\"],\n", " padding=\"max_length\",\n", " truncation=True,\n", " max_length=encoder_max_length,\n", " )\n", " outputs = tokenizer(\n", " batch[\"abstract\"],\n", " padding=\"max_length\",\n", " truncation=True,\n", " max_length=decoder_max_length,\n", " )\n", "\n", " batch[\"input_ids\"] = inputs.input_ids\n", " batch[\"attention_mask\"] = inputs.attention_mask\n", "\n", " # create 0 global_attention_mask lists\n", " batch[\"global_attention_mask\"] = len(batch[\"input_ids\"]) * [\n", " [0 for _ in range(len(batch[\"input_ids\"][0]))]\n", " ]\n", "\n", " # since above lists are references, the following line changes the 0 index for all samples\n", " batch[\"global_attention_mask\"][0][0] = 1\n", " batch[\"labels\"] = outputs.input_ids\n", "\n", " # We have to make sure that the PAD token is ignored\n", " batch[\"labels\"] = [\n", " [-100 if token == tokenizer.pad_token_id else token for token in labels]\n", " for labels in batch[\"labels\"]\n", " ]\n", "\n", " return batch\n", "\n", "\n", "# map train data\n", "pubmed_train = pubmed_train.map(\n", " process_data_to_model_inputs,\n", " batched=True,\n", " batch_size=batch_size,\n", " remove_columns=[\"article\", \"abstract\", \"section_names\"],\n", ")\n", "\n", "# map val data\n", "pubmed_val = pubmed_val.map(\n", " process_data_to_model_inputs,\n", " batched=True,\n", " batch_size=batch_size,\n", " remove_columns=[\"article\", \"abstract\", \"section_names\"],\n", ")\n", "\n", "# set Python list to PyTorch tensor\n", "pubmed_train.set_format(\n", " type=\"torch\",\n", " columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\n", ")\n", "\n", "# set Python list to PyTorch tensor\n", "pubmed_val.set_format(\n", " type=\"torch\",\n", " columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\n", ")\n", "\n", "# enable fp16 apex training\n", "training_args = Seq2SeqTrainingArguments(\n", " predict_with_generate=True,\n", " evaluation_strategy=\"steps\",\n", " per_device_train_batch_size=batch_size,\n", " per_device_eval_batch_size=batch_size,\n", " fp16=True,\n", " fp16_backend=\"apex\",\n", " output_dir=\"./\",\n", " logging_steps=250,\n", " eval_steps=5000,\n", " save_steps=500,\n", " warmup_steps=1500,\n", " save_total_limit=2,\n", " gradient_accumulation_steps=4,\n", ")\n", "\n", "\n", "# compute Rouge score during validation\n", "def compute_metrics(pred):\n", " labels_ids = pred.label_ids\n", " pred_ids = pred.predictions\n", "\n", " pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n", " labels_ids[labels_ids == -100] = tokenizer.pad_token_id\n", " label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\n", "\n", " rouge_output = rouge.compute(\n", " predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"]\n", " )[\"rouge2\"].mid\n", "\n", " return {\n", " \"rouge2_precision\": round(rouge_output.precision, 4),\n", " \"rouge2_recall\": round(rouge_output.recall, 4),\n", " \"rouge2_fmeasure\": round(rouge_output.fmeasure, 4),\n", " }\n", "\n", "\n", "# load model + enable gradient checkpointing & disable cache for checkpointing\n", "led = AutoModelForSeq2SeqLM.from_pretrained(\"allenai/led-large-16384\", gradient_checkpointing=True, use_cache=False)\n", "\n", "# set generate hyperparameters\n", "led.config.num_beams = 4\n", "led.config.max_length = 512\n", "led.config.min_length = 100\n", "led.config.length_penalty = 2.0\n", "led.config.early_stopping = True\n", "led.config.no_repeat_ngram_size = 3\n", "\n", "\n", "# instantiate trainer\n", "trainer = Seq2SeqTrainer(\n", " model=led,\n", " tokenizer=tokenizer,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " train_dataset=pubmed_train,\n", " eval_dataset=pubmed_val,\n", ")\n", "\n", "# start training\n", "trainer.train()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "CFtGA4yibG2u" }, "source": [ "## Evaluation" ] }, { "cell_type": "code", "metadata": { "id": "tLM3niQqhEzP" }, "source": [ "import torch\n", "\n", "from datasets import load_dataset, load_metric\n", "from transformers import LEDTokenizer, LEDForConditionalGeneration\n", "\n", "# load pubmed\n", "pubmed_test = load_dataset(\"scientific_papers\", \"pubmed\", ignore_verifications=True, split=\"test\")\n", "\n", "# load tokenizer\n", "tokenizer = LEDTokenizer.from_pretrained(\"patrickvonplaten/led-large-16384-pubmed\")\n", "model = LEDForConditionalGeneration.from_pretrained(\"patrickvonplaten/led-large-16384-pubmed\").to(\"cuda\").half()\n", "\n", "\n", "def generate_answer(batch):\n", " inputs_dict = tokenizer(batch[\"article\"], padding=\"max_length\", max_length=8192, return_tensors=\"pt\", truncation=True)\n", " input_ids = inputs_dict.input_ids.to(\"cuda\")\n", " attention_mask = inputs_dict.attention_mask.to(\"cuda\")\n", " global_attention_mask = torch.zeros_like(attention_mask)\n", " # put global attention on token\n", " global_attention_mask[:, 0] = 1\n", "\n", " predicted_abstract_ids = model.generate(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)\n", " batch[\"predicted_abstract\"] = tokenizer.batch_decode(predicted_abstract_ids, skip_special_tokens=True)\n", " return batch\n", "\n", "\n", "result = pubmed_test.map(generate_answer, batched=True, batch_size=4)\n", "\n", "# load rouge\n", "rouge = load_metric(\"rouge\")\n", "\n", "print(\"Result:\", rouge.compute(predictions=result[\"predicted_abstract\"], references=result[\"abstract\"], rouge_types=[\"rouge2\"])[\"rouge2\"].mid)\n" ], "execution_count": null, "outputs": [] } ] } ================================================ FILE: Fine_tuning_Wav2Vec2_for_English_ASR.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Fine-tuning Wav2Vec2 for English ASR", "provenance": [], "collapsed_sections": [], "toc_visible": true, "machine_shape": "hm", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "4fae0d04a34b4fbcbbac419ef522389d": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [ "IPY_MODEL_04889e8a534846219c60b6c637ff9703", "IPY_MODEL_dd0d9ccef1b34806a8989f882caca422", "IPY_MODEL_cfcf385787a94992ac39070cad8fcada", "IPY_MODEL_6d2c83e9574c4b0796e73ccc5ba2632e", "IPY_MODEL_8f15f29ae18640d3ba9215ca91e764b7" ], "layout": "IPY_MODEL_b58bd0d46cbc4d7ab932940941d2ad36" } }, "04889e8a534846219c60b6c637ff9703": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_b807a57d90ae41c1bfa3a54b121affd1", "placeholder": "​", "style": "IPY_MODEL_edbc56749e544622be7717992bd72dbc", "value": "

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"metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "LBSYoWbi-45k" }, "source": [ "# **Fine-tuning Wav2Vec2 for English ASR with 🤗 Transformers**" ] }, { "cell_type": "markdown", "metadata": { "id": "V7YOT2mnUiea" }, "source": [ "Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in [September 2020](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by Alexei Baevski, Michael Auli, and Alex Conneau.\n", "\n", "Using a novel contrastive pretraining objective, Wav2Vec2 learns powerful speech representations from more than 50.000 hours of unlabeled speech. Similar, to [BERT's masked language modeling](http://jalammar.github.io/illustrated-bert/), the model learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network.\n", "\n", "![wav2vec2_structure](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/wav2vec2.png)\n", "\n", "For the first time, it has been shown that pretraining, followed by fine-tuning on very little labeled speech data achieves competitive results to state-of-the-art ASR systems. Using as little as 10 minutes of labeled data, Wav2Vec2 yields a word error rate (WER) of less than 5% on the clean test set of [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) - *cf.* with Table 9 of the [paper](https://arxiv.org/pdf/2006.11477.pdf)." ] }, { "cell_type": "markdown", "metadata": { "id": "nT_QrfWtsxIz" }, "source": [ "In this notebook, we will give an in-detail explanation of how Wav2Vec2's pretrained checkpoints can be fine-tuned on any English ASR dataset. Note that in this notebook, we will fine-tune Wav2Vec2 without making use of a language model. It is much simpler to use Wav2Vec2 without a language model as an end-to-end ASR system and it has been shown that a standalone Wav2Vec2 acoustic model achieves impressive results. For demonstration purposes, we fine-tune the \"base\"-sized [pretrained checkpoint](https://huggingface.co/facebook/wav2vec2-base) on the rather small [Timit](https://huggingface.co/datasets/timit_asr) dataset that contains just 5h of training data." ] }, { "cell_type": "markdown", "metadata": { "id": "Gx9OdDYrCtQ1" }, "source": [ "Wav2Vec2 is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems and mainly in Automatic Speech Recognition and handwriting recognition. \n", "\n", "I highly recommend reading the blog post [Sequence Modeling with CTC (2017)](https://distill.pub/2017/ctc/) very well-written blog post by Awni Hannun." ] }, { "cell_type": "markdown", "metadata": { "id": "qW3J3rBizeds" }, "source": [ "First, let's try to get a good GPU in our colab! With Google Colab's free version it's sadly becoming much harder to get access to a good GPU. With Google Colab Pro, one has a much easier time getting access to a V100 or P100 GPU however." ] }, { "cell_type": "code", "metadata": { "id": "SLAufgh_xxj7", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "56da9201-624d-4dd2-ad8f-1d2e9c829723" }, "source": [ "gpu_info = !nvidia-smi\n", "gpu_info = '\\n'.join(gpu_info)\n", "if gpu_info.find('failed') >= 0:\n", " print('Not connected to a GPU')\n", "else:\n", " print(gpu_info)" ], "execution_count": 43, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Tue May 10 10:58:40 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla V100-SXM2... Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 38C P0 41W / 300W | 7183MiB / 16160MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "+-----------------------------------------------------------------------------+\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "e335hPmdtASZ" }, "source": [ "Before we start, let's install both `datasets` and `transformers` from master. Also, we need the `librosa` package to load audio files and the `jiwer` to evaluate our fine-tuned model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric ${}^1$." ] }, { "cell_type": "code", "metadata": { "id": "c8eh87Hoee5d" }, "source": [ "%%capture\n", "!pip install datasets==1.18.3\n", "!pip install transformers==4.17.0\n", "!pip install jiwer" ], "execution_count": 44, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "8_6kYmDMH9lR" }, "source": [ "Next we strongly suggest to upload your training checkpoints directly to the [🤗 Hub](https://huggingface.co/) while training. The [🤗 Hub](https://huggingface.co/) has integrated version control so you can be sure that no model checkpoint is getting lost during training. \n", "\n", "To do so you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!)" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 385, "referenced_widgets": [ "4fae0d04a34b4fbcbbac419ef522389d", "04889e8a534846219c60b6c637ff9703", "dd0d9ccef1b34806a8989f882caca422", "cfcf385787a94992ac39070cad8fcada", "6d2c83e9574c4b0796e73ccc5ba2632e", "8f15f29ae18640d3ba9215ca91e764b7", "b58bd0d46cbc4d7ab932940941d2ad36", "b807a57d90ae41c1bfa3a54b121affd1", "edbc56749e544622be7717992bd72dbc", "3328f4a664d24ba89dbc892734a3ed58", "e0c3115ae01a44848620d6f19a071542", "ecc433611c5144d3a909627ffb08eb77", "3378d5d15d0f4c45a286842339425003", "70914255a10141ef8882c4e69a4f4057", "5ca38ad0652b4d27ba646d6b55298498", "84c3f51a7956477eaa1c9ecdeba5c00a", "1eb4c98ebb084e8898b793265738b7fc" ] }, "id": "zFLBDyzQIA3R", "outputId": "fb99cad5-09f1-47b9-aeec-edd541e11603" }, "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "execution_count": 45, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n", "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", "\n", "git config --global credential.helper store\u001b[0m\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "zCyw5D23IQ1F" }, "source": [ "\n", "Then you need to install Git-LFS to upload your model checkpoints:" ] }, { "cell_type": "code", "metadata": { "id": "Q9BnQDhOITBC" }, "source": [ "%%capture\n", "!apt install git-lfs" ], "execution_count": 46, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Mn9swf6EQ9Vd" }, "source": [ "\n", "\n", "\n", "---\n", "\n", "${}^1$ Timit is usually evaluated using the phoneme error rate (PER), but by far the most common metric in ASR is the word error rate (WER). To keep this notebook as general as possible we decided to evaluate the model using WER." ] }, { "cell_type": "markdown", "metadata": { "id": "0mW-C1Nt-j7k" }, "source": [ "## Prepare Data, Tokenizer, Feature Extractor" ] }, { "cell_type": "markdown", "metadata": { "id": "BeBosnY9BH3e" }, "source": [ "ASR models transcribe speech to text, which means that we both need a feature extractor that processes the speech signal to the model's input format, *e.g.* a feature vector, and a tokenizer that processes the model's output format to text. \n", "\n", "In 🤗 Transformers, the Wav2Vec2 model is thus accompanied by both a tokenizer, called [Wav2Vec2CTCTokenizer](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#wav2vec2ctctokenizer), and a feature extractor, called [Wav2Vec2FeatureExtractor](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#wav2vec2featureextractor).\n", "\n", "Let's start by creating the tokenizer responsible for decoding the model's predictions." ] }, { "cell_type": "markdown", "metadata": { "id": "sEXEWEJGQPqD" }, "source": [ "### Create Wav2Vec2CTCTokenizer" ] }, { "cell_type": "markdown", "metadata": { "id": "tWmMikuNEKl_" }, "source": [ "The [pretrained Wav2Vec2 checkpoint]( ) maps the speech signal to a sequence of context representations as illustrated in the figure above. A fine-tuned Wav2Vec2 checkpoint needs to map this sequence of context representations to its corresponding transcription so that a linear layer has to be added on top of the transformer block (shown in yellow). This linear layer is used to classifies each context representation to a token class analogous how, *e.g.*, after pretraining a linear layer is added on top of BERT's embeddings for further classification - *cf.* with *\"BERT\"* section of this [blog post](https://huggingface.co/blog/warm-starting-encoder-decoder).\n", "\n", "The output size of this layer corresponds to the number of tokens in the vocabulary, which does **not** depend on Wav2Vec2's pretraining task, but only on the labeled dataset used for fine-tuning. So in the first step, we will take a look at Timit and define a vocabulary based on the dataset's transcriptions." ] }, { "cell_type": "markdown", "metadata": { "id": "bee4g9rpLxll" }, "source": [ "Let's start by loading the dataset and taking a look at its structure." ] }, { "cell_type": "code", "metadata": { "id": "2MMXcWFFgCXU", "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "aadac5aba97941f4922c2cdb74dadc6f", "4b4bd3b71ffc46deaeaed4d7ad7a768e", "e225f2981fe7472d899a1fff0eb6bb8a", "8ba58dd1b97644869a58a1b5a390e96d", "dc086ef489044898b7ec0830d8175f56", "a579ecba69ff45c096f3e1dabb535209", "1e596bd81f984654b5e965a9e8159caf", "162b2b0630244c2182042899074c222d", "425c9b6bd9f74bfbbc1d72ffdceec0d0", "4c2a4d2dadf5422aba969cd5ab6eb762", "3a1fb95b6101460fa5925cab3ba86cf6" ] }, "outputId": "9df1f651-8b7a-4900-9674-4af012de0aa3" }, "source": [ "from datasets import load_dataset, load_metric\n", "\n", "timit = load_dataset(\"timit_asr\")" ], "execution_count": 47, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Reusing dataset timit_asr (/root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/2 [00:00" ], "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
text
0Be careful not to plow over the flower beds.
1An official deadline cannot be postponed.
2You can build this vacation cottage yourself.
3He holds that goodness and badness lie in feelings of approval or disapproval.
4The avalanche triggered a minor earthquake.
5Then came coconuts, eggs, and rice wine.
6The local drugstore was charged with illegally dispensing tranquilizers.
7Who took the kayak down the bayou?
8As such it acts as an anchor for the people.
9She had your dark suit in greasy wash water all year.
" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "fowcOllGNNju" }, "source": [ "Alright! The transcriptions look very clean and the language seems to correspond more to written text than dialogue. This makes sense taking into account that [Timit](https://huggingface.co/datasets/timit_asr) is a read speech corpus." ] }, { "cell_type": "markdown", "metadata": { "id": "vq7OR50LN49m" }, "source": [ "We can see that the transcriptions contain some special characters, such as `,.?!;:`. Without a language model, it is much harder to classify speech chunks to such special characters because they don't really correspond to a characteristic sound unit. *E.g.*, the letter `\"s\"` has a more or less clear sound, whereas the special character `\".\"` does not.\n", "Also in order to understand the meaning of a speech signal, it is usually not necessary to include special characters in the transcription.\n", "\n", "In addition, we normalize the text to only have lower case letters and append a word separator token at the end." ] }, { "cell_type": "code", "metadata": { "id": "svKzVJ_hQGK6" }, "source": [ "import re\n", "chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"]'\n", "\n", "def remove_special_characters(batch):\n", " batch[\"text\"] = re.sub(chars_to_ignore_regex, '', batch[\"text\"]).lower() + \" \"\n", " return batch" ], "execution_count": 52, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XIHocAuTQbBR", "outputId": "4b4453ef-2972-4af5-d29f-dd0062e5a3ba" }, "source": [ "timit = timit.map(remove_special_characters)" ], "execution_count": 53, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Loading cached processed dataset at /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad/cache-542ed77e561b2d6a.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad/cache-e974e08c2913891f.arrow\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "RBDRAAYxRE6n", "outputId": "f579701c-24c2-4c11-dcb8-9c706eac425e" }, "source": [ "show_random_elements(timit[\"train\"].remove_columns([\"audio\", \"file\"]))" ], "execution_count": 54, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
text
0we're not drunkards she said
1the most recent geological survey found seismic activity
2alimony harms a divorced man's wealth
3our entire economy will have a terrific uplift
4don't ask me to carry an oily rag like that
5the gorgeous butterfly ate a lot of nectar
6the high security prison was surrounded by barbed wire
7they've never met you know
8she had your dark suit in greasy wash water all year
9she had your dark suit in greasy wash water all year
" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "jwfaptH5RJwA" }, "source": [ "Good! This looks better. We have removed most special characters from transcriptions and normalized them to lower-case only.\n", "\n", "In CTC, it is common to classify speech chunks into letters, so we will do the same here. \n", "Let's extract all distinct letters of the training and test data and build our vocabulary from this set of letters.\n", "\n", "We write a mapping function that concatenates all transcriptions into one long transcription and then transforms the string into a set of chars. \n", "It is important to pass the argument `batched=True` to the `map(...)` function so that the mapping function has access to all transcriptions at once." ] }, { "cell_type": "code", "metadata": { "id": "LwCshNbbeRZR" }, "source": [ "def extract_all_chars(batch):\n", " all_text = \" \".join(batch[\"text\"])\n", " vocab = list(set(all_text))\n", " return {\"vocab\": [vocab], \"all_text\": [all_text]}" ], "execution_count": 55, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "92b0669ac4c34f3daa4dac1eb3d12b1d", "cc5ef0d5c963487e9f630101ba0d9572", "5929b873c33d4c559b36935102f33e78", "21ceaf0f53d444c396801784d3f1db45", "1decb0d7a7504439825a6e12c21fe8ed", "285447fa0e1e4ae8b5346477ad397ceb", "381bc655291b4effaa6460f7476b0e9b", "8f88e6872d3d45fca03ed5b89d67e6ab", "68b713049fff4a10a14344a5ec358d5c", "7802f2a76e6b4435b4c9c8bb1d40e28e", "6013756a6b204b368184e09e40a4567c", "aaf920d2593846b1bc5824c610167721", "c9126b3964f141a2af5c611c339e8156", "2d8e6e61f14f4cba8bd07e73ca868caf", "efe617968ca54b26b0048c7d20af1ba8", "821399caca9242eca44d7232edafe213", "b3a6d272dc4940b4824c86b22068d8f8", "0c34c7a51c284cda95a8848bad451603", "28e9858f73c3469aaf7e94c819bc5304", "9ad678ebacc941e78a97b0bd728819eb", "29bc21f75fe84b62861e859304a70a4c", "dc7b87eab1a344be9c6c29e92e7278b8" ] }, "id": "_m6uUjjcfbjH", "outputId": "bf5e77dd-6ab3-4be1-e547-2d93b7fbbe74" }, "source": [ "vocabs = timit.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=timit.column_names[\"train\"])" ], "execution_count": 56, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/1 [00:00 main\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-google-colab/commit/293755a3628339986b575eb68038ecffe2789e5c'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 67 } ] }, { "cell_type": "markdown", "metadata": { "id": "KvL12DrNV4cx" }, "source": [ "Great, you can see the just created repository under `https://huggingface.co//wav2vec2-base-timit-demo-colab`" ] }, { "cell_type": "markdown", "metadata": { "id": "mYcIiR2FQ96i" }, "source": [ "### Create Wav2Vec2 Feature Extractor" ] }, { "cell_type": "markdown", "metadata": { "id": "Y6mDEyW719rx" }, "source": [ "Speech is a continuous signal and to be treated by computers, it first has to be discretized, which is usually called **sampling**. The sampling rate hereby plays an important role in that it defines how many data points of the speech signal are measured per second. Therefore, sampling with a higher sampling rate results in a better approximation of the *real* speech signal but also necessitates more values per second.\n", "\n", "A pretrained checkpoint expects its input data to have been sampled more or less from the same distribution as the data it was trained on. The same speech signals sampled at two different rates have a very different distribution, *e.g.*, doubling the sampling rate results in data points being twice as long. Thus, \n", "before fine-tuning a pretrained checkpoint of an ASR model, it is crucial to verify that the sampling rate of the data that was used to pretrain the model matches the sampling rate of the dataset used to fine-tune the model.\n", "\n", "Wav2Vec2 was pretrained on the audio data of [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) and LibriVox which both were sampling with 16kHz. Our fine-tuning dataset, [Timit](hhtps://huggingface.co/datasets/timit_asr), was luckily also sampled with 16kHz. If the fine-tuning dataset would have been sampled with a rate lower or higher than 16kHz, we first would have had to up or downsample the speech signal to match the sampling rate of the data used for pretraining. \n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "KuUbPW7oV-B5" }, "source": [ "A Wav2Vec2 feature extractor object requires the following parameters to be instantiated:\n", "\n", "- `feature_size`: Speech models take a sequence of feature vectors as an input. While the length of this sequence obviously varies, the feature size should not. In the case of Wav2Vec2, the feature size is 1 because the model was trained on the raw speech signal ${}^2$.\n", "- `sampling_rate`: The sampling rate at which the model is trained on.\n", "- `padding_value`: For batched inference, shorter inputs need to be padded with a specific value\n", "- `do_normalize`: Whether the input should be *zero-mean-unit-variance* normalized or not. Usually, speech models perform better when normalizing the input\n", "- `return_attention_mask`: Whether the model should make use of an `attention_mask` for batched inference. In general, models should **always** make use of the `attention_mask` to mask padded tokens. However, due to a very specific design choice of `Wav2Vec2`'s \"base\" checkpoint, better results are achieved when using no `attention_mask`. This is **not** recommended for other speech models. For more information, one can take a look at [this](https://github.com/pytorch/fairseq/issues/3227) issue. **Important** If you want to use this notebook to fine-tune [large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60), this parameter should be set to `True`." ] }, { "cell_type": "code", "metadata": { "id": "kAR0-2KLkopp" }, "source": [ "from transformers import Wav2Vec2FeatureExtractor\n", "\n", "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=False)" ], "execution_count": 68, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qUETetgqYC3W" }, "source": [ "Great, Wav2Vec2's feature extraction pipeline is thereby fully defined!\n", "\n", "To make the usage of Wav2Vec2 as user-friendly as possible, the feature extractor and tokenizer are *wrapped* into a single `Wav2Vec2Processor` class so that one only needs a `model` and `processor` object." ] }, { "cell_type": "code", "metadata": { "id": "KYZtoW-tlZgl" }, "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ], "execution_count": 69, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DrKnYuvDIoOO" }, "source": [ "Next, we can prepare the dataset." ] }, { "cell_type": "markdown", "metadata": { "id": "YFmShnl7RE35" }, "source": [ "### Preprocess Data\n", "\n", "So far, we have not looked at the actual values of the speech signal but just the transcription. In addition to `'text'`, our datasets include two more column names `'file'` and `'audio'`. `'file'` states the absolute path of the audio file. Let's take a look." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "TTCS7W6XJ9BG", "outputId": "fdf3468a-d37a-4931-c9a6-222fb18a2d77" }, "source": [ "timit[\"train\"][0][\"file\"]" ], "execution_count": 70, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'/root/.cache/huggingface/datasets/downloads/extracted/404950a46da14eac65eb4e2a8317b1372fb3971d980d91d5d5b221275b1fd7e0/data/TRAIN/DR4/MMDM0/SI681.WAV'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 70 } ] }, { "cell_type": "markdown", "metadata": { "id": "BwxprOw4Nzrl" }, "source": [ "`Wav2Vec2` expects the input in the format of a 1-dimensional array of 16 kHz. This means that the audio file has to be loaded and resampled.\n", "\n", " Thankfully, `datasets` does this automatically when calling the column `audio`. Let try it out. " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mk9QHuSsN7lf", "outputId": "1d665477-2012-44d9-b884-c16b28d0b13d" }, "source": [ "timit[\"train\"][0][\"audio\"]" ], "execution_count": 71, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'array': array([-2.1362305e-04, 6.1035156e-05, 3.0517578e-05, ...,\n", " -3.0517578e-05, -9.1552734e-05, -6.1035156e-05], dtype=float32),\n", " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/404950a46da14eac65eb4e2a8317b1372fb3971d980d91d5d5b221275b1fd7e0/data/TRAIN/DR4/MMDM0/SI681.WAV',\n", " 'sampling_rate': 16000}" ] }, "metadata": {}, "execution_count": 71 } ] }, { "cell_type": "markdown", "metadata": { "id": "wSBIGEiaKHMn" }, "source": [ "We can see that the audio file has automatically been loaded. This is thanks to the new [`\"Audio\"` feature](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=audio#datasets.Audio) introduced in `datasets == 4.13.3`, which loads and resamples audio files on-the-fly upon calling.\n", "\n", "The sampling rate is set to 16kHz which is what `Wav2Vec2` expects as an input." ] }, { "cell_type": "markdown", "metadata": { "id": "SOckzFd4Mbzq" }, "source": [ "Great, let's listen to a couple of audio files to better understand the dataset and verify that the audio was correctly loaded. \n", "\n", "**Note**: *You can click the following cell a couple of times to listen to different speech samples.*" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 94 }, "id": "dueM6U7Ev0OA", "outputId": "4220aef7-b6ef-452f-e7f9-905198a5b497" }, "source": [ "import IPython.display as ipd\n", "import numpy as np\n", "import random\n", "\n", "rand_int = random.randint(0, len(timit[\"train\"]))\n", "\n", "print(timit[\"train\"][rand_int][\"text\"])\n", "ipd.Audio(data=np.asarray(timit[\"train\"][rand_int][\"audio\"][\"array\"]), autoplay=True, rate=16000)" ], "execution_count": 72, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "the water contained too much chlorine and stung his eyes \n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {}, "execution_count": 72 } ] }, { "cell_type": "markdown", "metadata": { "id": "1MaL9J2dNVtG" }, "source": [ "It can be heard, that the speakers change along with their speaking rate, accent, etc. Overall, the recordings sound relatively clear though, which is to be expected from a read speech corpus.\n", "\n", "Let's do a final check that the data is correctly prepared, by printing the shape of the speech input, its transcription, and the corresponding sampling rate.\n", "\n", "**Note**: *You can click the following cell a couple of times to verify multiple samples.*" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Po2g7YPuRTx", "outputId": "5a99494b-4cf6-46d2-a986-81ed4e1240dd" }, "source": [ "rand_int = random.randint(0, len(timit[\"train\"]))\n", "\n", "print(\"Target text:\", timit[\"train\"][rand_int][\"text\"])\n", "print(\"Input array shape:\", np.asarray(timit[\"train\"][rand_int][\"audio\"][\"array\"]).shape)\n", "print(\"Sampling rate:\", timit[\"train\"][rand_int][\"audio\"][\"sampling_rate\"])" ], "execution_count": 73, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Target text: leave me your address \n", "Input array shape: (23962,)\n", "Sampling rate: 16000\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "M9teZcSwOBJ4" }, "source": [ "Good! Everything looks fine - the data is a 1-dimensional array, the sampling rate always corresponds to 16kHz, and the target text is normalized." ] }, { "cell_type": "markdown", "metadata": { "id": "k3Pbn5WvOYZF" }, "source": [ "Finally, we can process the dataset to the format expected by the model for training. We will make use of the `map(...)` function.\n", "\n", "First, we load and resample the audio data, simply by calling `batch[\"audio\"]`.\n", "Second, we extract the `input_values` from the loaded audio file. In our case, the `Wav2Vec2Processor` only normalizes the data. For other speech models, however, this step can include more complex feature extraction, such as [Log-Mel feature extraction](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum).\n", "Third, we encode the transcriptions to label ids.\n", "\n", "**Note**: This mapping function is a good example of how the `Wav2Vec2Processor` class should be used. In \"normal\" context, calling `processor(...)` is redirected to `Wav2Vec2FeatureExtractor`'s call method. When wrapping the processor into the `as_target_processor` context, however, the same method is redirected to `Wav2Vec2CTCTokenizer`'s call method.\n", "For more information please check the [docs](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#transformers.Wav2Vec2Processor.__call__)." ] }, { "cell_type": "code", "metadata": { "id": "eJY7I0XAwe9p" }, "source": [ "def prepare_dataset(batch):\n", " audio = batch[\"audio\"]\n", "\n", " # batched output is \"un-batched\" to ensure mapping is correct\n", " batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n", " batch[\"input_length\"] = len(batch[\"input_values\"])\n", " \n", " with processor.as_target_processor():\n", " batch[\"labels\"] = processor(batch[\"text\"]).input_ids\n", " return batch" ], "execution_count": 74, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "hVMZhH4-nP8-" }, "source": [ "Let's apply the data preparation function to all examples." ] }, { "cell_type": "code", "metadata": { "id": "-np9xYK-wl8q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "da1c6465-a1e7-4408-e813-8d805a50cf3a" }, "source": [ "timit = timit.map(prepare_dataset, remove_columns=timit.column_names[\"train\"], num_proc=4)" ], "execution_count": 75, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Loading cached processed dataset at /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad/cache-051ef7c66871c35b.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad/cache-c5ecff9383b34436.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad/cache-8aaf1056c35e6980.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad/cache-77b8d0383684d066.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad/cache-fabcc70b342ca8b2.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad/cache-71ac065da3a584f7.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad/cache-c575da350b265c95.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad/cache-a4f7354b3d691cfa.arrow\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "p_MuJSH8nTuQ" }, "source": [ "**Note**: Currently `datasets` make use of [`torchaudio`](https://pytorch.org/audio/stable/index.html) and [`librosa`](https://librosa.org/doc/latest/index.html) for audio loading and resampling. If you wish to implement your own costumized data loading/sampling, feel free to just make use of the `\"path\"` column instead and disregard the `\"audio\"` column." ] }, { "cell_type": "markdown", "metadata": { "id": "M4J0bU1WsvAg" }, "source": [ "Long input sequences require a lot of memory. Since `Wav2Vec2` is based on `self-attention` the memory requirement scales quadratically with the input length for long input sequences (*cf.* with [this](https://www.reddit.com/r/MachineLearning/comments/genjvb/d_why_is_the_maximum_input_sequence_length_of/) reddit post). For this demo, let's filter all sequences that are longer than 4 seconds out of the training dataset." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nqGobEPUvG3v", "outputId": "0cae58f9-2cd9-405a-ad60-7bb31e649911" }, "source": [ "max_input_length_in_sec = 4.0\n", "timit[\"train\"] = timit[\"train\"].filter(lambda x: x < max_input_length_in_sec * processor.feature_extractor.sampling_rate, input_columns=[\"input_length\"])" ], "execution_count": 76, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Loading cached processed dataset at /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad/cache-27a077ffa2911470.arrow\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "25Genil2v_Br" }, "source": [ "Awesome, now we are ready to start training!" ] }, { "cell_type": "markdown", "metadata": { "id": "gYlQkKVoRUos" }, "source": [ "## Training & Evaluation\n", "\n", "The data is processed so that we are ready to start setting up the training pipeline. We will make use of 🤗's [Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer) for which we essentially need to do the following:\n", "\n", "- Define a data collator. In contrast to most NLP models, Wav2Vec2 has a much larger input length than output length. *E.g.*, a sample of input length 50000 has an output length of no more than 100. Given the large input sizes, it is much more efficient to pad the training batches dynamically meaning that all training samples should only be padded to the longest sample in their batch and not the overall longest sample. Therefore, fine-tuning Wav2Vec2 requires a special padding data collator, which we will define below\n", "\n", "- Evaluation metric. During training, the model should be evaluated on the word error rate. We should define a `compute_metrics` function accordingly\n", "\n", "- Load a pretrained checkpoint. We need to load a pretrained checkpoint and configure it correctly for training.\n", "\n", "- Define the training configuration.\n", "\n", "After having fine-tuned the model, we will correctly evaluate it on the test data and verify that it has indeed learned to correctly transcribe speech." ] }, { "cell_type": "markdown", "metadata": { "id": "Slk403unUS91" }, "source": [ "### Set-up Trainer\n", "\n", "Let's start by defining the data collator. The code for the data collator was copied from [this example](https://github.com/huggingface/transformers/blob/9a06b6b11bdfc42eea08fa91d0c737d1863c99e3/examples/research_projects/wav2vec2/run_asr.py#L81).\n", "\n", "Without going into too many details, in contrast to the common data collators, this data collator treats the `input_values` and `labels` differently and thus applies to separate padding functions on them (again making use of Wav2Vec2's context manager). This is necessary because in speech input and output are of different modalities meaning that they should not be treated by the same padding function.\n", "Analogous to the common data collators, the padding tokens in the labels with `-100` so that those tokens are **not** taken into account when computing the loss." ] }, { "cell_type": "code", "metadata": { "id": "tborvC9hx88e" }, "source": [ "import torch\n", "\n", "from dataclasses import dataclass, field\n", "from typing import Any, Dict, List, Optional, Union\n", "\n", "@dataclass\n", "class DataCollatorCTCWithPadding:\n", " \"\"\"\n", " Data collator that will dynamically pad the inputs received.\n", " Args:\n", " processor (:class:`~transformers.Wav2Vec2Processor`)\n", " The processor used for proccessing the data.\n", " padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n", " Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n", " among:\n", " * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n", " sequence if provided).\n", " * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n", " maximum acceptable input length for the model if that argument is not provided.\n", " * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n", " different lengths).\n", " \"\"\"\n", "\n", " processor: Wav2Vec2Processor\n", " padding: Union[bool, str] = True\n", "\n", " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n", " # split inputs and labels since they have to be of different lenghts and need\n", " # different padding methods\n", " input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", "\n", " batch = self.processor.pad(\n", " input_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", " with self.processor.as_target_processor():\n", " labels_batch = self.processor.pad(\n", " label_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " # replace padding with -100 to ignore loss correctly\n", " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", "\n", " batch[\"labels\"] = labels\n", "\n", " return batch" ], "execution_count": 77, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "lbQf5GuZyQ4_" }, "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ], "execution_count": 78, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xO-Zdj-5cxXp" }, "source": [ "Next, the evaluation metric is defined. As mentioned earlier, the \n", "predominant metric in ASR is the word error rate (WER), hence we will use it in this notebook as well." ] }, { "cell_type": "code", "metadata": { "id": "9Xsux2gmyXso" }, "source": [ "wer_metric = load_metric(\"wer\")" ], "execution_count": 79, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "E1qZU5p-deqB" }, "source": [ "The model will return a sequence of logit vectors:\n", "$\\mathbf{y}_1, \\ldots, \\mathbf{y}_m$ with $\\mathbf{y}_1 = f_{\\theta}(x_1, \\ldots, x_n)[0]$ and $n >> m$.\n", "\n", "A logit vector $\\mathbf{y}_1$ contains the log-odds for each word in the vocabulary we defined earlier, thus $\\text{len}(\\mathbf{y}_i) =$ `config.vocab_size`. We are interested in the most likely prediction of the model and thus take the `argmax(...)` of the logits. Also, we transform the encoded labels back to the original string by replacing `-100` with the `pad_token_id` and decoding the ids while making sure that consecutive tokens are **not** grouped to the same token in CTC style ${}^1$." ] }, { "cell_type": "code", "metadata": { "id": "1XZ-kjweyTy_" }, "source": [ "def compute_metrics(pred):\n", " pred_logits = pred.predictions\n", " pred_ids = np.argmax(pred_logits, axis=-1)\n", "\n", " pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n", "\n", " pred_str = processor.batch_decode(pred_ids)\n", " # we do not want to group tokens when computing the metrics\n", " label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n", "\n", " wer = wer_metric.compute(predictions=pred_str, references=label_str)\n", "\n", " return {\"wer\": wer}" ], "execution_count": 80, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Xmgrx4bRwLIH" }, "source": [ "Now, we can load the pretrained `Wav2Vec2` checkpoint. The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of `Wav2Vec2ForCTC` also CTC's *blank token* ${}^2$. To save GPU memory, we enable PyTorch's [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html) and also set the loss reduction to \"*mean*\"." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e7cqAWIayn6w", "outputId": "0b87744a-98a0-4198-c9f6-2a0f46365058" }, "source": [ "from transformers import Wav2Vec2ForCTC\n", "\n", "model = Wav2Vec2ForCTC.from_pretrained(\n", " \"facebook/wav2vec2-base\",\n", " ctc_loss_reduction=\"mean\", \n", " pad_token_id=processor.tokenizer.pad_token_id,\n", ")" ], "execution_count": 81, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "loading configuration file https://huggingface.co/facebook/wav2vec2-base/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/c7746642f045322fd01afa31271dd490e677ea11999e68660a92619ec7c892b4.ce1f96bfaf3d7475cb8187b9668c7f19437ade45fb9ceb78d2b06a2cec198015\n", "/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py:357: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n", " \"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 \"\n", "Model config Wav2Vec2Config {\n", " \"activation_dropout\": 0.0,\n", " \"adapter_kernel_size\": 3,\n", " \"adapter_stride\": 2,\n", " \"add_adapter\": false,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 256,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": false,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"mean\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": false,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_norm\": \"group\",\n", " \"feat_proj_dropout\": 0.1,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"freeze_feat_extract_train\": true,\n", " \"gradient_checkpointing\": true,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 768,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.0,\n", " \"mask_channel_length\": 10,\n", " \"mask_channel_min_space\": 1,\n", " \"mask_channel_other\": 0.0,\n", " \"mask_channel_prob\": 0.0,\n", " \"mask_channel_selection\": \"static\",\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_min_masks\": 0,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_masks\": 2,\n", " \"mask_time_min_space\": 1,\n", " \"mask_time_other\": 0.0,\n", " \"mask_time_prob\": 0.05,\n", " \"mask_time_selection\": \"static\",\n", " \"model_type\": \"wav2vec2\",\n", " \"no_mask_channel_overlap\": false,\n", " \"no_mask_time_overlap\": false,\n", " \"num_adapter_layers\": 3,\n", " \"num_attention_heads\": 12,\n", " \"num_codevector_groups\": 2,\n", " \"num_codevectors_per_group\": 320,\n", " \"num_conv_pos_embedding_groups\": 16,\n", " \"num_conv_pos_embeddings\": 128,\n", " \"num_feat_extract_layers\": 7,\n", " \"num_hidden_layers\": 12,\n", " \"num_negatives\": 100,\n", " \"output_hidden_size\": 768,\n", " \"pad_token_id\": 29,\n", " \"proj_codevector_dim\": 256,\n", " \"tdnn_dilation\": [\n", " 1,\n", " 2,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"tdnn_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 1500\n", " ],\n", " \"tdnn_kernel\": [\n", " 5,\n", " 3,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"transformers_version\": \"4.17.0\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 32,\n", " \"xvector_output_dim\": 512\n", "}\n", "\n", "loading weights file https://huggingface.co/facebook/wav2vec2-base/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/ef45231897ce572a660ebc5a63d3702f1a6041c4c5fb78cbec330708531939b3.fcae05302a685f7904c551c8ea571e8bc2a2c4a1777ea81ad66e47f7883a650a\n", "Some weights of the model checkpoint at facebook/wav2vec2-base were not used when initializing Wav2Vec2ForCTC: ['project_q.weight', 'quantizer.weight_proj.bias', 'project_hid.bias', 'project_q.bias', 'quantizer.weight_proj.weight', 'project_hid.weight', 'quantizer.codevectors']\n", "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base and are newly initialized: ['lm_head.weight', 'lm_head.bias']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "1DwR3XLSzGDD" }, "source": [ "The first component of Wav2Vec2 consists of a stack of CNN layers that are used to extract acoustically meaningful - but contextually independent - features from the raw speech signal. This part of the model has already been sufficiently trained during pretrainind and as stated in the [paper](https://arxiv.org/abs/2006.11477) does not need to be fine-tuned anymore. \n", "Thus, we can set the `requires_grad` to `False` for all parameters of the *feature extraction* part." ] }, { "cell_type": "code", "source": [ "model.freeze_feature_encoder()" ], "metadata": { "id": "Et_NUAZjWppc" }, "execution_count": 82, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "lD4aGhQM0K-D" }, "source": [ "In a final step, we define all parameters related to training. \n", "To give more explanation on some of the parameters:\n", "- `group_by_length` makes training more efficient by grouping training samples of similar input length into one batch. This can significantly speed up training time by heavily reducing the overall number of useless padding tokens that are passed through the model\n", "- `learning_rate` and `weight_decay` were heuristically tuned until fine-tuning has become stable. Note that those parameters strongly depend on the Timit dataset and might be suboptimal for other speech datasets.\n", "\n", "For more explanations on other parameters, one can take a look at the [docs](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer#trainingarguments).\n", "\n", "During training, a checkpoint will be uploaded asynchronously to the hub every 400 training steps. It allows you to also play around with the demo widget even while your model is still training.\n", "\n", "**Note**: If one does not want to upload the model checkpoints to the hub, simply set `push_to_hub=False`." ] }, { "cell_type": "code", "metadata": { "id": "KbeKSV7uzGPP", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "502e0963-4b94-40c9-915a-531b35ef7e8a" }, "source": [ "from transformers import TrainingArguments\n", "\n", "training_args = TrainingArguments(\n", " output_dir=repo_name,\n", " group_by_length=True,\n", " per_device_train_batch_size=8,\n", " evaluation_strategy=\"steps\",\n", " num_train_epochs=30,\n", " fp16=True,\n", " gradient_checkpointing=True,\n", " save_steps=500,\n", " eval_steps=500,\n", " logging_steps=500,\n", " learning_rate=1e-4,\n", " weight_decay=0.005,\n", " warmup_steps=1000,\n", " save_total_limit=2,\n", ")" ], "execution_count": 83, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "PyTorch: setting up devices\n", "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "OsW-WZcL1ZtN" }, "source": [ "Now, all instances can be passed to Trainer and we are ready to start training!" ] }, { "cell_type": "code", "metadata": { "id": "rY7vBmFCPFgC", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7d004e64-8aa4-49d1-8635-f6dd920470b1" }, "source": [ "from transformers import Trainer\n", "\n", "trainer = Trainer(\n", " model=model,\n", " data_collator=data_collator,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " train_dataset=timit[\"train\"],\n", " eval_dataset=timit[\"test\"],\n", " tokenizer=processor.feature_extractor,\n", ")" ], "execution_count": 84, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Using amp half precision backend\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "UoXBx1JAA0DX" }, "source": [ "\n", "\n", "---\n", "\n", "${}^1$ To allow models to become independent of the speaker rate, in CTC, consecutive tokens that are identical are simply grouped as a single token. However, the encoded labels should not be grouped when decoding since they don't correspond to the predicted tokens of the model, which is why the `group_tokens=False` parameter has to be passed. If we wouldn't pass this parameter a word like `\"hello\"` would incorrectly be encoded, and decoded as `\"helo\"`.\n", "\n", "${}^2$ The blank token allows the model to predict a word, such as `\"hello\"` by forcing it to insert the blank token between the two l's. A CTC-conform prediction of `\"hello\"` of our model would be `[PAD] [PAD] \"h\" \"e\" \"e\" \"l\" \"l\" [PAD] \"l\" \"o\" \"o\" [PAD]`." ] }, { "cell_type": "markdown", "metadata": { "id": "rpvZHM1xReIW" }, "source": [ "### Training" ] }, { "cell_type": "markdown", "metadata": { "id": "j-3oKSzZ1hGq" }, "source": [ "Training will take between 90 and 270 minutes depending on the GPU allocated to this notebook. While the trained model yields satisfying results on *Timit*'s test data, it is by no means an optimally fine-tuned model. The purpose of this notebook is to demonstrate how Wav2Vec2's [base](https://huggingface.co/facebook/wav2vec2-base), [large](https://huggingface.co/facebook/wav2vec2-large), and [large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) checkpoints can be fine-tuned on any English dataset.\n", "\n", "In case you want to use this google colab to fine-tune your model, you should make sure that your training doesn't stop due to inactivity. A simple hack to prevent this is to paste the following code into the console of this tab (*right mouse click -> inspect -> Console tab and insert code*)." ] }, { "cell_type": "markdown", "metadata": { "id": "VYYAvgkW4P0m" }, "source": [ "```javascript\n", "function ConnectButton(){\n", " console.log(\"Connect pushed\"); \n", " document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n", "}\n", "setInterval(ConnectButton,60000);\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "PmY6yqd1zvzU" }, "source": [ "Depending on what GPU was allocated to your google colab it might be possible that you are seeing an `\"out-of-memory\"` error here. In this case, it's probably best to reduce `per_device_train_batch_size` to 16 or even less and eventually make use of [`gradient_accumulation`](https://huggingface.co/transformers/master/main_classes/trainer.html#trainingarguments)." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "_UEjJqGsQw24", "outputId": "ef86cb55-632e-44fd-b9b2-6b804e7808df" }, "source": [ "trainer.train()" ], "execution_count": 85, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", " FutureWarning,\n", "***** Running training *****\n", " Num examples = 3978\n", " Num Epochs = 30\n", " Instantaneous batch size per device = 8\n", " Total train batch size (w. parallel, distributed & accumulation) = 8\n", " Gradient Accumulation steps = 1\n", " Total optimization steps = 14940\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [14940/14940 1:24:12, Epoch 30/30]\n", "
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StepTraining LossValidation LossWer
5003.5137001.6718890.958032
10000.8324000.5546100.534147
15000.4365000.4567340.463510
20000.3058000.4429020.445386
25000.2284000.4733830.418648
30000.1892000.4190890.403005
35000.1542000.4521610.398456
40000.1364000.4749200.392185
45000.1239000.4950030.397698
50000.1092000.4467690.377851
55000.0956000.4896800.378885
60000.0897000.4926810.371787
65000.0792000.5242020.369926
70000.0731000.5202230.377162
75000.0681000.5046240.363724
80000.0620000.5336460.366412
85000.0556000.5017130.363311
90000.0556000.5466270.373648
95000.0461000.5489350.356626
100000.0439000.5398720.355937
105000.0397000.5154130.353938
110000.0346000.5170130.351251
115000.0338000.5235570.349183
120000.0342000.5287850.349252
125000.0282000.5146780.344911
130000.0251000.5091520.344222
135000.0268000.5093250.341327
140000.0210000.5309610.339880
145000.0220000.5185330.336986

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-500/preprocessor_config.json\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-1000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-1000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-1000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-1000/preprocessor_config.json\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-1500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-1500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-1500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-1500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-2000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-2000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-1000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-2500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-2500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-2500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-2500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-1500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-3000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-3000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-3000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-3000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-2000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-3500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-3500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-3500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-3500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-2500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-4000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-4000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-4000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-4000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-3000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-4500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-4500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-4500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-4500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-3500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-5000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-5000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-5000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-5000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-4000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-5500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-5500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-5500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-5500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-4500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-6000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-6000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-6000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-6000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-5000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-6500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-6500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-6500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-6500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-5500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-7000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-7000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-7000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-7000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-6000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-7500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-7500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-7500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-7500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-6500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-8000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-8000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-8000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-8000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-7000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-8500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-8500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-8500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-8500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-7500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-9000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-9000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-9000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-9000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-8000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-9500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-9500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-9500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-9500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-8500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-10000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-10000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-10000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-10000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-9000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-10500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-10500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-10500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-10500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-9500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-11000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-11000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-11000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-11000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-10000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-11500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-11500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-11500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-11500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-10500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-12000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-12000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-12000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-12000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-11000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-12500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-12500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-12500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-12500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-11500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-13000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-13000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-13000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-13000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-12000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-13500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-13500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-13500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-13500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-12500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-14000\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-14000/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-14000/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-14000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-13000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.\n", "***** Running Evaluation *****\n", " Num examples = 1680\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-14500\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-14500/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-14500/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-14500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-13500] due to args.save_total_limit\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=14940, training_loss=0.23359869193520091, metrics={'train_runtime': 5052.3634, 'train_samples_per_second': 23.621, 'train_steps_per_second': 2.957, 'total_flos': 3.0766610595969556e+18, 'train_loss': 0.23359869193520091, 'epoch': 30.0})" ] }, "metadata": {}, "execution_count": 85 } ] }, { "cell_type": "markdown", "metadata": { "id": "UCyp-v3n4Zlt" }, "source": [ "The final WER should be around 0.3 which is reasonable given that state-of-the-art phoneme error rates (PER) are just below 0.1 (see [leaderboard](https://paperswithcode.com/sota/speech-recognition-on-timit)) and that WER is usually worse than PER.\n", "\n", "You can now upload the result of the training to the Hub, just execute this instruction:" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 497, "referenced_widgets": [ "a7725ad7a1774178bba1b39b48bee36b", "acc6c975316f4fc9af3f449ccaf71646", "2b48e3c58b284b32ae51a3c3afb13d85", "7748d6bd4e224b3f836a06b321e72de0", "b29aab47d701449181b8d8c715b09657", "5cb2c62e3d6f426f8c17eec2fe6e23ae", "f44a8453ba314ddbb153e1278eb59eec", "6ba56300bd3240629256a72060f36fde", "d8f4c2ce266a4a9284572af28ad9ad1c", "4cdbe067372b4c32bc782f966aea7f42", "afe17562ac7948edab0233eb3804d9cb", "00a02ff8b284413f853da9d7c41afedd", "73b586087e9447e688c97318bf6eba11", "6ba8f8fde3fc4050a304df11cc1d2c7c", "0ceb294ca03a4bf2ae5586ffbbb6f0fc", "ffa2bd3495f54f1c81f15ebf50fd4fee", "e53ec4e8ee3d44bb8b3bdcf323335c8a", "ceef78fab0024c578976ad6c589c0d7f", "35a9027e670146f3a25db0d06035fd1b", "0cd65792216b4decaf24961bb33802d0", "968f456d45ab4f25aeaf57b0c0ddc190", "92ae2ab85efc40fa89da650ccbe85e21", "e1a34af59be04546808279b6e673c498", "91a9bd637428473192d5ec47ba137268", "f11e33bc060746758ad638204b71955c", "ef69985b8f7c4662912a11a377e99b59", "9721f00cdfc942e8ab8a4faf8080ef2d", "196c66080f944a61a537a0ddd9905248", "e7d54319900548208c3852623ebc3dcf", "adbd04cd03624b66a8c725f9a89647b6", "200b5fada23d45ff947953003d052605", "8c89f99cd7b9408eafbfbf8e0c56d39c", "a40561b6f013481ba77fa137ab43b1c2", "63ee5e2413434249bd47a8d662ec0da6", "d10215d60b9f471ab681a948ad143159", "bcb33e49c5ab412eb5b8cd823cf06706", "f23b78d30ff040f481b5584a83083b39", "fe39df4b5e9d41f6a0233bafb4522dd4", "6cac8e9fcefd4353a76244f89ff459e1", "4f70c2c356074adf9aa7c1fb6b20f8e3", "4cc884973b734098a135619736b5f599", "fd72ea27c82843ceb5fdb74886fc5b44", "28b1ff0d507e40fb84e4083537207de4", "bf2896c4b25147f0a303c748fa2b0cd9" ] }, "id": "JYGQYBhHNsvj", "outputId": "c60381d7-a4ec-4cfe-ec0b-0b16af963c3d" }, "source": [ "trainer.push_to_hub()" ], "execution_count": 86, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/content/wav2vec2-base-timit-demo-google-colab is already a clone of https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-google-colab. Make sure you pull the latest changes with `repo.git_pull()`.\n", "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab\n", "Configuration saved in wav2vec2-base-timit-demo-google-colab/config.json\n", "Model weights saved in wav2vec2-base-timit-demo-google-colab/pytorch_model.bin\n", "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/preprocessor_config.json\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.34k/360M [00:00 main\n", "\n", "Dropping the following result as it does not have all the necessary fields:\n", "{}\n", "remote: Enforcing permissions... \n", "remote: Allowed refs: all \n", "To https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-google-colab\n", " 629f0ba..38b281e main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-google-colab/commit/629f0ba808bdecd2134e2d91110e86e4d7d7647c'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 86 } ] }, { "cell_type": "markdown", "metadata": { "id": "djzwS5WeNu16" }, "source": [ "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier \"your-username/the-name-you-picked\" so for instance:" ] }, { "cell_type": "markdown", "metadata": { "id": "Adm0LngNNxq7" }, "source": [ "```python\n", "from transformers import AutoModelForCTC, Wav2Vec2Processor\n", "\n", "model = AutoModelForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-base-timit-demo-google-colab\")\n", "processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-base-timit-demo-google-colab\")\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "nD4XP5IHncsY" }, "source": [ "### Evaluate\n", "\n", "In the final part, we run our model on some of the validation data to get a feeling for how well it works.\n", "\n", "Let's load the `processor` and `model`." ] }, { "cell_type": "code", "metadata": { "id": "-rg0USDCnbo6", "colab": { "base_uri": "https://localhost:8080/", "height": 718, "referenced_widgets": [ "524b5795780746f5966c06b878cc0fbc", "8eb41fee8e1c452a9d69a07a266d871a", "f22febfc3bbe48058be84b2a20e906f5", "ab51fefefa1b464a9a780b80c6f0c020", "88fd1cb973f8485b97caa19b7801c3d3", "3234edc4a75e4658acd19feef174e88f", "6bfe6219ad934146a4d680e38ed19879", "63e9fc689ea54fbfa7226cc9918977cb", "dc034b4bf9e34963ab79f391c55d44ac", "6b3288ffeef34c2a925d2554c7c4f521", "116e45bbe2d640d18d6a47309692dc20", "66d5a397b26e4d66a97bc3b3159e96bd", "041870fd5bac43b394956252364a16c4", "26f6574d99724fb29159071dda6039b5", "55d0bfd5c746402f89e6b86a7ca37b3a", "b69bad861f014c20b4cb470c73b1fe37", "940e030ce7ae40d68136ddf1a1a67736", "c80bdd3bae794c08b81c950a33ff9ba0", "7b0c8bc5bb66433384aeb47bf99001ae", "6b03d462bd0f4fbeab0df76bbb881621", "be78bdab8638431e80c087a0fe9a7a8f", "d44a1f0b3216469fb1ac597951f9471a", "a0e897961bb049438847879cf082adf3", "820ac3ce474346728b80a609d47848ee", "045273b88fa04afba97bf7ba6027f164", "4847949da7b64782895b4fca8b432ec8", "427b99cf8b964a59bbbf0b4d04504906", "f652ea3e007c42669e445a7247bb0430", "527bf8b3612b4da89479424c5b66681e", "708ff309676040119bccd6f21d1684f6", "6a8694b9181f4828ba3ef4ff828fd1c3", "90dbfe5bab604315b329a6a8fcf131b1", "b1a51bfb07774b7bac783d1c4d5a680c", "6ca0c9b3d250425b9597de9baa6bd77b", "b7d46c7f5da646eba0ed9e38fed6b4ee", "7291673613bb43848bec1abee7447078", "5c49d625a7f742f1a64820bc6f749f3a", "a6a5bcb980df41d1b3e7b581701eae16", "6412d390b2ce416fae910d689c3f6e70", "255c276a050446ed9df81f8eeb7c1178", "ddd975596c234b0eacc40ef18ef40778", "a5fd824b8eb64bb7b8d83354961927e6", "17e3d063396848548af91c55c8f8e282", "a3b48bc669674ff2a0bcce75a50e81eb" ] }, "outputId": "ddfac637-7c6d-4df1-a07a-4f1278fff182" }, "source": [ "processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-base-timit-demo-google-colab\")" ], "execution_count": 87, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-google-colab/resolve/main/preprocessor_config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpjyd34ogd\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading: 0%| | 0.00/215 [00:00 to the vocabulary\n", "Adding to the vocabulary\n", "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "sSpFW3pDn23P", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "dd9ac831-3620-4b87-98b6-c85a57150a15" }, "source": [ "model = Wav2Vec2ForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-base-timit-demo-google-colab\").cuda()" ], "execution_count": 93, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "loading configuration file https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-google-colab/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/7606a293c66b1cba6bb9a342dcffe3831dc19a120cfeeb9e57cdf6ffb9630f3f.3202909be16e9c482257d0c4d35b4f4c2558cea585027c729eed803ba4bf0d8e\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"facebook/wav2vec2-base\",\n", " \"activation_dropout\": 0.0,\n", " \"adapter_kernel_size\": 3,\n", " \"adapter_stride\": 2,\n", " \"add_adapter\": false,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForCTC\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 256,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": false,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"mean\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": false,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_norm\": \"group\",\n", " \"feat_proj_dropout\": 0.1,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"freeze_feat_extract_train\": true,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 768,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.0,\n", " \"mask_channel_length\": 10,\n", " \"mask_channel_min_space\": 1,\n", " \"mask_channel_other\": 0.0,\n", " \"mask_channel_prob\": 0.0,\n", " \"mask_channel_selection\": \"static\",\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_min_masks\": 0,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_masks\": 2,\n", " \"mask_time_min_space\": 1,\n", " \"mask_time_other\": 0.0,\n", " \"mask_time_prob\": 0.05,\n", " \"mask_time_selection\": \"static\",\n", " \"model_type\": \"wav2vec2\",\n", " \"no_mask_channel_overlap\": false,\n", " \"no_mask_time_overlap\": false,\n", " \"num_adapter_layers\": 3,\n", " \"num_attention_heads\": 12,\n", " \"num_codevector_groups\": 2,\n", " \"num_codevectors_per_group\": 320,\n", " \"num_conv_pos_embedding_groups\": 16,\n", " \"num_conv_pos_embeddings\": 128,\n", " \"num_feat_extract_layers\": 7,\n", " \"num_hidden_layers\": 12,\n", " \"num_negatives\": 100,\n", " \"output_hidden_size\": 768,\n", " \"pad_token_id\": 29,\n", " \"proj_codevector_dim\": 256,\n", " \"tdnn_dilation\": [\n", " 1,\n", " 2,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"tdnn_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 1500\n", " ],\n", " \"tdnn_kernel\": [\n", " 5,\n", " 3,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.17.0\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 32,\n", " \"xvector_output_dim\": 512\n", "}\n", "\n", "loading weights file https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-demo-google-colab/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/666767bce47a87d50dfd1ceac759d5dc9db6eac8d185ae8f9a7dd388687c28c5.20bdf845b9896c1d0a213f03187b544f2a12b23d751b88c7b49a95d00bd946e1\n", "All model checkpoint weights were used when initializing Wav2Vec2ForCTC.\n", "\n", "All the weights of Wav2Vec2ForCTC were initialized from the model checkpoint at patrickvonplaten/wav2vec2-base-timit-demo-google-colab.\n", "If your task is similar to the task the model of the checkpoint was trained on, you can already use Wav2Vec2ForCTC for predictions without further training.\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "TUuZpB0v5jn_" }, "source": [ "Now, we will make use of the `map(...)` function to predict the transcription of every test sample and to save the prediction in the dataset itself. We will call the resulting dictionary `\"results\"`. \n", "\n", "**Note**: we evaluate the test data set with `batch_size=1` on purpose due to this [issue](https://github.com/pytorch/fairseq/issues/3227). Since padded inputs don't yield the exact same output as non-padded inputs, a better WER can be achieved by not padding the input at all." ] }, { "cell_type": "code", "metadata": { "id": "40naJl53n7jT" }, "source": [ "def map_to_result(batch):\n", " with torch.no_grad():\n", " input_values = torch.tensor(batch[\"input_values\"], device=\"cuda\").unsqueeze(0)\n", " logits = model(input_values).logits\n", "\n", " pred_ids = torch.argmax(logits, dim=-1)\n", " batch[\"pred_str\"] = processor.batch_decode(pred_ids)[0]\n", " batch[\"text\"] = processor.decode(batch[\"labels\"], group_tokens=False)\n", " \n", " return batch" ], "execution_count": 94, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "uPqH7gZqrGPi", "colab": { "base_uri": "https://localhost:8080/", "height": 48, "referenced_widgets": [ "8fbdb230253a4152be95bf6229049f09", "857f0c099d8549e98991a93c791db4c1", "16561789e2b9417a80ddcb6ef9d8676a", "050c9d6bfd1e4b6ebe67401183a8d397", "5874591082f944ff9b32202a1edbfd60", "8c968b160d3d4fbb998840734f9f07e4", "d2fd7c9e56b1419f9d23a10c260a7c7d", "4a03fb6d945f4102bc4f7192195d810d", "8ab2466b187047fbbe4d8b72455b1108", "89472d79793e4db9afa6845fb016c8ad", "f5e79b8c3a634eae8a555f18b9dc9bd6" ] }, "outputId": "1eb633f5-c487-4cd1-8be6-aac4af385a2e" }, "source": [ "results = timit[\"test\"].map(map_to_result, remove_columns=timit[\"test\"].column_names)" ], "execution_count": 95, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "0ex [00:00, ?ex/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "8fbdb230253a4152be95bf6229049f09" } }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "-mqpdB8R6rty" }, "source": [ "Let's compute the overall WER now." ] }, { "cell_type": "code", "metadata": { "id": "PmqAb4Isx8OK", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d70180e0-77e4-458b-daff-61df307f500c" }, "source": [ "print(\"Test WER: {:.3f}\".format(wer_metric.compute(predictions=results[\"pred_str\"], references=results[\"text\"])))" ], "execution_count": 96, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Test WER: 0.218\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "6Va94d8Y7Q98" }, "source": [ "21.8% WER - not bad! Our demo model would have probably made it on the official [leaderboard](https://paperswithcode.com/sota/speech-recognition-on-timit).\n", "\n", "Let's take a look at some predictions to see what errors are made by the model." ] }, { "cell_type": "code", "metadata": { "id": "odNDiFVRy53w", "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "outputId": "cb787bc7-5346-449e-e314-202df442185a" }, "source": [ "show_random_elements(results)" ], "execution_count": 97, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
pred_strtext
0aim to balenge your employ you benefits packageaim to balance your employee benefit package
1the fog prevented them from ariving on tomthe fog prevented them from arriving on time
2young children should avoid exposure to contageous diseasesyoung children should avoid exposure to contagious diseases
3art official intelligence is for realartificial intelligence is for real
4theire propes were two step latters a chair and a pame fentheir props were two stepladders a chair and a palm fan
5if people were more generous there would be no need for wealfarif people were more generous there would be no need for welfare
6the fish began to leep frantically on the surface of the smalleakuthe fish began to leap frantically on the surface of the small lake
7her rite hand aques whenever the baramatric presuer changesher right hand aches whenever the barometric pressure changes
8only lawyers loved milunearsonly lawyers love millionaires
9the nearest synnegu may not be within walk in distancethe nearest synagogue may not be within walking distance
" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "5HFCujhd9n4N" }, "source": [ "It becomes clear that the predicted transcriptions are acoustically very similar to the target transcriptions, but often contain spelling or grammatical errors. This shouldn't be very surprising though given that we purely rely on Wav2Vec2 without making use of a language model." ] }, { "cell_type": "markdown", "metadata": { "id": "a3ydKvUl9FTK" }, "source": [ "Finally, to better understand how CTC works, it is worth taking a deeper look at the exact output of the model. Let's run the first test sample through the model, take the predicted ids and convert them to their corresponding tokens." ] }, { "cell_type": "code", "metadata": { "id": "AqaM45t87uM4", "colab": { "base_uri": "https://localhost:8080/", "height": 73 }, "outputId": "2d181121-2184-48dc-a337-bb33194fd68b" }, "source": [ "model.to(\"cuda\")\n", "\n", "with torch.no_grad():\n", " logits = model(torch.tensor(timit[\"test\"][:1][\"input_values\"], device=\"cuda\")).logits\n", "\n", "pred_ids = torch.argmax(logits, dim=-1)\n", "\n", "# convert ids to tokens\n", "\" \".join(processor.tokenizer.convert_ids_to_tokens(pred_ids[0].tolist()))" ], "execution_count": 98, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'[PAD] [PAD] [PAD] [PAD] [PAD] t t h h e | | | b [PAD] [PAD] [PAD] u u n n g g [PAD] [PAD] l l l l [PAD] o o o | | w w a a s s | | [PAD] [PAD] [PAD] p l l [PAD] e s s s s [PAD] n n t t [PAD] l l l y y | | [PAD] s s s i i t t t [PAD] u u u u u [PAD] [PAD] [PAD] [PAD] a a t t [PAD] e e d d | | [PAD] n n e e a a r | | t t h e e | | s s h h [PAD] [PAD] [PAD] [PAD] o o r r r r | [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 98 } ] }, { "cell_type": "markdown", "metadata": { "id": "tdO8E28g-n5C" }, "source": [ "The output should make it a bit clearer how CTC works in practice. The model is to some extent invariant to speaking rate since it has learned to either just repeat the same token in case the speech chunk to be classified still corresponds to the same token. This makes CTC a very powerful algorithm for speech recognition since the speech file's transcription is often very much independent of its length.\n", "\n", "I again advise the reader to take a look at [this](https://distill.pub/2017/ctc) very nice blog post to better understand CTC." ] } ] } ================================================ FILE: Getting_the_most_out_of_LLMs.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "_bB0IiBfC_8C" }, "source": [ "# Getting the most out of LLMs\n", "\n", "Large Language Models (LLMs) such as GPT3/4, [Falcon](https://huggingface.co/tiiuae/falcon-40b), and [LLama](https://huggingface.co/meta-llama/Llama-2-70b-hf) are rapidly advancing in their ability to tackle human-centric tasks, establishing themselves as essential tools in modern knowledge-based industries.\n", "Deploying these models in real-world tasks remains challenging, however:\n", "\n", "- To exhibit near-human text understanding and generation capabilities, LLMs currently require to be composed of billions of parameters (see [Kaplan et al](https://arxiv.org/abs/2001.08361), [Wei et. al](https://arxiv.org/abs/2206.07682)). This consequently amplifies the memory demands for inference.\n", "- In many real-world tasks, LLMs need to be given extensive contextual information. This necessitates the model's capability to manage very long input sequences during inference.\n", "\n", "The crux of these challenges lies in augmenting the computational and memory capabilities of LLMs, especially when handling expansive input sequences.\n", "\n", "In this blog post, we will go over the most effective techniques to tackle these challenges for efficient LLM deployment:\n", "\n", "1. **Lower Precision**: Research has shown that operating at reduced numerical precision, namely 8-bit and 4-bit, can achieve computational advantages without a considerable decline in model performance.\n", "\n", "2. **Flash Attention:** Flash Attention is a variation of the attention algorithm that not only provides a more memory-efficient approach but also realizes increased efficiency due to optimized GPU memory utilization.\n", "\n", "3. **Architectural Innovations:** Considering that LLMs are always deployed in the same way during inference, namely autoregressive text generation with a long input context, specialized model architectures have been proposed that allow for more efficient inference. The most important advancement in model architectures hereby are [Alibi](https://arxiv.org/abs/2108.12409), [Rotary embeddings](https://arxiv.org/abs/2104.09864), [Multi-Query Attention (MQA)](https://arxiv.org/abs/1911.02150) and [Grouped-Query-Attention (GQA)]((https://arxiv.org/abs/2305.13245)).\n", "\n", "Throughout this notebook, we will offer an analysis of auto-regressive generation from a tensor's perspective. We delve into the pros and cons of adopting lower precision, provide a comprehensive exploration of the latest attention algorithms, and discuss improved LLM architectures. While doing so, we run practical examples showcasing each of the feature improvements.\n", "\n", "## 1. Harnessing the Power of Lower Precision\n", "\n", "Memory requirements of LLMs can be best understood by seeing the LLM as a set of weight matrices and vectors and the text inputs as a sequence of vectors. In the following, the definition *weights* will be used to signify all model weight matrices and vectors.\n", "\n", "At the time of writing this paper, LLMs consist of at least a couple billion parameters. Each parameter thereby is made of a decimal number, e.g. `4.5689` which is usually stored in either [float32](https://en.wikipedia.org/wiki/Single-precision_floating-point_format), [bfloat16](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format), or [float16](https://en.wikipedia.org/wiki/Half-precision_floating-point_format) format. This allows us to easily compute the memory requirement to load the LLM into memory:\n", "\n", "> *Loading the weights of a model having X billion parameters requires roughly 4 * X GB of VRAM in float32 precision*\n", "\n", "Nowadays, models are however rarely trained in full float32 precision, but usually in bfloat16 precision or less frequently in float16 precision. Therefore the role of thumb becomes:\n", "\n", "> *Loading the weights of a model having X billion parameters requires roughly 2 * X GB of VRAM in bfloat16/float16 precision*\n", "\n", "For shorter text inputs (less than 1024 tokens), the memory requirement for inference is very much dominated by the memory requirement to load the weights. Therefore, for now, let's assume that the memory requirement for inference is equal to the memory requirement to load the model into the GPU VRAM.\n", "\n", "To give some examples of how much VRAM it roughly takes to load a model in bfloat16:\n", "\n", "- **GPT3** requires 2 \\* 175 GB = **350 GB** VRAM\n", "- [**Bloom**](https://huggingface.co/bigscience/bloom) requires 2 \\* 176 GB = **352 GB** VRAM\n", "- [**Llama-2-70b**](https://huggingface.co/meta-llama/Llama-2-70b-hf) requires 2 \\* 70 GB = **140 GB** VRAM\n", "- [**Falcon-40b**](https://huggingface.co/tiiuae/falcon-40b) requires 2 \\* 40 GB = **80 GB** VRAM\n", "- [**MPT-30b**](https://huggingface.co/mosaicml/mpt-30b) requires 2 \\* 30 GB = **60 GB** VRAM\n", "- [**bigcode/starcoder**](https://huggingface.co/bigcode/starcoder) requires 2 \\* 15.5 = **31 GB** VRAM\n", "\n", "As of writing this document, the largest GPU chip on the market is the A100 offering 80GB of VRAM. Most of the models listed before require more than 80GB just to be loaded and therefore necessarily require [tensor parallelism](https://huggingface.co/docs/transformers/v4.15.0/parallelism#tensor-parallelism) and/or [pipeline parallelism](https://huggingface.co/docs/transformers/v4.15.0/parallelism#naive-model-parallel-vertical-and-pipeline-parallel)\n", "\n", "🤗 Transformers does not support tensor parallelism out of the box as it requires the model architecture to be written in a specific way. If you're interested in writing models in a tensor-parallelism-friendly way, feel free to have a look at [the text-generation-inference library](https://github.com/huggingface/text-generation-inference/tree/main/server/text_generation_server/models/custom_modeling).\n", "\n", "Naive pipeline parallelism is supported out of the box. For this, simply load the model with device=\"auto\" which will automatically place the different layers on the available GPUs as explained [here](https://huggingface.co/docs/accelerate/v0.22.0/en/concept_guides/big_model_inference). Note, however that while very effective, this naive pipeline parallelism does not tackle the issues of GPU idling. For this more advanced pipeline parallelism is required as explained [here](https://huggingface.co/docs/transformers/v4.15.0/parallelism#naive-model-parallel-vertical-and-pipeline-parallel).\n", "\n", "Pipeline parallelism is supported out of the box. For this, simply load the model with `device=\"auto\"` which will automatically place the different layers on the available GPUs as explained [here](https://huggingface.co/docs/accelerate/v0.22.0/en/concept_guides/big_model_inference).\n", "\n", "If you have access to an 8 x 80GB A100 node, you could load BLOOM as follows" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2rWj4FHKDNZd", "outputId": "c2758ec5-6814-47f8-80b5-27aec9c19ade" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.33.1)\n", "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.22.0)\n", "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.41.1)\n", "Requirement already satisfied: optimum in /usr/local/lib/python3.10/dist-packages (1.13.1)\n", "Requirement 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/usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets->optimum) (1.16.0)\n" ] } ], "source": [ "!pip install transformers accelerate bitsandbytes optimum" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "77_5IcYaDR_j" }, "outputs": [], "source": [ "# from transformers import AutoModelForCausalLM\n", "\n", "# model = AutoModelForCausalLM.from_pretrained(\"bigscience/bloom\", device_map=\"auto\")" ] }, { "cell_type": "markdown", "metadata": { "id": "hr94CKExDNvX" }, "source": [ "By using `device_map=\"auto\"` the attention layers would be equally distributed over all available GPUs.\n", "\n", "In this notebook, we will use [bigcode/octocoder](https://huggingface.co/bigcode/octocoder) as it can be run on a single 40 GB A100 GPU device chip. Note that all memory and speed optimizations that we will apply going forward, are equally applicable to models that require model or tensor parallelism.\n", "\n", "Since the model is loaded in bfloat16 precision, using our rule of thumb above, we would expect the memory requirement to run inference with `bigcode/octocoder` to be around 31 GB VRAM. Let's give it a try.\n", "\n", "We first load the model and tokenizer and then pass both to Transformers' [pipeline](https://huggingface.co/docs/transformers/main_classes/pipelines) object.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "2b2fe981999b48c7b3121f3086ff4d47", "bd818e7fe6b24c02bbdfa782e9ce1862", "385e13a69e4a4816b80b881c89798bdd", "8c129311170842e5bbccf6425562ad18", "f940f6f51ac042ebbb0b031979433f4d", "fc90fb527c0845a28a98c3ba58ab9527", "23a49faf621a4eb4adf662ff571c4301", "7e201f1cb93649e5b28a993e4e42caaa", "1ccf56fc5da5433e83803b73dc9c2464", "18ca55cc38b74f238ff91df93902ae90", "2034a324528d426c974b1e74e1a73060" ] }, "id": "ny3Mv-FlDYMr", "outputId": "6b1216c3-87ea-4d55-f55d-f58cea5d049d" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/7 [00:00 Almost all models are trained in bfloat16 nowadays, there is no reason to run the model in full float32 precision if [your GPU supports bfloat16](https://discuss.pytorch.org/t/bfloat16-native-support/117155/5). Float32 won't give better inference results than the precision that was used to train the model.\n", "\n", "Let's define a `flush(...)` function to free all allocated memory so that we can accurately measure the peak allocated GPU memory." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "paSs50gnGI-T" }, "outputs": [], "source": [ "model.to(\"cpu\")\n", "del pipe\n", "del model" ] }, { "cell_type": "code", "source": [ "import gc\n", "import torch\n", "\n", "def flush():\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", " torch.cuda.reset_peak_memory_stats()" ], "metadata": { "id": "TUvgmjoIOeMq" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "JViS_vqsGNx5" }, "source": [ "Let's call it now for the next experiment." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ncW8sIgmGJyo" }, "outputs": [], "source": [ "flush()" ] }, { "cell_type": "markdown", "metadata": { "id": "kg30PIPPGRIw" }, "source": [ "Now what if your GPU does not have 32 GB of VRAM? It has been found that model weights can be quantized to 8-bit or 4-bits without a significant loss in performance (see [Dettmers et al.](https://arxiv.org/abs/2208.07339)).\n", "Model can be quantized to even 3 or 2 bits with an acceptable loss in performance as shown in the recent [GPTQ paper](https://huggingface.co/docs/transformers/main_classes/quantization#general-usage) 🤯.\n", "\n", "Without going into too many details, quantization schemes aim at reducing the precision of weights while trying to keep the model's inference results as accurate as possible (*a.k.a* as close as possible to bfloat16).\n", "Note that quantization works especially well for text generation since all we care about is choosing the *most likely next token* and don't really care about the exact values of the next token *logit* distribution.\n", "All that matters is that the next token *logit* distribution stays roughly the same so that an `argmax` or `topk` operation gives the same results.\n", "\n", "There are various quantization techniques, which we won't discuss in detail here, but in general, all quantization techniques work as follows:\n", "\n", "- 1. Quantize all weights to the target precision\n", "- 2. Load the quantized weights, and pass the input sequence of vectors in bfloat16 precision\n", "- 3. Dynamically dequantize weights to bfloat16 to perform the computation with their input vectors in bfloat16 precision\n", "- 4. Quantize the weights again to the target precision after computation with their inputs.\n", "\n", "In a nutshell, this means that *inputs-weight matrix* multiplications, with $X$ being the *inputs*, $W$ being a weight matrix and $Y$ being the output:\n", "\n", "$Y = X * W$\n", "\n", "are changed to\n", "\n", "$ Y = X * \\text{dequantize}(W); \\text{quantize}(W) $\n", "\n", "for every matrix multiplication. Dequantization and re-quantization is performed sequentially for all weight matrices as the inputs run through the network graph.\n", "\n", "Therefore, inference time is often **not** reduced when using quantized weights, but rather increases.\n", "Enough theory, let's give it a try! To quantize the weights with Transformers, you need to make sure that\n", "the [`bitsandbytes`](https://github.com/TimDettmers/bitsandbytes) library is installed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "f1-PcCaFGS9A" }, "outputs": [], "source": [ "# !pip install bitsandbytes" ] }, { "cell_type": "markdown", "metadata": { "id": "HfrHvJyZGbBs" }, "source": [ "We can then load models in 8-bit quantization by simply adding a `load_in_8bit=True` flag to `from_pretrained`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yhwHj948GdQy", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "003fe528910a4bd7a546b81e17efcb4b", "5d588188327a4691b50de0d33f83db11", "65d69be1f3224eebb2830cdc2dfc6038", "a07ca8aa805344548d0be317e037aa79", "73ec1c4358d040e890290303e6be1d69", "30302479d3e24887aebcd7043e53fff6", "0447c45d26aa45b1a6fd0a8acdf710ed", "65b723d0dfa343ea88c83759dc7d8958", "68e714a8f33f4fa4902b489cfea08d59", "f0eba1e320fc4ac293f93ffe8a903cba", "89318b2079e34238baadf7d3024080fb" ] }, "outputId": "35914829-0f9e-423b-f26b-96916564f84e" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/7 [00:0015 billion parameter model.\n", "\n", "While we see very little degradation in accuracy for our model here, 4-bit quantization can in practice often lead to different results compared to 8-bit quantization or full `bfloat16` inference. It is up to the user to try it out.\n", "\n", "Also note that inference here was again a bit slower compared to 8-bit quantization which is due to the more aggressive quantization method used for 4-bit quantization leading to $\\text{quantize}$ and $\\text{dequantize}$ taking longer during inference." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "J-9IBH3HHAvK" }, "outputs": [], "source": [ "model.cpu()\n", "del model\n", "del pipe" ] }, { "cell_type": "code", "source": [ "flush()" ], "metadata": { "id": "ZHX7LH5eD1tr" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Zjk5gz1xHGcS" }, "source": [ "Overall, we saw that running OctoCoder in 8-bit precision reduced the required GPU VRAM from 32G GPU VRAM to only 15GB and running the model in 4-bit precision further reduces the required GPU VRAM to just a bit over 9GB.\n", "\n", "4-bit quantization allows the model to be run on GPUs such as RTX3090, V100, and T4 which are quite accessible for most people.\n", "\n", "For more information on quantization and to see how one can quantize models to require even less GPU VRAM memory than 4-bit, we recommend looking into the [`AutoGPTQ`](https://huggingface.co/docs/transformers/main/en/main_classes/quantization#autogptq-integration%60) implementation.\n", "\n", "> As a conclusion, it is important to remember that model quantization trades improved memory efficiency against accuracy and in most cases inference time.\n", "\n", "If GPU memory is not a constraint for your use case, there is often no need to look into quantization. However many GPUs simply can't run LLMs without quantization methods and in this case, 4-bit and 8-bit quantization schemes are extremely useful tools.\n", "\n", "For more in-detail usage information, we strongly recommend taking a look at the [Transformers Quantization Docs](https://huggingface.co/docs/transformers/main_classes/quantization#general-usage).\n", "Next, let's look into how we can improve computational and memory efficiency by using better algorithms and an improved model architecture." ] }, { "cell_type": "markdown", "metadata": { "id": "e9JmlMvBHRe1" }, "source": [ "# 2. Flash Attention: A Leap Forward\n", "\n", "Today's top-performing LLMs share more or less the same fundamental architecture that consists of feed-forward layers, activation layers, layer normalization layers, and most crucially, self-attention layers.\n", "\n", "Self-attention layers are central to Large Language Models (LLMs) in that they enable the model to understand the contextual relationships between input tokens.\n", "However, the peak GPU memory consumption for self-attention layers grows *quadratically* both in time and memory complexity with number of input tokens (also called *sequence length*) that we denote in the following by $N$.\n", "While this is not really noticeable for shorter input sequences (of up to 1000 input tokens), it becomes a serious problem for longer input sequences (at around 16000 input tokens).\n", "\n", "Let's take a closer look. The formula to compute the output $\\mathbf{O}$ of a self-attention layer for an input $\\mathbf{X}$ of length $N$ is:\n", "\n", "$$ \\textbf{O} = \\text{Attn}(\\mathbf{X}) = \\mathbf{V} \\times \\text{Softmax}(\\mathbf{QK}^T) \\text{ with } \\mathbf{Q} = \\mathbf{W}_q \\mathbf{X}, \\mathbf{V} = \\mathbf{W}_v \\mathbf{X}, \\mathbf{K} = \\mathbf{W}_k \\mathbf{X} $$\n", "\n", "$\\mathbf{X} = (\\mathbf{x}_1, ... \\mathbf{x}_{N})$ is thereby the input sequence to the attention layer. The projections $\\mathbf{Q}$ and $\\mathbf{K}$ will each consist of $N$ vectors resulting in the $\\mathbf{QK}^T$ being of size $N^2$.\n", "\n", "LLMs usually have multiple attention heads, thus doing multiple self-attention computations in parallel.\n", "Assuming, the LLM has 40 attention heads and runs in bfloat16 precision, we can calculate the memory requirement to store the $\\mathbf{QK^T}$ matrices to be $40 * 2 * N^2$ bytes. For $N=1000$ only around 50 MB of VRAM are needed, however, for $N=16000$ we would need 19 GB of VRAM, and for $N=100,000$ we would need almost 1TB just to store the $\\mathbf{QK}^T$ matrices.\n", "\n", "Long story short, the default self-attention algorithm quickly becomes prohibitively memory-expensive for large input contexts.\n", "\n", "As LLMs improve in text comprehension and generation, they are applied to increasingly complex tasks. While models once handled the translation or summarization of a few sentences, they now manage entire pages, demanding the capability to process extensive input lengths.\n", "\n", "How can we get rid of the exorbitant memory requirements for large input lengths? We need a new way to compute the self-attention mechanism that gets rid of the $QK^T$ matrix. [Tri Dao et al.](FlashAttention:%20Fast%20and%20Memory-Efficient%20Exact%20Attention%20with%20IO-Awareness) developed exactly such a new algorithm and called it **Flash Attention**.\n", "\n", "In a nutshell, Flash Attention breaks the $ \\mathbf{V} \\times \\text{Softmax}(\\mathbf{QK}^T) $ computation apart and instead computes smaller chunks of the output by iterating oven multiple softmax computation steps:\n", "\n", "$$ \\textbf{O}_i \\leftarrow s^a_{ij} * \\textbf{O}_i + s^b_{ij} * \\mathbf{V}_{j} \\times \\text{Softmax}(\\mathbf{QK}^T_{i,j}) \\text{ for multiple } i, j \\text{ iterations} $$\n", "\n", "with $s^a_{ij}$ and $s^b_{ij}$ being some softmax normalization statistics that need to be recomputed for every $i$ and $j$.\n", "\n", "Please note that the whole Flash Attention is a bit more complex and is greatly simplified here as going in too much depth is out of scope for this notebook. The reader is invited to take a look at the well-written [Flash Attention paper](https://arxiv.org/pdf/2205.14135.pdf) for more details.\n", "\n", "The main takeaway here is:\n", "\n", "> By keeping track of softmax normalization statistics and by using some smart mathematics, Flash Attention gives **numerical identical** outputs compared to the default self-attention layer at a memory cost that only increases linearly with $N$.\n", "\n", "Looking at the formula, one would intuitively say that Flash Attention must be much slower compared to the default self-attention formula as more computation needs to be done. Indeed Flash Attention requires more FLOPs compared to normal attenion as the softmax normalization statistics have to constantly be recomputed (see [paper](https://arxiv.org/pdf/2205.14135.pdf) for more details if interested)\n", "\n", "> However Flash Attenion is much faster in inference compared to default attention which comes from its ability to significantly reduce the demands on the slower, high-bandwidth memory of the GPU (VRAM), focusing instead on the faster on-chip memory (SRAM).\n", "\n", "Essentially, Flash Attention makes sure that all intermediate write and read operations can be done using the fast *on-chip* SRAM memory instead of having to access the slower VRAM memory to compute the output vector $\\mathbf{O}$.\n", "\n", "In practice, there is currently absolutely no reason to **not** use Flash Attention if available. The algorithm gives mathematically the same outputs, and is both faster and more memory-efficient.\n", "\n", "Let's look at a practical example.\n", "\n", "Our OctoCoder model now gets a significantly longer input prompt which includes a so-called *system prompt*. System prompts are used to steer the LLM into a better assistant that is tailored to the users' task.\n", "In the following, we use a system prompt that will make OctoCoder a better coding assistant." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hO-FB26EHL6v" }, "outputs": [], "source": [ "system_prompt = \"\"\"Below are a series of dialogues between various people and an AI technical assistant.\n", "The assistant tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble but knowledgeable.\n", "The assistant is happy to help with code questions and will do their best to understand exactly what is needed.\n", "It also tries to avoid giving false or misleading information, and it caveats when it isn't entirely sure about the right answer.\n", "That said, the assistant is practical really does its best, and doesn't let caution get too much in the way of being useful.\n", "\n", "The Starcoder models are a series of 15.5B parameter models trained on 80+ programming languages from The Stack (v1.2) (excluding opt-out requests).\n", "The model uses Multi Query Attention, was trained using the Fill-in-the-Middle objective, and with 8,192 tokens context window for a trillion tokens of heavily deduplicated data.\n", "\n", "-----\n", "\n", "Question: Write a function that takes two lists and returns a list that has alternating elements from each input list.\n", "\n", "Answer: Sure. Here is a function that does that.\n", "\n", "def alternating(list1, list2):\n", " results = []\n", " for i in range(len(list1)):\n", " results.append(list1[i])\n", " results.append(list2[i])\n", " return results\n", "\n", "Question: Can you write some test cases for this function?\n", "\n", "Answer: Sure, here are some tests.\n", "\n", "assert alternating([10, 20, 30], [1, 2, 3]) == [10, 1, 20, 2, 30, 3]\n", "assert alternating([True, False], [4, 5]) == [True, 4, False, 5]\n", "assert alternating([], []) == []\n", "\n", "Question: Modify the function so that it returns all input elements when the lists have uneven length. The elements from the longer list should be at the end.\n", "\n", "Answer: Here is the modified function.\n", "\n", "def alternating(list1, list2):\n", " results = []\n", " for i in range(min(len(list1), len(list2))):\n", " results.append(list1[i])\n", " results.append(list2[i])\n", " if len(list1) > len(list2):\n", " results.extend(list1[i+1:])\n", " else:\n", " results.extend(list2[i+1:])\n", " return results\n", "\n", "-----\n", "\"\"\"" ] }, { "cell_type": "markdown", "metadata": { "id": "hXA31-PkHWJ-" }, "source": [ "For demonstration purposes, we duplicate the system by ten so that the input length is long enough to observe Flash Attention's memory savings.\n", "We append the original text prompt `\"Question: Please write a function in Python that transforms bytes to Giga bytes.\\n\\nAnswer: Here\"`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ju0gGhGsHY-0" }, "outputs": [], "source": [ "long_prompt = 10 * system_prompt + prompt" ] }, { "cell_type": "markdown", "metadata": { "id": "fuxcrfwQHkGD" }, "source": [ "We instantiate our model again in bfloat16 precision." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A_6TPWlBHn1P", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "acc4045a4d1248229be925a1f30c9343", "ac6921ac317c45b2b3fc61232f769acf", "87813afbd74c4ea5b6d7d7752789fad7", "eddf05eee60d4190af6bad4fd9d1531b", "488fe5b3769d436ca3090ed76bb28c8c", "8ebc8c49fd554d689aec5422c93fa034", "15feb73d540140e69afcaf5ece4763b3", "fa3da205418c4d1ca58812409aa629f6", "fd4a5931ce304e9db6a5f2b3aaedd916", "297a44d6c29645cfa7ae94da93858e2f", "f91b7e7ae4154068805ace600918d6d1" ] }, "outputId": "6b2b4146-de54-4342-8fcb-98a7338ebfa2" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/7 [00:00 By doing so, the propability score between $\\mathbf{q}_i$ and $\\mathbf{q}_j$ is only affected if $i \\ne j$ and solely depends on the relative distance $i - j$ regardless of each vector's specific positions $i$ and $j$.\n", "\n", "*RoPE* is used in multiple of today's most important LLMs, such as:\n", "\n", "- [**Falcon**](https://huggingface.co/tiiuae/falcon-40b)\n", "- [**Llama**](https://arxiv.org/abs/2302.13971)\n", "- [**PaLM**](https://arxiv.org/pdf/2204.02311.pdf)\n", "\n", "As an alternative, *ALiBi* proposes a much simpler relative position encoding scheme. The relative distance that input tokens have to each other is added as a negative integer scaled by a pre-defined value `m` to each query-key entry of the $\\mathbf{QK}^T$ matrix right before the softmax computation.\n", "\n", "![](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/alibi.png)\n", "\n", "As shown in the [ALiBi](https://arxiv.org/abs/2108.12409) paper, this simple relative positional encoding allows the model to retain a high performance even at very long text input sequences.\n", "\n", "*ALiBi* is used in multiple of today's most important LLMs, such as:\n", "\n", "- [**MPT**](https://huggingface.co/mosaicml/mpt-30b)\n", "- [**BLOOM**](https://huggingface.co/bigscience/bloom)\n", "\n", "Both *RoPE* and *ALiBi* position encodings can extrapolate to input lengths not seen during training whereas it has been shown that extrapolation work much better out-of-the-box for ALiBi as compared to *RoPE*.\n", "For ALiBi, one simply increases the values of the lower triangular position matrix to match the length of the input sequence.\n", "For *RoPE*, keeping the same $\\theta$ that was used during training leads to poor results when passing text inputs much longer than those seen during training, *c.f* [Press et al.](https://arxiv.org/abs/2108.12409). However, the community has found a couple of effective tricks that adapt $\\theta$, thereby allowing *RoPE* position embeddings to work well for extrapolated text input sequences (see [here](https://github.com/huggingface/transformers/pull/24653)).\n", "\n", "> Both RoPE and ALiBi are relative positional embeddings that are *not* learned during training, but instead are based on the following intuitions:\n", " - Positional cues about the text inputs should be given directly to the $QK^T$ matrix of the self-attention layer\n", " - The LLM should be incentivized to learn a constant *relative* distance positional encodings have to each other\n", " - The further text input tokens are from each other, the lower the probability of their query-value probability. Both RoPE and ALiBi lower the query-key probability of tokens far away from each other. RoPE by decreasing their vector product by increasing the angle between the query-key vectors. ALiBi by adding large negative numbers to the vector product\n", "\n", "In conclusion, LLMs that are intended to be deployed in tasks that require handling large text inputs are better trained with relative positional embeddings, such as RoPE and ALiBi. Also note that even if an LLM with RoPE and ALiBi has been trained only on a fixed length of say $N_1 = 2048$ it can still be used in practice with text inputs much larger than $N_1$, like $N_2 = 8192 > N_1$ by extrapolating the positional embeddings.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "IZl--c1cIT_c" }, "source": [ "### 3.2 The key-value cache\n", "\n", "Auto-regressive text generation with LLMs works by iteratively putting in an input sequence, sampling the next token, appending the next token to the input sequence, and continuing to do so until the LLM produces a token that signifies that the generation has finished.\n", "\n", "Please have a look at [Transformer's Generate Text Tutorial](https://huggingface.co/docs/transformers/llm_tutorial#generate-text) to get a more visual explanation of how auto-regressive generation works.\n", "\n", "Let's run a quick code snippet to show how auto-regressive works in practice. We will simply take the most likely next token via `torch.argmax`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pR3ng1DmIVJE", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d2fed697-43a0-4ccc-b3a5-dd201414271c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "shape of input_ids torch.Size([1, 21])\n", "shape of input_ids torch.Size([1, 22])\n", "shape of input_ids torch.Size([1, 23])\n", "shape of input_ids torch.Size([1, 24])\n", "shape of input_ids torch.Size([1, 25])\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[' Here is a Python function']" ] }, "metadata": {}, "execution_count": 35 } ], "source": [ "input_ids = tokenizer(prompt, return_tensors=\"pt\")[\"input_ids\"].to(\"cuda\")\n", "\n", "for _ in range(5):\n", " next_logits = model(input_ids)[\"logits\"][:, -1:]\n", " next_token_id = torch.argmax(next_logits,dim=-1)\n", "\n", " input_ids = torch.cat([input_ids, next_token_id], dim=-1)\n", " print(\"shape of input_ids\", input_ids.shape)\n", "\n", "generated_text = tokenizer.batch_decode(input_ids[:, -5:])\n", "generated_text" ] }, { "cell_type": "markdown", "metadata": { "id": "Bkt-cYlDIZal" }, "source": [ "As we can see every time we increase the text input tokens by the just sampled token.\n", "\n", "With very few exceptions, LLMs are trained using the [causal language modeling objective](https://huggingface.co/docs/transformers/tasks/language_modeling#causal-language-modeling) and therefore mask the upper triangle matrix of the attention score - this is why in the two diagrams above the attention scores are left blank (*a.k.a* have 0 probability). For a quick recap on causal language modeling you can refer to the [*Illustrated Self Attention blog*](https://jalammar.github.io/illustrated-gpt2/#part-2-illustrated-self-attention).\n", "\n", "As a consequence, tokens *never* depend on previous tokens, more specifically the $\\mathbf{q}_i$ vector is never put in relation with any key, values vectors $\\mathbf{k}_j, \\mathbf{v}_j$ if $j > i$. Instead $\\mathbf{q}_i$ only attends to previous key-value vectors $\\mathbf{k}_{m < i}, \\mathbf{v}_{m < i} \\text{ , for } m \\in \\{0, \\ldots i - 1\\}$. In order to reduce unnecessary computation, one can therefore cache each layer's key-value vectors for all previous timesteps.\n", "\n", "In the following, we will tell the LLM to make user of the key-value cache by retrieving and forwarding it for each forward pass.\n", "In Transformers, we can retrieve the key-value cache by passing the `use_cache` flag to the `forward` call and can then pass it with the current token." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ABfZCOc2IaNR", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7b1766c4-5e50-43c8-edcc-d6f625df97a6" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "shape of input_ids torch.Size([1, 1])\n", "length of key-value cache 20\n", "shape of input_ids torch.Size([1, 1])\n", "length of key-value cache 21\n", "shape of input_ids torch.Size([1, 1])\n", "length of key-value cache 22\n", "shape of input_ids torch.Size([1, 1])\n", "length of key-value cache 23\n", "shape of input_ids torch.Size([1, 1])\n", "length of key-value cache 24\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[' Here', ' is', ' a', ' Python', ' function']" ] }, "metadata": {}, "execution_count": 44 } ], "source": [ "past_key_values = None # past_key_values is the key-value cache\n", "generated_tokens = []\n", "next_token_id = tokenizer(prompt, return_tensors=\"pt\")[\"input_ids\"].to(\"cuda\")\n", "\n", "for _ in range(5):\n", " next_logits, past_key_values = model(next_token_id, past_key_values=past_key_values, use_cache=True).to_tuple()\n", " next_logits = next_logits[:, -1:]\n", " next_token_id = torch.argmax(next_logits, dim=-1)\n", "\n", " print(\"shape of input_ids\", next_token_id.shape)\n", " print(\"length of key-value cache\", len(past_key_values[0][0])) # past_key_values are of shape [num_layers, 0 for k, 1 for v, batch_size, length, hidden_dim]\n", " generated_tokens.append(next_token_id.item())\n", "\n", "generated_text = tokenizer.batch_decode(generated_tokens)\n", "generated_text" ] }, { "cell_type": "markdown", "metadata": { "id": "P0J_LmVyIgyo" }, "source": [ "As one can see, when using the key-value cache the text input tokens are *not* increased in length, but remain a single input vector. The length of the key-value cache on the other hand is increased by one at every decoding step.\n", "\n", "> Making use of the key-value cache means that the $\\mathbf{QK}^T$ is essentially reduced to $\\mathbf{q}_c\\mathbf{K}^T$ with $\\mathbf{q}_c$ being the query projection of the currently passed input token which is *always* just a single vector.\n", "\n", "Using the key-value cache has two advantages:\n", "- Significant increase in computational efficiency as less computations are performed compared to computing the full $\\mathbf{QK}^T$ matrix. This leads to an increase in inference speed\n", "- The maximum required memory is not increased quadratically with the number of generated tokens, but only increases linearly.\n", "\n", "> One should *always* make use of the key-value cache as it leads to identical results and a significant speed-up for longer input sequences. Transformers has the key-value cache enabled by default when making use of the text pipeline or the [`generate` method](https://huggingface.co/docs/transformers/main_classes/text_generation).\n", "\n", "Note that the key-value cache is especially useful for applications such as chat where multiple passes of auto-regressive decoding are required. Let's look at an example.\n", "\n", "```\n", "User: How many people live in France?\n", "Assistant: Roughly 75 million people live in France\n", "User: And how many are in Germany?\n", "Assistant: Germany has ca. 81 million inhabitants\n", "```\n", "\n", "In this chat, the LLM runs auto-regressive decoding twice:\n", "- 1. The first time, the key-value cache is empty and the input prompt is `\"User: How many people live in France?\"` and the model auto-regressively generates the text `\"Roughly 75 million people live in France\"` while increasing the key-value cache at every decoding step.\n", "- 2. The second time the input prompt is `\"User: How many people live in France? \\n Assistant: Roughly 75 million people live in France \\n User: And how many in Germany?\"`. Thanks to the cache, all key-value vectors for the first two sentences are already computed. Therefore the input prompt only consists of `\"User: And how many in Germany?\"`. While processing the shortened input prompt, it's computed key-value vectors are concatenated to the key-value cache of the first decoding. The second Assistant's answer `\"Germany has ca. 81 million inhabitants\"` is then auto-regressively generated with the key-value cache consisting of encoded key-value vectors of `\"User: How many people live in France? \\n Assistant: Roughly 75 million people live in France \\n User: And how many are in Germany?\"`.\n", "\n", "Two things should be noted here:\n", "- 1. Keeping all the context is crucial for LLMs deployed in chat so that the LLM understands all the previous context of the conversation. E.g. for the example above the LLM needs to understand that the user refers to the population when asking `\"And how many are in Germany\"`.\n", "- 2. The key-value cache is extremely useful for chat as it allows us to continuously grow the encoded chat history instead of having to re-encode the chat history again from scratch (as e.g. would be the case when using an encoder-decoder architecture).\n", "\n", "There is however one catch. While the required peak memory for the $\\mathbf{QK}^T$ matrix is significantly reduced, holding the key-value cache in memory can become very memory expensive for long input sequence or multi-turn chat. Remember that the key-value cache needs to store the key-value vectors for all previous input vectors $\\mathbf{x}_i \\text{, for } i \\in \\{1, \\ldots, c - 1\\}$ for all self-attention layers and for all attention heads.\n", "\n", "Let's compute the number of float values that need to be stored in the key-value cache for the LLM `bigcode/octocoder` that we used before.\n", "The number of float values amounts to:\n", "\n", "$$ 2 \\times (\\text{seq_len} - 1) \\times \\text{num_attn_heads} \\times \\text{attn_head_dim} \\times \\text{num_layers} $$\n", "\n", "Computing this for our LLM at a hypothetical input sequence length of 16000 gives:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IdOR72ueIkmf", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "492ebf99-642a-45dc-a7d5-8b724df16a7a" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "7864320000" ] }, "metadata": {}, "execution_count": 45 } ], "source": [ "config = model.config\n", "2 * 16_000 * config.n_layer * config.n_head * config.n_embd // config.n_head" ] }, { "cell_type": "markdown", "metadata": { "id": "VB-rlYk4IRWF" }, "source": [ "Roughly 8 billion float values! Storing 8 billion float values in `float16` precision requires around 15 GB of RAM which is circa half as much as the model weights themselves!\n", "Researchers have proposed two methods that allow to significantly reduce the memory cost of storing the key-value cache:\n", "\n", "- 1. [Multi-Query-Attention (MQA)](https://arxiv.org/abs/1911.02150)\n", "\n", "Multi-Query-Attention was proposed in Noam Shazeer's *Fast Transformer Decoding: One Write-Head is All You Need* paper. As the title says, Noam found out that instead of using `n_head` key-value projections weights, one can use a single head-value projection weight pair that is shared across all attention heads without that the model's performance significantly degrades.\n", "\n", "> By using a single head-value projection weight pair, the key value vectors $\\mathbf{k}_i, \\mathbf{v}_i$ have to be identical across all attention heads which in turn means that we only need to store 1 key-value projection pair in the cache instead of `n_head` ones.\n", "\n", "As most LLMs use between 20 and 100 attention heads, MQA significantly reduces the memory consumption of the key-value cache. For the LLM used in this notebook we could therefore reduce the required memory consumption from 15 GB to less than 400 MB at an input sequence length of 16000.\n", "\n", "In addition to memory savings, MQA also leads to improved computational efficiency as explained in the following.\n", "In auto-regressive decoding, large key-value vectors need to be reloaded, concatenated with the current key-value vector pair to be then fed into the $\\mathbf{q}_c\\mathbf{K}^T$ computation at every step. For auto-regressive decoding, the required memory bandwidth for the constant reloading can become a serious time bottleneck. By reducing the size of the key-value vectors less memory needs to be accessed, thus reducing the memory bandwidth bottleneck. For more detail, please have a look into [Noam's paper](https://arxiv.org/abs/1911.02150).\n", "\n", "The important part to understand here is that reducing the number of key-value attention heads to 1 only makes sense if a key-value cache is used. The peak memory consumption of the model for a single forward pass without key-value cache stays unchanged as every attention head still has a unique query vector so that each attention head still has a different $\\mathbf{QK}^T$ matrix.\n", "\n", "MQA has seen wide adoption by the community and is now used by many of the most popular LLMs:\n", "\n", "- [**Falcon**](https://huggingface.co/tiiuae/falcon-40b)\n", "- [**PaLM**](https://arxiv.org/pdf/2204.02311.pdf)\n", "- [**MPT**](https://huggingface.co/mosaicml/mpt-30b)\n", "- [**BLOOM**](https://huggingface.co/bigscience/bloom)\n", "\n", "Also, the checkpoint used in this notebook - `bigcode/octocoder` - makes use of MQA.\n", "\n", "- 2. [Grouped-Query-Attention (GQA)](https://arxiv.org/abs/2305.13245)\n", "\n", "Grouped-Query-Attention, as proposed by Ainslie et al. from Google, found that using MQA can often lead to quality degradation compared to using vanilla multi-key-value head projections. The paper argues that more model performance can be kept by less drastically reducing the number of query head projection weights. Instead of using just a single key-value projection weight, `n < n_head` key-value projection weights should be used. By choosing `n` to a significantly smaller value than `n_head`, such as 2,4 or 8 almost all of the memory and speed gains from MQA can be kept while sacrificing less model capacity and thus arguably less performance.\n", "\n", "Moreover, the authors of GQA found out that existing model checkpoints can be *uptrained* to have a GQA architecture with as little as 5% of the original pre-training compute. While 5% of the original pre-training compute can still be a massive amount, GQA *uptraining* allows existing checkpoints to be useful for longer input sequences.\n", "\n", "GQA was only recently proposed which is why there is less adoption at the time of writing this notebook.\n", "The most notable application of GQA is [Llama-v2](https://huggingface.co/meta-llama/Llama-2-70b-hf).\n", "\n", "> As a conclusion, it is strongly recommended to make use of either GQA or MQA if the LLM is deployed with auto-regressive decoding and is required to handle large input sequences as is the case for example for chat.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "uwkIyhcLIpMx" }, "source": [ " ## Conclusion\n", "\n", "The research community is constantly coming up with new, nifty ways to speed up inference time for ever-larger LLMs. As an example, one such promising research direction is [speculative decoding](https://arxiv.org/abs/2211.17192) where \"easy tokens\" are generated by smaller, faster language models and only \"hard tokens\" are generated by the LLM itself. Going into more detail is out of the scope of this notebook, but can be read upon in this [nice blog post](https://huggingface.co/blog/assisted-generation).\n", "\n", "The reason massive LLMs such as GPT3/4, Llama-2-70b, Claude, PaLM can run so quickly in chat-interfaces such as [Hugging Face Chat](https://huggingface.co/chat/) or ChatGPT is to a big part thanks to the above-mentioned improvements in precision, algorithms, and architecture.\n", "Going forward, accelerators such as GPUs, TPUs, etc... will only get faster and allow for more memory, but one should nevertheless always make sure to use the best available algorithms and architectures to get the most bang for your buck 🤗" ] } ], "metadata": { "accelerator": "GPU", "colab": { "machine_shape": "hm", "provenance": [], "gpuType": "A100", "authorship_tag": "ABX9TyMb5chWDy4K1gSzlUHpl5rc", "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": 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"_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: How_to_evaluate_Longformer_on_TriviaQA_using_NLP.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "How to evaluate Longformer on TriviaQA using NLP", "provenance": [], "collapsed_sections": [], "toc_visible": true, "authorship_tag": "ABX9TyMobmJFrxuYygHuUuXMD52/", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "lbNZdYkugq7-", "colab_type": "text" }, "source": [ "# Evaluation of a model using 🤗nlp\n", "\n", "*This notebook shows how `nlp` can be leveraged to evaluate **Longformer** on **TriviaQA** .*\n", "\n", "- The [`nlp`](https://github.com/huggingface/nlp) library allows simple and intuitive access to nlp datasets and metrics.\n", "\n", "- **Longformer** is transformer-based model for long-range sequence modeling introduced by *Iz Beltagy, Matthew E. Peters, Arman Cohan* (see paper [here](https://arxiv.org/abs/2004.05150)) and can now be accessed via Transformers via the [docs](https://huggingface.co/transformers/model_doc/longformer.html).\n", "\n", "- **TriviaQA** is a reading comprehension dataset containing question-answer-evidence triplets (see paper here [here](https://homes.cs.washington.edu/~eunsol/papers/acl17jcwz.pdf))." ] }, { "cell_type": "markdown", "metadata": { "id": "ckDfoClXj-mm", "colab_type": "text" }, "source": [ "We will evaluate a pretrained `LongformerForQuestionAnswering` model on the *validation* dataset of **TriviaQA**. Along the way, this notebook will show you how `nlp` can be used for effortless preprocessing of data and analysis of the results." ] }, { "cell_type": "markdown", "metadata": { "id": "PGg2btE2mY0v", "colab_type": "text" }, "source": [ "Alright! Let's start by installing the `nlp` library and loading *TriviaQA*. " ] }, { "cell_type": "markdown", "metadata": { "id": "KLb6vCflSqcI", "colab_type": "text" }, "source": [ "### Installs and Imports" ] }, { "cell_type": "code", "metadata": { "id": "FXHkpUVuft4o", "colab_type": "code", "colab": {} }, "source": [ "# install nlp\n", "!pip install -qq nlp==0.2.0\n", "\n", "# Make sure that we have a recent version of pyarrow in the session before we continue - otherwise reboot Colab to activate it\n", "import pyarrow\n", "if int(pyarrow.__version__.split('.')[1]) < 16:\n", " import os\n", " os.kill(os.getpid(), 9)\n", "\n", "!pip install -qq transformers==2.11.0\n", "\n", "import nlp\n", "import torch" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "P3mV2lN2fe3u", "colab_type": "code", "colab": {} }, "source": [ "# ATTENTION. Rerunning this command remove the cached trivia qa dataset completely \n", "!rm -rf /root/.cache/" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "TVQCLkYbS0Nm", "colab_type": "text" }, "source": [ "### Data cleaning and preprocessing " ] }, { "cell_type": "markdown", "metadata": { "id": "gpOqRdcjnaBT", "colab_type": "text" }, "source": [ "The total *TriviaQA* dataset has a size of 17 GB once processed.\n", "Downloading and preprocessing the dataset will take around *15 minutes*. ☕\n", "Afterwards the data is serialized in *Arrow* format for quick reloading.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "YusOKo5FgkYH", "colab_type": "code", "colab": {} }, "source": [ "validation_dataset = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:5%]\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "G_LFkvD_sx9H", "colab_type": "text" }, "source": [ "First, let's get an overview of the dataset 🧐" ] }, { "cell_type": "code", "metadata": { "id": "mJTko97rwQGF", "colab_type": "code", "outputId": "b6c28652-0984-4d22-9e40-3c0b0472eb4b", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "validation_dataset" ], "execution_count": 18, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Dataset(schema: {'question': 'string', 'question_id': 'string', 'question_source': 'string', 'entity_pages': 'struct, filename: list, title: list, wiki_context: list>', 'search_results': 'struct, filename: list, rank: list, title: list, url: list, search_context: list>', 'answer': 'struct, normalized_aliases: list, matched_wiki_entity_name: string, normalized_matched_wiki_entity_name: string, normalized_value: string, type: string, value: string>'}, num_rows: 933)" ] }, "metadata": { "tags": [] }, "execution_count": 18 } ] }, { "cell_type": "markdown", "metadata": { "id": "QHsmt1ULwTug", "colab_type": "text" }, "source": [ "5% of the validation data corresponds to 933 examples, which we can use as a good snapshot of the actual dataset and get be used to get familiar with the dataset.\n", "\n", "Let's check out the datatset's structure." ] }, { "cell_type": "code", "metadata": { "id": "DjAVcrAitsU5", "colab_type": "code", "outputId": "11fb6010-1a1b-4f15-dfe1-7120888b8bf1", "colab": { "base_uri": "https://localhost:8080/", "height": 632 } }, "source": [ "# check out schema\n", "validation_dataset.schema" ], "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "question: string not null\n", "question_id: string not null\n", "question_source: string not null\n", "entity_pages: struct, filename: list, title: list, wiki_context: list> not null\n", " child 0, doc_source: list\n", " child 0, item: string\n", " child 1, filename: list\n", " child 0, item: string\n", " child 2, title: list\n", " child 0, item: string\n", " child 3, wiki_context: list\n", " child 0, item: string\n", "search_results: struct, filename: list, rank: list, title: list, url: list, search_context: list> not null\n", " child 0, description: list\n", " child 0, item: string\n", " child 1, filename: list\n", " child 0, item: string\n", " child 2, rank: list\n", " child 0, item: int32\n", " child 3, title: list\n", " child 0, item: string\n", " child 4, url: list\n", " child 0, item: string\n", " child 5, search_context: list\n", " child 0, item: string\n", "answer: struct, normalized_aliases: list, matched_wiki_entity_name: string, normalized_matched_wiki_entity_name: string, normalized_value: string, type: string, value: string> not null\n", " child 0, aliases: list\n", " child 0, item: string\n", " child 1, normalized_aliases: list\n", " child 0, item: string\n", " child 2, matched_wiki_entity_name: string\n", " child 3, normalized_matched_wiki_entity_name: string\n", " child 4, normalized_value: string\n", " child 5, type: string\n", " child 6, value: string" ] }, "metadata": { "tags": [] }, "execution_count": 5 } ] }, { "cell_type": "markdown", "metadata": { "id": "7XqHiM5mt5t-", "colab_type": "text" }, "source": [ "Alright, quite a lot of entries here! For Questions Answering, all we need is the *question*, the *context* and the *answer*. \n", "\n", "The **question** is a single entry, so we keep it.\n", "\n", "Because *Longformer* was trained on the Wikipedia part of *TriviaQA*, we will use `validation_dataset[\"entity_pages\"][\"search_context\"]` as our **context**. \n", "\n", "We can also see that there are multiple entries for the **answer**. In this use case, we define a correct output of the model as one that is one of the answer aliases `validation_dataset[\"answer\"][\"aliases\"]`. Lastly, we also keep `validation_dataset[\"answer\"][\"normalized_value\"]`. All other columns can be disregarded. \n", "\n", "We apply the `.map()` function to map the dataset into the format as defined above." ] }, { "cell_type": "code", "metadata": { "id": "GpEkZy_LvhEX", "colab_type": "code", "outputId": "32241e60-7850-4904-c878-1bbdad6178a6", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "# define the mapping function\n", "def format_dataset(example):\n", " # the context might be comprised of multiple contexts => me merge them here\n", " example[\"context\"] = \" \".join((\"\\n\".join(example[\"entity_pages\"][\"wiki_context\"])).split(\"\\n\"))\n", " example[\"targets\"] = example[\"answer\"][\"aliases\"]\n", " example[\"norm_target\"] = example[\"answer\"][\"normalized_value\"]\n", " return example\n", "\n", "# map the dataset and throw out all unnecessary columns\n", "validation_dataset = validation_dataset.map(format_dataset, remove_columns=[\"search_results\", \"question_source\", \"entity_pages\", \"answer\", \"question_id\"])" ], "execution_count": 19, "outputs": [ { "output_type": "stream", "text": [ "933it [00:01, 564.44it/s]\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "jRhh7AJrw3fM", "colab_type": "text" }, "source": [ "Now, we can check out a first example of the dataset." ] }, { "cell_type": "code", "metadata": { "id": "7I730mW9wNeo", "colab_type": "code", "outputId": "e4ca13cf-a822-4dca-d2b4-13cdc78e1821", "colab": { "base_uri": "https://localhost:8080/", "height": 612 } }, "source": [ "validation_dataset[8]" ], "execution_count": 20, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'context': '',\n", " 'norm_target': 'basket ball',\n", " 'question': 'The Naismith Award is presented in which sport?',\n", " 'targets': ['Basketball',\n", " 'Basketball gear',\n", " 'Bball',\n", " \"Boy's Basketball\",\n", " 'B Ball',\n", " 'Shoot hoops',\n", " 'Basketball parity worldwide',\n", " \"Men's Basketball\",\n", " 'High school basketball',\n", " 'Basketball Worldwide',\n", " 'Basketball club',\n", " 'B-ball',\n", " 'Basket-ball',\n", " 'Basketball team',\n", " '🏀',\n", " 'Basketball rim',\n", " 'Basketballer',\n", " 'Rim (basketball)',\n", " 'Basket ball',\n", " 'Basketball net',\n", " 'Baksetball',\n", " 'Basketball player',\n", " 'Basket-Ball',\n", " \"Women's hoops\",\n", " \"Men's basketball\",\n", " 'BasketBall',\n", " 'Basketball Parity Worldwide',\n", " 'Basket Ball',\n", " 'Baketball',\n", " 'Basketball Player',\n", " 'B ball',\n", " 'Unicycle basketball']}" ] }, "metadata": { "tags": [] }, "execution_count": 20 } ] }, { "cell_type": "markdown", "metadata": { "id": "dqcviRfJw-3M", "colab_type": "text" }, "source": [ "Great 🙂. That's exactly, the structure we wanted! Some examples might have an empty context so we will filter those examples out.\n", "For this we can use the convenient `.filter()` function of `nlp`." ] }, { "cell_type": "code", "metadata": { "id": "j0vSycFKxc0O", "colab_type": "code", "outputId": "133831fb-d416-4669-a08a-b5f9a51deb97", "colab": { "base_uri": "https://localhost:8080/", "height": 51 } }, "source": [ "validation_dataset = validation_dataset.filter(lambda x: len(x[\"context\"]) > 0)\n", "# check out how many samples are left\n", "validation_dataset" ], "execution_count": 21, "outputs": [ { "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 8.28it/s]\n" ], "name": "stderr" }, { "output_type": "execute_result", "data": { "text/plain": [ "Dataset(schema: {'question': 'string', 'context': 'string', 'targets': 'list', 'norm_target': 'string'}, num_rows: 467)" ] }, "metadata": { "tags": [] }, "execution_count": 21 } ] }, { "cell_type": "markdown", "metadata": { "id": "IMh8EvQoxsrF", "colab_type": "text" }, "source": [ "Looks like more or less half of our examples have no context and are now filtered out. Let's think about the evaluation on *Longformer* now. \n", "\n", "*Longformer* is able to process inputs of up to a length of **4096** tokens. As a rule of thumb, 4 is the average number of characters per word piece. Therefore, it is a good idea to check for how many examples, the *question* + *context* exceeds 4 * 4096 characters.\n", "Again we can apply the convenient `.map()` function." ] }, { "cell_type": "code", "metadata": { "id": "4ebRyrOmy4br", "colab_type": "code", "outputId": "ac8edb9e-e566-4cce-e914-be9e50e39a5c", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "print(\"\\n\\nLength for each example\")\n", "print(30 * \"=\")\n", "\n", "# length for each example\n", "validation_dataset.map(lambda x, i: print(f\"Id: {i} - Question Length: {len(x['question'])} - context Length: {len(x['context'])}\"), with_indices=True)\n", "print(30 * \"=\")\n", "\n", "print(\"\\n\")\n", "print(\"Num examples larger than 4 * 4096 characters: \")\n", "# filter out examples smaller than 4 * 4096\n", "short_validation_dataset = validation_dataset.filter(lambda x: (len(x['question']) + len(x['context'])) < 4 * 4096)\n", "short_validation_dataset\n" ], "execution_count": 34, "outputs": [ { "output_type": "stream", "text": [ "207it [00:00, 1071.45it/s]" ], "name": "stderr" }, { "output_type": "stream", "text": [ "\n", "\n", "Length for each example\n", "==============================\n", "Id: 0 - Question Length: 48 - context Length: 87872\n", "Id: 0 - Question Length: 48 - context Length: 87872\n", "Id: 0 - Question Length: 48 - context Length: 87872\n", "Id: 1 - Question Length: 85 - context Length: 39997\n", "Id: 2 - Question Length: 146 - context Length: 22353\n", "Id: 3 - Question Length: 114 - context Length: 73891\n", "Id: 4 - Question Length: 58 - context Length: 345\n", "Id: 5 - Question Length: 80 - context Length: 36373\n", "Id: 6 - Question Length: 68 - context Length: 1410\n", "Id: 7 - Question Length: 68 - context Length: 115858\n", "Id: 8 - Question Length: 81 - context Length: 1404\n", "Id: 9 - Question Length: 83 - context Length: 65529\n", "Id: 10 - Question Length: 75 - context Length: 32034\n", "Id: 11 - Question Length: 59 - context Length: 24511\n", "Id: 12 - Question Length: 111 - context Length: 46362\n", "Id: 13 - Question Length: 79 - context Length: 26408\n", "Id: 14 - Question Length: 190 - context Length: 52829\n", "Id: 15 - Question Length: 130 - context Length: 28293\n", "Id: 16 - 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We can see that only 114 examples have less than 4 * 4096 = 16384 characters...\n", "\n", "Most examples seem to have a very long context which will have to be cut to Longformer's maximum length." ] }, { "cell_type": "markdown", "metadata": { "id": "WDPrsXOTV6R2", "colab_type": "text" }, "source": [ "### Evaluation\n", "\n", "It's time to evaluate *Longformer* on *TriviaQA* 🚀.\n", "\n", "Let's write our evaluation function and import the pretrained `LongformerForQuestionAnswering` model. For more details on `LongformerForQuestionAnswering`, see [here](https://huggingface.co/transformers/model_doc/longformer.html?highlight=longformerforquestionanswering#transformers.LongformerForQuestionAnswering)." ] }, { "cell_type": "code", "metadata": { "id": "sHgcdBt41gby", "colab_type": "code", "colab": {} }, "source": [ "from transformers import LongformerTokenizerFast, LongformerForQuestionAnswering\n", "\n", "tokenizer = LongformerTokenizerFast.from_pretrained(\"allenai/longformer-large-4096-finetuned-triviaqa\")\n", "\n", "# download the 1.7 GB pretrained model. It might take ~1min\n", "model = LongformerForQuestionAnswering.from_pretrained(\"allenai/longformer-large-4096-finetuned-triviaqa\")\n", "model.to(\"cuda\")\n", "\n", "def evaluate(example):\n", " def get_answer(question, context):\n", " # encode question and context so that they are seperated by a tokenizer.sep_token and cut at max_length\n", " encoding = tokenizer.encode_plus(question, context, return_tensors=\"pt\", max_length=4096)\n", " input_ids = encoding[\"input_ids\"].to(\"cuda\")\n", " attention_mask = encoding[\"attention_mask\"].to(\"cuda\")\n", "\n", " # the forward method will automatically set global attention on question tokens\n", " # The scores for the possible start token and end token of the answer are retrived\n", " # wrap the function in torch.no_grad() to save memory\n", " with torch.no_grad():\n", " start_scores, end_scores = model(input_ids=input_ids, attention_mask=attention_mask)\n", "\n", " # Let's take the most likely token using `argmax` and retrieve the answer\n", " all_tokens = tokenizer.convert_ids_to_tokens(encoding[\"input_ids\"][0].tolist())\n", " answer_tokens = all_tokens[torch.argmax(start_scores): torch.argmax(end_scores)+1]\n", " answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))[1:].replace('\"', '') # remove space prepending space token and remove unnecessary '\"'\n", " \n", " return answer\n", "\n", " # save the model's output here\n", " example[\"output\"] = get_answer(example[\"question\"], example[\"context\"])\n", "\n", " # save if it's a match or not\n", " example[\"match\"] = (example[\"output\"] in example[\"targets\"]) or (example[\"output\"] == example[\"norm_target\"])\n", "\n", " return example\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "bh65UMh8WP2I", "colab_type": "text" }, "source": [ "We are interested in the performance of the model on short and long samples.\n", "First we evaluate the model on `short_validation_dataset`, which comprises only the samples that are shorter than 4 * 4096 characters." ] }, { "cell_type": "code", "metadata": { "id": "R2REN9qH4HR2", "colab_type": "code", "outputId": "506d0fe5-b2eb-4245-f940-7375097337eb", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "results_short = results_short.map(evaluate)" ], "execution_count": 38, "outputs": [ { "output_type": "stream", "text": [ "114it [00:00, 12381.87it/s]\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "ULyIPLZIbtIP", "colab_type": "text" }, "source": [ "Now let's check for how many questions we were correct!" ] }, { "cell_type": "code", "metadata": { "id": "kavPCIWKWzua", "colab_type": "code", "outputId": "5b2ff4a9-a5df-42ba-c99c-72c916dea6fd", "colab": { "base_uri": "https://localhost:8080/", "height": 972 } }, "source": [ "print(f\"\\nNum Correct examples: {sum(results_short['match'])}/{len(results_short)}\")\n", "wrong_results = results_short.filter(lambda x: x['match'] is False)\n", "print(f\"\\nWrong examples: \")\n", "wrong_results.map(lambda x, i: print(f\"{i} - Output: {x['output']} - Target: {x['norm_target']}\"), with_indices=True)" ], "execution_count": 39, "outputs": [ { "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 135.22it/s]\n", "46it [00:00, 5004.10it/s]" ], "name": "stderr" }, { "output_type": "stream", "text": [ "\n", "Num Correct examples: 68/114\n", "\n", "Wrong examples: \n", "0 - Output: Doctor Finlay - Target: dr finlay\n", "0 - Output: Doctor Finlay - Target: dr finlay\n", "0 - Output: Doctor Finlay - Target: dr finlay\n", "1 - Output: film industry - Target: film making\n", "2 - Output: kaleidoscopes - Target: kaleidoscope\n", "3 - Output: fruit and vegetables - Target: fruit\n", "4 - Output: - Target: motel 6\n", "5 - Output: - Target: fiddler on roof\n", "6 - Output: - Target: mutiny on bounty\n", "7 - Output: Tom Stoppard - Target: j g ballard\n", "8 - Output: Kentuckian - Target: kentuckian\n", "9 - Output: Roald Amundsen and his party on December 14, 1911. Amundsen named his camp Polheim and the entire plateau surrounding the Pole King Haakon VII Vidde in honour of King Haakon VII of Norway. Robert Falcon Scott returned to Antarctica with his second expedition, the Terra Nova Expedition, initially unaware of Amundsen's secretive expedition. Scott and four other men reached the South Pole on January 17, 1912, thirty-four days after Amundsen. On the return trip, Scott and his four companions all died of starvation and extreme cold. In 1914 Ernest Shackleton's Imperial Trans-Antarctic Expedition set out with the goal of crossing Antarctica via the South Pole, but his ship, the Endurance, was frozen in pack ice and sank 11 months later. The overland journey was never made. US Admiral Richard Evelyn Byrd, with the assistance of his first pilot Bernt Balchen, became the first person to fly over the South Pole on November 29, 1928. 1950–present It was not until 31 October 1956 that humans once again set foot at the South Pole, when a party led by Admiral George J. Dufek of the US Navy landed there in an R4D-5L Skytrain (C-47 Skytrain) aircraft. The US Amundsen-Scott South Pole Station was established by air over 1956–1957 for the International Geophysical Year and has been continuously staffed since then by research and support personnel. After Amundsen and Scott, the next people to reach the South Pole overland (albeit with some air support) were Edmund Hillary - Target: edmond hillary\n", "10 - Output: - Target: all in family\n", "11 - Output: Roy Orbison - Target: donny osmond\n", "12 - Output: Lisa Kudrow - Target: jennifer aniston\n", "13 - Output: - Target: george\n", "14 - Output: Bette Davis and Joan Crawford - Target: crawford\n", "15 - Output: La Grange - Target: chicken ranch\n", "16 - Output: - Target: belgium\n", "17 - Output: 1936–46 - Target: 13\n", "18 - Output: Gary Lewis - Target: gary lewis and playboys\n", "19 - Output: Collapsible baby buggy - Target: baby buggy\n", "20 - Output: - Target: basket ball\n", "21 - Output: sprint canoeist - Target: canoeing\n", "22 - Output: Jon Bon Jovi - Target: presley\n", "23 - Output: Cora Crippen - Target: his wife\n", "24 - Output: jingoism - Target: chauvinism\n", "25 - Output: muscle - Target: muscle tissue\n", "26 - Output: testes - Target: corpus luteum\n", "27 - Output: 11 - Target: 11 years and 302 days\n", "28 - Output: Nicholas Nick Berry - Target: nick berry\n", "29 - Output: File:Ido2.JPG|Istanbul - Target: istanbul\n", "30 - Output: horse racing - Target: horseracing\n", "31 - Output: Christmas Carol - Target: christmas carol\n", "32 - Output: Cliff Richard - Target: beatles\n", "33 - Output: Neruda - Target: pablo neruda\n", "34 - Output: - Target: razor\n", "35 - Output: Geena Davis and Hugh Laurie star as Eleanor and Frederick Little, with Jonathan Lipnicki as Stuart's big brother George Little and Nathan Lane as the voice of the family cat Snowbell. The film was released on December 17, 1999, by Columbia Pictures. It received an Academy Award for Best Visual Effects nomination, but lost to The Matrix. The film, the first in the film series, spawned a sequel in 2002, Stuart Little 2, the short-lived TV show Stuart Little: The Animated Series in 2003, and another sequel in 2005, the animated direct-to-video Stuart Little 3: Call of the Wild. This film was Estelle Getty's final film before her retirement in 2001 and her death in 2008. Plot Eleanor Little (Geena Davis - Target: geena davis\n", "36 - Output: Stuart - Target: house of dunkeld\n", "37 - Output: - Target: hippety hopper\n", "38 - Output: raspberries - Target: blackberry\n", "39 - Output: - Target: windy miller\n", "40 - Output: *Christina Ricci - Target: christina ricci\n", "41 - Output: Switzerland - Target: st moritz\n", "42 - Output: beetles - Target: beetle\n", "43 - Output: Steptoe and Son, eight series of which were aired between 1962 and 1974. Career The partnership's break in comedy writing came with the Derek Roy vehicle Happy Go Lucky, although this was not a success. The Hancock connection began with their involvement with later radio variety series, and from November 1954 continued with Hancock's Half Hour on radio; a series featuring their scripts for Hancock ran on television between 1956 and 1961. In October that year Hancock ended his professional relationship with the writers, and with Beryl Vertue who worked with the writers' at their agency Associated London Scripts. This writers' co-operative had been founded by Eric Sykes and Spike Milligan, with others involved, including Hancock for a time. After their association with Hancock had ended, they wrote a series of Comedy Playhouse (1961–62), ten one-off half-hour plays for the BBC. One play in the series, The Offer, was well received, and from this emerged Steptoe and Son - Target: steptoe and son\n", "44 - Output: Frosted Flakes (also known as Frosties) breakfast cereal - Target: frosties\n", "45 - Output: gray - Target: purple\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "\n" ], "name": "stderr" }, { "output_type": "execute_result", "data": { "text/plain": [ "Dataset(schema: {'question': 'string', 'context': 'string', 'targets': 'list', 'norm_target': 'string', 'output': 'string', 'match': 'bool'}, num_rows: 46)" ] }, "metadata": { "tags": [] }, "execution_count": 39 } ] }, { "cell_type": "markdown", "metadata": { "id": "ViCCSxzPc2Gk", "colab_type": "text" }, "source": [ "68/114 - not bad 🔥. Also we can see that many of the wrong outputs are very close to the correct solution or are the correct solution, but just written differently (Number 0,8, ...). One could obviously make a better post processing script that makes sure solutions like 0 and 8 are counted as correct solutions by adding a couple of lines to the `evaluate` function. " ] }, { "cell_type": "markdown", "metadata": { "id": "2Si1eo3tYBR_", "colab_type": "text" }, "source": [ "**Note**: *Longformer reached a new SOTA on TriviaQA - see Table 9 in [paper](https://arxiv.org/abs/2004.05150). In order to reproduce the exact results, please refer to the following [instructions](https://github.com/allenai/longformer/blob/master/scripts/cheatsheet.txt).*" ] }, { "cell_type": "markdown", "metadata": { "id": "Q5CtQILEVMay", "colab_type": "text" }, "source": [ "Second, we evaluate `LongformerForQuestionAnswering` on the all of the examples." ] }, { "cell_type": "code", "metadata": { "id": "jWNtljj3Wr9h", "colab_type": "code", "outputId": "63ea75c7-ec78-48d7-e301-ecd70b678b7a", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "results = validation_dataset.map(evaluate)" ], "execution_count": 42, "outputs": [ { "output_type": "stream", "text": [ "467it [09:31, 1.22s/it]\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "id": "wyDYG4YDXFV7", "colab_type": "code", "outputId": "a22aeea3-b95a-4743-af90-fa978a282f68", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "print(f\"Correct examples: {sum(results['match'])}/{len(results)}\")" ], "execution_count": 43, "outputs": [ { "output_type": "stream", "text": [ "Correct examples: 224/467\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "e80MqFyP4JIj", "colab_type": "text" }, "source": [ "Here, we now see a slight degradation. Less than half the samples are correct. It is still a very good score though." ] }, { "cell_type": "markdown", "metadata": { "id": "suv6-Rb5Xe9X", "colab_type": "text" }, "source": [ "Now you should have all the tools necessary to preprocess your data and evaluate your model with 🤗nlp in no time!\n", "\n", "🤗 🤗 **Finish** 🤗🤗\n", "\n", "Thanks goes out to Iz Beltagy for proof reading the notebook!" ] } ] } ================================================ FILE: LED_on_Arxiv.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "LED on Arxiv.ipynb", "provenance": [], "collapsed_sections": [], "authorship_tag": "ABX9TyMEcneTZn4uDJELuwu4ioaf", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "5306168224f64dcebb462d932abe6c6b": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "state": { "_view_name": "HBoxView", "_dom_classes": [], "_model_name": "HBoxModel", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_view_count": null, "_view_module_version": "1.5.0", "box_style": "", "layout": "IPY_MODEL_f9b189b8310f41d987bf99a19c626872", "_model_module": "@jupyter-widgets/controls", "children": [ "IPY_MODEL_e6c305e2b5da4c9587d080f6c10819a2", "IPY_MODEL_6b472b428bcf4901a39d1557cb1ed9d9" ] } }, "f9b189b8310f41d987bf99a19c626872": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { "_view_name": "LayoutView", "grid_template_rows": null, "right": null, "justify_content": null, "_view_module": "@jupyter-widgets/base", "overflow": null, "_model_module_version": "1.2.0", "_view_count": null, "flex_flow": null, "width": null, "min_width": null, "border": null, "align_items": null, "bottom": null, "_model_module": "@jupyter-widgets/base", "top": null, "grid_column": null, "overflow_y": null, "overflow_x": null, "grid_auto_flow": null, "grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } }, "e6c305e2b5da4c9587d080f6c10819a2": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "state": { "_view_name": "ProgressView", "style": "IPY_MODEL_efd154f98aac46d8be79615e9cc3b8fe", "_dom_classes": [], "description": "Downloading: ", "_model_name": "FloatProgressModel", "bar_style": "success", "max": 2168, "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "value": 2168, "_view_count": null, "_view_module_version": "1.5.0", "orientation": "horizontal", "min": 0, "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", "layout": "IPY_MODEL_70539716eafb4df29eea9287af52b60c" } }, "6b472b428bcf4901a39d1557cb1ed9d9": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "state": { "_view_name": "HTMLView", "style": "IPY_MODEL_f2957c701cfc404986003ae6826dc299", "_dom_classes": [], "description": "", "_model_name": "HTMLModel", "placeholder": "​", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "value": " 5.41k/? 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"grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "Y7R0VPXOcL8w" }, "source": [ "## 🤗 20-lines of code to reproduce SOTA on Arxiv with **Longformer Encoder-Decoder (LED)** 🤗" ] }, { "cell_type": "markdown", "metadata": { "id": "ObggiInJKsje" }, "source": [ "At the time of writing this notebook, the best-performing model on long-range summarization is the Longformer Encoder-Decoder (LED) model by [Beltagy et al. (2020)](https://arxiv.org/pdf/2004.05150.pdf).\n", "\n", "This notebook shows how to reproduce the official results in 20-some lines of code with 🤗Datasets and 🤗Transformers." ] }, { "cell_type": "markdown", "metadata": { "id": "0B19PhgrCHM1" }, "source": [ "First, let's try to get a GPU with at least 15GB RAM." ] }, { "cell_type": "code", "metadata": { "id": "4JnoCUoL-2Jz" }, "source": [ "# crash colab to get more RAM\n", "# !kill -9 -1" ], "execution_count": 1, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "tZ11Va-qCp96" }, "source": [ "To check that we are having enough RAM we can run the following command.\n", "If the randomely allocated GPU is too small, the above cells can be run \n", "to crash the notebook hoping to get a better GPU." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "o9IkphgF-90-", "outputId": "fd92008b-c6df-4092-8329-de92f7eac9a6" }, "source": [ "!nvidia-smi" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "text": [ "Wed Jan 13 15:19:41 2021 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 460.27.04 Driver Version: 418.67 CUDA Version: 10.1 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 61C P8 31W / 149W | 0MiB / 11441MiB | 0% Default |\n", "| | | ERR! |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "eHSLE5TdC7RL" }, "source": [ "Next, we install 🤗Transformers, 🤗Datasets, and `rouge_score`.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "j2LK20EFbnY2" }, "source": [ "%%capture\n", "!pip install datasets==1.2.1\n", "!pip install transformers==4.2.0\n", "!pip install rouge_score" ], "execution_count": 3, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "h8yJ2QysDCW4" }, "source": [ "We will evaluate **LED** on the **_arxiv_** dataset using the **Rouge-2** metric. Let's \n", "import the two loading functions `load_dataset` and `load_metric`." ] }, { "cell_type": "code", "metadata": { "id": "ODLQ8MUJfmi4" }, "source": [ "from datasets import load_dataset, load_metric" ], "execution_count": 4, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "hEZ20fa9DP4W" }, "source": [ "Let's download the arxiv dataset ([click to see on 🤗Datasets Hub](https://huggingface.co/datasets/scientific_papers)). This can take a couple of minutes **☕** ." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 273, "referenced_widgets": [ "5306168224f64dcebb462d932abe6c6b", "f9b189b8310f41d987bf99a19c626872", "e6c305e2b5da4c9587d080f6c10819a2", "6b472b428bcf4901a39d1557cb1ed9d9", "efd154f98aac46d8be79615e9cc3b8fe", "70539716eafb4df29eea9287af52b60c", "f2957c701cfc404986003ae6826dc299", "b4f79f15aa9846f7b44eb1fbcff09dc6", "aacf19ff21084ba58fe1b04205eb570d", "281621de4c6e440fb4fa3f937fea7e94", "2bffd38e6275482fbda5cd3df69426e6", "0587ca173bd64ca79253850ed3cc414b", "dc796f90f8494004a2677fc1bf0dbac7", "28d58d571709485281e106eb23f2043b", "ff8f7e471891405da1923a492d620020", "a11e5efb62da44ee822d2aa361d176ad", "52aea7c7d7bd4b26ad0c6f464dcd17ce", "68a9b45d01294ec696217d0c3ba7dc3f", "396fb2f0c4294ed0bb5306b7c7619133", "e95a2249db674292a64f12557e29d5e5", "8279726c380e4da1aafb90be814aed27", "91cf4f72349549fe836cd796cd011eb3", "65ec72aabe2b45be97ee4d7ba9e07396", "fbffa1eefcea4f8fabd393551d863df4", "138def5817814f57ae3caf22480755b3", "b3e819535fc2498cba6c19bfafb50440", "e434866361fb439a8a8a047ceaca544f", "cbaed06b24284a48a93d67298e2a8926", "b412614ff9e84bb3abad0ebbcf7fbd47", "e72b3c50e6a746c586b628124042482a", "a19a3324e7b2481a829ac98c4edf8018", "02ac238e416946bcb8aa1588f06d35cc", "fe722040e2d8490284097e8766a02705", "e682748fc0a54c8185671256b360a92f", "7c8eaed118174cbc856095a8c0ec51b5", "d8c43cc87d0147f381c9671b37f1454d", "68f66cd64be14f239084ae5c167c6388", "bcdb3a5e3b6d404faee5d486f41b8ff5", "38566d5bfc6d4caeb32a27a2982ba23c", "e8e25c2677b64d1da8408e1b2e988c0c", "27a59a0b6f274fc5a3484711df7c926a", "2a95563554a146bf850ac17d4d1f870b", "f5840ba81c0c4fb3969855f20b8b70e1", "ca17d16741f74a0990005b1b6067fbae", "0a3996ae847245e5a9dae4d475cfd5c2", "85bfd36fbdfa45e49a5410be523d8b2d", "40f94afe897e496ca9c463a410409c79", "4a1a2d1422964c3e8fc7be73f685cd51", "56a0663084d543c6bd6446b7b674128a", "6465fe21fdf2448cab80f6bac3e8c945", "23f94d75bd6048a2a4637f4905faff96", "21930201615a41429f99866eb5be3a4f", "dbe577dabdf14b24aba8ee3ade221522", "d95db77c001d4e759b884e46a390fb7e", "0233309273e240bdaab9989df465bc5e", "57ed5995424a4906bba0a84fbf3f4719" ] }, "id": "bR9A9aaMci2i", "outputId": "e518567b-ec70-4e94-b461-4c1b091e9e5d" }, "source": [ "test_dataset = load_dataset(\"scientific_papers\", \"arxiv\", split=\"test\")" ], "execution_count": 5, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5306168224f64dcebb462d932abe6c6b", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=2168.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "aacf19ff21084ba58fe1b04205eb570d", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1288.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "Downloading and preparing dataset scientific_papers/arxiv (download: 4.20 GiB, generated: 7.06 GiB, post-processed: Unknown size, total: 11.26 GiB) to /root/.cache/huggingface/datasets/scientific_papers/arxiv/1.1.1/043e40ed208b8a66ee9e8228c86874946c99d2fc6155a1daee685795851cfdfc...\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "52aea7c7d7bd4b26ad0c6f464dcd17ce", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=3624420843.0, style=ProgressStyle(descr…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "138def5817814f57ae3caf22480755b3", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=880225504.0, style=ProgressStyle(descri…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fe722040e2d8490284097e8766a02705", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\r" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "27a59a0b6f274fc5a3484711df7c926a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\r" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "56a0663084d543c6bd6446b7b674128a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\rDataset scientific_papers downloaded and prepared to /root/.cache/huggingface/datasets/scientific_papers/arxiv/1.1.1/043e40ed208b8a66ee9e8228c86874946c99d2fc6155a1daee685795851cfdfc. Subsequent calls will reuse this data.\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "y5jexaruDnYQ" }, "source": [ "Next, we import the LED model and LED tokenizer." ] }, { "cell_type": "code", "metadata": { "id": "y67iB27wdUqt" }, "source": [ "from transformers import LEDForConditionalGeneration, LEDTokenizer" ], "execution_count": 6, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "IazVN9ZMDrwo" }, "source": [ "The official checkpoint \"allenai/led-large-16384-arxiv\" ([click to see on 🤗Model Hub](https://huggingface.co/allenai/led-large-16384-arxiv)) has already been fine-tuned on arxiv. In this notebook, we are just interested in evaluating the model. To save memory, let's convert the model into fp16 using the `.half()` method." ] }, { "cell_type": "markdown", "metadata": { "id": "DnRaYv2aV-Gy" }, "source": [ "Next, we install 🤗Transformers, 🤗Datasets, and `rouge_score`.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "CxKbgLOmdfwy", "colab": { "base_uri": "https://localhost:8080/", "height": 317, "referenced_widgets": [ "5b245ce465d6483da86613ab84520f2e", "d0f1ab24cd224d29ae1207ed58479f29", "e9a6e4fef3ec4c739f790f51c9011e61", "c9dbbc61217c430ea6a4a267094229c3", "efdfe027984d4212b994053adb403534", "77ec666c1f664a8caf4fcbb81e50a6c4", "af85e26350654d87ac1b8d989fb210eb", "f98a72011e0c4f23b87b186218f5d3cb", "a755cf08fd4f44f08742d5cb6adb1ec0", "3e3971ab04a7401e979adcd9695140ca", "ba9ee96ae865480089c5618eb141ed2f", "002fe655cb1d49658694a540875d7ccc", "da6c626c8bd8436c9ecdf69bb6628ed4", "8717a3e4011d4711913f4a8b8cd93894", "42640dbd38904c578549b68eb8a99606", "b7fa6a2eaf3b4a2eada622bc2569f97d", "b4a7babd65444e6dac7c3ad7c5e0ff11", "c6bafadb097c4318b7bac32a76081b28", "10a6c7b1f9494278a2a7fac4a8e8f0f3", "36ab8f8ea21a4f26a72b8c73909a8e92", "ccc55b6e1abb48cbaf73325a779323dc", "cbbb4498932c4113a338730076eb3486", "67d9b5324d3748079cc9dd69450ada50", "45f67cd8bc864918baa0efbf34cb9799", "8e20f6774e504d65b55f96dc7211221d", "304df62458df4ff289ea929ea74eb2d8", "1e6d22897e8546dd8ed206ddd0422f36", "e4459b4efd674b7d9d65cdc61af4d332", "7fd9057dba734f419d63cd53d210e603", "ca02496309fe469594a5197273e8e239", "66f3342c6ef442feb40b7eedd4e35964", "c88a564681a74857ad437961a123ccc2", "109fd5fe604a40e7a9f777664af017d4", "633fcafb0bec4ac3b8643be8759a8387", "faa1dbbb0f354d6ab7e1cc061beeff24", "fff683a4f1ce4ff2b04c7e4da9136709", "645f4060314344c5973a3f4a23ded424", "54d9af89c31047de8219a2b3114407f1", "3c1be8eba58047f9a9eec25ae5936736", "269928a1c4434e6793cd310c1712c319", "65d42917e228487ba6f25d48be76ae63", "46ad5db99d2b4db3b848a78dfcec8c06", "602cdca008734b76a07e238f4590b74f", "0955272c121b452cb2e137a7ff8c3e06", "4458067c989c487a8c548cb4bf76233e", "71565883ef5843cc94a46ed3b819c4ea", "b75116081a1543c3b19707ad46ef4de6", "e2476b6785aa484485c3c0989a7bf9b0" ] }, "outputId": "c3d5d600-c446-42f1-9cda-1198e59a3544" }, "source": [ "tokenizer = LEDTokenizer.from_pretrained(\"allenai/led-large-16384-arxiv\")\n", "model = LEDForConditionalGeneration.from_pretrained(\"allenai/led-large-16384-arxiv\").to(\"cuda\").half()" ], "execution_count": 7, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5b245ce465d6483da86613ab84520f2e", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=898822.0, style=ProgressStyle(descripti…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a755cf08fd4f44f08742d5cb6adb1ec0", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b4a7babd65444e6dac7c3ad7c5e0ff11", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=772.0, style=ProgressStyle(description_…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8e20f6774e504d65b55f96dc7211221d", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=27.0, style=ProgressStyle(description_w…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "109fd5fe604a40e7a9f777664af017d4", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1291.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "65d42917e228487ba6f25d48be76ae63", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1839633783.0, style=ProgressStyle(descr…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "WH_1gGGOFRTI" }, "source": [ "Now we can write the evaluation function for LED.\n", "First, we tokenize each *article* up to a maximum length of 16384 tokens.\n", "Next, we make sure that the very first token of the encoder attends to all other tokens, but activating its `global_attention_mask`.\n", "We will make use of beam search (with `num_beams=4`) to generate the predicted *abstract* of the *article*. Finally, the predicted *abstract* tokens are decoded and the resulting predicted *abstract* string is saved in the batch.\n" ] }, { "cell_type": "code", "metadata": { "id": "45BU5o72d1Fa" }, "source": [ "import torch\n", "\n", "def generate_answer(batch):\n", " inputs_dict = tokenizer(batch[\"article\"], padding=\"max_length\", max_length=16384, return_tensors=\"pt\", truncation=True)\n", " input_ids = inputs_dict.input_ids.to(\"cuda\")\n", " attention_mask = inputs_dict.attention_mask.to(\"cuda\")\n", "\n", " global_attention_mask = torch.zeros_like(attention_mask)\n", " # put global attention on token\n", " global_attention_mask[:, 0] = 1\n", "\n", " predicted_abstract_ids = model.generate(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask, max_length=512, num_beams=4)\n", " batch[\"predicted_abstract\"] = tokenizer.batch_decode(predicted_abstract_ids, skip_special_tokens=True)\n", " return batch\n", "\n", " " ], "execution_count": 8, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "uvuESnn-Gkmq" }, "source": [ "Because of the very lange input size of over 16K tokens, in this notebook it would take over 12h to evaluate the whole test dataset. For the sake of this notebook, we'll only evaluate on the first 600 examples. Therefore, we cut the whole 6000+ samples dataset to just 600 samples using 🤗Datasets' convenient `.select()` functionality. " ] }, { "cell_type": "code", "metadata": { "id": "Va6-cCR1gX6i" }, "source": [ "dataset_small = test_dataset.select(range(600))" ], "execution_count": 9, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "w179g14ZHFgW" }, "source": [ "Alright, let's map each sample to the predicted *abstract*. This will take ca. 90 minutes if you're given a fast GPU." ] }, { "cell_type": "code", "metadata": { "id": "ZXKcGvduhNEI", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "aa509e3045134fb0be24b7e51cad5516", "f8f52421f2cb4868a76b14405d6ad7b2", "72a8a477e711406389cad546373d3a68", "8627f2182c2f4b97932b412468814740", "3ff24425a8dc4a7e8b6cf0a838b0268d", "dfe32f3186b14271a7e72c5153204c3a", "bfc6ebf08a97493db1268b41fba21b60", "d7ed9cbde5c24cd4a1401b51c45e2513" ] }, "outputId": "03db701f-1d2f-4260-d48f-fa093b85ee83" }, "source": [ "result_small = dataset_small.map(generate_answer, batched=True, batch_size=2)" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "aa509e3045134fb0be24b7e51cad5516", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=300.0), HTML(value='')))" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "fxtGDAG7HdS-" }, "source": [ "The only thing left to do is to evaluate our predictions now by making use of the *rouge* metric. Let's load the metric." ] }, { "cell_type": "code", "metadata": { "id": "2vmEf7P9fbzI" }, "source": [ "rouge = load_metric(\"rouge\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "36-yuvBmHuP6" }, "source": [ "Now, we can compute the rouge score on all predicted *abstracts*." ] }, { "cell_type": "code", "metadata": { "id": "WeCxlgLdC8wB" }, "source": [ "rouge.compute(predictions=result_small[\"predicted_abstract\"], references=result_small[\"abstract\"], rouge_types=[\"rouge2\"])[\"rouge2\"].mid" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "b7OG8vmdH1VI" }, "source": [ "For our 600 samples, we get a *Rouge-2* score of **19.39** 🔥🔥🔥. The [official paper](https://arxiv.org/pdf/2004.05150.pdf) reports a new state-of-the-art score of **19.62** on the whole test dataset which aligns very well with our observation here${}^1$. LED significantly outperforms [PEGASUS](https://huggingface.co/transformers/model_doc/pegasus.html) and also slightly outperforms [BigBird](https://arxiv.org/abs/2007.14062) despite *PEGASUS* and \n", "*BigBird* making use of a \"summarization-specific\" pre-training objective.\n", "\n", "The arxiv dataset contains many documents of lengths exceeding 14K tokens, which cannot be handled well by *PEGASUS* and *BigBird* as those models are limited to 1024 and 4096 tokens respectively.\n", "This shows the importance of LED's capability to process very long documents.\n", "\n", "Thanks to Iz Beltagy for open-sourcing the model checkpoints for useful tips.\n", "\n", "\n", "\n", "---\n", "\n", "The checkpoint was also evaluated on the complete dataset with the exact same hyperparameters as this notebook yielding a score of **19.43** which is close enough to the official results to confirm the effectiveness of LED.\n" ] } ] } ================================================ FILE: Leveraging_Pre_trained_Checkpoints_for_Encoder_Decoder_Models.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Leveraging Pre-trained Checkpoints for Encoder-Decoder Models.ipynb", "provenance": [], "collapsed_sections": [ "Gw3IZYrfKl4Z", "4M2uzGLV9a_O", "9pftbMBeJACc", "27iHqRfq6rne", "ZwQIEhKOrJpl" ], "toc_visible": true, "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "bf1fe072d3e7400cb466836c5f8e76b9": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "state": { "_view_name": "HBoxView", "_dom_classes": [], "_model_name": "HBoxModel", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_view_count": null, "_view_module_version": "1.5.0", "box_style": "", "layout": "IPY_MODEL_1bcb00bc10564dd29c188586da1f099e", "_model_module": "@jupyter-widgets/controls", "children": [ "IPY_MODEL_ee69043440a6494d997ff2b3b3dc5692", "IPY_MODEL_e9fec04c755b413f920ff6589204bade" ] } }, "1bcb00bc10564dd29c188586da1f099e": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { "_view_name": "LayoutView", "grid_template_rows": null, "right": null, "justify_content": null, "_view_module": "@jupyter-widgets/base", "overflow": null, "_model_module_version": "1.2.0", "_view_count": null, "flex_flow": null, "width": null, "min_width": null, "border": null, "align_items": null, "bottom": null, "_model_module": "@jupyter-widgets/base", "top": null, "grid_column": null, "overflow_y": null, "overflow_x": null, "grid_auto_flow": null, "grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } }, "ee69043440a6494d997ff2b3b3dc5692": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "state": { "_view_name": "ProgressView", "style": "IPY_MODEL_05d6668fb73b460ea183fde0336bae7a", "_dom_classes": [], "description": "Downloading: ", "_model_name": "FloatProgressModel", "bar_style": "success", "max": 3528, "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "value": 3528, "_view_count": null, "_view_module_version": "1.5.0", "orientation": "horizontal", "min": 0, "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", "layout": "IPY_MODEL_e46497be81cb42b6b4125bd912292cef" } }, "e9fec04c755b413f920ff6589204bade": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "state": { "_view_name": "HTMLView", "style": "IPY_MODEL_68a4cf8218944223ac44ec91af851e61", "_dom_classes": [], "description": "", "_model_name": "HTMLModel", "placeholder": "​", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "value": " 9.38k/? 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"max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "mIBy7uK3Od4B" }, "source": [ "# **Leveraging Pre-trained Language Model Checkpoints for Encoder-Decoder Models**\n", "\n", "Transformer-based encoder-decoder models were proposed in [Vaswani et al. (2017)](https://arxiv.org/pdf/1706.03762.pdf) and have recently experienced a surge of interest, *e.g.* [Lewis et al. (2019)](https://arxiv.org/abs/1910.13461), [Raffel et al. (2019)](https://arxiv.org/abs/1910.10683), [Zhang et al. (2020)](https://arxiv.org/abs/1912.08777), [Zaheer et al. (2020)](https://arxiv.org/abs/2007.14062), [Yan et al. (2020)](https://arxiv.org/pdf/2001.04063.pdf).\n", "\n", "Similar to BERT and GPT2, massive pre-trained encoder-decoder models have shown to significantly boost performance on a variety of *sequence-to-sequence* tasks [Lewis et al. (2019)](https://arxiv.org/abs/1910.13461), [Raffel et al. (2019)](https://arxiv.org/abs/1910.10683). However, due to the enormous computational cost attached to pre-training encoder-decoder models, the development of such models is mainly limited to large companies and institutes.\n", "\n", "In [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks (2020)](https://arxiv.org/pdf/1907.12461.pdf), Sascha Rothe, Shashi Narayan and Aliaksei Severyn initialize encoder-decoder model with pre-trained *encoder and/or decoder-only* checkpoints (*e.g.* BERT, GPT2) to skip the costly pre-training. The authors show that such *warm-started* encoder-decoder models yield competitive results to large pre-trained encoder-decoder models, such as [*T5*](https://arxiv.org/abs/1910.10683), and [*Pegasus*](https://arxiv.org/abs/1912.08777) on multiple *sequence-to-sequence* tasks at a fraction of the training cost.\n", "\n", "In this notebook, we will explain in detail how encoder-decoder models can be warm-started, give practical tips based on [Rothe et al. (2020)](https://arxiv.org/pdf/1907.12461.pdf), and finally go over a complete code example showing how to warm-start encoder-decoder models with 🤗Transformers.\n", "\n", "This notebook is divided into 4 parts:\n", "\n", "- **Introduction** - *Short summary of pre-trained language models in NLP and the need for warm-starting encoder-decoder models.*\n", "- **Warm-starting encoder-decoder models (Theory)** - *Illustrative explanation on how encoder-decoder models are warm-started?* \n", "- **Warm-starting encoder-decoder models (Analysis)** - *Summary of [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks (2020)](https://arxiv.org/pdf/1907.12461.pdf) - What model combinations are effective to warm-start encoder-decoder models; How does it differ from task to task?*\n", "- **Warm-starting encoder-decoder models with 🤗Transformers (Practice)** - *Complete code example showcasing in-detail how to use the `EncoderDecoderModel` framework to warm-start transformer-based encoder-decoder models.*\n", "\n", "It is highly recommended (probably even necessary) to have read [this blog post](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Encoder_Decoder_Model.ipynb) about transformer-based encoder-decoder models.\n", "\n", "Let's start by giving some back-ground on warm-starting encoder-decoder models.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Gw3IZYrfKl4Z" }, "source": [ "## **Introduction**\n", "\n", "Recently, pre-trained language models${}^1$ have revolutionized the field of natural language processing (NLP). \n", "\n", "The first pre-trained language models were based on recurrent neural networks (RNN) as proposed [Dai et al. (2015)](https://arxiv.org/pdf/1511.01432.pdf). *Dai et. al* showed that pre-training an RNN-based model on unlabelled data and subsequently fine-tuning${}^2$ it on a specific task yields better results than training a randomly initialized model directly on such a task.\n", "However, it was only in 2018, when pre-trained language models become widely accepted in NLP. [ELMO by Peters et al.](https://arxiv.org/abs/1802.05365) and [ULMFit by Howard et al.](https://arxiv.org/pdf/1801.06146.pdf) were the first pre-trained language model to significantly improve the state-of-the-art on an array of natural language understanding (NLU) tasks. Just a couple of months later, OpenAI and Google published *transformer-based* pre-trained language models, called [GPT by Radford et al.](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) and [BERT by Devlin et al.](https://arxiv.org/abs/1810.04805) respectively.\n", "The improved efficiency of *transformer-based* language models over RNNs allowed GPT2 and BERT to be pre-trained on massive amounts of unlabeled text data. Once pre-trained, BERT and GPT were shown to require very little fine-tuning to shatter state-of-art results on more than a dozen NLU tasks ${}^3$.\n", "\n", "The capability of pre-trained language models to effectively transfer *task-agnostic* knowledge to *task-specific* knowledge turned out to be a great catalyst for NLU. Whereas engineers and researchers previously had to train a language model from scratch, now publicly available checkpoints of large pre-trained language models can be fine-tuned at a fraction of the cost and time.\n", "This can save millions in industry and allows for faster prototyping and better benchmarks in research.\n", "\n", "Pre-trained language models have established a new level of performance on NLU tasks and more and more research has been built upon leveraging such pre-trained language models for improved NLU systems. However, standalone BERT and GPT models have been less successful for *sequence-to-sequence* tasks, *e.g.* *text-summarization*, *machine translation*, *sentence-rephrasing*, etc. \n", "\n", "Sequence-to-sequence tasks are defined as a mapping from an input sequence $\\mathbf{X}_{1:n}$ to an output sequence $\\mathbf{Y}_{1:m}$ of *a-priori* unknown output length $m$. Hence, a sequence-to-sequence model should define the conditional probability distribution of the output sequence $\\mathbf{Y}_{1:m}$ conditioned on the input sequence $\\mathbf{X}_{1:n}$:\n", "\n", "$$ p_{\\theta_{\\text{model}}}(\\mathbf{Y}_{1:m} | \\mathbf{X}_{1:n}). $$\n", "\n", "Without loss of generality, an input word sequence of $n$ words is hereby represented by the vector sequnece $\\mathbf{X}_{1:n} = \\mathbf{x}_1, \\ldots, \\mathbf{x}_n$ and an output sequence of $m$ words as $\\mathbf{Y}_{1:m} = \\mathbf{y}_1, \\ldots, \\mathbf{y}_m$.\n", "\n", "Let's see how BERT and GPT2 would be fit to model sequence-to-sequence tasks.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "p3CAvlBZ58Fp" }, "source": [ "### **BERT**\n", "\n", "BERT is an *encoder-only* model, which maps an input sequence $\\mathbf{X}_{1:n}$ to a *contextualized* encoded sequence $\\mathbf{\\overline{X}}_{1:n}$:\n", "\n", "$$ f_{\\theta_{\\text{BERT}}}: \\mathbf{X}_{1:n} \\to \\mathbf{\\overline{X}}_{1:n}. $$\n", "\n", " BERT's contextualized encoded sequence $\\mathbf{\\overline{X}}_{1:n}$ can then further be processed by a classification layer for NLU classification tasks, such as *sentiment analysis*, *natural language inference*, etc. To do so, the classification layer, *i.e.* typically a pooling layer followed by a feed-forward layer, is added as a final layer on top of BERT to map the contextualized encoded sequence $\\mathbf{\\overline{X}}_{1:n}$ to a class $c$:\n", "\n", " $$\n", " f_{\\theta{\\text{p,c}}}: \\mathbf{\\overline{X}}_{1:n} \\to c.\n", " $$ \n", "\n", "It has been shown that adding a pooling- and classification layer, defined as $\\theta_{\\text{p,c}}$, on top of a pre-trained BERT model $\\theta_{\\text{BERT}}$ and subsequently fine-tuning the complete model $\\{\\theta_{\\text{p,c}}, \\theta_{\\text{BERT}}\\}$ can yield state-of-the-art performances on a variety of NLU tasks, *cf.* to [BERT by Devlin et al.](https://arxiv.org/abs/1810.04805).\n", "\n", "Let's visualize BERT.\n", "\n", " ![texte du lien](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/bert.png)\n", "\n", " The BERT model is shown in grey. The model stacks multiple *BERT blocks*, each of which is composed of *bi-directional* self-attention layers (shown in the lower part of the red box) and two feed-forward layers (short in the upper part of the red box). \n", " \n", " Each BERT block makes use of **bi-directional** self-attention to process an input sequence $\\mathbf{x'}_1, \\ldots, \\mathbf{x'}_n$ (shown in light grey) to a more \"refined\" contextualized output sequence $\\mathbf{x''}_1, \\ldots, \\mathbf{x''}_n$ (shown in slightly darker grey) ${}^4$. The contextualized output sequence of the final BERT block, *i.e.* $\\mathbf{\\overline{X}}_{1:n}$, can then be mapped to a single output class $c$ by adding a *task-specific* classification layer (shown in orange) as explained above.\n", "\n", "*Encoder-only* models can only map an input sequence to an output sequence of *a priori* known output length. In conclusion, the output dimension does not depend on the input sequence, which makes it disadvantageous and impractical to use encoder-only models for sequence-to-sequence tasks.\n", "\n", "As for all *encoder-only* models, BERT's architecture corresponds exactly to the architecture of the encoder part of *transformer-based* encoder-decoder models as shown in the \"Encoder\" section in the [Encoder-Decoder notebook](https://colab.research.google.com/drive/19wkOLQIjBBXQ-j3WWTEiud6nGBEw4MdF?usp=sharing).\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "W15QvjRh6IQr" }, "source": [ "### **GPT2**\n", "\n", "GPT2 is a *decoder-only* model, which makes use of *uni-directional* (*i.e.* \"causal\") self-attention to define a mapping from an input sequence $\\mathbf{Y}_{0: m - 1}$ ${}^1$ to a \"next-word\" logit vector sequence $\\mathbf{L}_{1:m}$:\n", "\n", "$$ f_{\\theta_{\\text{GPT2}}}: \\mathbf{Y}_{0: m - 1} \\to \\mathbf{L}_{1:m}. $$\n", "\n", "By processing the logit vectors $\\mathbf{L}_{1:m}$ with the *softmax* operation, the model can define the probability distribution of the word sequence $\\mathbf{Y}_{1:m}$. To be exact, the probability distribution of the word sequence $\\mathbf{Y}_{1:m}$ can be factorized into $m-1$ conditional \"next word\" distributions:\n", "\n", "$$ p_{\\theta_{\\text{GPT2}}}(\\mathbf{Y}_{1:m}) = \\prod_{i=1}^{m} p_{\\theta_{\\text{GPT2}}}(\\mathbf{y}_i | \\mathbf{Y}_{0:i-1}). $$\n", "\n", "$p_{\\theta_{\\text{GPT2}}}(\\mathbf{y}_i | \\mathbf{Y}_{0:i-1})$ hereby presents the probability distribution of the next word $\\mathbf{y}_i$ given all previous words $\\mathbf{y}_0, \\ldots, \\mathbf{y}_{i-1}$ ${}^3$ and is defined as the softmax operation applied on the logit vector $\\mathbf{l}_i$. To summarize, the following equations hold true.\n", "\n", "$$ p_{\\theta_{\\text{gpt2}}}(\\mathbf{y}_i | \\mathbf{Y}_{0:i-1}) = \\textbf{Softmax}(\\mathbf{l}_i) = \\textbf{Softmax}(f_{\\theta_{\\text{GPT2}}}(\\mathbf{Y}_{0: i - 1})).$$\n", "\n", "For more detail, please refer to the [decoder](https://huggingface.co/blog/encoder-decoder#decoder) section of the encoder-decoder blog post.\n", "\n", "Let's visualize GPT2 now as well.\n", "\n", " ![texte du lien](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/gpt2.png)\n", "\n", "Analogous to BERT, GPT2 is composed of a stack of *GPT2 blocks*. In contrast to BERT block, GPT2 block makes use of **uni-directional** self-attention to process some input vectors $\\mathbf{y'}_0, \\ldots, \\mathbf{y'}_{m-1}$ (shown in light blue on the bottom right) to an output vector sequence $\\mathbf{y''}_0, \\ldots, \\mathbf{y''}_{m-1}$ (shown in darker blue on the top right). In addition to the GPT2 block stack, the model also has a linear layer, called *LM Head*, which maps the output vectors of the final GPT2 block to the logit vectors $\\mathbf{l}_1, \\ldots, \\mathbf{l}_m$. As mentioned earlier, a logit vector $\\mathbf{l}_i$ can then be used to sample of new input vector $\\mathbf{y}_i$ ${}^5$.\n", "\n", "GPT2 is mainly used for *open-domain* text generation. First, an input prompt $\\mathbf{Y}_{0:i-1}$ is fed to the model to yield the conditional distribution $p_{\\theta_{\\text{gpt2}}}(\\mathbf{y} | \\mathbf{Y}_{0:i-1})$. Then the next word $\\mathbf{y}_i$ is sampled from the distribution (represented by the grey arrows in the graph above) and consequently append to the input. In an auto-regressive fashion the word $\\mathbf{y}_{i+1}$ can then be sampled from $p_{\\theta_{\\text{gpt2}}}(\\mathbf{y} | \\mathbf{Y}_{0:i})$ and so on.\n", "\n", "GPT2 is therefore well-suited for *language generation*, but less so for *conditional* generation. By setting the input prompt $\\mathbf{Y}_{0: i-1}$ equal to the sequence input $\\mathbf{X}_{1:n}$, GPT2 can very well be used for conditional generation. However, the model architecture has a fundamental drawback compared to the encoder-decoder architecture as explained in [Raffel et al. (2019)](https://arxiv.org/abs/1910.10683) on page 17. In short, uni-directional self-attention forces the model's representation of the sequence input $\\mathbf{X}_{1:n}$ to be unnecessarily limited since $\\mathbf{x}_i$ cannot depend on $\\mathbf{x}_{i+1}, \\forall i \\in \\{1,\\ldots, n\\}$.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "FBRkPI-P6Mn2" }, "source": [ "\n", "### **Encoder-Decoder**\n", "\n", "Because *encoder-only* models require to know the output length *a priori*, they seem unfit for sequence-to-sequence tasks. *Decoder-only* models can function well for sequence-to-sequence tasks, but also have certain architectural limitations as explained above.\n", "\n", "The current predominant approach to tackle *sequence-to-sequence* tasks are\n", "*transformer-based* **encoder-decoder** models - often also called *seq2seq transformer* models. Encoder-decoder models were introduced in [Vaswani et al. (2017)](https://arxiv.org/abs/1706.03762) and since then have been shown to perform better on *sequence-to-sequence* tasks than stand-alone language models (*i.e.* decoder-only models), *e.g.* [Raffel et al. (2020)](https://arxiv.org/pdf/1910.10683.pdf). In essence, an encoder-decoder model is the combination of a *stand-alone* encoder, such as BERT, and a *stand-alone* decoder model, such as GPT2. For more details on the exact architecture of transformer-based encoder-decoder models, please refer to [this blog post](https://huggingface.co/blog/encoder-decoder). \n", "\n", "Now, we know that freely available checkpoints of large pre-trained *stand-alone* encoder and decoder models, such as *BERT* and *GPT*, can boost performance and reduce training cost for many NLU tasks, We also know that encoder-decoder models are essentially the combination of *stand-alone* encoder and decoder models.\n", "This naturally brings up the question of how one can leverage stand-alone model checkpoints for encoder-decoder models and which model combinations are most performant on certain *sequence-to-sequence* tasks.\n", " \n", "In 2020, Sascha Rothe, Shashi Narayan, and Aliaksei Severyn investigated exactly this question in their paper [**Leveraging Pre-trained Checkpoints for Sequence Generation Tasks**](https://arxiv.org/abs/1907.12461). The paper offers a great analysis of different encoder-decoder model combinations and fine-tuning techniques, which we will study in more detail later.\n", "\n", "Composing an encoder-decoder model of pre-trained stand-alone model checkpoints is defined as *warm-starting* the encoder-decoder model.\n", "The following sections show how warm-starting an encoder-decoder model works in theory, how one can put the theory into practice with 🤗Transformers, and also gives practical tips for better performance.\n", "\n", "---\n", "${}^1$ A *pre-trained language model* is defined as a neural network:\n", "- that has been trained on *unlabeled* text data, *i.e.* in a task-agnostic, unsupervised fashion, and\n", "- that processes a sequence of input words into a *context-dependent* embedding. *E.g.* the *continuous bag-of-words* and *skip-gram* model from [Mikolov et al. (2013)](https://arxiv.org/abs/1301.3781) is not considered a pre-trained language model because the embeddings are context-agnostic.\n", "\n", "${}^2$ *Fine-tuning* is defined as the *task-specific* training of a model that has been initialized with the weights of a pre-trained language model.\n", "\n", "${}^3$ The input vector $\\mathbf{y}_0$ corresponds hereby to the $\\text{BOS}$ embedding vector required to predict the very first output word $\\mathbf{y}_1$.\n", "\n", "${}^4$ Without loss of generalitiy, we exclude the normalization layers to not clutter the equations and illustrations.\n", "\n", "${}^5$ For more detail on why uni-directional self-attention is used for \"decoder-only\" models, such as GPT2, and how sampling works exactly, please refer to the [decoder](https://huggingface.co/blog/encoder-decoder#decoder) section of the encoder-decoder blog post." ] }, { "cell_type": "markdown", "metadata": { "id": "4M2uzGLV9a_O" }, "source": [ "## **Warm-starting encoder-decoder models (Theory)**\n", "\n", "Having read the introduction, we are now familiar with *encoder-only*- and *decoder-only* models. We have noticed that the encoder-decoder model architecture is essentially a composition of a *stand-alone* encoder model and a *stand-alone* decoder model, which led us to the question of how one can *warm-start* encoder-decoder models from *stand-alone* model checkpoints.\n", "\n", "There are multiple possibilities to warm-start an encoder-decoder model. One can\n", "\n", "1. initialize both the encoder and decoder part from an *encoder-only* model checkpoint, *e.g.* BERT,\n", "2. initialize the encoder part from an *encoder-only* model checkpoint, *e.g.* BERT, and the decoder part from and a *decoder-only* checkpoint, *e.g.* GPT2,\n", "3. initialize only the encoder part with an *encoder-only* model checkpoint, or\n", "4. initialize only the decoder part with a *decoder-only* model checkpoint.\n", "\n", "In the following, we will put the focus on possibilities 1. and 2. Possibilities 3. and 4. are trivial after having understood the first two.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "aJ7VLfMh6Ssm" }, "source": [ "### **Recap Encoder-Decoder Model**\n", "\n", "First, let's do a quick recap of the encoder-decoder architecture.\n", "\n", " ![texte du lien](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/encoder_decoder_reap.png)\n", "\n", "The encoder (shown in green) is a stack of *encoder blocks*. Each encoder block is composed of a *bi-directional self-attention* layer, and two feed-forward layers ${}^1$. The decoder (shown in orange) is a stack of *decoder blocks*, followed by a dense layer, called *LM Head*. Each decoder block is composed of a *uni-directional self-attention* layer, a *cross-attention* layer, and two feed-forward layers.\n", "\n", " The encoder maps the input sequence $\\mathbf{X}_{1:n}$ to a contextualized encoded sequence $\\mathbf{\\overline{X}}_{1:n}$ in the exact same way BERT does. \n", " The decoder then maps the contextualized encoded sequence $\\mathbf{\\overline{X}}_{1:n}$ and a target sequence $\\mathbf{Y}_{0:m-1}$ to the logit vectors $\\mathbf{L}_{1:m}$. Analogous to GPT2, the logits are then used to define the distribution of the target sequence $\\mathbf{Y}_{1:m}$ conditioned on the input sequence $\\mathbf{X}_{1:n}$ by means of a *softmax* operation.\n", "\n", "To put it into mathematical terms, first, the conditional distribution is factorized into $m - 1$ conditional distributions of the next word $\\mathbf{y}_i$ by Bayes' rule.\n", "\n", " $$\n", " p_{\\theta_{\\text{enc, dec}}}(\\mathbf{Y}_{1:m} | \\mathbf{X}_{1:n}) = p_{\\theta_{\\text{dec}}}(\\mathbf{Y}_{1:m} | \\mathbf{\\overline{X}}_{1:n}) = \\prod_{i=1}^m p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i -1}, \\mathbf{\\overline{X}}_{1:n}), \\text{ with } \\mathbf{\\overline{X}}_{1:n} = f_{\\theta_{\\text{enc}}}(\\mathbf{X}_{1:n}).\n", " $$\n", "\n", "Each \"next-word\" conditional distributions is thereby defined by the *softmax* of the logit vector as follows.\n", "\n", " $$ p_{\\theta_{\\text{dec}}}(\\mathbf{y}_i | \\mathbf{Y}_{0: i -1}, \\mathbf{\\overline{X}}_{1:n}) = \\textbf{Softmax}(\\mathbf{l}_i). $$\n", "\n", "For more detail, please refer to the [Encoder-Decoder notebook](https://colab.research.google.com/drive/19wkOLQIjBBXQ-j3WWTEiud6nGBEw4MdF?usp=sharing).\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "L-LyGs5Q6X1P" }, "source": [ "### **Warm-staring Encoder-Decoder with BERT**\n", "\n", "Let's now illustrate how a pre-trained BERT model can be used to warm-start the encoder-decoder model. BERT's pre-trained weight parameters are used to both initialize the encoder's weight parameters as well as the decoder's weight parameters. \n", "To do so, BERT's architecture is compared to the encoder's architecture and all layers of the encoder that also exist in BERT will be initialized with the pre-trained weight parameters of the respective layers. All layers of the encoder that do not exist in BERT will simply have their weight parameters be randomly initialized.\n", "\n", "Let's visualize.\n", "\n", " ![texte du lien](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/encoder_decoder/leverage_encoder.png)\n", "\n", "We can see that the encoder architecture corresponds 1-to-1 to BERT's architecture.\n", "The weight parameters of the *bi-directional self-attention layer* and the two *feed-forward layers* of **all** encoder blocks are initialized with the weight parameters of the respective BERT blocks.\n", "This is illustrated examplary for the second encoder block (red boxes at botton) whose weight parameters $\\theta_{\\text{enc}}^{\\text{self-attn}, 2}$ and $\\theta_{\\text{enc}}^{\\text{feed-forward}, 2}$ are set to BERT's weight parameters $\\theta_{\\text{BERT}}^{\\text{feed-forward}, 2}$ and $\\theta_{\\text{BERT}}^{\\text{self-attn}, 2}$, respectively at initialization.\n", "\n", "Before fine-tuning, the encoder therefore behaves exactly like a pre-trained BERT model. Assuming the input sequence $\\mathbf{x}_1, \\ldots, \\mathbf{x}_n$ (shown in green) passed to the encoder is equal to the input sequence $\\mathbf{x}_1^{\\text{BERT}}, \\ldots, \\mathbf{x}_n^{\\text{BERT}}$ (shown in grey) passed to BERT, this means that the respective output vector sequences $\\mathbf{\\overline{x}}_1, \\ldots, \\mathbf{\\overline{x}}_n$ (shown in darker green) and $\\mathbf{\\overline{x}}_1^{\\text{BERT}}, \\ldots, \\mathbf{\\overline{x}}_n^{\\text{BERT}}$ (shown in darker grey) also have to be equal.\n", "\n", "\n", "Next, let's illustrate how the decoder is warm-started.\n", "\n", " ![texte du lien](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/encoder_decoder/leverage_decoder.png)\n", "\n", "The architecture of the decoder is different from BERT's architecture in three ways.\n", "\n", "1. First, the decoder has to be conditioned on the contextualized encoded sequence $\\mathbf{\\overline{X}}_{1:n}$ by means of cross-attention layers.\n", "Consequently, randomly initialized cross-attention layers are added between the self-attention layer and the two feed-forward layers in each BERT block. This is represented exemplary for the second block by $+\\theta_{\\text{dec}}^{\\text{cross-attention, 2}}$ and illustrated by the newly added fully connected graph in red in the lower red box on the right. This necessarily changes the behavior of each modified BERT block so that an input vector, *e.g.* $\\mathbf{y'}_0$ now yields a random output vector $\\mathbf{y''}_0$ (highlighted by the red border around the output vector $\\mathbf{y''}_0$).\n", "\n", "2. Second, BERT's *bi-directional* self-attention layers have to be changed to *uni-directional* self-attention layers to comply with auto-regressive generation. Because both the bi-directional and the uni-directional self-attention layer are based on the same *key*, *query* and *value* projection weights, the decoder's self-attention layer weights can be initialized with BERT's self-attention layer weights. *E.g.* the query, key and value weight parameters of the decoder's uni-directional self-attention layer are initialized with those of BERT's bi-directional self-attention layer:\n", "$$\n", "\\theta_{\\text{BERT}}^{\\text{self-attn}, 2} = \\{\\mathbf{W}_{\\text{BERT}, k}^{\\text{self-attn}, 2}, \\mathbf{W}_{\\text{BERT}, v}^{\\text{self-attn}, 2}, \\mathbf{W}_{\\text{BERT}, q}^{\\text{self-attn}, 2} \\} \\to\n", "\\theta_{\\text{dec}}^{\\text{self-attn}, 2} = \\{\\mathbf{W}_{\\text{dec}, k}^{\\text{self-attn}, 2}, \\mathbf{W}_{\\text{dec}, v}^{\\text{self-attn}, 2}, \\mathbf{W}_{\\text{dec}, q}^{\\text{self-attn}, 2} \\}.\n", "$$\n", "However, in *uni-directional* self-attention each token only attends to all previous tokens, so that the decoder's self-attention layers yield different output vectors than BERT's self-attention layers even though they share the same weights. Compare *e.g.*, the decoder's causally connected graph in the right box versus BERT's fully connected graph in the left box. \n", "\n", "3. Third, the decoder outputs a sequence of logit vectors $\\mathbf{L}_{1:m}$ in order to define the conditional probability distribution $p_{\\theta_{\\text{dec}}}(\\mathbf{Y}_{1:n} | \\mathbf{\\overline{X}})$. As a result, a *LM Head* layer is added on top of the last decoder block. The weight parameters of the *LM Head* layer usually correspond to the weight parameters of the word embedding $\\mathbf{W}_{\\text{emb}}$ and thus are not randomly initialized. This is illustrated in the top by the initialization $\\theta_{\\text{BERT}}^{\\text{word-emb}} \\to \\theta_{\\text{dec}}^{\\text{lm-head}}$.\n", "\n", "To conclude, when warm-starting the decoder from a pre-trained BERT model only the cross-attention layer weights are randomly initialized. All other weights including those of the self-attention layer and LM Head are initialized with BERT's pre-trained weight parameters.\n", "\n", "Having warm-stared the encoder-decoder model, the weights are then fine-tuned on a *sequence-to-sequence* downstream task, such as summarization.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "SKaGyb446cmb" }, "source": [ "### **Warm-staring Encoder-Decoder with BERT and GPT2**\n", "\n", "Instead of warm-starting both the encoder and decoder with a BERT checkpoint, we can instead leverage the BERT checkpoint for the encoder and a GPT2 checkpoint for the decoder. At first glance, a decoder-only GPT2 checkpoint seems to be better-suited to warm-start the decoder because it has already been trained on causal language modeling and uses *uni-directional* self-attention layers.\n", "\n", "Let's illustrate how a GPT2 checkpoint can be used to warm-start the decoder.\n", "\n", "![texte du lien](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/encoder_decoder/leverage_decoder_gpt2.png)\n", "\n", "We can see that decoder is more similar to GPT2 than it is to BERT. The weight parameters of decoder's *LM Head* can directly be initialized with GPT2's *LM Head* weight parameters, *e.g.* $\\theta_{\\text{GPT2}}^{\\text{lm-head}} \\to \\theta_{\\text{dec}}^{\\text{lm-head}}$. \n", "In addition, the blocks of the decoder and GPT2 both make use of *uni-directional* self-attention so that the output vectors of the decoder's self-attention layer are equivalent to GPT2's output vectors assuming the input vectors are the same, *e.g.* $\\mathbf{y'}_0^{\\text{GPT2}} = \\mathbf{y'}_0$.\n", "In contrast to the BERT-initialized decoder, the GPT2-initialized decoder, therefore, keeps the causal connected graph of the self-attention layer as can be seen in the red boxes on the bottom.\n", "\n", "Nevertheless, the GPT2-initialized decoder also has to condition the decoder on $\\mathbf{\\overline{X}}_{1:n}$. Analoguos to the BERT-initialized decoder, randomly initialized weight parameters for the cross-attention layer are therefore added to each decoder block. This is illustrated *e.g.* for the second encoder block by $+\\theta_{\\text{dec}}^{\\text{cross-attention, 2}}$.\n", "\n", "Even though GPT2 resembles the decoder part of an encoder-decoder model more than BERT, a GPT2-initialized decoder will also yield random logit vectors $\\mathbf{L}_{1:m}$ without fine-tuning due to randomly initialized cross-attention layers in every decoder block. It would be interesting to investigate whether a GPT2-initialized decoder yields better results or can be fine-tuned more efficiently.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "tDwUkF7O6g_1" }, "source": [ "### **Encoder-Decoder Weight Sharing**\n", "\n", "In [Raffel et al. (2020)](https://arxiv.org/pdf/1910.10683.pdf), the authors show that a randomly-initialized encoder-decoder model that shares the encoder's weights with the decoder, and therefore reduces the memory footprint by half, performs only slightly worse than its \"non-shared\" version.\n", "Sharing the encoder's weights with the decoder means that all layers of the decoder that are found at the same position in the encoder share the same weight parameters, *i.e.* the same node in the network's computation graph. \n", " *E.g.* the query, key, and value projection matrices of the self-attention layer in the third encoder block, defined as $\\mathbf{W}^{\\text{self-attn}, 3}_{\\text{Enc}, k}$, $\\mathbf{W}^{\\text{self-attn}, 3}_{\\text{Enc}, v}$, $\\mathbf{W}^{\\text{self-attn}, 3}_{\\text{Enc}, q}$ are identical to the respective query, key, and value projections matrices of the self-attention layer in the third decoder block ${}^2$:\n", "\n", " $$ \\mathbf{W}^{\\text{self-attn}, 3}_{k} = \\mathbf{W}^{\\text{self-attn}, 3}_{\\text{enc}, k} \\equiv \\mathbf{W}^{\\text{self-attn}, 3}_{\\text{dec}, k}, $$\n", " $$ \\mathbf{W}^{\\text{self-attn}, 3}_{q} = \\mathbf{W}^{\\text{self-attn}, 3}_{\\text{enc}, q} \\equiv \\mathbf{W}^{\\text{self-attn}, 3}_{\\text{dec}, q}, $$\n", " $$ \\mathbf{W}^{\\text{self-attn}, 3}_{v} = \\mathbf{W}^{\\text{self-attn}, 3}_{\\text{enc}, v} \\equiv \\mathbf{W}^{\\text{self-attn}, 3}_{\\text{dec}, v}, $$\n", "\n", "As a result, the key projection weights $\\mathbf{W}^{\\text{self-attn}, 3}_{k}, \\mathbf{W}^{\\text{self-attn}, 3}_{v}, \\mathbf{W}^{\\text{self-attn}, 3}_{q}$ are updated twice for each backward propagation pass - once when the gradient is backpropagated through the third decoder block and once when the gradient is backprapageted thourgh the third encoder block.\n", "\n", "In the same way, we can warm-start an encoder-decoder model by sharing the encoder weights with the decoder.\n", "Being able to share the weights between the encoder and decoder requires the decoder architecture (excluding the cross-attention weights) to be identical to the encoder architecture. Therefore, *encoder-decoder weight sharing* is only relevant if the encoder-decoder model is warm-started from a single *encoder-only* pre-trained checkpoint. \n", "\n", "Great! That was the theory about warm-starting encoder-decoder models. Let's now look at some results.\n", "\n", "\n", "---\n", "\n", "${}^1$ Without loss of generality, we exclude the normalization layers to not clutter the equations and illustrations.\n", "\n", "${}^2$ For more detail on how self-attention layers function, please refer to [this section](https://huggingface.co/blog/encoder-decoder#encoder) of the transformer-based encoder-decoder model blog post for the encoder-part (and [this section](https://huggingface.co/blog/encoder-decoder#decoder) for the decoder part respectively)." ] }, { "cell_type": "markdown", "metadata": { "id": "9pftbMBeJACc" }, "source": [ "## **Warm-starting encoder-decoder models (Analysis)**\n", "\n", "In this section, we will summarize the findings on warm-starting encoder-decoder models as presented in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, and Aliaksei Severyn. The authors compared the performance of warm-started encoder-decoder models to randomly initialized encoder-decoder models on multiple *sequence-to-sequence* tasks, notably *summarization*, *translation*, *sentence splitting*, and *sentence fusion*. \n", "\n", "To be more precise, the publicly available pre-trained checkpoints of **BERT**, **RoBERTa**, and **GPT2** were leveraged in different variations to warm-start an encoder-decoder model. *E.g.* a BERT-initialised encoder was paired with a BERT-initialized decoder yielding a BERT2BERT model *or* a RoBERTa-initialized encoder was paired with a GPT2-initialized decoder to yield a *RoBERTa2GPT2* model.\n", "Additionally, the effect of sharing the encoder and decoder weights (as explained in the previous section) was investigated for RoBERTa, *i.e.* **RoBERTaShare**, and for BERT, *i.e.* **BERTShare**. Randomly or partly randomly initialized encoder-decoder models were used as a baseline, such as a fully randomly initialized encoder-decoder model, coined **Rnd2Rnd** or a BERT-initialized decoder paired with a randomly initialized encoder, defined as **Rnd2BERT**.\n", "\n", "The following table shows a complete list of all investigated model variants including the number of randomly initialized weights, *i.e.* \"random\", and the number of weights initialized from the respective pre-trained checkpoints, *i.e.* \"leveraged\". All models are based on a 12-layer architecture with 768-dim hidden size embeddings, corresponding to the `bert-base-cased`, `bert-base-uncased`, `roberta-base`, and `gpt2` checkpoints in the 🤗Transformers model hub.\n", "\n", " Model | random | leveraged | total |\n", ":--- | :--- | :--- | :--- |\n", "Rnd2Rnd | 221M | 0 | 221M |\n", "Rnd2BERT | 112M | 109M | 221M |\n", "BERT2Rnd | 112M | 109M | 221M |\n", "Rnd2GPT2 | 114M | 125M | 238M |\n", "BERT2BERT | 26M | 195M | 221M |\n", "BERTShare | 26M | 109M | 135M |\n", "RoBERTaShare | 26M | 126M | 152M |\n", "BERT2GPT2 | 26M | 234M | 260M |\n", "RoBERTa2GPT2 | 26M | 250M | 276M |\n", "\n", "The model *Rnd2Rnd*, which is based on the BERT2BERT architecture, contains 221M weight parameters - all of which are randomly initialized. The other two \"BERT-based\" baselines *Rnd2BERT* and *BERT2Rnd* have roughly half of their weights, *i.e.* 112M parameters, randomly initialized. The other 109M weight parameters are leveraged from the pre-trained `bert-base-uncased` checkpoint for the encoder- or decoder part respectively. The models *BERT2BERT*, *BERT2GPT2*, and *RoBERTa2GPT2* have all of their encoder weight parameters leveraged (from `bert-base-uncased`, `roberta-base` respectively) and most of the decoder weight parameter weights as well (from `gpt2`, `bert-base-uncased` respectively). 26M decoder weight parameters, which correspond to the 12 cross-attention layers, are thereby randomly initialized. RoBERTa2GPT2 and BERT2GPT2 are compared to the *Rnd2GPT2* baseline. \n", "Also, it should be noted that the shared model variants *BERTShare* and *RoBERTaShare* have significantly fewer parameters because all encoder weight parameters are shared with the respective decoder weight parameters.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "27iHqRfq6rne" }, "source": [ "### **Experiments**\n", "\n", "The above models were trained and evaluated on four sequence-to-sequence tasks of increasing complexity: sentence-level fusion, sentence-level splitting, translation, and abstractive summarization. The following table shows which datasets were used for each task.\n", "\n", "Seq2Seq Task | Datasets | Paper | 🤗datasets |\n", ":--- | :--- | :--- | :--- |\n", "Sentence Fusion | DiscoFuse | [Geva et al. (2019)](https://arxiv.org/abs/1902.10526) | [link](https://huggingface.co/nlp/viewer/?dataset=discofuse&config=discofuse-wikipedia) |\n", "Sentence Splitting | WikiSplit | [Botha et al. (2018)](https://arxiv.org/abs/1808.09468) | - |\n", "Translation | WMT14 EN => DE| [Bojar et al. (2014)](http://www.aclweb.org/anthology/W/W14/W14-3302) | [link](https://huggingface.co/nlp/viewer/?dataset=wmt14&config=de-en) |\n", " | WMT14 DE => EN | [Bojar et al. (2014)](http://www.aclweb.org/anthology/W/W14/W14-3302) | [link](https://huggingface.co/nlp/viewer/?dataset=wmt14&config=de-en)|\n", "Abstractive Summarizaion | CNN/Dailymail | [Hermann et al. (2015)](http://arxiv.org/abs/1704.04368) | [link](https://huggingface.co/nlp/viewer/?dataset=cnn_dailymail&config=3.0.0) |\n", " | BBC XSum | [Narayan et al. (2018a)](https://arxiv.org/abs/1808.08745) | [link](https://huggingface.co/nlp/viewer/?dataset=xsum) |\n", " | Gigaword | [Napoles et al. (2012)](http://dx.doi.org/10.18653/v1/D15-1044) | [link](https://huggingface.co/nlp/viewer/?dataset=gigaword) |\n", "\n", "\n", "Depending on the task, a slightly different training regime was used. *E.g.* according to the size of the dataset and the specific task, the number of training steps ranges from 200K to 500K, the batch size is set to either 128 or 256, the input length ranges from 128 to 512 and the output length varies between 32 to 128. It shall be emphasized however that within each task, all models were trained and evaluated using the same hyperparameters to ensure a fair comparison. For more information on the task-specific hyperparameter settings, the reader is advised to see the *Experiments* section in the [paper](https://arxiv.org/pdf/1907.12461.pdf).\n", "\n", "We will now give a condensed overview of the results for each task.\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "g_XGrp_D7Cv2" }, "source": [ "#### **Sentence Fusion and -Splitting (DiscoFuse, WikiSplit)**\n", "\n", "**Sentence Fusion** is the task of combining multiple sentences into a single coherent sentence. *E.g.* the two sentences: \n", "\n", "_As a run-blocker, Zeitler moves relatively well._\n", "_Zeitler too often struggles at the point of contact in space._ \n", "\n", "should be connected with a fitting *linking word*, such as:\n", "\n", " _As a run-blocker, Zeitler moves relatively well. **However**, **he** too often struggles at the point of contact in space._\n", "\n", " As can be seen the linking word \"however\" provides a coherent transition from the first sentence to the second one. A model that is capable of generating such a linking word has arguably learned to infer that the two sentences above contrast to each other. \n", " \n", " The inverse task is called **Sentence splitting** and consists of splitting a single complex sentence into multiple simpler ones that together retain the same meaning. Sentence splitting is considered as an important task in text simplification, *cf.* to [Botha et al. (2018)](https://arxiv.org/pdf/1808.09468.pdf). \n", "\n", " As an example, the sentence:\n", "\n", " _Street Rod is the first in a series of two games released for the PC and Commodore 64 in 1989_\n", "\n", " can be simplified into \n", "\n", " _Street Rod is the first in a series of two games**. It** was released for the PC and Commodore 64 in 1989_\n", "\n", "It can be seen that the long sentence tries to convey two important pieces of information. One is that the game was the first of two games being released for the PC, and the second being the year in which it was released.\n", "Sentence splitting, therefore, requires the model to understand which part of the sentence should be divided into two sentences, making the task more difficult than sentence fusion.\n", "\n", "A common metric to evaluate the performance of models on sentence fusion resp. -splitting tasks is *SARI* [(Wu et al. (2016)](https://www.aclweb.org/anthology/Q16-1029/), which is broadly based on the F1-score of label and model output.\n", "\n", "Let's see how the models perform on sentence fusion and -splitting.\n", "\n", " Model | 100% DiscoFuse (SARI) | 10% DiscoFuse (SARI) | 100% WikiSplit (SARI) |\n", ":--- | :---: | :---: | :---: |\n", "Rnd2Rnd | 86.9 | 81.5 | 61.7 |\n", "Rnd2BERT | 87.6 | 82.1 | 61.8 |\n", "BERT2Rnd | 89.3 | 86.1 | 63.1 |\n", "Rnd2GPT2 | 86.5 | 81.4 | 61.3 |\n", "BERT2BERT | 89.3 | 86.1 | 63.2 |\n", "BERTShare | 89.2 | 86.0 | **63.5** |\n", "RoBERTaShare | 89.7 | 86.0 | 63.4 |\n", "BERT2GPT2 | 88.4 | 84.1 | 62.4 |\n", "RoBERTa2GPT2 | **89.9** | **87.1** | 63.2 |\n", "--- | --- | --- | ---|\n", "RoBERTaShare (large) | **90.3** | **87.7** | **63.8**\n", "\n", "The first two columns show the performance of the encoder-decoder models on the DiscoFuse evaluation data. The first column states the results of encoder-decoder models trained on all (100%) of the training data, while the second column shows the results of the models trained only on 10% of the training data. We observe that warm-started models perform significantly better than the randomly initialized baseline models *Rnd2Rnd*, *Rnd2Bert*, and *Rnd2GPT2*. A warm-started *RoBERTa2GPT2* model trained only on 10% of the training data is on par with an *Rnd2Rnd* model trained on 100% of the training data. Interestingly, the *Bert2Rnd* baseline performs equally well as a fully warm-started *Bert2Bert* model, which indicates that warm-starting the encoder-part is more effective than warm-starting the decoder-part. The best results are obtained by *RoBERTa2GPT2*, followed by *RobertaShare*. Sharing encoder and decoder weight parameters does seem to slightly increase the model's performance.\n", "\n", "On the more difficult sentence splitting task, a similar pattern emerges. Warm-started encoder-decoder models significantly outperform encoder-decoder models whose encoder is randomly initialized and encoder-decoder models with shared weight parameters yield better results than those with uncoupled weight parameters. On sentence splitting the *BertShare* models yields the best performance closely followed by *RobertaShare*.\n", "\n", "In addition to the 12-layer model variants, the authors also trained and evaluated a 24-layer *RobertaShare (large)* model which outperforms all 12-layer models significantly." ] }, { "cell_type": "markdown", "metadata": { "id": "XsNS0xyi6-0l" }, "source": [ "#### **Machine Translation (WMT14)**\n", "\n", "Next, the authors evaluated warm-started encoder-decoder models on the probably most common benchmark in machine translation (MT) - the *En* $\\to$ *De* and *De* $\\to$ *En* WMT14 dataset. In this notebook, we present the results on the *newstest2014* eval dataset. Because the benchmark requires the model to understand both an English and a German vocabulary the BERT-initialized encoder-decoder models were warm-started from the multilingual pre-trained checkpoint `bert-base-multilingual-cased`. Because there is no publicly available multilingual RoBERTa checkpoint, RoBERTa-initialized encoder-decoder models were excluded for MT. \n", "GPT2-initialized models were initialized from the `gpt2` pre-trained checkpoint as in the previous experiment. The translation results are reported using the BLUE-4 score metric ${}^1$.\n", "\n", " Model | En $\\to$ De (BLEU-4) | De $\\to$ En (BLEU-4) |\n", ":--- | :---: | :---: |\n", "Rnd2Rnd | 26.0 | 29.1 | \n", "Rnd2BERT | 27.2 | 30.4 |\n", "BERT2Rnd | **30.1** | **32.7** |\n", "Rnd2GPT2 | 19.6 | 23.2 |\n", "BERT2BERT | **30.1** | **32.7** |\n", "BERTShare | 29.6 | 32.6 |\n", "BERT2GPT2 | 23.2 | 31.4 |\n", "--- | --- | --- |\n", "BERT2Rnd (large, custom) | **31.7** | **34.2**\n", "BERTShare (large, custom) | 30.5 | 33.8 |\n", "\n", "Again, we observe a significant performance boost by warm-starting the encoder-part, with *BERT2Rnd* and *BERT2BERT* yielding the best results on both the *En* $\\to$ *De* and *De* $\\to$ *En* tasks. *GPT2* initialized models perform significantly worse even than the *Rnd2Rnd* baseline on *En* $\\to$ *De*. Taking into consideration that the `gpt2` checkpoint was trained only on English text, it is not very surprising that *BERT2GPT2* and *Rnd2GPT2* models have difficulties generating German translations. This hypothesis is supported by the competitive results (*e.g.* 31.4 vs. 32.7) of *BERT2GPT2* on the *De* $\\to$ *En* task for which GPT2's vocabulary fits the English output format. \n", "Contrary to the results obtained on sentence fusion and sentence splitting, sharing encoder and decoder weight parameters does not yield a performance boost in MT. Possible reasons for this as stated by the authors include \n", " - *the encoder-decoder model capacity is an important factor in MT, and* \n", " - *the encoder and decoder have to deal with different grammar and vocabulary*\n", "\n", "Since the *bert-base-multilingual-cased* checkpoint was trained on more than 100 languages, its vocabulary is probably undesirably large for *En* $\\to$ *De* and *De* $\\to$ *En* MT. Thus, the authors pre-trained a large BERT encoder-only checkpoint on the English and German subset of the Wikipedia dump and subsequently used it to warm-start a *BERT2Rnd* and *BERTShare* encoder-decoder model. Thanks to the improved vocabulary, another significant performance boost is observed, with *BERT2Rnd (large, custom)* significantly outperforming all other models." ] }, { "cell_type": "markdown", "metadata": { "id": "22l31xtp67Ms" }, "source": [ "#### **Summarization (CNN/Dailymail, BBC XSum, Gigaword)**\n", "\n", "Finally, the encoder-decoder models were evaluated on the arguably most challenging sequence-to-sequence task - *summarization*. The authors picked three summarization datasets with different characteristics for evaluation: Gigaword (*headline generation*), BBC XSum (*extreme summarization*), and CNN/Dailymail (*abstractive summarization*).\n", "\n", "The Gigaword dataset contains sentence-level abstractive summarizations, requiring the model to learn sentence-level understanding, abstraction, and eventually paraphrasing.\n", "A typical data sample in Gigaword, such as\n", "\n", "\"*venezuelan president hugo chavez said thursday he\n", "has ordered a probe into a suspected coup plot\n", "allegedly involving active and retired military\n", "officers .*\",\n", "\n", "would have a corresponding headline as its label, *e.g.*:\n", "\n", "\"*chavez orders probe into suspected coup plot*\".\n", "\n", "The BBC XSum dataset consists of much longer *article-like* text inputs with the labels being mostly single sentence summarizations. This dataset requires the model not only to learn document-level inference but also a high level of abstractive paraphrasing. Some data samples of the BBC XSUM datasets are shown [here](https://huggingface.co/nlp/viewer/?dataset=xsum).\n", "\n", "For the CNN/Dailmail dataset, documents, which are of similar length than those in the BBC XSum dataset, have to be summarized to bullet-point story highlights. The labels therefore often consist of multiple sentences. Besides document-level understanding, the CNN/Dailymail dataset requires models to be good at copying the most salient information. Some examples can be viewed [here](https://huggingface.co/nlp/viewer/?dataset=cnn_dailymail).\n", "\n", "The models are evaluated using the [Rouge metric](https://www.aclweb.org/anthology/N03-1020/), whereas the Rouge-2 scores are shown below.\n", "\n", "Alright, let's take a look at the results.\n", "\n", " Model | CNN/Dailymail (Rouge-2) | BBC XSum (Rouge-2) | Gigaword (Rouge-2) |\n", ":--- | :---: | :---: | :---: |\n", "Rnd2Rnd | 14.00 | 10.23 | 18.71 |\n", "Rnd2BERT | 15.55 | 11.52 | 18.91 |\n", "BERT2Rnd | 17.76 | 15.83 | 19.26 |\n", "Rnd2GPT2 | 8.81 | 8.77 | 18.39 |\n", "BERT2BERT | 17.84 | 15.24 | 19.68 |\n", "BERTShare | 18.10 | 16.12 | **19.81** |\n", "RoBERTaShare | **18.95** | **17.50** | 19.70 |\n", "BERT2GPT2 | 4.96 | 8.37 | 18.23 |\n", "RoBERTa2GPT2 | 14.72 | 5.20 | 19.21 |\n", "--- | --- | --- | ---|\n", "RoBERTaShare (large) | 18.91 | **18.79** | 19.78 |\n", "\n", "We observe again that warm-starting the encoder-part gives a significant improvement over models with randomly-initialized encoders, which is especially visible for document-level abstraction tasks, *i.e.* CNN/Dailymail and BBC XSum. This shows that tasks requiring a high level of abstraction benefit more from a pre-trained encoder part than those requiring only sentence-level abstraction. Except for Gigaword GPT2-based encoder-decoder models seem to be unfit for summarization.\n", "\n", "Furthermore, the shared encoder-decoder models are the best performing models for summarization. *RoBERTaShare* and *BERTShare* are the best performing models on all datasets whereas the margin is especially significant on the BBC XSum dataset on which *RoBERTaShare (large)* outperforms *BERT2BERT* and *BERT2Rnd* by *ca.* 3 Rouge-2 points and *Rnd2Rnd* by more than 8 Rouge-2 points. As brought forward by the authors, \"*this is probably because the BBC summary sentences follow\n", "a distribution that is similar to that of the sentences in the document, whereas this is not necessarily the case for the Gigaword headlines and the\n", "CNN/DailyMail bullet-point highlights*\". Intuitively this means that in BBC XSum, the input sentences processed by the encoder are very similar in structure to the single sentence summary processed by the decoder, *i.e.* same length, similar choice of words, similar syntax.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "lYiF_PXB6zie" }, "source": [ "### **Conclusion**\n", "\n", "Alright, let's draw a conclusion and try to derive some practical tips. \n", "\n", "- We have observed on all tasks that a warm-started encoder-part gives a significant performance boost compared to encoder-decoder models having a randomly initialized encoder. On the other hand, warm-starting the decoder seems to be less important, with *BERT2BERT* being on par with *BERT2Rnd* on most tasks. An intuitive reason would be that since a BERT- or RoBERTa-initialized encoder part has none of its weight parameters randomly initialized, the encoder can fully exploit the acquired knowledge of BERT's or RoBERTa's pre-trained checkpoints, respectively. In contrast, the warm-started decoder always has parts of its weight parameters randomly initialized which possibly makes it much harder to effectively leverage the knowledge acquired by the checkpoint used to initialize the decoder. \n", "\n", "- Next, we noticed that it is often beneficial to share encoder and decoder weights, especially if the target distribution is similar to the input distribution (*e.g.* BBC XSum). However, for datasets whose target data distribution differs more significantly from the input data distribution and for which model capacity${}^2$ is known to play an important role, *e.g.* WMT14, encoder-decoder weight sharing seems to be disadvantageous.\n", "\n", "- Finally, we have seen that it is very important that the vocabulary of the pre-trained \"stand-alone\" checkpoints fit the vocabulary required to solve the sequence-to-sequence task. *E.g.* a warm-started BERT2GPT2 encoder-decoder will perform poorly on *En* $\\to$ *De* MT because GPT2 was pre-trained on English whereas the target language is German. The overall poor performance of the *BERT2GPT2*, *Rnd2GPT2*, and *RoBERTa2GPT2* compared to *BERT2BERT*, *BERTShared*, and *RoBERTaShared* suggests that it is more effective to have a shared vocabulary. Also, it shows that initializing the decoder part with a pre-trained GPT2 checkpoint is *not* more effective than initializing it with a pre-trained BERT checkpoint besides GPT2 being more similar to the decoder in its architecture.\n", "\n", "For each of the above tasks, the most performant models were ported to 🤗Transformers and can be accessed here:\n", "\n", "- *RoBERTaShared (large)* - *Wikisplit*: [google/roberta2roberta_L-24_wikisplit](https://huggingface.co/google/roberta2roberta_L-24_wikisplit).\n", "- *RoBERTaShared (large)* - *Discofuse*: [google/roberta2roberta_L-24_discofuse](https://huggingface.co/google/roberta2roberta_L-24_discofuse).\n", "- *BERT2BERT (large)* - *WMT en $\\to$ de*: [google/bert2bert_L-24_wmt_en_de](https://huggingface.co/google/bert2bert_L-24_wmt_en_de).\n", "- *BERT2BERT (large)* - *WMT de $\\to$ en*: [google/bert2bert_L-24_wmt_de_en](https://huggingface.co/google/bert2bert_L-24_wmt_de_en).\n", "- *RoBERTaShared (large)* - *CNN/Dailymail*: [google/roberta2roberta_L-24_cnn_daily_mail](https://huggingface.co/google/roberta2roberta_L-24_cnn_daily_mail).\n", "- *RoBERTaShared (large)* - *BBC XSum*: [google/roberta2roberta_L-24_bbc](https://huggingface.co/google/roberta2roberta_L-24_bbc).\n", "- *RoBERTaShared (large)* - *Gigaword*: [google/roberta2roberta_L-24_gigaword](https://huggingface.co/google/roberta2roberta_L-24_gigaword).\n", "\n", "---\n", "\n", "${}^1$ To retrieve BLEU-4 scores, a script from the Tensorflow Official Transformer implementation\n", "https://github.com/tensorflow/models/tree\n", "master/official/nlp/transformer was used. Note\n", "that, differently from the tensor2tensor/utils/\n", "`get_ende_bleu.sh` used by Vaswani et al. (2017), this\n", "script does not split noun compounds, but utf-8\n", "quotes were normalized to ascii quotes after having noted that the pre-processed\n", "training set contains only ascii quotes.\n", "\n", "${}^2$ Model capacity is an informal definition of how good the model is at modeling complex patterns. It is also sometimes defined as *the ability of a model to learn from more and more data*. Model capacity is broadly measured by the number of trainable parameters - the more parameters, the higher the model capacity." ] }, { "cell_type": "markdown", "metadata": { "id": "H1F58j028eTV" }, "source": [ "## **Warm-starting encoder-decoder models with 🤗Transformers (Practice)**\n", "\n", "We have explained the theory of warm-starting encoder-decoder models, analyzed empirical results on multiple datasets, and have derived practical conclusions. Let's now walk through a complete code example showcasing how a **BERT2BERT** model can be warm-started and consequently fine-tuned on the *CNN/Dailymail* summarization task. We will be leveraging the 🤗datasets and 🤗Transformers libraries.\n", "\n", "In addition, the following list provides a condensed version of this and other notebooks on warm-starting other combinations of encoder-decoder models. \n", "\n", "- for **BERT2BERT** on *CNN/Dailymail* (a condensed version of this notebook), click [here](https://colab.research.google.com/drive/1Ekd5pUeCX7VOrMx94_czTkwNtLN32Uyu?usp=sharing).\n", "- for **RoBERTaShare** on *BBC XSum*, click [here](https://colab.research.google.com/drive/1vHZHXOCFqOXIvdsF8j4WBRaAOAjAroTi?usp=sharing).\n", "- for **BERT2Rnd** on *WMT14 En $\\to$ De*, click [here]() (Work in progress).\n", "- for **RoBERTa2GPT2** on *DiscoFuse*, click [here]() (Work in progress).\n", "\n", "***Note***: This notebook only uses a few training, validation, and test data samples for demonstration purposes. To fine-tune an encoder-decoder model on the full training data, the user should change the training and data preprocessing parameters accordingly as highlighted by the comments.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "3FO5ESocXvlK" }, "source": [ "### **Data Preprocessing**\n", "\n", "In this section, we show how the data can be pre-processed for training. More importantly, we try to give the reader some insight into the process of deciding how to preprocess the data.\n", "\n", "We will need datasets and transformers to be installed." ] }, { "cell_type": "code", "metadata": { "id": "w67vkz3KP9eZ" }, "source": [ "%%capture\n", "!pip install datasets==1.0.2\n", "!pip install transformers==4.2.1" ], "execution_count": 1, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "igRJfr-tINdv" }, "source": [ "Let's start by downloading the *CNN/Dailymail* dataset." ] }, { "cell_type": "code", "metadata": { "id": "scAYCFJ-IGZ_", "colab": { "base_uri": "https://localhost:8080/", "height": 423, "referenced_widgets": [ "bf1fe072d3e7400cb466836c5f8e76b9", "1bcb00bc10564dd29c188586da1f099e", "ee69043440a6494d997ff2b3b3dc5692", "e9fec04c755b413f920ff6589204bade", "05d6668fb73b460ea183fde0336bae7a", "e46497be81cb42b6b4125bd912292cef", "68a4cf8218944223ac44ec91af851e61", "b49ec75758c84fc7b68dc9b1a3fead2f", "aceefe07ee974ca7bb37ea42d63c3888", "bb98f3b9a4e141d79b4abfd75b40cb36", "0d262c9ff97a4e1ab9b5ad0508c6e3ea", "3c677c0e6e0743cd97799b6d2a0da24e", "5248e9ee6ddb4c7c9dc138e9404ae5d0", "52ff2ad04ace4d7aba5d8590e7016b9f", "a418b45315e3444990ff5fb6a159d4d9", "262fe956e0da4c519337f1a8f58fc2dc", "957a69bdd26f43b1b6d56cb85e693e7b", "1719ed620194400aa2e79d4135c7fca7", "e74fabe5bef4422e894a27ada6a98e7e", "24a22683803d43ec8a3c9ac9f6b684dc", "6f58a42dba6d4b4a9087c2d267b6d79e", "8bddd0bd190a418eb03140cdde7d6b94", "45fc0de077824302b77fe55a950c1363", "b76b0bd91b5e45b7b4ecb85c4cf1e792", "80689fcea72f465f8d443334e56b20e8", "a7d161772df541699ca39a411e52df56", "fd9c0edca0874890b9f9e5ceb00349b8", "7741905f0dd44879a61e7307070647c3", "36cf71742e164a8ebbb6ef3b10e470f1", "e0978fdb61694bc888913ca692cb2a21", "4a6b9a5dc3d942ff874f35f4ab317410", "08a32b2f537c4e56bb869d984644946c", "a3ecc806d20b473ab25e0fe566b25037", "2dcebae636af4bc5907094b7a58e7180", "20b764117c7847bbacd93835009cc1ad", "b47ec192bd05446bbc2a239ef002bf10", "dcf9d98ed5a445aa87a57c244bdca337", "c0290be87f3649c79f38aa18f6d89edd", "18b2e106001744569b0eeec58c2d1683", "5e4f2d92c4e64c3eb862ed87b140cc2b", "d87f257a64564d2b9ee33d5db10b1126", "6143f52dae094f20a857f9413d9c51c4", "18f6e5afbe1f489aa83049a2f1a5a7de", "a24c858596104fc68995f5b613bc10c5", "e951c819f0f44a4682ad7f0793a981af", "1f37e6e50a53443d8c305cad2955c444", "9770be23bcce43e68b275b6ae40adc8f", "4590ee1bceca471eb1e08fa2638f0738", "e36431b588f14957911637056dfc1206", "fbb9a66ae47b4e9fa4b4a533449b99fc", "bb6113030b6c4dda93e59e81931866d9", "dbd5405d4ac746f0afc39156f54f9755", "c003a32f77bf430c92d8a2ad234e3e5f", "9d5f03d0863a400babd3bbcab2f4fc8e", "5b18870bb7d34562a8d78bd4fa42f016", "f952ad32f3ae419b9373d1b6d4fb3ac8", "4051e10068164e489019a8088698c30f", "cc5a8b020665465a8948edb60017869f", "3f3615007ba14312a13095329a561dc3", "097ae35f92e14522a8a8d2966adc8ad1", "2d7d1e05991149a3a98e836300c08c71", "1797b738d38641c79c209ab1c34922b4", "77fa4331e68c428abad7e7793575b033", "cb5bbc781596424a945b40d9dcb095d9", "8b43674464604d168b168404777981b2", "51a2a1769f0c491cac310c68f131f63d", "b65ef65d45a74982b2db9f69c3a76a25", "520ca65c464c47f7939926226900d011", "449673bfbb3d4c92b2a513dbe45b7c40", "e61b8d6b2e5f4c509dd2d1ed8e6a13b0", "ce4d8e074ec948e2a3e0a3a9ded82580", "e1cedc23d8e14dfbb7e009330f5bdce5", "adc770c55fa2435589a9c5c286d455cf", "9c43d7e740e04fbc8c5b6189ba608e26", "8e06697519854389998361bd2e812d43", "0bfa1402b07f4216b58060661932bec0", "7e34d08745e243098933a49baa5c729a", "c0e11a348fb8469bb704b495aa4a5aac", "d17a408d094942a18efe5745ee44cdfa", "a8233d4136d44e5f833f14a9bfe4b27d" ] }, "outputId": "3f5792e2-f874-4d7e-9d42-fda52263f5e1" }, "source": [ "import datasets\n", "train_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")" ], "execution_count": 2, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bf1fe072d3e7400cb466836c5f8e76b9", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=3528.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "aceefe07ee974ca7bb37ea42d63c3888", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1610.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "957a69bdd26f43b1b6d56cb85e693e7b", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Downloading', max=1.0, style=ProgressSt…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "80689fcea72f465f8d443334e56b20e8", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Downloading', max=1.0, style=ProgressSt…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a3ecc806d20b473ab25e0fe566b25037", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=572061.0, style=ProgressStyle(descripti…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d87f257a64564d2b9ee33d5db10b1126", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=12259516.0, style=ProgressStyle(descrip…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e36431b588f14957911637056dfc1206", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=660943.0, style=ProgressStyle(descripti…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4051e10068164e489019a8088698c30f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\r" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8b43674464604d168b168404777981b2", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\r" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "adc770c55fa2435589a9c5c286d455cf", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\rDataset cnn_dailymail downloaded and prepared to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602. Subsequent calls will reuse this data.\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "C3lywsK4TXV5" }, "source": [ "Alright, let's get a first impression of the dataset.\n", "Alternatively, the dataset can also be visualized using the awesome [datasets viewer](https://huggingface.co/nlp/viewer/?dataset=cnn_dailymail&config=3.0.0) online.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "tQiACfE3Z3yw", "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "outputId": "4d93bb0a-52a6-4e65-d75e-aaf0fea50b15" }, "source": [ "train_data.info.description" ], "execution_count": 3, "outputs": [ { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'CNN/DailyMail non-anonymized summarization dataset.\\n\\nThere are two features:\\n - article: text of news article, used as the document to be summarized\\n - highlights: joined text of highlights with and around each\\n highlight, which is the target summary\\n'" ] }, "metadata": { "tags": [] }, "execution_count": 3 } ] }, { "cell_type": "markdown", "metadata": { "id": "yDota3xcZ8EH" }, "source": [ "Our input is called *article* and our labels are called *highlights*. Let's now print out the first example of the training data to get a feeling for the data." ] }, { "cell_type": "code", "metadata": { "id": "lMbFZz9LTVSf", "colab": { "base_uri": "https://localhost:8080/", "height": 845 }, "outputId": "2a11f1c9-36b3-4798-ce73-e7f5c03699a6" }, "source": [ "import pandas as pd\n", "from IPython.display import display, HTML\n", "from datasets import ClassLabel\n", "\n", "df = pd.DataFrame(train_data[:1])\n", "del df[\"id\"]\n", "for column, typ in train_data.features.items():\n", " if isinstance(typ, ClassLabel):\n", " df[column] = df[column].transform(lambda i: typ.names[i])\n", "display(HTML(df.to_html()))" ], "execution_count": 4, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
articlehighlights
0It's official: U.S. President Barack Obama wants lawmakers to weigh in on whether to use military force in Syria. Obama sent a letter to the heads of the House and Senate on Saturday night, hours after announcing that he believes military action against Syrian targets is the right step to take over the alleged use of chemical weapons. The proposed legislation from Obama asks Congress to approve the use of military force \"to deter, disrupt, prevent and degrade the potential for future uses of chemical weapons or other weapons of mass destruction.\" It's a step that is set to turn an international crisis into a fierce domestic political battle. There are key questions looming over the debate: What did U.N. weapons inspectors find in Syria? What happens if Congress votes no? And how will the Syrian government react? In a televised address from the White House Rose Garden earlier Saturday, the president said he would take his case to Congress, not because he has to -- but because he wants to. \"While I believe I have the authority to carry out this military action without specific congressional authorization, I know that the country will be stronger if we take this course, and our actions will be even more effective,\" he said. \"We should have this debate, because the issues are too big for business as usual.\" Obama said top congressional leaders had agreed to schedule a debate when the body returns to Washington on September 9. The Senate Foreign Relations Committee will hold a hearing over the matter on Tuesday, Sen. Robert Menendez said. Transcript: Read Obama's full remarks . Syrian crisis: Latest developments . U.N. inspectors leave Syria . Obama's remarks came shortly after U.N. inspectors left Syria, carrying evidence that will determine whether chemical weapons were used in an attack early last week in a Damascus suburb. \"The aim of the game here, the mandate, is very clear -- and that is to ascertain whether chemical weapons were used -- and not by whom,\" U.N. spokesman Martin Nesirky told reporters on Saturday. But who used the weapons in the reported toxic gas attack in a Damascus suburb on August 21 has been a key point of global debate over the Syrian crisis. Top U.S. officials have said there's no doubt that the Syrian government was behind it, while Syrian officials have denied responsibility and blamed jihadists fighting with the rebels. British and U.S. intelligence reports say the attack involved chemical weapons, but U.N. officials have stressed the importance of waiting for an official report from inspectors. The inspectors will share their findings with U.N. Secretary-General Ban Ki-moon Ban, who has said he wants to wait until the U.N. team's final report is completed before presenting it to the U.N. Security Council. The Organization for the Prohibition of Chemical Weapons, which nine of the inspectors belong to, said Saturday that it could take up to three weeks to analyze the evidence they collected. \"It needs time to be able to analyze the information and the samples,\" Nesirky said. He noted that Ban has repeatedly said there is no alternative to a political solution to the crisis in Syria, and that \"a military solution is not an option.\" Bergen: Syria is a problem from hell for the U.S. Obama: 'This menace must be confronted' Obama's senior advisers have debated the next steps to take, and the president's comments Saturday came amid mounting political pressure over the situation in Syria. Some U.S. lawmakers have called for immediate action while others warn of stepping into what could become a quagmire. Some global leaders have expressed support, but the British Parliament's vote against military action earlier this week was a blow to Obama's hopes of getting strong backing from key NATO allies. On Saturday, Obama proposed what he said would be a limited military action against Syrian President Bashar al-Assad. Any military attack would not be open-ended or include U.S. ground forces, he said. Syria's alleged use of chemical weapons earlier this month \"is an assault on human dignity,\" the president said. A failure to respond with force, Obama argued, \"could lead to escalating use of chemical weapons or their proliferation to terrorist groups who would do our people harm. In a world with many dangers, this menace must be confronted.\" Syria missile strike: What would happen next? Map: U.S. and allied assets around Syria . Obama decision came Friday night . On Friday night, the president made a last-minute decision to consult lawmakers. What will happen if they vote no? It's unclear. A senior administration official told CNN that Obama has the authority to act without Congress -- even if Congress rejects his request for authorization to use force. Obama on Saturday continued to shore up support for a strike on the al-Assad government. He spoke by phone with French President Francois Hollande before his Rose Garden speech. \"The two leaders agreed that the international community must deliver a resolute message to the Assad regime -- and others who would consider using chemical weapons -- that these crimes are unacceptable and those who violate this international norm will be held accountable by the world,\" the White House said. Meanwhile, as uncertainty loomed over how Congress would weigh in, U.S. military officials said they remained at the ready. 5 key assertions: U.S. intelligence report on Syria . Syria: Who wants what after chemical weapons horror . Reactions mixed to Obama's speech . A spokesman for the Syrian National Coalition said that the opposition group was disappointed by Obama's announcement. \"Our fear now is that the lack of action could embolden the regime and they repeat his attacks in a more serious way,\" said spokesman Louay Safi. \"So we are quite concerned.\" Some members of Congress applauded Obama's decision. House Speaker John Boehner, Majority Leader Eric Cantor, Majority Whip Kevin McCarthy and Conference Chair Cathy McMorris Rodgers issued a statement Saturday praising the president. \"Under the Constitution, the responsibility to declare war lies with Congress,\" the Republican lawmakers said. \"We are glad the president is seeking authorization for any military action in Syria in response to serious, substantive questions being raised.\" More than 160 legislators, including 63 of Obama's fellow Democrats, had signed letters calling for either a vote or at least a \"full debate\" before any U.S. action. British Prime Minister David Cameron, whose own attempt to get lawmakers in his country to support military action in Syria failed earlier this week, responded to Obama's speech in a Twitter post Saturday. \"I understand and support Barack Obama's position on Syria,\" Cameron said. An influential lawmaker in Russia -- which has stood by Syria and criticized the United States -- had his own theory. \"The main reason Obama is turning to the Congress: the military operation did not get enough support either in the world, among allies of the US or in the United States itself,\" Alexei Pushkov, chairman of the international-affairs committee of the Russian State Duma, said in a Twitter post. In the United States, scattered groups of anti-war protesters around the country took to the streets Saturday. \"Like many other Americans...we're just tired of the United States getting involved and invading and bombing other countries,\" said Robin Rosecrans, who was among hundreds at a Los Angeles demonstration. What do Syria's neighbors think? Why Russia, China, Iran stand by Assad . Syria's government unfazed . After Obama's speech, a military and political analyst on Syrian state TV said Obama is \"embarrassed\" that Russia opposes military action against Syria, is \"crying for help\" for someone to come to his rescue and is facing two defeats -- on the political and military levels. Syria's prime minister appeared unfazed by the saber-rattling. \"The Syrian Army's status is on maximum readiness and fingers are on the trigger to confront all challenges,\" Wael Nader al-Halqi said during a meeting with a delegation of Syrian expatriates from Italy, according to a banner on Syria State TV that was broadcast prior to Obama's address. An anchor on Syrian state television said Obama \"appeared to be preparing for an aggression on Syria based on repeated lies.\" A top Syrian diplomat told the state television network that Obama was facing pressure to take military action from Israel, Turkey, some Arabs and right-wing extremists in the United States. \"I think he has done well by doing what Cameron did in terms of taking the issue to Parliament,\" said Bashar Jaafari, Syria's ambassador to the United Nations. Both Obama and Cameron, he said, \"climbed to the top of the tree and don't know how to get down.\" The Syrian government has denied that it used chemical weapons in the August 21 attack, saying that jihadists fighting with the rebels used them in an effort to turn global sentiments against it. British intelligence had put the number of people killed in the attack at more than 350. On Saturday, Obama said \"all told, well over 1,000 people were murdered.\" U.S. Secretary of State John Kerry on Friday cited a death toll of 1,429, more than 400 of them children. No explanation was offered for the discrepancy. Iran: U.S. military action in Syria would spark 'disaster' Opinion: Why strikes in Syria are a bad idea .Syrian official: Obama climbed to the top of the tree, \"doesn't know how to get down\"\\nObama sends a letter to the heads of the House and Senate .\\nObama to seek congressional approval on military action against Syria .\\nAim is to determine whether CW were used, not by whom, says U.N. spokesman .
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "OsC2qcvvV18z" }, "source": [ "\n", "The input data seems to consist of short news articles. Interestingly, the labels appear to be bullet-point-like summaries. At this point, one should probably take a look at a couple of other examples to get a better feeling for the data.\n", "\n", "One should also notice here that the text is *case-sensitive*. This means that we have to be careful if we want to use *case-insensitive* models.\n", "As *CNN/Dailymail* is a summarization dataset, the model will be evaluated using the *ROUGE* metric. \n", "Checking the description of *ROUGE* in 🤗datasets, *cf.* [here](https://huggingface.co/metrics/rouge), we can see that the metric is *case-insensitive*, meaning that *upper case* letters will be normalized to *lower case* letters during evaluation. Thus, we can safely leverage *uncased* checkpoints, such as `bert-base-uncased`.\n", "\n", "Cool! Next, let's get a sense of the length of input data and labels. \n", "\n", "As models compute length in *token-length*, we will make use of the `bert-base-uncased` tokenizer to compute the article and summary length.\n", "\n", "First, we load the tokenizer." ] }, { "cell_type": "code", "metadata": { "id": "M0FWEkj8LThp", "colab": { "base_uri": "https://localhost:8080/", "height": 117, "referenced_widgets": [ "e1141bb39c5745e0b9ffe04dbf97653c", "14d8de86645340379a46b9bc296e5768", "43178b7b33e94498b84fada70321dcb8", "5bd54dbb07404b19b9335beae5a9701c", "5194f4260ca24fa7a0b5fcd70f61042d", "1a33f8cd8b184cb3aba1b25002eb08ee", "c0f54b1cac1e4508af11809fcfbe1c34", "bf8f2e9b334b4c48b226f9e802f811ce", "d09e94997e21475fbf4a6628ac961e0f", "43aee4d360794daa83e19c74f3870e13", "116ce10e900440ab904efcf65c19f05a", "e11eea9b0a294e9491e38c141f2c7f8d", "713ed6f585574e40b4ba4888bd842a6f", "97c9b85bb1c248fe8b31c662a04744e6", "a89aedc642d74810907b2388f12c87e6", "9469912fa82c406d884fa69d59abe2a9" ] }, "outputId": "b0d35c2c-793a-4038-f312-cfcc2aaf8c23" }, "source": [ "from transformers import BertTokenizerFast\n", "tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-uncased\")" ], "execution_count": 5, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e1141bb39c5745e0b9ffe04dbf97653c", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d09e94997e21475fbf4a6628ac961e0f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=466062.0, style=ProgressStyle(descripti…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "uUNOm0AALxLA" }, "source": [ "Next, we make use of `.map()` to compute the length of the article and its summary. Since we know that the maximum length that `bert-base-uncased` can process amounts to 512, we are also interested in the percentage of input samples being longer than the maximum length.\n", "Similarly, we compute the percentage of summaries that are longer than 64, and 128 respectively.\n", "\n", " We can define the `.map()` function as follows." ] }, { "cell_type": "code", "metadata": { "id": "wakECgirV1ZN" }, "source": [ "# map article and summary len to dict as well as if sample is longer than 512 tokens\n", "def map_to_length(x):\n", " x[\"article_len\"] = len(tokenizer(x[\"article\"]).input_ids)\n", " x[\"article_longer_512\"] = int(x[\"article_len\"] > 512)\n", " x[\"summary_len\"] = len(tokenizer(x[\"highlights\"]).input_ids)\n", " x[\"summary_longer_64\"] = int(x[\"summary_len\"] > 64)\n", " x[\"summary_longer_128\"] = int(x[\"summary_len\"] > 128)\n", " return x" ], "execution_count": 8, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "GkzX1bM1N6oz" }, "source": [ "It should be sufficient to look at the first 10000 samples. We can speed up the mapping by using multiple processes with `num_proc=4`." ] }, { "cell_type": "code", "metadata": { "id": "5yAVH5fkMwLr", "colab": { "base_uri": "https://localhost:8080/", "height": 235, "referenced_widgets": [ "f7cb6b3b8aab4b3aa1097402c2742324", "edbb1dd7178b4c85a8fb74b08bc1493a", "0dd37d97b76a4c1a8e3563fb61b946f2", "e34b61575cc14dfeb5fcc2dda8c87832", "8279f59690164bc791c911cd4311936f", "9a027820adc34468a6e630ee6bcf826d", "78e7743dfbfa4b21ac85808bff290855", "26209b5ebaec4df89debff1835f7d457", "689a86f8bd1e4d2c9e061ef40373a56b", "eb8ed86909904a1498bca4bc61415cc7", "c31377550c21484fa2f41e40f337df7e", "358b567670af467eaa87fa6d5f759ac2", "607d7dccafe3459f98f5fa030c390228", "ba26d5e398214a0ab04dd4de10bbfbee", "b8956b1ca2b84209a5eaa74ffd9493ee", "e6c9f68e7489410081cc9e83636cdb2c", "88a5cc33c5964da68ba41aa6bc1e157e", "9c1fa9968a104e1eb62077379257773c", "3a15d8b099594af5b105203a53a6bd79", "03c528668cbd4a16813c342ba2ef8ae3", "43f16e36fd54490b8beeacbb03557649", "ed91c7987efe4f4e96b50b608bb62624", "9f0d3a53bc304b509d005106aacd52c5", "e57add6af45a4d98944c6c18c55567d5", "3b622480c1824e8ea2200884d83d8685", "2c37f5926d844ae1b7eded47993c3773", "f17b4915be884252a4f7d0c4586cb86f", "0f28c3757fc14485ac6b1b3ae318e080", "01adba9e1e424647981f43c151b32900", "1cc85d9c5a1240d4a9e5d877c1230199", "5496c468512b4e34898477ad0e8678bb", "ed165e83068e4aadadb45d7fff049877" ] }, "outputId": "05e9602e-a3a7-43d3-808a-35be49e0f63c" }, "source": [ "sample_size = 10000\n", "data_stats = train_data.select(range(sample_size)).map(map_to_length, num_proc=4)\n" ], "execution_count": 9, "outputs": [ { "output_type": "stream", "text": [ " " ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f7cb6b3b8aab4b3aa1097402c2742324", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='#2', max=2500.0, style=ProgressStyle(description_width='i…" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "689a86f8bd1e4d2c9e061ef40373a56b", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='#3', max=2500.0, style=ProgressStyle(description_width='i…" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "88a5cc33c5964da68ba41aa6bc1e157e", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='#0', max=2500.0, style=ProgressStyle(description_width='i…" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3b622480c1824e8ea2200884d83d8685", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='#1', max=2500.0, style=ProgressStyle(description_width='i…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "\n", "\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "3eluYonnRL-c" }, "source": [ "Having computed the length for the first 10000 samples, we should now average them together. For this, we can make use of the `.map()` function with `batched=True` and `batch_size=-1` to have access to all 10000 samples within the `.map()` function." ] }, { "cell_type": "code", "metadata": { "id": "kALWVxHlOU4A", "colab": { "base_uri": "https://localhost:8080/", "height": 85, "referenced_widgets": [ "e551a5336a7f4303be2100c890507fdc", "50c2ae0d8fb44c8a85d3dd2296072acc", "7200027561274dff8e6174a46be5e4b0", "d597f89e8ae743d7826c9ebd0bd88335", "cfd5ec6781734adbb59f55e4a41173ab", "8616ce10c6404ba4af8af82f8c53cebb", "acc1d0607d184f00913b79c0260ac7db", "1d654e942b9c41b5aeb8dc7bba9f0295" ] }, "outputId": "2d9dfe82-5c2a-4e93-9c94-032b9598ec40" }, "source": [ "def compute_and_print_stats(x):\n", " if len(x[\"article_len\"]) == sample_size:\n", " print(\n", " \"Article Mean: {}, %-Articles > 512:{}, Summary Mean:{}, %-Summary > 64:{}, %-Summary > 128:{}\".format(\n", " sum(x[\"article_len\"]) / sample_size,\n", " sum(x[\"article_longer_512\"]) / sample_size, \n", " sum(x[\"summary_len\"]) / sample_size,\n", " sum(x[\"summary_longer_64\"]) / sample_size,\n", " sum(x[\"summary_longer_128\"]) / sample_size,\n", " )\n", " )\n", "\n", "output = data_stats.map(\n", " compute_and_print_stats, \n", " batched=True,\n", " batch_size=-1,\n", ")" ], "execution_count": 10, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e551a5336a7f4303be2100c890507fdc", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Article Mean: 847.6216, %-Articles > 512:0.7355, Summary Mean:57.7742, %-Summary > 64:0.3185, %-Summary > 128:0.0\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "e5fW44r9uJo5" }, "source": [ "We can see that on average an article contains 848 tokens with *ca.* 3/4 of the articles being longer than the model's `max_length` 512. The summary is on average 57 tokens long. Over 30% of our 10000-sample summaries are longer than 64 tokens, but none are longer than 128 tokens.\n", "\n", "`bert-base-cased` is limited to 512 tokens, which means we would have to cut possibly important information from the article. Because most of the important information is often found at the beginning of articles and because we want to be computationally efficient, we decide to stick to `bert-base-cased` with a `max_length` of 512 in this notebook. This choice is not optimal but has shown to yield [good results](https://arxiv.org/abs/1907.12461) on CNN/Dailymail. Alternatively, one could leverage long-range sequence models, such as [Longformer](https://huggingface.co/allenai/longformer-large-4096) to be used as the encoder.\n", "\n", "Regarding the summary length, we can see that a length of 128 already includes all of the summary labels. 128 is easily within the limits of `bert-base-cased`, so we decide to limit the generation to 128.\n", "\n", "Again, we will make use of the `.map()` function - this time to transform each training batch into a batch of model inputs.\n", "\n", "`\"article\"` and `\"highlights\"` are tokenized and prepared as the Encoder's `\"input_ids\"` and Decoder's `\"decoder_input_ids\"` respectively.\n", "\n", "`\"labels\"` are shifted automatically to the left for language modeling training.\n", "\n", "Lastly, it is very important to remember to ignore the loss of the padded labels. In 🤗Transformers this can be done by setting the label to -100. Great, let's write down our mapping function then." ] }, { "cell_type": "code", "metadata": { "id": "yoN2q0hZUbXN" }, "source": [ "encoder_max_length=512\n", "decoder_max_length=128\n", "\n", "def process_data_to_model_inputs(batch):\n", " # tokenize the inputs and labels\n", " inputs = tokenizer(batch[\"article\"], padding=\"max_length\", truncation=True, max_length=encoder_max_length)\n", " outputs = tokenizer(batch[\"highlights\"], padding=\"max_length\", truncation=True, max_length=decoder_max_length)\n", "\n", " batch[\"input_ids\"] = inputs.input_ids\n", " batch[\"attention_mask\"] = inputs.attention_mask\n", " batch[\"decoder_input_ids\"] = outputs.input_ids\n", " batch[\"decoder_attention_mask\"] = outputs.attention_mask\n", " batch[\"labels\"] = outputs.input_ids.copy()\n", "\n", " # because BERT automatically shifts the labels, the labels correspond exactly to `decoder_input_ids`. \n", " # We have to make sure that the PAD token is ignored\n", " batch[\"labels\"] = [[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch[\"labels\"]]\n", "\n", " return batch" ], "execution_count": 11, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "oxeMUmjxbqdP" }, "source": [ "In this notebook, we train and evaluate the model just on a few training examples for demonstration and set the `batch_size` to 4 to prevent out-of-memory issues. \n", "\n", "The following line reduces the training data to only the first `32` examples. The cell can be commented out or not run for a full training run. Good results were obtained with a `batch_size` of 16." ] }, { "cell_type": "code", "metadata": { "id": "rQ2nmUCRbzxY" }, "source": [ "train_data = train_data.select(range(32))" ], "execution_count": 12, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "tYvdujyfro1i" }, "source": [ "Alright, let's prepare the training data." ] }, { "cell_type": "code", "metadata": { "id": "Z396M38-beJE", "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "8083ecec72c843cab8aec17a6b3ac9be", "afcaba4553e74092a92b9fb81a2e9c9b", "f112ba23351d40918e47a3519c40ff05", "4a5ad27137614db481871deca5570aa3", "2b0f6ba181914467ab69aa880896a291", "8046ab13c4e2485ca4650beff1fd45a5", "056cf01dc1124b4a81a62b80d681b840", "18241c5e45e842f79c6eb46a5e33b57e" ] }, "outputId": "a4d52b29-89b8-4a37-81e8-0c5ace8b64bf" }, "source": [ "# batch_size = 16\n", "batch_size=4\n", "\n", "train_data = train_data.map(\n", " process_data_to_model_inputs, \n", " batched=True, \n", " batch_size=batch_size, \n", " remove_columns=[\"article\", \"highlights\", \"id\"]\n", ")" ], "execution_count": 13, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8083ecec72c843cab8aec17a6b3ac9be", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=8.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "YN4Qx8-lr2m7" }, "source": [ "Taking a look at the processed training dataset we can see that the column names `article`, `highlights`, and `id` have been replaced by the arguments expected by the `EncoderDecoderModel`." ] }, { "cell_type": "code", "metadata": { "id": "GFBL22RxsN1Q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "06748a60-9566-423e-861c-3a40c5eeb63c" }, "source": [ "train_data" ], "execution_count": 14, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Dataset(features: {'attention_mask': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'decoder_attention_mask': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'decoder_input_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'input_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'labels': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}, num_rows: 32)" ] }, "metadata": { "tags": [] }, "execution_count": 14 } ] }, { "cell_type": "markdown", "metadata": { "id": "lL9HI9E3sPRB" }, "source": [ "So far, the data was manipulated using Python's `List` format. Let's convert the data to PyTorch Tensors to be trained on GPU." ] }, { "cell_type": "code", "metadata": { "id": "-vZKuZVvqxlC" }, "source": [ "train_data.set_format(\n", " type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\n", ")" ], "execution_count": 15, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "EPCjQ5E6Lg89" }, "source": [ "Awesome, the data processing of the training data is finished. \n", "Analogous, we can do the same for the validation data.\n", "\n", "First, we load 10% of the validation dataset:" ] }, { "cell_type": "code", "metadata": { "id": "lB-bX6qktl1i", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "5a060356-dd3f-4e45-ea51-e7f87e4b07ac" }, "source": [ "val_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"validation[:10%]\")" ], "execution_count": 16, "outputs": [ { "output_type": "stream", "text": [ "Reusing dataset cnn_dailymail (/root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602)\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "ktlQfG-YtrG2" }, "source": [ "For demonstration purposes, the validation data is then reduced to just 8 samples," ] }, { "cell_type": "code", "metadata": { "id": "c0_fTpFTtxSX" }, "source": [ "val_data = val_data.select(range(8))" ], "execution_count": 17, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "CLXcp_rJtzbt" }, "source": [ "the mapping function is applied," ] }, { "cell_type": "code", "metadata": { "id": "ZIYccglk7FYj", "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "a4f6e831e01b4bba8fa5a74f4a01ce0f", "4491850f9333479f89615de35b8e460d", "1cac1351e420401693c4a8560762b6da", "dd15adbe47544a43b82d56736df65d2a", "e8e88de9c1cc468291e460aed2f28f81", "c5dde7211adf46a181c869940d00256f", "b132657d8d81447d8e7755fbb7c922d3", "647739b9152b4391870e6ac489affb51" ] }, "outputId": "2579514f-1356-4230-e26f-2c96c71eb626" }, "source": [ "val_data = val_data.map(\n", " process_data_to_model_inputs, \n", " batched=True, \n", " batch_size=batch_size, \n", " remove_columns=[\"article\", \"highlights\", \"id\"]\n", ")" ], "execution_count": 18, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a4f6e831e01b4bba8fa5a74f4a01ce0f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "ovXI_tnDt5dU" }, "source": [ "and, finally, the validation data is also converted to PyTorch tensors." ] }, { "cell_type": "code", "metadata": { "id": "6r2-M5hYt-Vw" }, "source": [ "val_data.set_format(\n", " type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\n", ")" ], "execution_count": 19, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "1WRG1QRwMUVd" }, "source": [ "Great! Now we can move to warm-starting the `EncoderDecoderModel`." ] }, { "cell_type": "markdown", "metadata": { "id": "aEjb026cNC38" }, "source": [ "### **Warm-starting the Encoder-Decoder Model**\n", "\n", "This section explains how an Encoder-Decoder model can be warm-started using the `bert-base-cased` checkpoint.\n", "\n", "Let's start by importing the `EncoderDecoderModel`. For more detailed information about the `EncoderDecoderModel` class, the reader is advised to take a look at the [documentation](https://huggingface.co/transformers/model_doc/encoderdecoder.html)." ] }, { "cell_type": "code", "metadata": { "id": "kRwMBv0hNMNM" }, "source": [ "from transformers import EncoderDecoderModel" ], "execution_count": 20, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "SpqSPjsLe1uL" }, "source": [ "In contrast to other model classes in 🤗Transformers, the `EncoderDecoderModel` class has two methods to load pre-trained weights, namely:\n", "\n", "1. the \"standard\" `.from_pretrained(...)` method is derived from the general `PretrainedModel.from_pretrained(...)` method and thus corresponds exactly to the the one of other model classes. The function expects a single model identifier, *e.g.* `.from_pretrained(\"google/bert2bert_L-24_wmt_de_en\")` and will load a single `.pt` checkpoint file into the `EncoderDecoderModel` class.\n", "\n", "2. a special `.from_encoder_decoder_pretrained(...)` method, which can be used to warm-start an encoder-decoder model from two model identifiers - one for the encoder and one for the decoder. The first model identifier is thereby used to load the *encoder*, via `AutoModel.from_pretrained(...)` (see doc [here](https://huggingface.co/transformers/master/model_doc/auto.html?highlight=automodel#automodel)) and the second model identifier is used to load the *decoder* via `AutoModelForCausalLM` (see doc [here](https://huggingface.co/transformers/master/model_doc/auto.html#automodelforcausallm).\n", "\n", "Alright, let's warm-start our *BERT2BERT* model. As mentioned earlier we will warm-start both the encoder and decoder with the `\"bert-base-cased\"` checkpoint." ] }, { "cell_type": "code", "metadata": { "id": "tS0UndNoQh8t", "colab": { "base_uri": "https://localhost:8080/", "height": 227, "referenced_widgets": [ "9b3e57df385f44128f945b38f914b53b", "06c7cd1310ba40e9bb4c6a85075b30d0", "5a441ddec5fa4577a5e115baa5f8d94b", "597432ca73b241ed9d2aac17473ee19e", "afdf5674b9964a71921f8edf4045dd69", "e8a0a62aefc645f9a9a44162f6427e34", "33159d649adb4a80a523f4481ee806e3", "78c46a053e5149de8c533a0ef77383bc", "eb8f50f635694e7eba985f5af2e76b05", "94be102d037d4d48aeb01c165be7e075", "fa89d11069d84262ad4990f0d53244f0", "706ed461fcec4eec94f0d7706664943d", "a1a278eafe364c1ca3b95e3e34ade614", "518699234a7b4bc4a3d9bb5e6bd9fab9", "df4ae967a11c46adba3649ffe5c893ac", "87e17fd04044428f87dbeec6023268bb" ] }, "outputId": "550ef593-862d-468a-ae0f-08e033d2e73d" }, "source": [ "bert2bert = EncoderDecoderModel.from_encoder_decoder_pretrained(\"bert-base-uncased\", \"bert-base-uncased\")" ], "execution_count": 21, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9b3e57df385f44128f945b38f914b53b", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "eb8f50f635694e7eba985f5af2e76b05", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=440473133.0, style=ProgressStyle(descri…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertLMHeadModel: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n", "- This IS expected if you are initializing BertLMHeadModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing BertLMHeadModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of BertLMHeadModel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['bert.encoder.layer.0.crossattention.self.query.weight', 'bert.encoder.layer.0.crossattention.self.query.bias', 'bert.encoder.layer.0.crossattention.self.key.weight', 'bert.encoder.layer.0.crossattention.self.key.bias', 'bert.encoder.layer.0.crossattention.self.value.weight', 'bert.encoder.layer.0.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.output.dense.weight', 'bert.encoder.layer.0.crossattention.output.dense.bias', 'bert.encoder.layer.0.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.0.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.1.crossattention.self.query.weight', 'bert.encoder.layer.1.crossattention.self.query.bias', 'bert.encoder.layer.1.crossattention.self.key.weight', 'bert.encoder.layer.1.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.self.value.bias', 'bert.encoder.layer.1.crossattention.output.dense.weight', 'bert.encoder.layer.1.crossattention.output.dense.bias', 'bert.encoder.layer.1.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.2.crossattention.self.query.weight', 'bert.encoder.layer.2.crossattention.self.query.bias', 'bert.encoder.layer.2.crossattention.self.key.weight', 'bert.encoder.layer.2.crossattention.self.key.bias', 'bert.encoder.layer.2.crossattention.self.value.weight', 'bert.encoder.layer.2.crossattention.self.value.bias', 'bert.encoder.layer.2.crossattention.output.dense.weight', 'bert.encoder.layer.2.crossattention.output.dense.bias', 'bert.encoder.layer.2.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.2.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.3.crossattention.self.query.weight', 'bert.encoder.layer.3.crossattention.self.query.bias', 'bert.encoder.layer.3.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.self.key.bias', 'bert.encoder.layer.3.crossattention.self.value.weight', 'bert.encoder.layer.3.crossattention.self.value.bias', 'bert.encoder.layer.3.crossattention.output.dense.weight', 'bert.encoder.layer.3.crossattention.output.dense.bias', 'bert.encoder.layer.3.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.3.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.4.crossattention.self.query.weight', 'bert.encoder.layer.4.crossattention.self.query.bias', 'bert.encoder.layer.4.crossattention.self.key.weight', 'bert.encoder.layer.4.crossattention.self.key.bias', 'bert.encoder.layer.4.crossattention.self.value.weight', 'bert.encoder.layer.4.crossattention.self.value.bias', 'bert.encoder.layer.4.crossattention.output.dense.weight', 'bert.encoder.layer.4.crossattention.output.dense.bias', 'bert.encoder.layer.4.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.4.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.5.crossattention.self.query.weight', 'bert.encoder.layer.5.crossattention.self.query.bias', 'bert.encoder.layer.5.crossattention.self.key.weight', 'bert.encoder.layer.5.crossattention.self.key.bias', 'bert.encoder.layer.5.crossattention.self.value.weight', 'bert.encoder.layer.5.crossattention.self.value.bias', 'bert.encoder.layer.5.crossattention.output.dense.weight', 'bert.encoder.layer.5.crossattention.output.dense.bias', 'bert.encoder.layer.5.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.5.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.6.crossattention.self.query.weight', 'bert.encoder.layer.6.crossattention.self.query.bias', 'bert.encoder.layer.6.crossattention.self.key.weight', 'bert.encoder.layer.6.crossattention.self.key.bias', 'bert.encoder.layer.6.crossattention.self.value.weight', 'bert.encoder.layer.6.crossattention.self.value.bias', 'bert.encoder.layer.6.crossattention.output.dense.weight', 'bert.encoder.layer.6.crossattention.output.dense.bias', 'bert.encoder.layer.6.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.6.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.7.crossattention.self.query.weight', 'bert.encoder.layer.7.crossattention.self.query.bias', 'bert.encoder.layer.7.crossattention.self.key.weight', 'bert.encoder.layer.7.crossattention.self.key.bias', 'bert.encoder.layer.7.crossattention.self.value.weight', 'bert.encoder.layer.7.crossattention.self.value.bias', 'bert.encoder.layer.7.crossattention.output.dense.weight', 'bert.encoder.layer.7.crossattention.output.dense.bias', 'bert.encoder.layer.7.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.7.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.8.crossattention.self.query.weight', 'bert.encoder.layer.8.crossattention.self.query.bias', 'bert.encoder.layer.8.crossattention.self.key.weight', 'bert.encoder.layer.8.crossattention.self.key.bias', 'bert.encoder.layer.8.crossattention.self.value.weight', 'bert.encoder.layer.8.crossattention.self.value.bias', 'bert.encoder.layer.8.crossattention.output.dense.weight', 'bert.encoder.layer.8.crossattention.output.dense.bias', 'bert.encoder.layer.8.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.8.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.9.crossattention.self.query.weight', 'bert.encoder.layer.9.crossattention.self.query.bias', 'bert.encoder.layer.9.crossattention.self.key.weight', 'bert.encoder.layer.9.crossattention.self.key.bias', 'bert.encoder.layer.9.crossattention.self.value.weight', 'bert.encoder.layer.9.crossattention.self.value.bias', 'bert.encoder.layer.9.crossattention.output.dense.weight', 'bert.encoder.layer.9.crossattention.output.dense.bias', 'bert.encoder.layer.9.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.9.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.10.crossattention.self.query.weight', 'bert.encoder.layer.10.crossattention.self.query.bias', 'bert.encoder.layer.10.crossattention.self.key.weight', 'bert.encoder.layer.10.crossattention.self.key.bias', 'bert.encoder.layer.10.crossattention.self.value.weight', 'bert.encoder.layer.10.crossattention.self.value.bias', 'bert.encoder.layer.10.crossattention.output.dense.weight', 'bert.encoder.layer.10.crossattention.output.dense.bias', 'bert.encoder.layer.10.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.10.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.11.crossattention.self.query.weight', 'bert.encoder.layer.11.crossattention.self.query.bias', 'bert.encoder.layer.11.crossattention.self.key.weight', 'bert.encoder.layer.11.crossattention.self.key.bias', 'bert.encoder.layer.11.crossattention.self.value.weight', 'bert.encoder.layer.11.crossattention.self.value.bias', 'bert.encoder.layer.11.crossattention.output.dense.weight', 'bert.encoder.layer.11.crossattention.output.dense.bias', 'bert.encoder.layer.11.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.11.crossattention.output.LayerNorm.bias']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "hMkM5q7Ehrdk" }, "source": [ "For once, we should take a good look at the warning here. \n", "We can see that two weights corresponding to a `\"cls\"` layer were not used. This should not be a problem because we don't need BERT's CLS layer for *sequence-to-sequence* tasks. \n", "Also, we notice that a lot of weights are \"newly\" or randomly initialized. When taking a closer look these weights all correspond to the cross-attention layer, which is exactly what we would expect after having read the theory above.\n", "\n", "Let's take a closer look at the model." ] }, { "cell_type": "code", "metadata": { "id": "BiCX7C0zxjAp", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "79b4f16f-10b9-414c-9872-ae688ec96eb0" }, "source": [ "bert2bert" ], "execution_count": 22, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "EncoderDecoderModel(\n", " (encoder): BertModel(\n", " (embeddings): BertEmbeddings(\n", " (word_embeddings): Embedding(30522, 768, padding_idx=0)\n", " (position_embeddings): Embedding(512, 768)\n", " (token_type_embeddings): Embedding(2, 768)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (encoder): BertEncoder(\n", " (layer): ModuleList(\n", " (0): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (1): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (2): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (3): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (4): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (5): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (6): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (7): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (8): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (9): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): 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BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (11): BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (crossattention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " )\n", " )\n", " (cls): BertOnlyMLMHead(\n", " (predictions): BertLMPredictionHead(\n", " (transform): BertPredictionHeadTransform(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " )\n", " (decoder): Linear(in_features=768, out_features=30522, bias=True)\n", " )\n", " )\n", " )\n", ")" ] }, "metadata": { "tags": [] }, "execution_count": 22 } ] }, { "cell_type": "markdown", "metadata": { "id": "5wRcnvzXxyOB" }, "source": [ "We see that `bert2bert.encoder` is an instance of `BertModel` and that `bert2bert.decoder` one of `BertLMHeadModel`. \n", "However, both instances are now combined into a single `torch.nn.Module` and can thus be saved as a single `.pt` checkpoint file. \n", "\n", "Let's try it out using the standard `.save_pretrained(...)` method." ] }, { "cell_type": "code", "metadata": { "id": "1GR3W0pOs7Na" }, "source": [ "bert2bert.save_pretrained(\"bert2bert\")" ], "execution_count": 23, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "y53B8J_RwoD3" }, "source": [ "Similarly, the model can be reloaded using the standard `.from_pretrained(...)` method." ] }, { "cell_type": "code", "metadata": { "id": "fpZaCNWWwoeg" }, "source": [ "bert2bert = EncoderDecoderModel.from_pretrained(\"bert2bert\")" ], "execution_count": 24, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "gA8qu45YzEPz" }, "source": [ "Awesome. Let's also checkpoint the config." ] }, { "cell_type": "code", "metadata": { "id": "8lWokpytzKNk", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "5df2bfad-287a-49c0-c453-5485f0b863b0" }, "source": [ "bert2bert.config" ], "execution_count": 25, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "EncoderDecoderConfig {\n", " \"_name_or_path\": \"bert2bert\",\n", " \"architectures\": [\n", " \"EncoderDecoderModel\"\n", " ],\n", " \"decoder\": {\n", " \"_name_or_path\": \"bert-base-uncased\",\n", " \"add_cross_attention\": true,\n", " \"architectures\": [\n", " \"BertForMaskedLM\"\n", " ],\n", " \"attention_probs_dropout_prob\": 0.1,\n", " \"bad_words_ids\": null,\n", " \"bos_token_id\": null,\n", " \"chunk_size_feed_forward\": 0,\n", " \"decoder_start_token_id\": null,\n", " \"diversity_penalty\": 0.0,\n", " \"do_sample\": false,\n", " \"early_stopping\": false,\n", " \"eos_token_id\": null,\n", " \"finetuning_task\": null,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout_prob\": 0.1,\n", " \"hidden_size\": 768,\n", " \"id2label\": {\n", " \"0\": \"LABEL_0\",\n", " \"1\": \"LABEL_1\"\n", " },\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"is_decoder\": true,\n", " \"is_encoder_decoder\": false,\n", " \"label2id\": {\n", " \"LABEL_0\": 0,\n", " \"LABEL_1\": 1\n", " },\n", " \"layer_norm_eps\": 1e-12,\n", " \"length_penalty\": 1.0,\n", " \"max_length\": 20,\n", " \"max_position_embeddings\": 512,\n", " \"min_length\": 0,\n", " \"model_type\": \"bert\",\n", " \"no_repeat_ngram_size\": 0,\n", " \"num_attention_heads\": 12,\n", " \"num_beam_groups\": 1,\n", " \"num_beams\": 1,\n", " \"num_hidden_layers\": 12,\n", " \"num_return_sequences\": 1,\n", " \"output_attentions\": false,\n", " \"output_hidden_states\": false,\n", " \"output_scores\": false,\n", " \"pad_token_id\": 0,\n", " \"position_embedding_type\": \"absolute\",\n", " \"prefix\": null,\n", " \"pruned_heads\": {},\n", " \"repetition_penalty\": 1.0,\n", " \"return_dict\": true,\n", " \"return_dict_in_generate\": false,\n", " \"sep_token_id\": null,\n", " \"task_specific_params\": null,\n", " \"temperature\": 1.0,\n", " \"tie_encoder_decoder\": false,\n", " \"tie_word_embeddings\": true,\n", " \"tokenizer_class\": null,\n", " \"top_k\": 50,\n", " \"top_p\": 1.0,\n", " \"torchscript\": false,\n", " \"transformers_version\": \"4.2.1\",\n", " \"type_vocab_size\": 2,\n", " \"use_bfloat16\": false,\n", " \"use_cache\": true,\n", " \"vocab_size\": 30522,\n", " \"xla_device\": null\n", " },\n", " \"encoder\": {\n", " \"_name_or_path\": \"bert-base-uncased\",\n", " \"add_cross_attention\": false,\n", " \"architectures\": [\n", " \"BertForMaskedLM\"\n", " ],\n", " \"attention_probs_dropout_prob\": 0.1,\n", " \"bad_words_ids\": null,\n", " \"bos_token_id\": null,\n", " \"chunk_size_feed_forward\": 0,\n", " \"decoder_start_token_id\": null,\n", " \"diversity_penalty\": 0.0,\n", " \"do_sample\": false,\n", " \"early_stopping\": false,\n", " \"eos_token_id\": null,\n", " \"finetuning_task\": null,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout_prob\": 0.1,\n", " \"hidden_size\": 768,\n", " \"id2label\": {\n", " \"0\": \"LABEL_0\",\n", " \"1\": \"LABEL_1\"\n", " },\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"is_decoder\": false,\n", " \"is_encoder_decoder\": false,\n", " \"label2id\": {\n", " \"LABEL_0\": 0,\n", " \"LABEL_1\": 1\n", " },\n", " \"layer_norm_eps\": 1e-12,\n", " \"length_penalty\": 1.0,\n", " \"max_length\": 20,\n", " \"max_position_embeddings\": 512,\n", " \"min_length\": 0,\n", " \"model_type\": \"bert\",\n", " \"no_repeat_ngram_size\": 0,\n", " \"num_attention_heads\": 12,\n", " \"num_beam_groups\": 1,\n", " \"num_beams\": 1,\n", " \"num_hidden_layers\": 12,\n", " \"num_return_sequences\": 1,\n", " \"output_attentions\": false,\n", " \"output_hidden_states\": false,\n", " \"output_scores\": false,\n", " \"pad_token_id\": 0,\n", " \"position_embedding_type\": \"absolute\",\n", " \"prefix\": null,\n", " \"pruned_heads\": {},\n", " \"repetition_penalty\": 1.0,\n", " \"return_dict\": true,\n", " \"return_dict_in_generate\": false,\n", " \"sep_token_id\": null,\n", " \"task_specific_params\": null,\n", " \"temperature\": 1.0,\n", " \"tie_encoder_decoder\": false,\n", " \"tie_word_embeddings\": true,\n", " \"tokenizer_class\": null,\n", " \"top_k\": 50,\n", " \"top_p\": 1.0,\n", " \"torchscript\": false,\n", " \"transformers_version\": \"4.2.1\",\n", " \"type_vocab_size\": 2,\n", " \"use_bfloat16\": false,\n", " \"use_cache\": true,\n", " \"vocab_size\": 30522,\n", " \"xla_device\": null\n", " },\n", " \"is_encoder_decoder\": true,\n", " \"model_type\": \"encoder-decoder\"\n", "}" ] }, "metadata": { "tags": [] }, "execution_count": 25 } ] }, { "cell_type": "markdown", "metadata": { "id": "7k95iWQyzQqG" }, "source": [ "The config is similarly composed of an encoder config and a decoder config both of which are instances of `BertConfig` in our case.\n", "However, the overall config is of type `EncoderDecoderConfig` and is therefore saved as a single `.json` file.\n", "\n", "In conclusion, one should remember that once an `EncoderDecoderModel` object is instantiated, it provides the same functionality as any other Encoder-Decoder model in 🤗Transformers, *e.g.* [BART](https://huggingface.co/transformers/model_doc/bart.html), [T5](https://huggingface.co/transformers/model_doc/t5.html), [ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html), ...\n", "The only difference is that an `EncoderDecoderModel` provides the additional `from_encoder_decoder_pretrained(...)` function allowing the model class to be warm-started from any two encoder and decoder checkpoints." ] }, { "cell_type": "markdown", "metadata": { "id": "5bPQd9-ZxjmJ" }, "source": [ "On a side-note, if one would want to create a shared encoder-decoder model, the parameter `tie_encoder_decoder=True` can additionally be passed as follows:" ] }, { "cell_type": "code", "metadata": { "id": "EZokD01chq3x", "colab": { "base_uri": "https://localhost:8080/", "height": 245, "referenced_widgets": [ "fb0fa4a04fa24d6aa8ca78a27b8101bd", "a7f78c64b3eb4023a20070e9ad7814d1", "df7836f8bdd146e1a767fdd0616a2ea8", "5256e610f71a4eec954f8076ede8f601", "cbef7ccd4db24c26a5312b22a80d7fa9", "cc6036768dac47df945c05a191325b86", "683a0016ec7949f2a259a5b9e61c57a3", "ef08f10698b642efb0258d5cc6938033", "a2d68e27c77d495e95dcb8162da19d9f", "b00ac998a73e4a27b5ca66131dd72d84", "69adb5512323462e8d037f138ce8da70", "8a5362171d5a49bdb1a233c13e5cb5f2", "71b3d267b54d485389fb17ea91cedbdd", "ddd8ad68541949a98ca4a70c1536a111", "fa4e241af77e4c5b8ebe750b9107ae5a", "2f19c20492024a2484c1c245d2d139cf" ] }, "outputId": "0975951c-37f9-40c1-febb-b49ca239b911" }, "source": [ "shared_bert2bert = EncoderDecoderModel.from_encoder_decoder_pretrained(\"bert-base-cased\", \"bert-base-cased\", tie_encoder_decoder=True)\n" ], "execution_count": 26, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fb0fa4a04fa24d6aa8ca78a27b8101bd", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a2d68e27c77d495e95dcb8162da19d9f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=435779157.0, style=ProgressStyle(descri…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertLMHeadModel: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n", "- This IS expected if you are initializing BertLMHeadModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing BertLMHeadModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of BertLMHeadModel were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['bert.encoder.layer.0.crossattention.self.query.weight', 'bert.encoder.layer.0.crossattention.self.query.bias', 'bert.encoder.layer.0.crossattention.self.key.weight', 'bert.encoder.layer.0.crossattention.self.key.bias', 'bert.encoder.layer.0.crossattention.self.value.weight', 'bert.encoder.layer.0.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.output.dense.weight', 'bert.encoder.layer.0.crossattention.output.dense.bias', 'bert.encoder.layer.0.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.0.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.1.crossattention.self.query.weight', 'bert.encoder.layer.1.crossattention.self.query.bias', 'bert.encoder.layer.1.crossattention.self.key.weight', 'bert.encoder.layer.1.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.self.value.bias', 'bert.encoder.layer.1.crossattention.output.dense.weight', 'bert.encoder.layer.1.crossattention.output.dense.bias', 'bert.encoder.layer.1.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.2.crossattention.self.query.weight', 'bert.encoder.layer.2.crossattention.self.query.bias', 'bert.encoder.layer.2.crossattention.self.key.weight', 'bert.encoder.layer.2.crossattention.self.key.bias', 'bert.encoder.layer.2.crossattention.self.value.weight', 'bert.encoder.layer.2.crossattention.self.value.bias', 'bert.encoder.layer.2.crossattention.output.dense.weight', 'bert.encoder.layer.2.crossattention.output.dense.bias', 'bert.encoder.layer.2.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.2.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.3.crossattention.self.query.weight', 'bert.encoder.layer.3.crossattention.self.query.bias', 'bert.encoder.layer.3.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.self.key.bias', 'bert.encoder.layer.3.crossattention.self.value.weight', 'bert.encoder.layer.3.crossattention.self.value.bias', 'bert.encoder.layer.3.crossattention.output.dense.weight', 'bert.encoder.layer.3.crossattention.output.dense.bias', 'bert.encoder.layer.3.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.3.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.4.crossattention.self.query.weight', 'bert.encoder.layer.4.crossattention.self.query.bias', 'bert.encoder.layer.4.crossattention.self.key.weight', 'bert.encoder.layer.4.crossattention.self.key.bias', 'bert.encoder.layer.4.crossattention.self.value.weight', 'bert.encoder.layer.4.crossattention.self.value.bias', 'bert.encoder.layer.4.crossattention.output.dense.weight', 'bert.encoder.layer.4.crossattention.output.dense.bias', 'bert.encoder.layer.4.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.4.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.5.crossattention.self.query.weight', 'bert.encoder.layer.5.crossattention.self.query.bias', 'bert.encoder.layer.5.crossattention.self.key.weight', 'bert.encoder.layer.5.crossattention.self.key.bias', 'bert.encoder.layer.5.crossattention.self.value.weight', 'bert.encoder.layer.5.crossattention.self.value.bias', 'bert.encoder.layer.5.crossattention.output.dense.weight', 'bert.encoder.layer.5.crossattention.output.dense.bias', 'bert.encoder.layer.5.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.5.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.6.crossattention.self.query.weight', 'bert.encoder.layer.6.crossattention.self.query.bias', 'bert.encoder.layer.6.crossattention.self.key.weight', 'bert.encoder.layer.6.crossattention.self.key.bias', 'bert.encoder.layer.6.crossattention.self.value.weight', 'bert.encoder.layer.6.crossattention.self.value.bias', 'bert.encoder.layer.6.crossattention.output.dense.weight', 'bert.encoder.layer.6.crossattention.output.dense.bias', 'bert.encoder.layer.6.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.6.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.7.crossattention.self.query.weight', 'bert.encoder.layer.7.crossattention.self.query.bias', 'bert.encoder.layer.7.crossattention.self.key.weight', 'bert.encoder.layer.7.crossattention.self.key.bias', 'bert.encoder.layer.7.crossattention.self.value.weight', 'bert.encoder.layer.7.crossattention.self.value.bias', 'bert.encoder.layer.7.crossattention.output.dense.weight', 'bert.encoder.layer.7.crossattention.output.dense.bias', 'bert.encoder.layer.7.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.7.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.8.crossattention.self.query.weight', 'bert.encoder.layer.8.crossattention.self.query.bias', 'bert.encoder.layer.8.crossattention.self.key.weight', 'bert.encoder.layer.8.crossattention.self.key.bias', 'bert.encoder.layer.8.crossattention.self.value.weight', 'bert.encoder.layer.8.crossattention.self.value.bias', 'bert.encoder.layer.8.crossattention.output.dense.weight', 'bert.encoder.layer.8.crossattention.output.dense.bias', 'bert.encoder.layer.8.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.8.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.9.crossattention.self.query.weight', 'bert.encoder.layer.9.crossattention.self.query.bias', 'bert.encoder.layer.9.crossattention.self.key.weight', 'bert.encoder.layer.9.crossattention.self.key.bias', 'bert.encoder.layer.9.crossattention.self.value.weight', 'bert.encoder.layer.9.crossattention.self.value.bias', 'bert.encoder.layer.9.crossattention.output.dense.weight', 'bert.encoder.layer.9.crossattention.output.dense.bias', 'bert.encoder.layer.9.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.9.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.10.crossattention.self.query.weight', 'bert.encoder.layer.10.crossattention.self.query.bias', 'bert.encoder.layer.10.crossattention.self.key.weight', 'bert.encoder.layer.10.crossattention.self.key.bias', 'bert.encoder.layer.10.crossattention.self.value.weight', 'bert.encoder.layer.10.crossattention.self.value.bias', 'bert.encoder.layer.10.crossattention.output.dense.weight', 'bert.encoder.layer.10.crossattention.output.dense.bias', 'bert.encoder.layer.10.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.10.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.11.crossattention.self.query.weight', 'bert.encoder.layer.11.crossattention.self.query.bias', 'bert.encoder.layer.11.crossattention.self.key.weight', 'bert.encoder.layer.11.crossattention.self.key.bias', 'bert.encoder.layer.11.crossattention.self.value.weight', 'bert.encoder.layer.11.crossattention.self.value.bias', 'bert.encoder.layer.11.crossattention.output.dense.weight', 'bert.encoder.layer.11.crossattention.output.dense.bias', 'bert.encoder.layer.11.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.11.crossattention.output.LayerNorm.bias']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n", "The following encoder weights were not tied to the decoder ['bert/pooler']\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "-KFgnQ2nuNjw" }, "source": [ "As a comparison, we can see that the tied model has much fewer parameters as expected." ] }, { "cell_type": "code", "metadata": { "id": "8UsfN3ayuMbm", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "e2e67b9f-c78b-4734-8700-d05f019faffc" }, "source": [ "print(f\"\\n\\nNum Params. Shared: {shared_bert2bert.num_parameters()}, Non-Shared: {bert2bert.num_parameters()}\")" ], "execution_count": 27, "outputs": [ { "output_type": "stream", "text": [ "\n", "\n", "Num Params. Shared: 137298244, Non-Shared: 247363386\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "1XpiyR9LuiPQ" }, "source": [ "In this notebook, we will however train a non-shared *Bert2Bert* model, so we continue with `bert2bert` and not `shared_bert2bert`." ] }, { "cell_type": "code", "metadata": { "id": "41Lo4Yruudwx" }, "source": [ "# free memory\n", "del shared_bert2bert" ], "execution_count": 28, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "rTet0_tyj0Ly" }, "source": [ "We have warm-started a `bert2bert` model, but we have not defined all the relevant parameters used for beam search decoding yet.\n", "\n", "Let's start by setting the special tokens.\n", "`bert-base-cased` does not have a `decoder_start_token_id` or `eos_token_id`, so we will use its `cls_token_id` and `sep_token_id` respectively. \n", "Also, we should define a `pad_token_id` on the config and make sure the correct `vocab_size` is set." ] }, { "cell_type": "code", "metadata": { "id": "JD2jv3GkyjR-" }, "source": [ "bert2bert.config.decoder_start_token_id = tokenizer.cls_token_id\n", "bert2bert.config.eos_token_id = tokenizer.sep_token_id\n", "bert2bert.config.pad_token_id = tokenizer.pad_token_id\n", "bert2bert.config.vocab_size = bert2bert.config.encoder.vocab_size" ], "execution_count": 29, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "WOPZ-pDpvSP9" }, "source": [ "Next, let's define all parameters related to beam search decoding. Since `bart-large-cnn` yields good results on CNN/Dailymail, we will just copy its beam search decoding parameters.\n", "\n", "For more details on what each of these parameters does, please take a look at [this](https://huggingface.co/blog/how-to-generate) blog post or the [docs](https://huggingface.co/transformers/main_classes/model.html#generative-models)." ] }, { "cell_type": "code", "metadata": { "id": "t004XKklvU6q" }, "source": [ "bert2bert.config.max_length = 142\n", "bert2bert.config.min_length = 56\n", "bert2bert.config.no_repeat_ngram_size = 3\n", "bert2bert.config.early_stopping = True\n", "bert2bert.config.length_penalty = 2.0\n", "bert2bert.config.num_beams = 4" ], "execution_count": 30, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "h4GwOGxAtKo5" }, "source": [ "Alright, let's now start fine-tuning the warm-started *BERT2BERT* model." ] }, { "cell_type": "markdown", "metadata": { "id": "u98CLZiTkgzv" }, "source": [ "### **Fine-Tuning Warm-Started Encoder-Decoder Models**" ] }, { "cell_type": "markdown", "metadata": { "id": "-gYzA-w96wCt" }, "source": [ "In this section, we will show how one can make use of the `Seq2SeqTrainer` to fine-tune a warm-started encoder-decoder model.\n", "\n", "Let's first import the `Seq2SeqTrainer` and its training arguments `Seq2SeqTrainingArguments`." ] }, { "cell_type": "code", "metadata": { "id": "pyiwaF0noA5c" }, "source": [ "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments" ], "execution_count": 31, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "WaUYsq5YKgjb" }, "source": [ "In addition, we need a couple of python packages to make the `Seq2SeqTrainer` work." ] }, { "cell_type": "code", "metadata": { "id": "CFf3BB7rKeBc" }, "source": [ "%%capture\n", "!pip install git-python==1.0.3\n", "!pip install rouge_score\n", "!pip install sacrebleu" ], "execution_count": 32, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "dPUAgo7pxH24" }, "source": [ "The `Seq2SeqTrainer` extends 🤗Transformer's Trainer for encoder-decoder models.\n", "In short, it allows using the `generate(...)` function during evaluation, which is necessary to validate the performance of encoder-decoder models on most *sequence-to-sequence* tasks, such as *summarization*. \n", "\n", "For more information on the `Trainer`, one should read through [this](https://huggingface.co/transformers/training.html#trainer) short tutorial.\n", "\n", "Let's begin by configuring the `Seq2SeqTrainingArguments`.\n", "\n", "The argument `predict_with_generate` should be set to `True`, so that the `Seq2SeqTrainer` runs the `generate(...)` on the validation data and passes the generated output as `predictions` to the `compute_metric(...)` function which we will define later.\n", "The additional arguments are derived from `TrainingArguments` and can be read upon [here](https://huggingface.co/transformers/main_classes/trainer.html#trainingarguments).\n", "For a complete training run, one should change those arguments as needed. Good default values are commented out below.\n", "\n", "For more information on the `Seq2SeqTrainer`, the reader is advised to take a look at the [code](https://github.com/huggingface/transformers/blob/master/examples/seq2seq/seq2seq_trainer.py)." ] }, { "cell_type": "code", "metadata": { "id": "LAaTxUpdzshF" }, "source": [ "training_args = Seq2SeqTrainingArguments(\n", " predict_with_generate=True,\n", " evaluation_strategy=\"steps\",\n", " per_device_train_batch_size=batch_size,\n", " per_device_eval_batch_size=batch_size,\n", " fp16=True, \n", " output_dir=\"./\",\n", " logging_steps=2,\n", " save_steps=10,\n", " eval_steps=4,\n", " # logging_steps=1000,\n", " # save_steps=500,\n", " # eval_steps=7500,\n", " # warmup_steps=2000,\n", " # save_total_limit=3,\n", ")" ], "execution_count": 33, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "z0FWPCFnCgf6" }, "source": [ "\n", "Also, we need to define a function to correctly compute the ROUGE score during validation. Since we activated `predict_with_generate`, the `compute_metrics(...)` function expects `predictions` that were obtained using the `generate(...)` function. \n", "Like most summarization tasks, CNN/Dailymail is typically evaluated using the ROUGE score. \n", "\n", "Let's first load the ROUGE metric using the 🤗datasets library." ] }, { "cell_type": "code", "metadata": { "id": "68IHmFYLx09W", "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "c409212f98b54d14b2d39d574931e5f7", "b718d9d9b3374519a56559174b47958c", "d166ae2021854e95937a7c7c4fe0a81f", "1750417c22d84e08ad9c3bbc74aa4bce", "aae2c21b7e1b416b907ab8e7690e9eaf", "fd8d81f6d3e04881a24191618c227d79", "289fd33789dc4b8191e9c065937f1c22", "50254085964d4dc9a7048daea97510db" ] }, "outputId": "01743f87-0817-44b7-d3f7-b21de62137e7" }, "source": [ "rouge = datasets.load_metric(\"rouge\")" ], "execution_count": 34, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c409212f98b54d14b2d39d574931e5f7", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1656.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "y8UgiZP7N_tB" }, "source": [ "Next, we will define the `compute_metrics(...)` function. The `rouge` metric computes the score from two lists of strings. Thus we decode both the `predictions` and `labels` - making sure that `-100` is correctly replaced by the `pad_token_id` and remove all special characters by setting `skip_special_tokens=True`." ] }, { "cell_type": "code", "metadata": { "id": "iVKkr2xfNyRh" }, "source": [ "def compute_metrics(pred):\n", " labels_ids = pred.label_ids\n", " pred_ids = pred.predictions\n", "\n", " pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n", " labels_ids[labels_ids == -100] = tokenizer.pad_token_id\n", " label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\n", "\n", " rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\n", "\n", " return {\n", " \"rouge2_precision\": round(rouge_output.precision, 4),\n", " \"rouge2_recall\": round(rouge_output.recall, 4),\n", " \"rouge2_fmeasure\": round(rouge_output.fmeasure, 4),\n", " }" ], "execution_count": 35, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "1ik4hZb2yV-b" }, "source": [ "Great, now we can pass all arguments to the `Seq2SeqTrainer` and start finetuning. Executing the following cell will take *ca.* 10 minutes \n", "☕.\n", "\n", "Finetuning *BERT2BERT* on the complete *CNN/Dailymail* training data takes *ca.* model takes *ca.* 8h on a single *TITAN RTX* GPU." ] }, { "cell_type": "code", "metadata": { "id": "cy7i1Q3D1qSI", "colab": { "base_uri": "https://localhost:8080/", "height": 335 }, "outputId": "2471ef83-813b-467d-8148-6a13411423c9" }, "source": [ "# instantiate trainer\n", "trainer = Seq2SeqTrainer(\n", " model=bert2bert,\n", " tokenizer=tokenizer,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " train_dataset=train_data,\n", " eval_dataset=val_data,\n", ")\n", "trainer.train()" ], "execution_count": 37, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", " \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "text/html": [ "\n", "

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StepTraining LossValidation LossRouge2 PrecisionRouge2 RecallRouge2 FmeasureRuntimeSamples Per Second
410.52510010.8117330.0000000.0000000.0000009.3461000.856000
88.3935007.8518840.0078000.0078000.0078009.5804000.835000
127.5651007.8339860.0000000.0000000.0000009.8241000.814000
167.5630007.7554280.0055000.0148000.0080009.6507000.829000
207.2282007.6954100.0051000.0109000.0069009.6774000.827000
247.1507007.6692250.0000000.0000000.0000009.6282000.831000

" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=24, training_loss=8.299681742986044, metrics={'train_runtime': 158.7532, 'train_samples_per_second': 0.151, 'total_flos': 91188038615040, 'epoch': 3.0})" ] }, "metadata": { "tags": [] }, "execution_count": 37 } ] }, { "cell_type": "markdown", "metadata": { "id": "YQ7shq14JRJG" }, "source": [ "Awesome, we should now be fully equipped to finetune a warm-started encoder-decoder model. To check the result of our fine-tuning let's take a look at the saved checkpoints." ] }, { "cell_type": "code", "metadata": { "id": "t2Yv4OkuJb1B", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "943a6b13-582e-4006-fab9-5419ceed8e2c" }, "source": [ "!ls" ], "execution_count": 38, "outputs": [ { "output_type": "stream", "text": [ "bert2bert checkpoint-10 checkpoint-20 runs sample_data\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "CU1xAmVcJeP6" }, "source": [ "Finally, we can load the checkpoint as usual via the `EncoderDecoderModel.from_pretrained(...)` method." ] }, { "cell_type": "code", "metadata": { "id": "J2MvxTwmKVeV" }, "source": [ "dummy_bert2bert = EncoderDecoderModel.from_pretrained(\"./checkpoint-20\")" ], "execution_count": 39, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ZwQIEhKOrJpl" }, "source": [ "### **Evaluation**\n", "\n", "In a final step, we might want to evaluate the *BERT2BERT* model on the test data.\n", "\n", "To start, instead of loading the dummy model, let's load a *BERT2BERT* model that was finetuned on the full training dataset. Also, we load its tokenizer, which is just a copy of `bert-base-cased`'s tokenizer." ] }, { "cell_type": "code", "metadata": { "id": "TM2czn1xKxC0", "colab": { "base_uri": "https://localhost:8080/", "height": 267, "referenced_widgets": [ "733d1d2cdcff49d4846821d885bf9c72", "042d6c88c60f443bab9c5560bfd54da2", "33635b79d70d45a38b7d56bcd03648e8", "a7fdbaa4181242d6aa0f11eeca18372c", "60604e9f2e8a4489b9798896075b3b28", "cf27e6f99b204c149e6c55b036ca6897", "1981e1b7048345c09a916f83e5b79168", "317aae4515264050967327982268e466", "69f8b027080d4152a9f0cc784da2bb42", "c6fab56903434ec4b69b1a1e29bba1a8", "2639a20ec4334d24ae5a890e8429dbd6", "6dbb4d09fe89431d9b155b1f7fafc4e7", "51d0bded8c814402b69bdd3beb6bcb68", "844ccda51e314c0c9d9c198d9b30c529", "96e553e61b514412825022be6a0fc562", "4aaf5dff62a24215b3e40bd747d4354d", "2aa2dd61983f404d97d6230a25b32d2b", "621090f530fe4e148e22a53d8977d6bd", "5801d23969e845d78fe421db51c5e89d", "4c6c3b955b9c43269ce4d3b932362417", "c71acb21b40c42da9620ded419519dfa", "ee624b803e9d401cac7ff616515e8af5", "64690980c8c44853b0c25bbfd4739033", "1ff55ca032f74693a37b74a3c7e32cef", "c312cbd0caa24b1b8cf82a5b301870b8", "a3c44374ac7d4fba9bcc32329b48eea1", "4072064c7e6b423eb514a39a276322b0", "389c193c575848a7a720ecd6f1e0b843", "5fcef0ea8e4d472591e9599ec0d295a2", "0692181b0f89403ebffab1cdd2c9edbb", "5201c7a54db7417992ed96c71079e470", "a591585795d045e08d8548835f417c14", "994f3e58043040afbe09974ce6eafe2a", "db4c2a97175341a0ba52c820ad28e0fa", "281e2d74bc7c4aab82a7d949e24ca540", "bbf6dc7ceb354c85ab6025b380f853f2", "5ca3868eaa2b415bbe05a1524bbb7f4c", "f429f7e427f943f8990f64f22f17c9bf", "ccfb3ef3a8ac473b9afce7b04721cdb1", "93c10ea821874fc091ebcf72f7e9bc04" ] }, "outputId": "0d32980c-c5e5-43e6-ed13-2d0bf3c9aea0" }, "source": [ "from transformers import BertTokenizer\n", "\n", "bert2bert = EncoderDecoderModel.from_pretrained(\"patrickvonplaten/bert2bert_cnn_daily_mail\").to(\"cuda\")\n", "tokenizer = BertTokenizer.from_pretrained(\"patrickvonplaten/bert2bert_cnn_daily_mail\")" ], "execution_count": 40, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "733d1d2cdcff49d4846821d885bf9c72", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=3660.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "69f8b027080d4152a9f0cc784da2bb42", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=989691346.0, style=ProgressStyle(descri…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2aa2dd61983f404d97d6230a25b32d2b", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c312cbd0caa24b1b8cf82a5b301870b8", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=156.0, style=ProgressStyle(description_…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "994f3e58043040afbe09974ce6eafe2a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=252.0, style=ProgressStyle(description_…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "jOOslzG9K1hM" }, "source": [ "Next, we load just 2% of *CNN/Dailymail's* test data. For the full evaluation, one should obviously use 100% of the data." ] }, { "cell_type": "code", "metadata": { "id": "oOoSrwWarJAC", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "0369a876-b111-486c-cb9f-380e2851318f" }, "source": [ "test_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"test[:2%]\")" ], "execution_count": 41, "outputs": [ { "output_type": "stream", "text": [ "Reusing dataset cnn_dailymail (/root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602)\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "XVD967erLrnM" }, "source": [ "Now, we can again leverage 🤗dataset's handy `map()` function to generate a summary for each test sample.\n", "\n", "For each data sample we:\n", "\n", "- first, tokenize the `\"article\"`,\n", "- second, generate the output token ids, and\n", "- third, decode the output token ids to obtain our predicted summary." ] }, { "cell_type": "code", "metadata": { "id": "x274feeXLqrT" }, "source": [ "def generate_summary(batch):\n", " # cut off at BERT max length 512\n", " inputs = tokenizer(batch[\"article\"], padding=\"max_length\", truncation=True, max_length=512, return_tensors=\"pt\")\n", " input_ids = inputs.input_ids.to(\"cuda\")\n", " attention_mask = inputs.attention_mask.to(\"cuda\")\n", "\n", " outputs = bert2bert.generate(input_ids, attention_mask=attention_mask)\n", "\n", " output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n", "\n", " batch[\"pred_summary\"] = output_str\n", "\n", " return batch" ], "execution_count": 42, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "pZecWodQsr0O" }, "source": [ "Let's run the map function to obtain the *results* dictionary that has the model's predicted summary stored for each sample. Executing the following cell may take *ca.* 10min ☕." ] }, { "cell_type": "code", "metadata": { "id": "bYmdx-W1NAky", "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "67c3a738d1f94256b73dc427342d6304", "01fa66f9a6164b10b7a6e02d6591da85", "3b6d077ddf6b4b10b518bccd88cc8771", "9d5a46724e714795b455eb9f67a568ab", "9f7a0107fddd47cf953c36c62f81f308", "6b927ce40d754c03919f4865c0ddea64", "93a0438f7f164251b9eb288fd1849b3e", "b5de42ee03544dfc82ef5ce6f96ce5ec" ] }, "outputId": "8c0798fc-b32c-4d93-dea8-f355839a2256" }, "source": [ "batch_size = 16 # change to 64 for full evaluation\n", "\n", "results = test_data.map(generate_summary, batched=True, batch_size=batch_size, remove_columns=[\"article\"])" ], "execution_count": 43, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "67c3a738d1f94256b73dc427342d6304", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=15.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "0ZhjRk8oNW0J" }, "source": [ "Finally, we compute the ROUGE score." ] }, { "cell_type": "code", "metadata": { "id": "yfhyBz-ZsVJe", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2e515efc-b297-4b26-9e2a-56a6c80884a9" }, "source": [ "rouge.compute(predictions=results[\"pred_summary\"], references=results[\"highlights\"], rouge_types=[\"rouge2\"])[\"rouge2\"].mid" ], "execution_count": 44, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Score(precision=0.10389454113300968, recall=0.1564771201053348, fmeasure=0.12175271663717585)" ] }, "metadata": { "tags": [] }, "execution_count": 44 } ] }, { "cell_type": "markdown", "metadata": { "id": "7zdm50ZotZqb" }, "source": [ "That's it. We've shown how to warm-start a *BERT2BERT* model and fine-tune/evaluate it on the CNN/Dailymail dataset.\n", "\n", "The fully trained *BERT2BERT* model is uploaded to the 🤗model hub under [patrickvonplaten/bert2bert_cnn_daily_mail](https://huggingface.co/patrickvonplaten/bert2bert_cnn_daily_mail). \n", "\n", "The model achieves a ROUGE-2 score of **18.22** on the full evaluation data, which is even a little better than reported in the paper.\n", "\n", "For some summarization examples, the reader is advised to use the online inference API of the model, [here](https://huggingface.co/patrickvonplaten/bert2bert_cnn_daily_mail)." ] }, { "cell_type": "markdown", "metadata": { "id": "LlrCkQGuA4Bw" }, "source": [ "Thanks a lot to Sascha Rothe, Shashi Narayan, and Aliaksei Severyn from Google Research, and Victor Sanh, Sylvain Gugger, and Thomas Wolf from 🤗Hugging Face for proof-reading and giving very much appreciated feedback." ] } ] } ================================================ FILE: ODSC_2022_🤗_Transformers_Workshop.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "7-Quw0_gXyDR" }, "source": [ "# **Using 🤗 Transformers and 🤗 Datasets filter customer feedback filtering**" ] }, { "cell_type": "markdown", "source": [ "Before diving into this notebook, we strongly recommend \n", "going through **all** the chapters of the official [🤗 Hugging Face course](https://huggingface.co/course/chapter1/1). This will make it much easier for \n", "you to follow this notebook and transfer the knowledge to **your** tasks." ], "metadata": { "id": "da6TrUsUZzi7" } }, { "cell_type": "markdown", "source": [ "In this notebook, we will simulate a real-world use \n", "case and try to solve it using tools of the Hugging Face ecosystem.\n", "\n", "We strongly recommend using this notebook as a template/example to \n", "solve **your** real-world use case. Before the official workshop at the OSCD workshop starts, it will be very advantageous to have adapted the google colab for your use case so that the Hugging Face team can help you solve your specific questions." ], "metadata": { "id": "H4mgysOXZZWD" } }, { "cell_type": "markdown", "source": [ "# **Defining Task, Dataset & Model**\n", "\n", "Before jumping into the actual coding part, it's important to have a clear definition of the use case that you would like to automate or partly automate.\n", "A clear definition of the use case helps in identifying the most suitable task, dataset to use, and model to apply for your use case." ], "metadata": { "id": "B_tTKBgzNNP0" } }, { "cell_type": "markdown", "source": [ "## **Define your NLP task**\n", "\n", "Alright, let's dive into a hypothetical problem we wish to save using models of natural language processing. Let's assume, we are selling a product and our customer support team receives thousands of messages including feedback, complaints, and questions which ideally should all be answered. \n", "\n", "Quickly, it becomes obvious though that customer support is by no means able to reply to every message. Thus, we decide to only reply \n", "to the most unsatisfied custmoers and set the goal of replying to 100% of very unsatisfied messages.\n", "\n", "Assuming that a) messages of very unsatisfied customers represent only a fraction of all messages and b) that we can filter out unsatisfied messages in an automated way, customer support should be able to reach this goal.\n", "\n", "To filter out unsatisfied messages in an automated way, we plan on applying natural language processing technologies. \n", "\n", "\n", "The first step is now to map our use case - *filtering out unsatisfied messages* - to a natural language processing task.\n", "\n", "To do so, it is recommended to go over all available tasks on the Hugging Face Hub [here](https://huggingface.co/tasks). If you are not sure which task applies to your use case, you should click on all of the different tasks to better understand them, *e.g.* \n", "\n", "Automatically replying The task of finding messages of the most unsatisfied customers can be labeled as a text classification task: Classify a message into one of *very unsatisfied*, *unsatisfied*, *neutral*, *satisfied*, or *very satisfied*.\n", "\n" ], "metadata": { "id": "SCP5jic6ZCEG" } }, { "cell_type": "markdown", "source": [ "## **Find suitable datasets**\n", "\n", "Having decided on the task, next we should find the data the model will be trained on. This is usually more important for the downstream performance of your use case than picking the right model architecture.\n", "Keep in mind that a model is **only as good as the data it has been trained on**. Thus, we should be very careful when curating and/or selecting the dataset.\n", "\n", "Since we consider the hypothetical use case of *filtering out unsatisfied messages*, let's look into what datasets are available to us.\n", "\n", "For your real-world use case, it is **very likely** that you have internal data that best represents the actual data your NLP system is supposed to handle. Therefore, you should use such internal data to train your NLP system.\n", "It can nevertheless be helpful to also include publicly available to improve the generalizability of your model.\n", "\n", "Let's take a look at all available Datasets on the [Hugging Face Hub](https://huggingface.co/datasets). On the left side, you can filter the datasets according to *Task Categories* as well as *Tasks* which are more specific. Our use case corresponds to *Text Classification* -> *Sentiment Analysis* so let's select [these filters](https://huggingface.co/datasets?task_categories=task_categories:text-classification&task_ids=task_ids:sentiment-classification&sort=downloads). We are left with *ca.* 80 datasets at the time of writing this notebook. Two aspects should be evaluated when picking a dataset:\n", "\n", "- **Quality**: Is the dataset of high quality? More specifically: Does the data correspond to the data you expect to deal with in your use case? Is the data diverse, unbiased, ...?\n", "- **Size**: How big is the dataset? Usually one can safely say the bigger the dataset, the better.\n", "\n", "It's quite difficult to efficiently evaluate whether a dataset is of high quality and it's even more difficult to know whether and how the dataset is biased.\n", " An efficient and reasonable heuristic for high quality is to look at the download statistics. The more downloads, the more usage, the higher chance that the dataset is of high quality. The size is easy to evaluate as it can usually be quickly read upon. Let's take a look at the most downloaded datasets:\n", "\n", "- [Glue](https://huggingface.co/datasets/glue)\n", "- [Amazon polarity](https://huggingface.co/datasets/amazon_polarity)\n", "- [Tweet eval](https://huggingface.co/datasets/tweet_eval)\n", "- [Yelp review full](https://huggingface.co/datasets/yelp_review_full)\n", "- [Amazon reviews multi](https://huggingface.co/datasets/amazon_reviews_multi)\n", "\n", "Now we can inspect those datasets in more detail by reading through the dataset card which ideally should give all relevant and important information. In addition, the [dataset viewer](https://huggingface.co/datasets/glue/viewer/cola/test) is an incredibly powerful tool to inspect whether the data suits your use case.\n", "\n", "Let's quickly go over the dataset cards of the models above: \n", "- *GLUE* is a collection of small datasets that mostly serves as a means to compare new model architectures for researchers. The datasets are too small and don't correspond enough to our use case.\n", "- *Amazon polarity* is huge and a well-suited dataset for customer feedback since the data deals with customer reviews. However, it only has binary labels (positive/negative) whereas we are looking for more granularity in the sentiment classification. \n", "- *Tweet eval* uses different emojis as labels which cannot that easily be mapped to a scale going from unsatisfied to satisfied.\n", "- *Amazon reviews multi* seems to be the most suited dataset here. We have sentiment labels ranging from 1-5 corresponding to 1-5 stars on Amazon. These labels can very well be mapped to *very unsatisfied, unsatisfied, neutral, satisfied, very satisfied*. Having inspected some examples on [the dataset viewer](https://huggingface.co/datasets/amazon_reviews_multi/viewer/en/train) we can see that the reviews look very similar to how customer feedback reviews would look, so this seems like a very good dataset. In addition, each review has a `product_category` label so we could even go as far as to only use reviews of a product category that corresponds to the one we are working in. The dataset is multi-lingual, but we are just interested in the English version for now.\n", "- *Yelp review full* looks like a very suitable dataset. It's large and contains product reviews and sentiment labels from 1 to 5. Sadly, the dataset viewer is not working here at the moment and the dataset card is also relatively sparse requiring some more time to inspect the dataset. At this point, we should read the paper, but given the time-constraint of this blog post, we'll choose to go for *Amazon reviews multi*.\n", "\n", "As a conclusion, let's focus on the [*Amazon reviews multi*](https://huggingface.co/datasets/amazon_reviews_multi) dataset considering all training examples.\n", "\n", "As a final note, we recommend making use of Hub's dataset functionality even when working with private datasets. The Hugging Face Hub, Transformers, and Datasets are flawlessly integrated, which makes it trivial to use them in combination when training models.\n", "\n", "In addition, the Hugging Face Hub offers:\n", "\n", "- [A dataset viewer for every dataset](https://huggingface.co/datasets/amazon_reviews_multi)\n", "- [Easy demoing of every model using widgets](https://huggingface.co/docs/hub/main#whats-a-widget)\n", "- [Private and Public models](https://huggingface.co/docs/hub/adding-a-model#creating-a-repository)\n", "- [Git version control for repositories](https://huggingface.co/docs/hub/main#whats-a-repository)\n", "- [Highest security mechanisms](https://huggingface.co/docs/hub/security)" ], "metadata": { "id": "k_MrbAHndCAy" } }, { "cell_type": "markdown", "source": [ "## **Find a suitable model**\n", "\n", "Having decided on the task and the dataset that best describes our use case, we can now look into choosing a model to be used.\n", "\n", "Most likely, you will have to fine-tune a pretrained model for your use case, but it is worth checking whether they are already fine-tuned models on the Hub that perform well. In this case, you might reach a higher performance by just continuing to fine-tune such a model on your dataset.\n", "\n", "Let's take a look at all models that have been fine-tuned on Amazon Reviews Multi, you can find the list of models on the bottom right corner - clicking on *Browse models trained on this dataset* you can see [a list of all models fine-tuned on the dataset that are publicly available](https://huggingface.co/models?dataset=dataset:amazon_reviews_multi). Note that we are only interested in the English version of the dataset because our customer feedback will only be in English. It looks like most of the most downloaded models are trained on the multi-lingual version of the dataset and those that don't seem to be multi-lingual have very little information or poor performance. At this point, \n", "it might be more sensible to fine-tune a purely pretrained model instead of using one of the already fine-tuned ones shown in the link above.\n", "\n", "Alright, the next step now is to find a suitable pretrained model to be used for fine-tuning. This is actually more difficult than it seems given the large amount of pretrained and fine-tuned models that are the [Hugging Face Hub](https://huggingface.co/models) . The best option is usually to simply try out a variety of different models to see which one performs best. \n", "We still haven't found the perfect way of comparing different model checkpoints to each other at Hugging Face, but we provide some resources that are worth looking into:\n", "\n", "- The [model summary](https://huggingface.co/docs/transformers/model_summary) gives a short overview of different model architectures.\n", "- A task-specific search on the Hugging Face Hub, *e.g.* [a search on text-classification models](https://huggingface.co/models), shows you the most downloaded checkpoints which is also an indication of how well those checkpoints perform.\n", "\n", "Both of the above resources are currently however a bit suboptimal. The model summary is not always kept up to date. The speed at which new model architectures are released and old model architectures become outdated makes it extremely difficult to have an up-to-date summary of all model architectures.\n", "Similarly, it doesn't necessarily mean that the most downloaded model checkpoint is the best one. E.g. [`bert-base-cased`](https://huggingface.co/bert-base-uncased) is amongst the most downloaded model checkpoints but is not the best performing checkpoint anymore. \n", "\n", "The best is often to try out a variety of different model architectures, stay up to date with new model architectures by following experts in the field and checking well-known leaderboards.\n", "\n", "For text-classification, the important benchmarks to look at are [GLUE](https://gluebenchmark.com/leaderboard) and [SuperGLUE](https://super.gluebenchmark.com/leaderboard). Both benchmarks evaluate pretrained models on a variety of text-classification tasks, such as grammatical correctness, natural language inference, Yes/No question answering, etc..., which are quite similar to our target task of sentiment analysis. Thus, it is reasonable to choose one of the leading models of these benchmarks for our task.\n", "\n", "At the time of writing this notebook, the best performing models are very large models containing more than 10 billion parameters most of which are not open-sourced, *e.g.* *ST-MoE-32B*, *Turing NLR v5*, or\n", "*ERNIE 3.0*. One of the top-ranking models that is easily accessible is [DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta). Because Let's try out DeBERTa's newest base version - *i.e.* [`microsoft/deberta-v3-base`](https://huggingface.co/microsoft/deberta-v3-base)." ], "metadata": { "id": "1IucFk0GQt0Q" } }, { "cell_type": "markdown", "source": [ "# **Training / Fine-tuning a model with 🤗 Transformers and 🤗 Datasets**\n", "\n", "In this section, we will jump into the technical details of how to \n", "fine-tune a model end-to-end to be able to automatically filter out very unsatisfied customer feedback messages.\n", "\n", "Cool, let's start by installing all necessary pip packages and by setting up our code environment, then look into preprocessing the dataset and finally start training the model.\n", "\n", "The following notebook can be run online in a google colab pro with the GPU runtime environment enabled." ], "metadata": { "id": "F4IQ0Bb6IRNQ" } }, { "cell_type": "markdown", "source": [ "## **Install all necessary packages**\n", "\n", "To begin with, let's install [`git-lfs`](https://git-lfs.github.com/) so that we can automatically upload our trained checkpoints to the Hub during training." ], "metadata": { "id": "7ntAgMY2TKRx" } }, { "cell_type": "code", "source": [ "!apt install git-lfs" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kL476PVciBlB", "outputId": "f35e6774-d0bd-4803-c631-2b26671ebd21" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Reading package lists... Done\n", "Building dependency tree \n", "Reading state information... Done\n", "The following NEW packages will be installed:\n", " git-lfs\n", "0 upgraded, 1 newly installed, 0 to remove and 39 not upgraded.\n", "Need to get 2,129 kB of archives.\n", "After this operation, 7,662 kB of additional disk space will be used.\n", "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 git-lfs amd64 2.3.4-1 [2,129 kB]\n", "Fetched 2,129 kB in 0s (18.5 MB/s)\n", "Selecting previously unselected package git-lfs.\n", "(Reading database ... 156210 files and directories currently installed.)\n", "Preparing to unpack .../git-lfs_2.3.4-1_amd64.deb ...\n", "Unpacking git-lfs (2.3.4-1) ...\n", "Setting up git-lfs (2.3.4-1) ...\n", "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "B683CTrxXyDY" }, "source": [ "Also, we install the 🤗 Transformers and 🤗 Datasets libraries to run this notebook. Since we will be using [DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta-v2#debertav2) in this notebook, we also need to install the [`sentencepiece`](https://github.com/google/sentencepiece) library for its tokenizer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OyR6R9jQXyDY" }, "outputs": [], "source": [ "%%capture\n", "!pip install datasets transformers[sentencepiece]" ] }, { "cell_type": "markdown", "source": [ "Next, let's login into our [Hugging Face account](https://huggingface.co/join) so that models are uploaded correctly under your name tag." ], "metadata": { "id": "AnukCvT4UG-S" } }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 385, "referenced_widgets": [ "ecf82f9bb19a43b0b0f85ba677d7fdaf", "b0b24ae83353474b84ac532037b0ec74", "5f9c324715554952bd0a04aa9c7ec62b", "1b4c3fb1903340e39a65dad195140d2e", "f0cb87644ad84421871f0d413fea8c9d", "8c778cf5552749898775ee3e43f46bc6", "daf8b0e163f148fe84d9e98ba54cb558", "4585585a9717423aacd8df5d57e0edd7", "efdc4418401e4b1dbce2b31b6ef854fd", "05395208365a42d78491771367ce29b2", "7cb8615b59014c0a83f4c72f2c97e54a", "22c5377c8e1045c0893036b8e087a0a5", "52a831fe62094909abddf7fef6a4ffe3", "f6afe52a6aea40399b0cc100f87947eb", "6b0339a84011471abd1dbd67da679139", "9049522a19ed486aa524c741125974a2", "6ef126eb487a4485a554943777678a9f" ] }, "id": "l8Jbx6h2y9w2", "outputId": "4a107d0b-58e1-4a38-f410-31a3b2eb1d5b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n", "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", "\n", "git config --global credential.helper store\u001b[0m\n" ] } ] }, { "cell_type": "markdown", "source": [ "## **Preprocess the dataset**\n", "\n", "Before we can start training the model, we should bring the dataset in a format \n", "that is understandable by the model.\n", "\n", "Thankfully, the 🤗 Datasets library makes this extremely easy as you will see in the following cells.\n", "\n", "The `load_dataset` function loads the dataset, nicely arranges it into predefined attributes, such as `review_body` and `stars`, and finally saves the newly arranged data using the [arrow format](https://arrow.apache.org/#:~:text=Format,data%20access%20without%20serialization%20overhead.) on disk. \n", "The arrow format allows for fast and memory-efficient data reading and writing.\n", "\n", "Let's load and prepare the English version of the `amazon_reviews_multi` dataset." ], "metadata": { "id": "XPz26fv8VC9k" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UUjKAtEgXyDa", "outputId": "7c79db7e-ce2a-4b94-d93a-5f19b82afde2", "colab": { "base_uri": "https://localhost:8080/", "height": 457, "referenced_widgets": [ "599b3b02cc884b808f4675c539e7c6c6", "c810d5857cb94e7b8defcc28a03293c1", "643b2827becc497ba4f2f8edc75e6c5c", "9e15b7840cbe46a4a2f64277a0d59cd7", "38979bc856aa4786a0302f4261fdbf1a", "21b89528860744609492123f7cfc4bdc", "89eef2ea65214940bac035d8346997e1", "6805f97b712c462abeed5f7e9a29cb55", "27d6f6d186a1470781037fa3b48e2aad", "7091548c8802428a92511382b2318302", "f253f7aa84154daf8f88a727e4487836", "cf07002c1741489db4864a9ff08350c9", "a4870b08b93346099e8e7456afbb3cfb", "4352386bc421483491f78d8e6fd68d5b", "f23a8f3bc79a487d8b7a203134b5aefd", "1f3d8c4e66764a6584975c236edd6cf5", "4f3d864687214961805cd797ee9264c4", "515fde7da4c54e69905340bb284accdb", "19664fb4a3274f82817220dd15455e24", "c6eac7d2ae6e47c1bb6f998d33118c5f", "aa19af312d7c403f91a717c1b31e235f", "d014e9b63bb84a919ac462ffc01a85ca", "8643bc90a4754ac2a6d59bc5bf22be99", "1d3fa80b678c49439227c3446fa0e2ee", "1c04500963dd4823b9072b6993ef4178", "04b9be6e059541839ec9e5c670c8a5bb", "e1b95e1441f54166ad66eea413fe9a4e", "4632fb7a0d094dad95731d3dfe5f3d3b", "c7042a4015fa404cb7f8c44810bc47f5", "ba54f8093ea44524acc948211e930c54", "1a4cc5805bee43ff86bb603bb8bc02e2", "56e4b599c78c46dda2e89379a592d02d", "f781383ed8fd4a8fa34092d18f6e604c", "3a5d870bf21d4751aa022549455c8e60", "a390b8c7e8494e799459991e0596f1c6", "881d5655a9a34f16857bbf95fff9abc4", "524960ddd1d04b288cce7f96f452aecd", "dd3c4674834c4565ad617964e4445bc6", "88aa1f054f674001a8490850c7b361fa", "0554e5c172304cfdbc521fdeb11c3a78", "48b2fd1d719747fc89b2abed973632cd", "a73b55c85c414b208df55729e66518ac", "e8e8c6bffd134d53a978589bddc77230", "39de9bf9d32f432e82cf78204a2d1761", "c3754b13402f49668002ca84e0eced7e", "f30df8faf007400388f0869fa32b0423", "6fbcc1108902464283c8d94406a74d1f", 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StepTraining LossValidation LossAccuracy
50000.9312000.9796020.585600

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-5000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-5000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-5000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-5000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-5000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-5000/added_tokens.json\n", "tokenizer config file saved in deberta_amazon_reviews_v1/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/added_tokens.json\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

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StepTraining LossValidation LossAccuracy
50000.9312000.9796020.585600
100000.9316000.9336070.597400
150000.9076000.9170620.602600
200000.9024000.9194140.604600
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300000.8067000.9339230.609200
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450000.7950000.9189470.616800
500000.7836000.9075720.618400

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-10000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-10000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-10000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-10000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-10000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-10000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-15000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-15000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-15000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-15000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-15000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-15000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-20000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-20000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-20000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-20000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-20000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-20000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-25000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-25000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-25000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-25000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-25000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-25000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-30000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-30000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-30000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-30000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-30000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-30000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-35000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-35000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-35000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-35000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-35000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-35000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-40000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-40000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-40000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-40000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-40000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-40000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-45000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-45000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-45000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-45000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-45000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-45000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-50000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-50000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-50000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-50000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-50000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-50000/added_tokens.json\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "Cool, we see that the model seems to learn something! Training loss and validation loss is going down and the accuracy also ends up being well over random chance (20%). Interestingly, we see accuracy of around **58.6 %** already after 5000 steps which doesn't improve that much anymore afterward. Choosing a bigger model or training for longer would have probably given better results here, but that's good enough for our hypothetical use case!\n", "\n", "Alright, finally let's upload the model checkpoint to the Hub." ], "metadata": { "id": "CaLVY2nfmcgS" } }, { "cell_type": "code", "source": [ "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 505, "referenced_widgets": [ "f1b47b308fc44d0db5d5c9c0e220ca20", "4a061a1f37194e6baf6a69af5cb21810", "6a0648b8a0ef4572bb2b40de2c8841eb", "200396843e4946838c4da86b6db63da7", "b485f836368e47b68a6429ef021f6bfe", "fe2303f666f8497ea53a47b031ef666f", "ab931e51f4054133a5fb5c9a966574a0", "325bd958d1fa4301a99d00a2aa6269c7", "a070818e87534bf19d3135b0f12397ca", "4c28fc60b50445d3a0692132aadeb48b", "6ed5123c732448b895de96331c1c3e4c", "07209e50ade04003b6f1a8e0e4c31153", "0ab586f91c1f433d93c0174f31a023bc", "e77e2908f7774ff89d8d604e39e90ff5", "559d95a66cc34e54ae1d8aa47a5d1304", "dee389b6c91142b3b94f07b6eefa4937", "832f027ab6ca4d2bbb59b5c67b83307a", "15f910b522cc4bdd8cae4bed4e5741be", "2753294c27b54c17b3f3fbc80087a748", "a55e8bdb7d7044f7840f44915661cdd1", "629612eaf9324a9ea983a7012a888954", "cb74137376914463866f1dec3ebe68b5" ] }, "id": "HmjV2dhkibLc", "outputId": "b3f1c6e2-2a0e-41b1-d79e-92a9cd0deaf9" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to deberta_amazon_reviews_v1\n", "Configuration saved in deberta_amazon_reviews_v1/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/added_tokens.json\n", "Several commits (2) will be pushed upstream.\n", "The progress bars may be unreliable.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.34k/704M [00:00 main\n", "\n", "remote: tput: No value for $TERM and no -T specified \n", "remote: tput: No value for $TERM and no -T specified \n", "remote: tput: No value for $TERM and no -T specified \n", "remote: tput: No value for $TERM and no -T specified \n", "To https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1\n", " 7f3172e..599b891 main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "'https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1/commit/7f3172e3457e9f20305db66972ae64b21d70d9b1'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 24 } ] }, { "cell_type": "markdown", "source": [ "## **Evaluate / Analyse the model**\n", "\n", "Now that we have fine-tuned the model we need to be very careful about analyzing its performance. It's usually not enough to just look at basic metrics defining the quality of a model purely on a metric, such as *accuracy*.\n", "The better approach is to find a metric that best describes the actual use case of the model.\n", "\n", "Let's dive into evaluating the model 🤿." ], "metadata": { "id": "FDg4g1vxvv4t" } }, { "cell_type": "markdown", "source": [ "The model has been uploaded to the Hub under [`deberta_v3_amazon_reviews`](https://huggingface.co/patrickvonplaten/deberta_v3_amazon_reviews) after training, so in a first step, let's download it from there again. If this notebook is run all at once the following cell will simply load the model from the cache." ], "metadata": { "id": "wvefwWDdwkl4" } }, { "cell_type": "code", "source": [ "from transformers import AutoModelForSequenceClassification\n", "\n", "model = AutoModelForSequenceClassification.from_pretrained(\"patrickvonplaten/deberta_v3_amazon_reviews\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "e6a77953f35e4e9d95a380c31115809e", "8ee7908b2de54c6ab1d5e04b1834f5ef", "0276f3a079954474a0d47abd3e98c215", "d85be80c14664ea69a361f3d94a0d246", "88d90dfccaa2481a926cfb0061ddd9f0", "543addda1f3744428934dee014f1f760", "6c986eaae73c43d7a268c2b6a9849691", "65848249df3a488aaa72963ed93fb7e1", "382bdf1be42a4076b961ff663646f321", "6c3174b1750c402288a13057714f4195", "bc2b0917ed68425db053692a10a1e3a0", "b6cf632bf0cc4798b19e895cd28df0c7", "c92923f5af844bedb2128a90a9b0ae5a", "ff350d87b3fa4ef3af82f1e810913147", "b220417f8e5f409c9d15d27e826710f2", "65f463342fde410a8daf118af2557b4b", "47f91be6ba0347d99cf7f795dc199083", "463d53eaa0aa418b9848f8241a56791d", "693a053d799642dc91ac3ea751cd0f4c", "179a46d90c37483e81706a87da825c3c", "d5266627c61446ab815031e24bf874d4", "ac39e7e4fa39452ab36bb82ef2527cbe" ] }, "id": "HShL8goGqMYF", "outputId": "41d3671f-9aa7-4024-bcb5-e53a9c14e073" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading: 0%| | 0.00/1.10k [00:00" ], "text/html": [ "\n", "

\n", " \n", " \n", " [625/625 00:21]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "{'test_accuracy': 0.608,\n", " 'test_loss': 0.9637690186500549,\n", " 'test_runtime': 21.9574,\n", " 'test_samples_per_second': 227.714,\n", " 'test_steps_per_second': 28.464}" ] }, "metadata": {}, "execution_count": 22 } ] }, { "cell_type": "markdown", "source": [ "The results are very similar to performance on the validation dataset, which is usually a good sign as it shows that the model didn't overfit the test dataset.\n", "\n", "However, 60% accuracy is far from being perfect on a 5-class classification problem, but do we need very high accuracy for all classes?\n", "\n", "Since we are mostly concerned with very negative customer feedback, let's maybe just focus on how well the model performs on classifying reviews of the most unsatisfied customers. Also, we decide to help the model a bit - all feedback classified as either **very unsatisfied** or **unsatisfied** will be handled by us - to catch close to 99% of the **very unsatisfied** messages. At the same time, we also measure how many **unsatisfied** messages we can answer this way and how much unnecessary work we do by answering messages of neutral, satisfied, and very satisfied customers.\n", "\n", "Great, let's write a new `compute_metrics` function." ], "metadata": { "id": "paCsFboh12TI" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "\n", "def compute_metrics(pred):\n", " pred_logits = pred.predictions\n", " pred_classes = np.argmax(pred_logits, axis=-1)\n", " labels = np.asarray(pred.label_ids)\n", "\n", " # First let's compute % of very unsatisfied messages we can catch\n", " very_unsatisfied_label_idx = (labels == 0)\n", " very_unsatisfied_pred = pred_classes[very_unsatisfied_label_idx]\n", "\n", " # now both 0 and 1 labels are 0 labels the rest is > 0\n", " very_unsatisfied_pred = very_unsatisfied_pred * (very_unsatisfied_pred - 1)\n", " \n", " # Let's count how many labels are 0 -> that's the \"very unsatisfied\"-accuracy\n", " true_positives = sum(very_unsatisfied_pred == 0) / len(very_unsatisfied_pred)\n", "\n", " # Second let's compute how many satisfied messages we unnecessarily reply to\n", " satisfied_label_idx = (labels > 1)\n", " satisfied_pred = pred_classes[satisfied_label_idx]\n", "\n", " # how many predictions are labeled as unsatisfied over all satisfied messages?\n", " false_positives = sum(satisfied_pred <= 1) / len(satisfied_pred)\n", "\n", " return {\"%_unsatisfied_replied\": round(true_positives, 2), \"%_satisfied_incorrectly_labels\": round(false_positives, 2)}" ], "metadata": { "id": "Wr0whRk1HsNt" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "We again instantiate the Trainer to easily run the evaluation." ], "metadata": { "id": "Z8FDaPF8LV5v" } }, { "cell_type": "code", "source": [ "trainer = Trainer(\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " model=model,\n", " tokenizer=tokenizer,\n", " data_collator=data_collator,\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "myFZM-YOKjra", "outputId": "b6a31d76-98b1-4d61-ec63-4bb8220d3c24" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/content/deberta_amazon_reviews_v1 is already a clone of https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1. Make sure you pull the latest changes with `repo.git_pull()`.\n" ] } ] }, { "cell_type": "markdown", "source": [ "And let's run the evaluation again with our new metric computation which is better suited for our use case." ], "metadata": { "id": "tG5ejdE8LbSx" } }, { "cell_type": "code", "source": [ "prediction_metrics = trainer.predict(tokenized_datasets[\"test\"]).metrics\n", "prediction_metrics" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 203 }, "id": "7oLwsTAxKnpu", "outputId": "bc80fe4d-6a9e-439d-dd28-776eadc6a686" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "***** Running Prediction *****\n", " Num examples = 5000\n", " Batch size = 8\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [625/625 03:06]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "{'test_%_satisfied_incorrectly_labels': 0.11733333333333333,\n", " 'test_%_unsatisfied_replied': 0.949,\n", " 'test_loss': 0.9637690186500549,\n", " 'test_runtime': 22.8964,\n", " 'test_samples_per_second': 218.375,\n", " 'test_steps_per_second': 27.297}" ] }, "metadata": {}, "execution_count": 28 } ] }, { "cell_type": "markdown", "source": [ "Cool! This already paints a pretty nice picture. We catch around 95% of **very unsatisfied** customers automatically at a cost of wasting our efforts on 10% of satisfied messages.\n", "\n", "Let's do some quick math. We receive daily around 10,000 messages for which we expect ca. 500 to be very negative. Instead of having to answer to all 10,000 messages, using this automatic filtering, we would only need to look into 500 + 0.12 * 10,000 = 1700 messages and only reply to 475 messages while incorrectly missing 5% of the messages. Pretty nice - a 83% reduction in human effort at missing only 5% of very unsatisfied customers!\n", "\n", "Obviously, the numbers don't represent the gained value of an actual use case, but we could come close to it with enough high-quality training data of your real-world example!" ], "metadata": { "id": "jZMdyrfYLnjN" } }, { "cell_type": "markdown", "source": [ "Let's save the results" ], "metadata": { "id": "MLPzAXRa2Qnp" } }, { "cell_type": "code", "source": [ "trainer.save_metrics(\"prediction\", prediction_metrics)" ], "metadata": { "id": "Jx-akRTgNVI5" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "and again upload everything on the Hub." ], "metadata": { "id": "8Q8yORv7NfTj" } }, { "cell_type": "code", "source": [ "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 294 }, "id": "dklENjigNjCV", "outputId": "c9c324b3-d571-4b93-d4aa-c52b863c97ba" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to deberta_amazon_reviews_v1\n", "Configuration saved in deberta_amazon_reviews_v1/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/added_tokens.json\n", "To https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1\n", " 599b891..ad77e6d main -> main\n", "\n", "Dropping the following result as it does not have all the necessary fields:\n", "{'task': {'name': 'Text Classification', 'type': 'text-classification'}}\n", "To https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1\n", " ad77e6d..13e5ddd main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "'https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1/commit/ad77e6da4fdbd07678a91ac57af6652ecb3d3b83'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 30 } ] }, { "cell_type": "markdown", "source": [ "The data is now saved [here](https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1/blob/main/prediction_results.json).\n", "\n", "That's it for today 😎. As a final step, it would also make a lot of sense to try the model out on actual real-world data. This can be done directly on the inference widget on [the model card](https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1):\n", "\n", "![example.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlIAAAHbCAIAAACP6uOpAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAgAElEQVR4Xuzdd1QUVxcA8Lt9l947UgQEGwgoRETFEFGxoGIJ9hKJopEYY0tiTL6oKWoiiSVqYoxdVIy9dw0WRFFRKUpVpLdl2TrfHy+O61BEZQHd+zscz+x9b97Mzuzu9c28mWFRFAVqKsUSPV2RegQhhBB6Z7CZAYQQQujdhWkPIYSQFsG0hxBCSItg2kMIIaRFMO0hhBDSIpj2EEIIaRFMewghhLQIpj2EEEJahMsMAFSKJcwQQgghpAFNf4OUWtJe068EQggh1DTwICdCCCEtgmkPIYSQFsG0hxBCSItg2kMIIaRFMO0hhBDSIpj2EEIIaRFMewghhLQIpj2EEEJaBNMeQgghLYJpDyGEkBbBtIcQQkiLYNpDCCGkRTDtIYQQ0iKY9hBCCGkRTHsIIYS0CKY9hBBCWgTTHkIIIS2CaQ8hhJAWwbSHEEJIi2DaQwghpEUw7SGEENIimPYQQghpEc2nPVkpM4IQQgg1E42nPfndVcwQQggh1Ew0m/ZUeeeVeReY0Zbq/v37LBarf//+jHivXr3s7e3z8/MZ8Zo2btzo7OwsFAojIiKYZVoj72lhUUkZM4oQQi2DZtOeLHEJM1QHKysroVCoHgkODmaxWGlpaerBBpo3bx6LxSosLGQWvJaCgoLS0lKZTMYseFFeXt6UKVMqKirmz58fHh7OLG5Jvv76axaLJRQKy8qe56e1a9eyntHV1e3YsePixYvlcjkprbmDaoq/fjvu0OnHTwtT07PiDp1OTLrPrIEQQs2Nyww0HlXeeVXeRbZVN2aB5mVnZzNDDcBisZghAABITEyUy+UikYhZ8KLU1FSFQjFgwICvv/6aWVY3pVLJ4XCYUQ3buXOnq6trampqXFzc+PHj1Yt69+4dGBhYUlJy+PDhL7/8MicnZ82aNeoV6nLo+IX2Hi7+vh3oSGb2k0PHL4T2DlSrhRBCzUyDvb2Gd/Ua4uHDh0OGDDE0NDQ1NR0/fjx9yDE3N3fIkCHGxsaGhoYjR45MTU01MzPbtm0bAJibm7NYrJycHABIS0sbOHCgvr6+ubl5VFSUWCwmsxcWFo4cOdLAwMDFxWXLli3PlvYCX19fHR2dyspKAIiOjmaz2deuXevdu7eOjo6rq+vRo0cB4PDhw++//z4AbNy4kcVi3blzBwCOHz/epUsXkUjk7Oy8cuVK0tq+fftYLNY///wzevRooVC4YsUKqGP1SM1t27ZNnjzZyMjIyspq6dKlz1YKqqurv/zyS0dHRz09vYCAgLNnzwJAZWXljBkzLCws9PX1Q0NDHz58SNenJSYmPnjw4LPPPjM3N9+xYwejNCgo6Msvv1y+fPn169cNDAzq2iYM8ddvt/dwcbC3BoA799JvJN0HAAd76/YeLmQaIYRaCE2lPdLVAwBV3sWqjXpVG/Wqj/RhVmqw7Oxsf3//gwcPjhgxIiQkZNOmTQEBAeXl5QAQGRl54MCBWbNmzZ079+7du6WlpcuXL2/VqhUALFu2bM2aNcbGxrm5uV27dr158+acOXNGjx69fv366dOnA4BCoejbt+/OnTu9vb379u37119/vbjY2lEUFRgY6OHhMW/evMzMzPHjx8vlcjc3t0mTJgFAly5dfvrpJ2tr66NHj/bt25fD4XzzzTfe3t7R0dGbNm2iG4mMjExJSVm0aFFoaGhdq0eMHj26pKRk4cKFPB5vwYIFSUlJAKBUKvv167d48WILC4vIyEixWExRFAn+8ccfo0ePnjt3bkJCQkhIiEKhoJsitm/fDgD9+vXr27fvqVOnCgoKGBUIHR0dkUikUCgoimKW1fDkaQHJeQDQ3qO1d0d3Mu1gb52Z/fh5PYQQanbUiyoqqxiR1yM5HCL+U1f9T/nkHLOSGktLS+aaAQBAamoqRVEkDaxcuZJUnjVrFgAsX76coigXFxdjY+P8/Hz11nx8fACgoKCAvJw5cyYA3L59Wy6Xy+XycePGCQQCqVR64MABAAgKClKpVBRF3b59GwBCQ0PVm6IoytPTEwAqKiropjZs2ECKQkJCAODBgwcURZ05cwYAJk2aRM/VqlWryspKuVwuk8mcnZ2DgoIoioqLiwOANm3aSCQSUrOu1SM1R48eTaqRrt7vv/9ON9KjRw+lUklK6eCqVatIO3/++ScAnDv3wmZXqVQODg5eXl4URcXGxgLA6tWrSRE5mDl79uzU1NQrV6589NFHABAWFkZKLS0tBQIB3Y66wuLSf68l0S8PHjufkpZJv/z3WlJhcSn9EiGEmpdGzu3RXT0a26ob26q7eqQmDoejfp7pyJEjjx//11G4evUqAISGhpKXAwYMWLFiBQl+9dVXkyZNcnBwGDZs2GeffdaxY8dnDTz377//AkCHDs9POwFAWVlZQkICAAwcOJCc1eNyG7o16CRtZmYGACUlJS8UA0gkklu3bgGAnp4eHVQfEtK/f3/6ZV2rRyZqXRaZJSIigs1+3l8nwaioqKioKDr49OlTehoALl++nJmZGRoampGR4e7uzuFwduzYMXXqVLrCsmXLli1bRqY/+OCDDRs20EUN5O3p8eRp4wwmQgihRtfQH/pXUvOsHr/TAkakJi6Xq/4jGxwcTKc9iqJAbcgJmSDBsWPH+vv7//bbbxs3btyxY8euXbsGDRr0XxPPkJonTpwwMjKig6ampiTPvfmIEtJ+zYizs/POnTvpoK6u7vMaaupavec11JDKjA2iXvT999+Ts4yEq6vr8xoA5GTe6tWrV69eTSIXLlzIycmxs7MjL0eNGhUWFqajo+Ph4eHk5ETPWA9TY8PzT2s/UgoAT54WqI9zQQih5qWRc3vCvkd1JlTqTKgkwzgb0tWrn5+fHwAcPnyYvDx48CAdLCsrc3Nzi4mJuXHjhkwm27hxIzzrY1VUVJD6nTt3BoC7d+/6qmGz2W3atAGAEydOkGp1neV6DTo6Om3bts3IyOBwOPQSPTw8mPUAoO7VY9ZTQ2bZtWsXnQWVSiUJxsfHq7djaGhIz6VUKmNjY21tbeOemTFjBkVRu3btouu0b98+PDy8X79+Dcx5hIO9TWb2EwBITc+Kv347NT0rNT0LADKznzjY2zBrI4RQ89FIb4+hIV29+s2ZM2fHjh2fffZZcnKyWCzevHmzi4vL5MmTc3NzO3XqFBAQEBQURM7M+fr6AkCXLl3OnTs3ZcqUrl27fvrpp2T22bNnJycnu7q6njt3LigoaNasWQMHDnR0dDxw4EBoaKidnZ16z+zNLVmyZPDgwcHBwVOnThWJRHv27Fm/fj056chQ1+ox66kJCwvr3LnzyZMne/To8d57750+fXrQoEFz58718/Pbt29f3759g4OD79+/X1ZWpp7Szpw58/Tp02nTpoWFhZFIp06dfv311+3bt9e/uJfy7uh+6PgFAHBt3cq1dSsSzMx+cudeGl7AgBBqUTSe9t68qwcA9vb2//777+zZs7ds2cLlciMiIpYtW2ZgYKCrq/vtt9/+9ttvhw8fNjc3nz179ty5cwFg3rx59+/fP3PmzK1btwYMGODr6xsfHz937txdu3axWCxvb29yIo3P5x88eHDq1KknT550cHDYvHkzGcfRKAYNGnTkyJFvv/32559/NjY27t69u76+PrMSAAA4ODjUunr14PF4x48fnz9/flxc3M2bN729vQMDA3k83okTJ7788svdu3efP3++TZs2ZHApjRzhHDhwIB0hw1uuX7/+ercFUBfaO/BG0v0bSfcc7W2kMvmTp4UO9taY8xBCLQ2LcV6qUizR033JddkNV32kD7/TgjdPe+gt8uRpIZ/PMzV+fnAVIYRaDs329jhWgZjztI21pRkzhBBCLYZme3sgKwX+89GJCCGEUPOqb7hgI8CchxBCqCXRcNpDCCGEWhJMewghhLQIpj2EEEJaBNMeQgghLYJpDyGEkBbBtIcQQkiLYNpDCCGkRTDtIYQQ0iKY9hBCCGkRTHsIIYS0CKY9hNDbh3EzYfS2aAk7TrNPYEAIocYllcoVSiWbBUpV8/+AolfFYbNVFMXlsAUCPrOsqWDaQwi9HVQqVZVEKhDwRDw+m41Hqt5WKpVKpVJViiU6IkGz7MdmWCRCCL2GKolURyTgcbnN8luJGgubzeZyuToiQZVEyixrEvjpQQi9BaRSuVDAw4T3zmCz2QI+TyqVMws0Dz9DCKG3gEKpxJz3jmFz2EqlkhnVPPwYIYRaOoqi2CzAtPeO4bDZLBar6cd24scIIdTSsVgsHLf5TlKqVCwWixnVsKZIe0XFpeovJZJq9ZcIIYRQk9F42ktNy/h68crUtIyc3Lyi4tJbd+5//uUPmPkQQgg1C+Zx1UqxRE9XpB55QxJJ9a079z3bu//+5w6RSDj2w7CU9AzP9u7qdX5Z9Vd5RaVEUi2Tyw0N9AFgeuQYE2ND9To13bpz387GytTESD1YWVm1d/+xB6mPlCpVx/Zthg4MEQj40XO+W/rNbJFIqF6zgVat2+LX2cu3U/uMrNxNW/fyeLyJY8Jj1mz67JNJjEXX5cz5+KDu/gAgk8kXLYlp+IwIIdqr/jRt3bn/TnIK/a1vyE/K63mUmbNj98H5n33MLEAN8Kq7tVFo/HJ1kUjo39kLAMZ8GKYjEhYVl165douR9qKjxgPAhcvXU9MzJo4JVy+qR/zVm726+6unEIVCGbNmk0+n9qNGDAKAazeSVG98snTIwBAjIwMAuJl0z6tj20Gh7wPARxNGNPwrdOjoWZL2+HzeK82IEHoTfT7o3qNbF2YUNYac3LzDx8+NGTlIvTshkVRv3vFPv9497Gyt1Oq2OBpPe8SZ8/Gp6ZlTJoyQSKqrqiQSSXX9fS+5QrF739Hs7CcA0K2rb1e/TttiDxjo6/XvE3Qz6d75y9fsbKwepD7MLygyMzWeOjmCzHXz9j2hUBASHEheknSr7vqN2xcuX6+qkgiEgkljhxkbGTxIffTPwZNcLkdXV2fimPBKcdWmrXtVKhWLxfpw2AArS7O9+4916+rL5XCvXLsJLFZZWfnYiMErV/1Fuo9pD7Pi9h9XKBU6ItHEseElJWUHj56pqBBLqqURw/q7uTr9suqvaql02coN7m6t+/cNomcsLinbHnuguKSMy+X07xPUoV0bAJi38KfgoIDM7NzHT/JD+wR5e7ZlrD9C6PUolMofVqwLD+vTxtUpNu6Ivp5uSHD3g0dOP0h7VFlZ5WBvM27UEDabdfzUxcLiErlcUVBQZGJi5O3Z7sr1WwWFxb3f79bFp+OjzJxdew5bW1tUV0vLyyuGhvVxcrBTX0pKWsaBw6cAQEdHNHrEIH193dPn/r2WcJvD5bi2diT/aX435DzOS7pz/5fVpdHTxpEfc4mk+pfVm3If53Vs30Yb015qeqadjeXf2/dJJNWkJ2dn899WcHVxjHYZr1a3dgePnLG1tvwwvL9SqVq2ckM7D5fBA3p/v3xtZ+8Oh46d/Wj8CAtzk9T0jCEDeru6ONJzZec8ae3s8LyVGqytLKKmjObzeXEHTpw+9+/QQSGHj50d0K+XR5vWFRViHo97KT7BydF+UOj7YrFEJBLQM7bzcOns01FHJOzzQXc6WFEhXr9xx+Rxw11dHItLyvT1dOVyRcSwAUZGBneSU/ceOD5vVuTUyRGfLVg6e+Zkei5i/cadXf29A7v6FhaV/LRyw+xPJpmbmVRXS83NTYKDuqY/ytoRexDTHkJv4uiJ8+cuXgUAv85eIe93GzV84Jad/0waNyw1LWPOrCksFrRv69a/by8AWLZyw917KeR/n0+fFkZNGc3jcRd+t9LczCRy4sic3Lx1G3d08ekIAOUVldOmjNLX001Ny/h7W9zX82fQi6uoEO/df2zm1HEikTDh5t3Dx8+Fh/X559Cpn76by+fzKirEdM13gH9nr9S0jCvXb/2yelP0tHEAQHKen69nzf5GS9P4aa+ouHTl6r9GjxwUGtKzqOS/MZyuLo7q+emlbibdMzYyuH33AQCoKKq0rMLB3mbwgN4rftvYs1sXC3MT5gwA0IB7e1tamt26fT81LSPncZ6+ni4A+Hfx2r3vaPeAzl18PQGgY3v3jZt3s1gQ4O+j+7IjzvdS0q0szcn7IocuTYwNU9Mzz1y4kl9QVFhYwqhPKyktf/K0oNt7PgBgZmrs7uZ8JzmFHAh1a+0IAGamJqXlFS/OhBB6NYyDnI4Otm1cnVau+mvSuOFcDgcALMxNL1y+lpmVK66qKnj2hbW3s+bzeQBgamLU2qkVAJiZmZSW/fd91NPTIT8dLq0di0vKKiurSBwA7j1Ir6gQ/7l5NwAolSpDQ30Oh93Fp+Pq9Vt7Bvp1bN+GrvluGPNhGACQzAcAJOeRYAvX+GnP1MRo5tRxdrZWKekZxWqXLpw5H0+f52uIQf2DGQcQ8p4W6OnqSKrrvI2bva31hcvXmFE1a9Zvc3VxDA3pmZL26GpCEgC816VT+7Zul68kfr/89xkfj2llZ73g86mJt5J/+31zWP8PPDu8cA6SgaIoLveFDXj05IXHj5+G9ukpEgkXLYlRL3olLNbLUzhC6FU9zS/U1dWRSqUAUFEpXrl6U9/ePUYMDd2z/5iKUjFrP8Oq7b/UCqVCqVQyrjmzsbaImjJaPTJqxMC8p4Wnzl4+dfbyp9MnstlNfY2aRtGZDwDelpwHGrqAwdXFUSQS5uTm5eTm0cGUtIyc3LzUtIyVqze99AIGzw7uR0+cVyiUAFBcUgYAT/OLrt24/fnMyXeSUzIycwFAJBJWiJ//VwsAvDp6VIqrTp65rFJRKpXq4r8JWdmPAQBYLApAJpM/fJQV3LOrnp5uRlYumeXho2x9Pd2Q97uZm5nkPn6alfMEKOji09G/S6fU9Ay1tmvh7uacmZVLVqaouFQmkyfdud+9WxdLC7OHj7JJHT6fx+Fw1P9LCADGRgbWluYX/00AgMKikvspD9u3dVOvgBBqdFeu3dLT1f1o/IjYuCOSamlW9mMDfT0fr3YqlepJXgGzdh3EVRJyuPL0uXhnp1a6uiIWsEhO9GjT+lFmTmpaBgBUS2XiKolcocjKfmxlaTYiPPRJXoGk+iW/e2+jMR+G9Qz06xno97bkPNBEb6+ouPTrxSvJQU46KJFUh4f1MTUxysnNEwqfnzOry4B+vfb+c+y7H1fp6epYW1lEDB+4PfbAwND3hULBiKGhW3f+M/ezyKBA/9i4I2cvXImeNo7ctYjH486cNm73vqMLv/tFqVS29XD19e4AAK2dWh05fm7ooJBuXX2//f43K0szZ0d7AFCpVDdv39t74DiXw7EwN23X1vVawu3YuCMcNpvH45LhoPUwNNCfODZ82679FICenk7EsAG9uvv/tXWPhZlpOw9XAZ8PACwW64OggGUxG9q6uwwf0o+ed/L44dtjD5y9cIXDZkcMH2BuVvthW4TQazt8/NyZ8/FkOjys76FjZ2ZNn2hkZNDZp+Pef46Fh/U5efby4p/WWFmaWVmavThrndgs1rbYA0/zCw309caPGgIAZmbGVVWSm0n3vDp6TJkwcu/+4ypKJeDzB/UPNjM1PnnmcklZOQugf99eujovOW/ylgoP68MMtWwauW7v0LGz5GBmcXEpOfW1efu+nMd58z/7OOnOA9fWDvUP40QIIYZG+Wl6Q48yc3bsPjT/s0hmAXpdzbJbNXKQMzSkp6mJ0e59Rw8dO0si/UJ6jhkZRgZEqR/5RAihtwrzJB9662ikt0ereX1ezQhCCL1U4/40oRaiWXarRnp7tJoZrmYEIYQQajKaTXsIIfTmKIri4MP23kUcNrvmxSGahp8khFBLx2KxVBSlUtV5aR16GylVKoqi3s3n7SGE0BvicNhKJaa9d4pKpeJwOMyo5mHaQwi9BYQCvlQmxw7fO0OlUkmlcoGAxyzQvMa/XB0hhDRBRySokkgFAh6bzcZTfW8vpUqlUqqkMrmO2u3+mxKmPYTQ24HNZuvpiqRSuVwmY7FAqWrqoRDozZExLBwOp+mvW6Bh2kMIvU0EAh4Ar1mGQqA31xJ2HB4oQAi9fZr9pxO9npaw4zDtIYQQ0iKY9hBCCGkRTHsIIYS0CKY9hBBCWgTTHkIIIS2CaQ8hhJAWwbSHEEJIi2DaQwghpEUw7SGEENIimPYa3xffrCgtLWdGa4i/dnPhd7988c2KX1b9RQelUtnnX/xwLeH283qv63L8ja079zOjCCGk3fCenM3myvVbY0aGubo4qgf5fH7P7n72dtbqQYQQQo1FI729qwlJn3z+v4oKMXmpUqnmLfzpwJHTL9aq06p1W+4kpzCjAABw6879ouJSZlRN9JzvpFIZM9oiKRVKZgiAxYLQkJ5WlmbMAoQQQo1BU709I0P9qwlJ7/d8DwDu3kvj8xvnWYLxV2/26u5vamLELGhs5RWVi39c/cP/5pCXs7/4ftGCT/R0dQ4cOV0priovrywqLrWyNBvzYRiPywWAawm39x8+JVcoQkN6qrdz917aP4dOVlaKHVrZjhoxUE9Xp0pSvfjH1QBQKa7a8Hcsl8MxNzOJjhr/NL8oZs0mAKioFM+J/sjO1oq0sOLXP9u4OT/KyCkuKfXz9QwJDqyrZQAQV0k2b9+Xlp5pZ2vl3qb1fyuBEELoGU2lPY82LtcTb5O0dzXhVvu2bUg8K/vxwaNnKirEkmppxLD+bq5OSqVqy85/CgqKKAqCg7p28mxLN3Li9KXsnCcTxoSTe3bv3X/8QerD/IIiM1PjqZMjUtIy4g4cl8sVero6Hw7rb2nxvIdEUdTvf+5o6+7SPaBzSlrGgcOnAEBHRzR6xCB9fd1V67a0dmr1+El+YVGJq4vj4AEfiMWSJcvWREWOtrGyoBupS2lp+ceTPmSxWBs377kcf6NHty6lpeU79xya+lGEs2Oru/dSxVUSUrOgsHjH7oMzPh5rYW5y9sKVTVvjoqaM0hEJF389CwBW/PrngL696IOclhamJE6Sojo+jzc9crRMJl+ybE2Hdm1srC1qbRkA9h8+JeDzlyz6TK5QbNnxD8mFCCGEaBo5yAkAPB7X1MQoO+eJRFJdVl5pZvpf/0xPTzdi2IC5s6aEh/XZe+A4AKQ/zCwoLJ49c/KsGRPdXJ3oFu4kpybduT8mIox+TsWQgb0tLcxGDg2dOjmirLxy/V87R48Y9OWcad0DOq/ZsF2l9szJfw6eNDM17h7QuaJCvHf/sWkfjfrsk0ldfD0PHz9HKoirJBPGhH86fcKlfxMqK6tEImFwUICJcYM6kU4OduTZGW3cnHNy8wAg+UF6W3eX1k6tWCxo39ZVRyQkNa8mJPl19rQwNwGAHt38HmZkVVZWqbXUUM5O9gDA5/OcHO1zHudB3S3fTU7t27sHn8/T1RG193B9sRmEEEIa6+2pVCr/zl5XE5KsLM19O7WXyeUkbmJsmJqeeebClfyCosLCEgCwt7OmVNTf2/cFvufr5GhHqj3JKzh68nzUlNHkEGJND1IeOtjb2NpYAoC3V7vd+44+fvKUHBi8mpB08d+Epd/MBoB7D9IrKsR/bt4NAEqlytBQn8zu0tqBxQIej2tgoFdWXqGnpxPU3e956w3D5XLkCgUAVFSKRTr/pTpQe6BUSUnZ/ZSHt+8+IC+FAgFZFl3zVXE5HIVcAXW3XFEp1nm2Ji3huVYIIdTS1J5UGkVbd9eDR888ySuYMGbov1cTSfDoyQuPHz8N7dNTJBIuWhIDACKRcPbMyalpj/45dNKxlW3YgA8A4OTZyyHvBx44fPqTqePq/vWuveDfq4k+ndofP3UxtE9PALCxtoiaMppZSQ1FPe8m0rhcrkyuoF/WWoemr6ebm5tHv6QrGxrq9wz0Cw7qShc1lrpa1tfTraqqNtDXg5etM0IIaSdNHeQEADab1cbVWSQS6OqI6GDSnfvdu3WxtDB7+CibRIqKS8vKK9xcnQaFBic/SCfBwQM+6P1+N6FQcCk+gZ4XAEQiYYW4CgDauDlnZufmPn4KADdu3uXz+TbWlqTOpLHDBg/sfTXh1uO8fI82rR9l5qSmZQBAtVRGn3VjoCjqwuXr6kNAdURCXR1R4q1kALh7L02ulgJrcndzTn6Q9igzBwCS76dVPVuKr3eHf68mkqGnD1Ifrf1ju/pcb6Kultt5uB49eV4uV0iqpXfupb4wD0IIIY329gCgzwfdGcP0e3X3/2vrHgsz03YergI+HwDKyysPHj1DjhYOG9yXVCNjMYYN7rs85o/2bd2Mnh2cDAr0j407cvbClehp4yaPG755xz4ypOXjSR+y2f91/vR0dQQC/uABvbfu2P/ZJ5OmTBi5d/9xFaUS8PmD+ge3dmpFqqkTV0kOHz/X2qmVjfXzIS2jRw7aHntwx55DIe93q38kqomx4fDB/f74O1apVPX9oLvus4Ek1pbmA0PfX7thW2WVxNjIYPiQfiT+9eIYmUwmrpKs+2snl8MJeM+nf5+gp/lFv6zaCADiKsmva/9ms9nhg/v6eLWjl6KurpYH9Ou1efu++V8vs7O1cndzrv9iD4QQ0kIsxqGwSrFET/d55wwhhBB6l2jwICdCCCHU0mDaQwghpEUw7SGEENIimPYQQghpEUx7CCGEtAimPYQQQloE0x5CCCEtgmkPIYSQFmmitIf3h0QIIW3WcrKAZm9OJhZLpDK5XKFgASiUKmYxQggh7cDlciiK4nG5Aj5Pt1nvBaapm5OpKKq4uIzH47IAWGxg1/0YBYQQQtpARVGUCigAuUJpamzQXA9H00hvT0VRRcVlQgGPzQLWM8xKCCGEtAmboig2RVEUm8UpLC4zMzFsltSgkbRXXFwmFPC4HDaLxWKz2c3yxhBCCLU0FEWpVCo2mxICFJWUm5kYMmtoXuOnPbFYwuNxuRw2DfBJ3wghpPXIOTUWi6VSqbhcUKlAXCVRfyBr02j8tCeVyblcNpvN5nA46l09zHwIIaS1KIpipAMWWymVyps+7TX+BQxyhfSRW4oAACAASURBVIL7LOGpY9ZDCCGkNehcQLIDm83mcjjkAeNNrJHTHkVRLAAul0O/N2YNhBBCWozODlwuh1XjaoIm0Mhpj8VikevzMOEhhBCqC8kRCqWy6ZNFI6c9ounfBkIIobdLc2UKjaQ9aL73gxBCqOVrxhyhqbSHEEIItUCY9hBCCGkRTHsIIYS0CKa9/3h5eW3ZsgUASktLWSxWTk4Os0a9NmzY4OXlRaY7deq0bt26F8ubQmxsrKurq1KpZBa8gdGjR0dHR6tHXm/7MNBbGzWQJnauQqEIDw83MjL68MMPmWWoDtHR0ZMnT2ZGG4MmdvFraJQveAvXbGlv2bJlwcHBzOg7Yfbs2T179mRGNa9Lly7z58/ncDjMAjU//vjj9OnTmVHNu3r1avv27ZnRJtRcb7yxNGTnvqoTJ06cO3cuOzt78+bNzDLU5DSxixuoX79+u3fvZkbfXY1/c7IWQqVSkduBNg31UUmjRo1SK2k6Dg4OEydOZEZfVFJSwgzVq67RVnXF61Jzua/awhuquQIa8oYfvLpmb8jOfVX5+fm2trb6+vrMgsZT19t5q2noo6uJXdxANb8dGnqPLUTL+kTu3bs3ICBAX1/fycmJ/t9HdHT03LlzJ06c6OHh4ezsnJiY+MMPP/j4+Dg4OOzdu/fFBsDLy2v//v3dunUjfYukpKTAwEBTU1NfX9+rV68CgFQqXbZsmbu7u76+vq+v7927dxkt0DZu3NixY0f65YIFCyZMmKBWDvn5+UOGDDEwMGjfvv358+fpOH0ELycnJzg42Nra2sPDY8OGDQCQl5f3ySef2NraGhkZDR48uKysDABKS0tFItGyZcu6d+/u7u4eHh5OPoV1xQEgMTGxR48eenp6bm5uf/31Fwnu27fPxcWFTHt5eW3cuHHgwIFubm6+vr6PHj0CgKioqJiYmL/++svOzm716tUAEBsb6+7ubmFhMXDgwKdPn5J5r1275ufnp6+v36tXr9zcXBJkOHjwoIeHh6Gh4dChQ4uKitLT0/l8Pt3C8ePHnZ2d6cpbtmz58MMPHzx4YGdnN2bMGBIsKioKCwvT0dGhVw9q21/q7t6927t3b0NDQ1dX102bNgHAb7/95u/vT0qrq6tZLNb9+/cB4OzZsx06dLC2tn7vvfcuX75c843XtQF79+793Xff+fj4WFpabtq06eDBg4GBgXZ2dgsWLCB1lErlokWLrK2t7ezsZs2aJZfLyYwDBw5cvny5s7Mz2dEEYzVIsOY2Z8z+4Ycfzpo1i26kc+fOf//9t/rOValUS5cudXBwMDExGTRoUEFBQa3NqtuxY0fbtm319PS6du0aHx8PAMePH58xY8bt27eNjIyOHTtGqtX1mS8sLBw1apSRkZGlpeW8efNkMhkA3Llzh8ViVVdXk8r+/v5r166FGm+Hbo0UeXl5GRgYdOnS5dq1a/AGO2Lfvn3+/v7Tp0/v1q2bq6vrggULVCoVaXDo0KEmJiYWFhbz589Xb/CHH37w9/e3s7P77rvvoO432xBz5swxMjJycHA4cuQIiTR8uQBQXFw8dOhQR0dHoVDIYrFYLFZ8fPxLv78AcPPmTT8/P0dHR3KXE6FQSOKEmZnZ0aNHyfS8efNGjhxJpr28vBYuXBgcHOzl5eXv75+UlPR8HgBvb+9r165FRkba2dklJiaSYGJiore3t56eXnh4ONnd8LLP2NuEelFFZRUj8qpynxTIZDKlUqlSqZhlan766af333+fEdywYUNCQoJCodi2bZuBgUFlZSVFUTNnzrSzs7t9+zZFUVOnTtXV1Y2NjaUoavPmzVZWVowWPD09PTw8MjMzKYp6+vSpubn5li1bVCrVqVOn7O3tq6qqFArFjz/+mJGRoVQqp06d2qtXL3rGzZs3UxRFUkt2drZYLDY0NLxx4wap4O7ufuzYsWfLoSiK8vf3/+CDD4qLi/Py8oYMGeLp6UnidFOTJk2KjIykKCovLy8+Pp6iqIyMjLVr1xYXF1dWVnp7e3/77bfUsyUuXbpUpVIplcrBgwdPmDChnvjjx4+NjY1//vlnqVR66dIlY2Pj3bt3UxQVFxfXunVreh169uxZWFhIUdTw4cOnTJlC4uPGjYuKiiLTJ06csLCwSExMVKlU3377bVhYGEVRT548MTIymj9/vkwmu3XrloeHx8yZM0l9gqzVRx999OTJk/z8/G7duo0bN46iqODg4BUrVpA6kZGR8+fPV59r48aN7dq1o196enq6ubldvXq1rKwsKCiIrF6t+4ueJS8vz8zMbO7cuVKpNDU1lWzPX3/91c/Pj1SQSCQAcO/ePYqiWrduvW3bNoqiEhMTMzIyqBffeD0bkMfj7dq1i6KonTt3crnczz//XKFQPHz4kMvlkpa//PLLgICA/Pz8qqqqfv36/fLLL2RGHR2db775hrRPq7katW5zxuwHDx60t7cnX5+MjAyRSFReXq6+c7/66isbG5vk5GSpVLpnzx6FQlFrs7QjR47o6+ufPn1aJpOtX79eX1//0aNHFEVt3LiR/tASdX3m/f39R4wYUVZWlp2d7ePjM336dIqibt++DQASiYRU9vPzW7NmDVXj7dBOnDjB5/P37NmjVCpPnz79+PHjN9kRcXFxurq6x48fpyiqqKjIyclp06ZNFEUdP3780KFDEokkJSVFT0/v/PnzdIPbt2+nKOrOnTscDicnJ6euN1u/mTNnGhgYbN26VSwWz5s3z83NjcQbvlyKosaPHz9mzBiVSvXo0SNLS8vk5GSFQvHS769KpXJyclq/fj1FUXv27GnXrl1paSmpT5iamh45coRMz507d8SIEWTa09Nz6NChFRUVFEWtWLHC1dVVoVDQc1EU5eDgQH5UqWdf8NDQ0Ozs7IcPH5qbm5PPcP2fsVdFftNkMlnukwJmmea1rLRHpKSkxMbGAgD5RM6cOXPSpEmkaNu2bfQnIzMzEwCKi4vpGSmK8vT0/PXXX8n08uXL+/TpQxcFBASQzyJFUXK5PCEh4auvvjIwMCCRmmmPoqioqCjyo3/37l0LCwv1z8qNGzfYbHZeXh55uX79+pppb968eb6+viRhq6uoqDh//nxYWNjAgQOpF5dIUdSxY8eMjY3riS9btqxjx47/tUVRX331Vc+ePakaaY+sA0VRMTEx3bt3J9Pqv/4DBgz4/vvvyXR1dbVQKFQoFCtWrFDPT6NGjao17dFrdeDAAbJWsbGxZAsolUorK6ukpCT1uWqmPXr1fv75Z7J69ewvUtqhQwf6JVFX2vP39x83bhy9d6gX33hDNuDjx4/V36aTk9PevXtVKpWhoSHJuBRFnThxgvy3KS4ujvHxIGquRq3bnDG7XC43Nze/dOkSRVE//fTTsGHDKLV1I+tA0gOt1mbp0v79+3/yySf0y8DAwEWLFlG1pT2qts/89evXWSxWUVERqXDq1CmhUFhdXV1P2qt1awwYMIDkS9pr7whGHYqi5s+fP3jwYPplXl7ekSNHXF1dyX/FGJVNTExOnz5N1fZm6Tp1Uf85SkxMZLPZ6nM1cLmenp5bt24lwffeey8uLo5RudbvL/nq5ebmUhQllUoBoKSkhNQh6kl7dGsSiYTNZt+6dYu8JGqmPXqDDxo0aOHChdTLPmOvqnnTXss6yLlr1y5vb+8lS5bk5eXxeDyxWMyowOPx6Gk+nw8AZPerMzIyIhOZmZnXr1/3eiYnJ6e8vFylUi1cuLBLly5///23TCaruQh1U6ZM2b59O/lhGjZsmPrZ5tzcXENDQ0tLS7XqTIsXLx41atTgwYN79Ohx69YtACgqKvrwww/79u174sQJDodT69KNjY1LSkrI0bNa41lZWa6urnTc1dU1OztbrSITj8eruZUAIDMzc+3atWTj+Pn5GRsbl5eX5+bmurm5MavWzcrKiqzVoEGD8vLykpKS/v33X1NT0w4dOjCr1kEoFJLVq3V/0dUyMzPbtGnzfLZ6HT582NjY2NPTc+zYsUVFRYzShmxA9U8aAPD5fKlUWlpaWlZWNnnyZLKG0dHRKpWKVNDX1685GKHmatS6zRmzc7ncESNGkP/57d69m3GqmKyDu7u7erCuZomGvF9azc98VlaWiYmJiYkJqeDq6lpdXU2OrNal1q2RmZnJWO2GrFitO0I9QhgbG+fn5wPAzZs3e/ToMWXKlKSkJKFQWOtXjP5G1HyzzKr1EgqFKpWKfFVfabnBwcG7d+9WKBTJyckPHjzwejYIvFb0XEZGRj4+Ptu2bQOA7du3u7u7GxoaMmu/jFAoFAqFZFs1hPrXs57P2NulBQ1pycjIGDNmTHJycuvWrQFg9uzZzBqvyN7e3svL68SJE+rBzZs37969OyEhQSQSxcfHL1u2TL2UoWPHjk5OTidPnty/f/+KFSvUi2xtbUtLS0tLS+ksWxObzY6Ojp45c+bKlSuDgoIKCgrmzJkjFAovXLgAAN9///3JkyeZ8wDcuXPHxsaG8W1Xj7dq1ercuXN0PC0tzd7eXq1ifdTHF9jb27/33ntffPGFWjnY2tqePn1aPVK/5ORkem3Hjx9PjofUHBDfkHENte4vmp2dXc0VEwgE9I8gnYEAgBw6W7JkyYQJEyZOnPjPP/+or8Brb0BDQ0N9ff21a9cGBAQwy2pTczVq3eY1jR49esSIEZ9//nlqamrfvn3ViwwNDfX09O7fv9+uXTs6WH+zrVq1SktLo1+mpaX16tVLrfwFNT/zrVq1Ki4uLi4uJpkvLS1NKBSam5uT7rVUKhUKhfDi9q+VnZ0dOfNKe+0dUdOdO3ccHBwAYOjQoQsXLhw3bhwA1PzAMNR8s6/tlZZL9m+PHj0oitqzZ4+joyOzRh3Gjh27efPmXbt2mZubnzhxgjHwpK6vg7pHjx5VVVWRbUVr4Nezns/Y2+Xl77bJVFZWAoBCoaiqqlq2bBl9HvW1RUREJCQkrFmzRi6XV1dXX7p0CQDKysrIfxjz8/NjYmLoylwul3xiuFwuqHUiIyMjf/755/z8/K5du9KVAaBjx47u7u6ff/55ZWXlgwcP/vjjD/VS4qeffkpJSWGxWO3atSOX45SVlfF4PJlMdvv27Z07d6pX/vvvv8Vi8d27d7/99tvIyMh64hEREVlZWTExMXK5/MqVK7/99lvDh+aTs6Tk8EJUVNSKFSvOnTtHUVRhYSHpjw4ePPju3bt//PGHVCo9efLkqVOnmE0AAMC2bdvEYvG9e/e++eYbem0/+uij2NjY/fv30+fSaXZ2dllZWcXFxfXs1lr3l3ppVlbW4sWLpVJpVlYW2Xpt2rRJTk6+detWYWHh1KlTyb4rLy9fsmRJcXGxSCRycXFRKBTw4ht/7Q3IZrOnTZsWHR2dnp4OACkpKfVc3lTratS6zWvy8/Pj8/mLFi0aMmQIOapBY7PZkZGRCxYsuHfvnkwm27lzZ15eXv3NRkVFbdy48dy5cwqF4s8//0xMTCS/znVhfOZ9fHz8/PxmzJhRUVGRm5s7d+7cyZMnCwQCOzs7HR2dnTt3ki9scnIys6EXTZ069c8//zx8+LBSqbxy5cqFCxdee0cQT58+3b9/v0wmO3DgwM6dOz/66CMAIMPEFArFgQMHrl+/zpynBsabvXTp0rhx40jOWLt27Q8//AAABQUFI0aMyMjIeHHWF7zScleuXDl9+vRLly5dvnz5lS52+vnnn//888+rV68eOnTIzs6OUdqmTZu4uLiqqqqjR49u3bpVvWjfvn1PnjwpKiqKiooKCgpS72QDABnMQlFUzYNMtPo/Y2+XFpT22rdvHxUV1blz544dO+rr63fq1IlZ4xXZ2NicPn16165dZmZm7u7u5OD12LFjLS0t7ezsQkNDIyIi6Mr9+/ePjo6urq7W09Pr1asXfUXqiBEjrly5Mnz4cMZ/rDgczt69e1NSUqysrMaMGTN27Fj1UgCgKIoMtDM1NZ0+ffrWrVs5HM5XX311+fJlMzOzhQsXMgYrl5aWenh4BAYGDh8+nB6uVmvc2tr6xIkTu3btMjIyGjVq1I8//jh06NDnDdVrypQpVVVVJiYm33//fd++fdesWRMVFWVsbNy9e/cbN24AgKOj4+7du5cvX25iYrJu3Tr1TUQzNDQUCATt2rULCAgIDw+n17Z169bOzs4GBgakv66uZ8+effr0cXJyGjhwIKOIVuv+okttbW3JqAEzM7OQkJCKigoA6N69+5gxY7p16xYQEDBhwgTyQ8BmsysqKjp06GBubn7u3Lmff/4ZXnzjb7IBv/vuu/79+/fo0cPU1HTChAn1pL1aV6PWbV6r0aNHr1+/vtbtv3Tp0rCwsKCgIBsbm9jYWLlcXn+zffr0Wb16dWRkpKGh4e+//3706NH6uxc1P/P79+9XKpX29vadOnXq3r378uXLAUAkEq1YseKLL75wcHDg8/nDhg17oZUa+vfvv2rVqujoaCMjo1mzZrFYrDfZEQBgYmKyf/9+KyurTz75ZP369SR/xMTEzJs3z9ra+uzZs+Hh4cx5amC82UePHl28eJEMT7158yYZblpcXHzx4sX6xy6+0nL79+8/Z84cPT09fX19W1vbiRMn1nrktqb+/fv7+/vr6ekZGBi4uLgweqhLliy5dOmSubn5nj17vv76a/UiQ0PDkJAQBwcHgUDA+A83AHz99debN282MzNT73wz1P8Ze8v8d47vmSYb0vK2qK6utrKyYpwBblzqZ6obEm/JwsLCYmJimFH0VmmCz/ybi4uLc3V1ZUZfXdO/Wblc3qZNmzt37pCXmZmZpqamZEhq/e7du9e+fXsyvl2pVB4+fJjNZldXVzPr1aA+gqaFaN4hLS3o3F4LpFKp/ve//3l7e6tf36MhdR2Lryve0lAUdfz48cuXL+Ndx95qTfmZf0Nv/tVoljcrk8kyMjLolSfTbdu2fbFWLbKzs6VSKTkOyWKxbt++3bFjR4FAwKxXmzffVu8STHt1un//vq+vr7e3d82L4lFNzs7ObDb7wIEDurq6zDL0ltCqz3xzvVkdHZ2tW7eSq105HI6lpeXJkydtbW2Z9WoIDg4ePXp0QEAAGUHq6+tL32cAvRIWpXb6BAAqxRI9XZF65FU9zis0NzXkcDjk7gPMYoQQQtqNHGxUKpUFRWU2VmbMYg1rQUNaEEIIIU3DtIcQQkiLYNpDCCGkRTDtIYQQ0iKY9hBCCGkRTHsIIYS0CKY9hBBCWqTFpb3o6Gj6fpgaUlpaymKxyN0UY2NjXV1dyX2ivby8duzYwazdYOrNqlNfRD06deq0bt06ZvRdof7k6IbEEUJIQ1pc2mtiXbp0mT9//qs+Z4vWr1+/3bt3M6MvauAiZs+e/Ur3Ym9RGrIdXg9FUSwWizywRn0aAHr06LF27doXaiOE0Mu0xJuTNeW9XRwcHBhPQngl5G7R6mqufAMXwXia6Nul5naoqeaWaQgWi8Xj8cjDd9SnAUAgEDAeyoMQQi/VbL296OjocePGDR06tEuXLm3btmU8C2POnDlGRkYODg5HjhwhkcLCwlGjRhkZGVlaWs6bN49+bJuXl9fGjRsHDhzo5ubm6+v76NEjEk9KSgoMDDQ1NfX19b169SoJ1lTrQbbs7GxbW1vyvNN62vH29r527VpkZCR5WhUJJiYment76+nphYeHk5VUX8SqVascHR3t7OwGDx78+PFjuikA8PLyIjdxzsnJCQ4Otra29vDw2LBhg3qd3377zd/fn0xXV1fTXZ/o6Oh58+ZFRkZ6eno6OTnRGy0xMXHo0KEmJiYWFhbz58+n29m3b5+Xl5eBgUGXLl3Io1Xu3r3bu3dvQ0NDV1fXTZs2AUBxcfGoUaNMTU1dXFz++usvMqOXl9fChQuDg4O9vLz8/f2TkpKgxnaoa6EAsHTpUisrK2tr63nz5tU86qtUKhctWmRtbW1nZzdr1iz60V8CgYA8yLSeaYQQaqhnj2L4T5M9eGjmzJkeHh5paWkURV27dk0gEKSnp5O4gYHB1q1bxWLxvHnz3NzcSH1/f/8RI0aUlZVlZ2f7+PhMnz6dxD09PXv27FlYWEhR1PDhw6dMmUJR1NOnT83Nzbds2aJSqU6dOmVvb19V9fx9ka5JdnY2RVFxcXGtW7emm9q+fXtVVZW3t/dvv/320nYoinJwcCCPhaOeNRsaGpqdnf3w4UNzc3PyqHF6EVlZWXw+PzMzU6VSHT16VCqVqjfl6em5efNmiqImTZoUGRlJUVReXl58fLx6nV9//dXPz49Mk2db37t3j6KomTNn2trakoenrFq1it5o5AF1EokkJSVFT0/v/PnzFEWdOHGCz+fv2bNHqVSePn368ePHeXl5ZmZmc+fOlUqlqampZKGBgYFRUVHkma6Ojo43btygKMrT03Po0KEVFRUURa1YscLV1VWhUFAvbodaFxoXF8fn82NiYioqKm7evGlhYbFx40YSp7f/l19+GRAQkJ+fX1VV1a9fv19++YXETU1Nyf5lTA8dOnT37t1kGiH0FmneBw81W28PALp27UqeR0rug37w4EESHzZsWEREhI6OzogRI9LS0pRKZUJCwpUrV1avXm1gYGBnZ/fjjz9u2LCBfjDjpEmTTE1NAaBbt26k97NlyxYfH59Ro0axWKxevXq1atXqpc87pk2ePNnLyysqKgpeq521a9fa2dk5OTl17dqVPgtFiEQiPp+/b9++6urqkJCQug7QmZubJyQk3Llzx9LS0s/Pj1lchz59+pCHpwQGBpKNBgAffPBBv379ysrK0tPTra2tycrHxMRMmTJlyJAhbDY7KCjI2tp669at1tbW33//PZ/Pd3Fx8fPzu3Hjxq1bt37++Wc+n29vbz9q1Ch674SFhenp6QHA1KlT09PT7969+3wlAKCOhQKAvb39jBkz9PT0PD09x4wZs3//fvW5KIr69ddfly9fbm5uLhKJPv30U7qCUCike3V1TSOEUAO1lHN7xsbG+fn5jCB5voZcLs/KyjIxMTExMSFxV1fX6urqgoIC8jRtGo/HI7kwMzPz+vXrXl5eJF5aWlpeXq5esy4xMTGZmZmk/8FisV67HQAQCoWMJyabmZnFx8d/8803S5YsmTZt2oIFC7jcWrb/4sWLLS0tBw8ebGNjExMT4+npyaxRLx6PRzYah8O5efPmzJkzjYyMyMNKxGIxAGRmZoaEhKjPkpmZ2aZNG0ZEJpN17tyZvKyqqqr57GySdWrutVoXymBlZRUfH68eKS0tLSsrmzx5Mhn7o1AozM3NSZFAIKAfKlbXNEIINVAtP7tNT6VSJScnh4WFMQueadWqVXFxcXFxMcl8aWlpQqGQ/lmsyd7e3svLi5yceyUlJSVJSUm9e/det25dZGTkS9ths1+tu9yuXbtdu3bl5ub27t1bX1//008/ZdYAYLPZ0dHRM2fOXLlyZVBQUEFBAT0KVCAQ0Km0Ic+NHDp06MKFC8eNGwcAp0+fJkE7OztGN9TOzo4uJezt7blc7pUrV+rJK48ePaqqqnJwcIAXt0OtC2VITk4mM9IMDQ319fXXrl0bEBCgHgeAkSNH0v8/UJ/u0aNHq1atntdDCKEGeLVf7cYVHx9/8+ZNiUTyzTffVFZWDhkyhFnjGR8fHz8/vxkzZlRUVOTm5s6dO3fy5Mn1/CJHREQkJCSsWbNGLpdXV1dfunRJvZT8bjK6YsRXX31lamq6evXq+fPnP378uP52AIAM4qAoih5/UY+bN2/+8ccfUqnU3Nzc0tJSoVAwawAAwE8//ZSSksJisdq1a8cY99GmTZvk5ORbt24VFhZOnTq11s6iurKyMgBQKBQHDhygDzZOnTr1zz//PHz4sFKpvHLlyoULFyIiIrKyshYvXkzO5O3cudPX17d9+/Yff/xxWVmZSqW6ePEivbb79u178uRJUVFRVFRUUFCQq6srvLgdal0oAOTn5x86dEgmkx06dGj79u0fffQRAHC5XLIj2Gz2tGnToqOj09PTASAlJYVcAVlSUpKenv7gwQPGNAAkJCTUPMSKEEL1a860Z2NjM2fOHAsLi8OHDx85coScn6vL/v37lUqlvb19p06dunfvvnz5cmYNNTY2NqdPn961a5eZmZm7uzsZbUGX6unp9erVq9aL4kmvxc/PLzw8fPr06fW3AwBff/315s2bzczMzp07px6vlaGh4alTp2xtbe3t7a2tradNm8asAUBRlImJyaBBg0xNTadPn75161b1C/66d+8+ZsyYbt26BQQETJgwgXGMt6aYmJh58+ZZW1ufPXs2PDycBPv3779q1aro6GgjI6NZs2axWCxbW1syDsXMzCwkJKSiogIADhw4oFQqnZycbG1tV6xYUVRURGY3NDQMCQlxcHAQCAT0+Fv17VDrQgGgS5cuhw4dsrGxmT59+rp168hFij4+PgCwaNEiAPjuu+/69+/fo0cPU1PTCRMmkLRXWlp64cIFMupVfRoALl68yOi2IoTQSzXb09Wjo6Orqqre4fuSvJO8vLzmzJkTERHBLEAIoQYjIyq18enqDTk7hVoa3GsIobdac6Y9hBBCqIk120FOhBBC2kl7D3IihBBCTQzTHkIIIS2CaQ8hhFBT+/jjj5mhptLIaY+iKC73JQ+WQwghpM0iIyN///13AOByOYzxJU2gkdMei8WiKIo8e4FZhhBCSOuRnKdSqZQqFUVRTT/ysZHTHgDwuFy5gvkoNYQQQoju5wGAQqHkvewOi5rQ+GlPIOCpVNjVQwgh9AL1nPfxxx+rVJRAwHuxSlNo/LSnqyOSyRUymZxcmcEsRgghpH3onEdRVGRk5Jo1a+Ryha7OG10m/noaP+0BgKmxgbiqWiqVYeZDCCHEyHmrV68uKas0MTZg1msSjX+XFoKiqKKScj6Py+VyeFwuDu9ECCEtp1Ao5QqFQqGQy5UmxgZNP5iF0FTaI8RVEqlULlcoWAAKJd7CGCGEtBS5VoHH5QoEvGY5tknT7CgaXi2gDAAAIABJREFUXR0ReXvNMkoVIYRQC9FysoBGzu3V1ELeLUIIoWbRcrJAE6U9hBBCqCXAtIcQQkiLYNpDCCGkRTDtIYQQ0iJNlPbe1YvW39X3hRBC7yrNXsAgFkuksnf5ur3nV6LwebqNd70jQgghDdFU2lNRVFFxGZfD4fK4QqGAw2mibmXTUypVCqVSJldIikpNTQxbziBdhBBCNWkk7alUqsLiMh2RkMfTSPstCofD5nDYwOfJ5YrColJTE0M2+53N8Qgh9LbTSFoqKikXCQXN8tjcZsTlcoRCQXFJuZmpEbMMIYRQy9D4aU8slpBjm1qU8Z7h8rhyhUJcJWneO84hhBCqS+OnPalMzuGwQZv6eeo4bLZUKse0hxBCLVPjpz25QsHji7Q16wGbw6mWyphRhBBCLUMjpz2KolgAHDYLQEvzHofNYgGr5dxrHCGEkLpGTnssFkuhVGltV49QKJWY8xBCqGXCofYIIYS0SCP39gitum4BIYTQW0QzaY8ZQAghhFoEjaQ9rb16ASGEUAunkXN71Kv/Xbp8aeCA/rY2Vq1a2X0yY3pJaWnNOhr9Gzp0cNy+uJrx1/hDCCHUYmkk7QFFvdLfqRMnhg4O69O3b9LtO2fPnNPV1X2Yllazmkb/SktKawZf8w8hhFBLpZG0VzMR1P83Z87n8+Yt+PjjacbGpo5OzouXfN/J24eioLCwcPKkia3sbV1aO329cKFUKiP1uwW89/emTeFDh7Rv59EnpHdOTu6MGdO7dPYN6Or/4MEDioLSklJLC7OYlSv79gnx9fEeM2Z0SXEJRcHdu8mGBnoSSTVp5/1eQX/8sYGiIDAw4MaNhOiZMz3c3W7dvKVQKJcuWeLm2trD3W3+/HkymbzmOtfzhxBCqMXSTNpjJoL6/tIfpqWlpQ4fObJm0cgRw5VKZdKd5DPnLpw9e+aLL+Y/K4LY3bvWrd+QkHirorIitF+fj6dOi796rZO39/dLl5A61dXVcqXi4OEj8VevqVTKL75cQOLqqwf/HZakzp2/aGNr+/MvK+/ee9DBs+PSpYvPnj198XL89Rs301JTN2xYV3Pd6vljbg6EEEIthkbSHvNkV71/hQWFAGBuZs6I30xMvH792rLlKwz0DWxtbL/59n+b/94krZYCBQAwKmK0kZExj8vz9fENDAz0cPcACrq+F/Ag5QGpAADDh41gAYvNYk+YMOnQwYP/tay+eoxpAKCAUlHr1v3+v++WmJmaiYSiqdOijhw+rL5iL/9DCCHUUmlkJOcr9XhMTE0B4EneExsbG/V4dk62sbGxkbExac3Z2bm6urqgsMDW1hb+yy8UAHB5PIVSSaZ5fJ5MKqW7XPSEoZFhaWmJTC4jSUmtT0aBWv+MxEvLSsrLyj75ZDqHwwEAhUJhZmr2Su8IIYRQi6WRtPdKOaK1c2tHR6fYnTtnRn+qHreztSspKSkpLjY2NgGAhw8fCoVCM1MzkvEA6v33xYl7yfesra15XB6fLwAAWbVUKBACgEpF0XXYLDaZNtA31NfXX7H8Fz9/f6C9yjtCCCHUYmnkICfpNjX8b/HSpT/++P36DesKCguys7PmzJn9yy8rPL28fHx95875vLyi/PHj3EWLFo4eM5Yv4FNAMRah9vL50gFg587tleLKe/eSf/rxh3HjJ1BAWdtY6+jo7I3bI64S//rrygcP7lPP6tvY2CQl3VJRKoVSMXHSpAUL5j589JACKjUtNTc3R31xL/1T3xQIIYRaFM2kvZqpoN6/kJC+W7Zu37d3r7dXx549AiUSyegx4ygKtmzdoVQqO7Zv17NH4HvvBXz7v8WkPkBtiY76r+dGx0tLy97z6xLar8+gsMHRn35GUSAUiv733ZLF3/3Pq2N7Ho8/cFAYvbafz5m7a+dO19ZOly5enL/gqw969xnQv5+Ls+OMqGm5uY/V1/alfwghhFosFuP+mZViiZ7uGz0i9XFeoYDPY0abVllZWWtnhzt37llZWzPLNE8qk9tYmTGjCCGEWgCNnNtr9g4PWQElpWr2NUEIIdSiaCTtNf+RPrICeMwRIYTQizSS9po91RgYGuYXlkALWBOEEEItikbSHmYbhBBCLZNG0h6FeQ8hhFCL1Mhpj6IoLo9DqZhxrcLlcSiKYrFYzAKEEELNrZHTHovFolQUAEuLD3SyKBXmPIQQaqEaOe0BAI/HlUkVoK0/+xSomv2yRYQQQnVp/LQnFAiUCpVSqWQWaAcOmyXk85lRhBBCLUPjpz0dkUBcJQGtPMpJURSHzdHRETILEEIItQyNn/YAwMzUKD+/WKXSupEtbDbLzMSQGUUIIdRiaORW1CwAC3NjHpejVQM7eFyupbkJDmZBCKGWTCO9PQBgsVjmZsbiKolUKpcrFCwAhfId7PxxuRyKonhcrkDA09V5o1t4I4QQagKaSnuEro6IJIN39Tq2d/V9IYTQu0ojBzlreldzw7v6vhBC6F3VRGkPIYQQagkw7SGEENIimPYQQghpEUx7CCGEtAimPYQQQloE0x5CCCEtgmkPIYSQFsG0hxBCSIto6i4tD9Iyk+6myOQKV+dWnTu1a/SrurNzn5qZGIlEAmYBQEp6pltrBwA4/+8NBztrB3trZo260c2mpmcVl5b7+bRn1mhJxFWSU+euyuTyTh3cWzvZ0fHyisprN+5WVFaFhQYBAEVRpy9cKy4pt7e19PftAAC5T/LjryWx2Oygbr7GRgb0jAgh9M7TSG8vKzfvQWrGgD49Iob2MTHWyK9qekZOVXU1MwoAAHeS08iEV3s3GyvzFwtfgm62lZ1VO3dnZnELk5h039vTfVC/njfvPFCqPe+Cz+O1drKnnj366WFGjoGe7rBBwRWV4oKiEgC4lni3X+/AHl19rt64S8+FEELaQCO9veT7D/19OwgFfABwcbIHgCpJ9dmL18VVElMTo+5dvbkczoV/E0UiQWlZRWVllbenx4PUDHGVpFf3zgb6ehf+TWSxoFoqk0ikAX6eJsaGJ87Gd+rgbmZqdC/lkVQq09UVZefklZSWuzjZ2dtZX7meVFFZpa+n+0FP/6s37pSWVx48dt7b0yP9UU5rRzsba/O8/KLLV28plf9v797joqr2v4F/93C/K4oDiICAIooCKTI43CXEu5VmpnIyKch6zmPn+OQl6vzUzpHMnup3zvmpaf7wqE9eymunUkxJFAk1UMRLICogl0GQ+8AMsJ4/9sxmswHrV0DqfN4vX7zWfNd3rb1mM8zXtWdg2kZ4uPqP9W5sUp/J+NHG2rK2rsFRPni8nw+/7PzCImFaCwvz2tr68f6jH7LOJnXzmYwfNVrtADubsOCnOu7+zcK8G7dkHBc43td1qGPB7eLyivutrW0PauvG+492HerIpzHGzl+4cq+s0sLcdHJ40L0y1Z2iUo7jmpqafUYO9/IYdvX6Lcbax44eUVPXcD7r8tRoZdq5S24ujsPdhvIzlKuqlEEBHEdDBturKqud5IP5uLm5maN8MOXyt6i4tGLMKE8iGu42tPhehZmpqYW5uYW5mYW5WX1Do/BnRe9X1Zz7IYfjuBGerj4jh+sGAwA8Wfpkt/egpm7woIHiSEbWZW8vt3mzn7aztb589Sc+aGpiEh0e5OTocOt28dORitGjPH4qKOK7BtkPiA4PChrv2+12ZISHq8PggWGTnho7eoSpiXGIImDurGhTU5N7ZSrFhLFmZqYzpoQJ+7z29vbvz12KDg+aOyu6tLyytLySiGrq6if4j572dMhPt+4y/efhiqfVH4qo53VmXsxVBI6bFRtuaWFeUlrBJ1c9qL1ZcPeZGVHTng49n3VFo9ESUWNTc7hyfIRyQt71W8K0fAmfNzs6KnSihbkZEclksslhE2OjJ126fL3bj6f3Gj5MfGIZY/zVY0sLsyZ193tfImpqaubntzQ3b1I3N6l1N4nI3MxU3dzCt2/k337Kz2fW1PDhrs7CWACAJ0yflD0ikny4eoWqynP4MCLy9nITKsRg+wFEZGVpPngQ37AQrlva2VgT0RAH++qaWj7SE0sL88ZG9Q+XrtbW1jc0Nkm7iapr6uxsrW1trDiOG+HhWlKqIiIbK0tzczOO48xMTTVajXRMZz2ts6y88sKPV1PTMlWV1S0tWj65tKzSw32osZGRhYWZk3xwRWU1EQ22t+M4ztLSQqgxRHSvVMVvqoRPY7ezsSIiE2NjG2vLuoZu7ouL8xAba0tplIgR/ZIXTxkxSRoj4vRD3V2dL12+fvvuPXN9UQQAePL0yUXOAXY2lVU1cgd7aQevU0EkIo4PccRJutra2vmtGMdxwitVEvmFRaVllYFPjTE3N2XCxq2zjjfUcCQ5PMdRD4O6kq6TEUWHB3X9EAahkBCR+HAckXiFjJjMqPv/drS2tXFEHEftD10bx3GMEceRWt1i6dzjp/1ZWlqo1S3WVpZqdYulhbmlhbla/9+LlhaNubkp33Zxlg8cYJude7O0vFIZ5N8xHgDgCdL90+5v5OPtkXXpaotGyxjLu3GrublliIP97bv3iCj/VtFQ5yHSAV3UNTQS0U+37g51ciAiczPT2tqG9nZ2r0zFJ5iaGLe0aIjoXpnKe4S7hbn5gwd1fJeM49pEH2lrP8C2pra+obGJMVZQWDzU6WFHF6b9JRyHDLqRf4eIxBcYnR0HF96919bW3tyiKVfdH+IwSOiScJIPvpl/l4jU6hZtaysR1Tc0MUYPauqam1vsbG3MzcxqaxuIqLiknB9yr0wl3tE6yQeXlFZoW1sr71cPGTywtq6hXFUl9Apch8pvF5US0e2ie8OGOtraWDU3a9TNLVXVtfwmmE+7X11jZWkRPGEsv0MFAHgi9cluz32YU1OT+sjXp1tb2zzcXczMTCdN9Es7e/HS5esDB9iGTxovHdBF8b3yazdumZiaTA6bSETeI9xPncnKu3FruPvQ9rZ2Ihrp5XY2M8fDfegID9fvz10aYGdja2PFj/Ue4X7436fG+4/mb8pksrBJ40+czmxvb/dwdxnqNKSxSa0/jpQw7QA7G2lfF5Mm+n1/7tKN/NsW5uaRoRPMTE2JaJD9gBEeww79+xTHcUHjx5qZmkiH6Y0e5Xn2fPaBIyctLMz4d8Q0NqmPfpum0bSGTRrPcTRsqDz3ev4XR0+OG6N7rTH/VpGri6O1le46p/9Yb90vMIwbJZPJyiruVz+odRwy6GLOtaLisrr6xkP/PhU2afxwt6G3i0oPHDk5bKicv04bGDDm6xPp/C8w6JdD98pUGVmXZRznP9ZbCAIAPGE4yYXBhka1tVWPl8v6R/r5bM/hLv/T3z143BXcLq6taxDeVgoAAH2hTy5y9oKHvqb1xPrFLzMCAMCv8yju9gAAAPrIo7rbAwAA6AMoewAAYEBQ9gAAwICg7AEAgAFB2QMAAAOCsgcAAAYEZQ8AAAwIyh4AABgQlD0AADAgKHsAAGBAUPYAAMCAoOwBAIABQdkDAAADgrIHAAAGBGUPAAAMiLE08JsV3WzPPdtW/4A4jhgjjiMiIo6IEXGiZncRfUAX6hrR5YtnJo6IcRzHGOM4jhHjhMHEOOIYMa5jAD9FR77+MF3G6vO7ztD9sTpFhEV3XW2nSE/nQXS39fmdz0PXSHdnRhz5VWdGiHSX3zXCH4zjRGdFvEjpkrqJ/OwJESKih5buoD/z7etyB7v/9onGPuyEdJcvOiFd7q8QEA7Y7b3rcmYk+bqIaGxPZ7JThKjrN6trpNN56Hpmej6TQqT7GURjO+d3Xm1HuLszI9xfXUB0wM4R6QxEJqZk78BZ2fJJAH2w2+uoefqHXcfjv+Ph2NHNcZwuQqSvGRwxfVyICDkcxz+PC1OLf5w6fe3yg83/KPIR3Y+fvt3pWPr5OepYW8dqRb3S1eoyhS/89PxQHU50ZvglCPm6uBDRH5G6PdbPnxlxpPunoa5nptP/BvhIp/OmP2J3Z0Y4b8QYccQY44gYMSLSfZox07X5IwsYI+LXoksh4tNJH9Hn6zp13zfhhm7+jmPpJ2LEuC5Hl6xNmIHTfeqy7it/fF2F6xwh/kTp2/wSdbVBtB5+vbpjCWsQrVYXF1YrytSvTXRmSJiST6eOg+uJI7oV6DK7HEt0rjrWJmR26RWdSf39FZ0Z1rFQ/ZkRIqKz2vlMEtM9EvTHEtbQ9cwI+eLMju+jfqj+hq7JiIi0LVRdyafAr5Senu7j4+Ph4fHaa6+1tbVJu4mI6KuvvnJ0dPT39/f39x85cuS0adOErk8//dTExCQnJ4eIVqxY4eXlxac5Oztv3LixY4r+0vtljyd6xOoeg7rHqu7h+PAfdfEDWh8RckQ/Kvx84h+nTl87noY6nph0vcKPn77d6Vjd/KiLVivqla5Wlyl84afnh+ow0ZnhlyDk6+JCRH9E6vZYP39mxJHOT0M9n5mOJyY+LnoKY6Kld3tmhPNG+hLIiPjyLCmQ/JEFHEfEr0WXQsSnkz6iz9d16r5vwg3d/B3H0k/0kPIsrE2YgfGlXf+VPz5/QiQR4k+Uvs0vUfSfCf0R9evptAbRanVxYbWiTP3aRGeGhCn5dOo4uJ44oluBLrPLsUTnqmNtQmaXXtGZ1N9f0ZnhOhaqPzNCRHRWO59J4nSPBP2xhDV0PTNCvjiz4/uoH6q/oWvqOgh+C41GEx8fn5qaWlhYaGxsvGPHDmmGXnx8fE5OTk5OzssvvxwdHc0Hm5qaUlJSYmNjhbQtW7bwaePGjRPS+lPvl72xIUY2A/nHpOhhJ/pRlD6gu/lRFz+g9REhR/Sjws8n/nHq9LXjaajjiUnXK/z46dudjtXNj7potaJe6Wp1mcIXfnp+qA4nOjP8EoR8XVyI6I9I3R7r58+MONL5aajnM9PxxMTHRU9hnGjp3Z4Z4byRvgRyRHx5lhRI/sgCxoj4tehSiPh00kf0+bpO3fdNuKGbv+NY+okeUp6FtQkzcHxp13/lj8+fEEmE+BOlb/NLFP1nQn9E/Xo6rUG0Wl1cWK0oU7820ZkhYUo+nToOrieO6Fagy+xyLNG56libkNmlV3Qm9fdXdGZYx0L1Z0aIiM5q5zNJTPdI0B9LWEPXMyPkizM7vo/6ofobuiYjIjIxIXsHjuDXysjICAwMdHFx0Wq1CQkJR44ckWYQEVFgYODChQuJqL29fefOnX/4wx/4+Mcff5yYmGhmZsbfXLJkiZ+fHxHdunXrwYMHTz31lH6C/tP7r+25estcvXu/mgLAI4OvIo/OV+hDZWVlcrk8JSVl48aN33//fUlJiTSDiIjkcrlcLieib775ZsKECYMGDSKiqqqqb7/9Ni0t7ejRo3zamDFj+MaWLVteffVV/eh+1ftlDwAAnhj89nru3LnBwcFE+is6Pdu8efOqVav49nvvvbdq1SqZTLoRamlpOXjw4JUrVyTx/iFdDQAAgMDJyUmlUllbW3t7e1dUVDg7O0szRO7evVtUVBQSEsK3r1+/Ln5vi2D//v2xsbFWVlbSjn6BsgcAAD1SKpVZWVmlpaVEtH379lmzZgldycnJ/PszBVu3bo2Pj+fb58+fLykpUSgUCoXi9OnTcXFxZ86c4bs2b96ckJDQMax/4SInAAD0yNTUdOvWrVFRUVqtNioqaunSpXxcq9X+53/+p62trb+/Px/RaDSff/55dnY2f/OFF1544YUX+PbcuXOTkpL4zMuXLzPGxo0bx3f1P/6NVR0aGtXWVhbiCAAAwBMDFzl1fH19pSGimJiYrVu3SqOdqVSqp556SqFQzJs3Twi+9957/Ht5fwkvLy9pCAAA+gYuchIRNTc3azQaaZRo48aND3/9lojy8/NHjx69e/ducfAPf/hDQ0ODOAIAAI+CPtntvfjii5mZmXw7Ojq6qKiIiNLT0yMjIydPnrxw4cL6+noiWrNmzcSJE319fdetW+fs7Lx+/fqMjAxhk3Tt2rU5c+bop+zk5MmTwqumM2bMuHHjBhHNmTNn5cqVwcHB48ePLygo4HtXrFgRFhYWHR2dkJDA16F9+/YFBweHhIQsXry4tbWViBYuXBgREVFUVMS/9PrFF18Q0YYNGxQKRUxMjPgttpLZ8vLyFArFq6++evz4cWG3x7cjIyP37NkjDOx638vKysLCwsaPH7927VohDQAA+hzrrL6hSRL5FQ4dOvTWW28xxqqqqkJCQhhjarU6KiqqqamJMbZjx47Vq1fzmfX19fb29t98840wdsKECQ8ePGCM/elPfzp69KgQF0tNTV26dCnfnj59+vXr1xljs2fPPnDgAGNs9+7dfO/NmzdjYmL4tMzMTI1GI26sWLHi4MGDfC9jzNPTU2gLli5dmpqayre7nY0xlp6evnDhQr4t2LVr19tvv823u73viYmJ27ZtY4zdvXvXwcFBNBQAAPpQn1zkjI2NXb9+PREdO3bsmWeeIaLc3NwbN27wbY1G4+bmJiTb29uL/1zbokWL9uzZ8+qrr546dep/+ldKR40aRUSBgYE7d+4kIhcXF5VKtXLlSl9f36ioKBMTEyJqbGycN28ex3H5+fmenp6SGR6i29l+iW7ve1ZW1l/+8hcicnV1tbXFH4cHAOgnfVL2zM3Nvb298/LyDh8+/Mknn/DB4OBg/vqhhPC32nhxcXEzZ850cnKaPn26kZGRuEvAcVx7e7s0qieTyfheS0vLzMzMzMzMvLy8mTNn7t69e/To0fHx8ZcuXRo4cOCmTZukIx+q29mkST3oet9Z5zfQAgBA/+iT1/aI6Pnnn9+1a1dtba2rqysRjR07Ni8vr7i4mIi2bNly+PBh6QC9gQMHDh8+/J133hF+O6QrV1fXnJyc1tbWlpaWwsJCabdeTk7O119/HR4evmzZsrCwsLy8PMZYa2urpaUlEWVlZYmTeyqxgq6zSTN60O19DwoK4v9IXVFRUV1dnWQIAAD0FclFz155bY8xplarBw0a9OGHHwqRM2fOKBSKwMDAOXPm1NTUMMaSkpKCg4MtLS2VSmV4eLiQ+dVXX0VHRws3u7VixYqRI0fOnTs3KipKeG0vNzeXMZafnz958mTGWH19/ZIlS3x9fQMCAhYtWtTc3MwY27x5s5+fX3h4+Pbt2729vW/evMlPGBcXFxoaqlQqv/zyS8ZYcnKyUqmUy+W+vr5KpbKkpKTrbCqVSqlUjh071sHBQalUJicnM8aOHz+uVCpHjhw5bNgwpVJ57Ngx1t19Ly0tDQkJ8fPzW7t2bbcvKwIAQF94FH9dffXq1f7+/vPnz5d2AAAA/DZ9dZHz16msrJw0aVJOTs6zzz4r7QMAAPjNHsXdHgAAQB95tHZ7AAAAfQplDwAADAjKHgAAGBCUPQAAMCAoewAAYEBQ9gAAwICg7AEAgAFB2QMAAAOCsgcAAAYEZQ8AAAwIyh4AABgQlD0AADAgKHsAAGBAUPYAAMCAGEsDv9nNi+1XM9patcTJiIg4Tv+VE9ocEeM4jjjW0SbGyURtjhPlE9HPzSYd23nmjsjPzsb3dju26zq7G9s18ktnYxzHcUY0aAhnZ08AANAXen+3d/1CW6uWOI6I8TVJ/5U4fUT/LE9CmzhO6BXyicS1RNortBnHccTEszHRsST54tn4tmQ2fiWdK5O0t/Ns9Ctm6/FeUztVqySfgQgA8HtKT0/38fHx8PB47bXX2trapN16//rXv/z8/MaMGbNs2bL29nYiKi0tDQ0N9fLyioyMrKys5NPs7Ox89W7fvt1pin7R+2VvqKeMiBgj4oh//tZ9JaaP8J9tyzES2sSY0Cvkk26enmbTtTnGGHHi2TjRsST54tn4tmQ2fiX6eYTZOvV2no1+xWwPu9fWdroiCgDwu9NoNPHx8ampqYWFhcbGxjt27JBmEBFRUVHRe++9d+7cudzc3OLi4sOHDxNRYmLimjVrCgoK3njjjeXLlxNRY2NjYGDgVb3hw4dLJ+p7vX+RMzDGKDDGSBo1dHwd+yVfAQAeIRkZGYGBgS4uLlqtNiEhYdWqVa+88oo0icjV1TU7O9vKyoqIJkyYUFRUpNVqc3Nzp06dqtVqn3vuuT//+c9EVFtbO3DgQOng/tX7uz0AAHhilJWVyeXylJQUPz8/uVxeUlIizdDja15bW9s333wTGRl5//59e3v727dvW1palpeXW1lZ1dTU1NTU8FVw7Nix69evl07RL1D2AACgR/zrL3Pnzj106BCR/l0IPVu+fPnkyZP9/Pz4gW5ubllZWY6OjkTEcZy9vX1oaOiePXvOnz9/4sSJ48ePS8f3PZQ9AADokZOTk0qlsra29vb2rqiocHZ2lmboMcZef/11Kyurv/71r0Tk4OBQXV0tk8kCAgKIqLGx0c7OztHR8c9//rO5ubm1tfW0adNyc3Ols/Q9lD0AAOiRUqnMysoqLS0lou3bt8+aNUvoSk5OzsnJ4dutra1xcXGenp7Jycl8xMTExNfX98SJE0R0+PDh4OBgIsrOzt6wYQMRabXatLQ0viL2M91bCgUNjWprKwtxBAAADFlaWlpiYqJWq42Kitq8ebOxsTERabVaNze3pKSkZcuWEdG5c+eio6M9PT35Ib6+vnv37i0pKZk/f75KpXJ2dt6/f79cLm9paXnzzTdPnTplYmKyYMGCNWvWiA/UP1D2AADAgDweFzk//vjj7du3S4JpaWnu7u6SYFepqanjxo1TKBT8zpoXExOzdetWUVaPrl69OmfOHGkUAAAeT73/e3t9oba21traWhIMDAzcs2ePJNhVVlbWH//4x/j4eHFw48aND3lhFgAAnlR9sttLSkravXs33/by8hIaK1asCAgImDlzZktLCxHV1NTMnj07Kipq8uTJmzZt4tNWr16tUCiCgoI++ugjIjp9+rRCofjss882bNigUChiY2OJqLKyUqFQhIaGrly5kh9FRNnZ2ZGRkZMnT46OjubfFLtr1y6FQrFlyxZ+LL/b49sxMTFXrlwRxv7jH/+IiIgIDw9fu3YtH0lPT/fXCgb8AAAdn0lEQVT19Q0NDT148KCQBgAAjz3WWX1DkyTyK7z99tu7du3i256ennxjyJAh9+7dY4zFx8fzvVu3bv3b3/7GGGtvbz979ixjrK2t7dy5c3z+xIkTq6qq+PaGDRu2bdvGtwXFxcVKpVK4uWDBgoyMDMZYQ0PDpUuXhPj69eu7jl26dGlqairf/vHHH19++WW+HRcXd/z4ccbY2LFjr127xhj7/PPPZ8+erR8HAACPt/67yGljY8NfVwwMDCwqKuIbS5YsaWtrGzNmzNNPP01EMpksOzs7OTnZyMjo7t27KpXK3v6XfhhBWFhYUlLS9OnT/f39IyIipN09O3nyZFZWFr+PrK2tLSwsrK+v12g0Pj4+ROTr6ysdAAAAj60+KXscx/F/frtbMpmM7w0ICDh16lRmZubly5fXrVt34cKFmzdv7t+///Tp0zKZbMaMGdKRD5WYmDhlypSLFy8eOnRo586dO3fulGb0LCEh4Y033hBu1tfXizoBAODJ0Sev7bm6ul64cIGI7ty509zcLO3WO3DgQFlZ2bRp01atWsVx3IMHD9RqtaWlpUwma2xszMvLEzKNjH7+b1t/8sknAwcOnDdv3tq1a7Ozs6XdPYuOjt67d29LSwtjLCEhoaioyMbGxtTUlF/A1atXpQMAAOCx1Se7vRdffPHAgQN+fn6zZs0yNzeXdusFBAQkJiZWV1e3trYuXrzYwcHBwcHByckpKCjI0dExPj4+Pj7+7NmzRBQREbFs2bKUlJQBAwZ89dVXlZWVzzzzjEajuXnzZkhIyJQpU955552RI0dGRUUZGRm1t7d/8MEHRHTixIl169YVFxebmpqmpKS8++67MTEx77///rFjxwoKCn744Qc7O7t9+/YFBAQsWLBAqVS2tbVNnTrV1dWViP75z3/OmzfPzs5u2rRpkmUDAMDjC7+uDgAABqRPLnICAAA8mlD2AADAgKDsAQCAAUHZAwAAA4KyBwAABgRlDwAADAjKHgAAGBCUPQAAMCAoewAAYEBQ9gAAwICg7AEAgAFB2QMAAAOCsgcAAAYEZQ8AAAwIyh4AABiQ3v+Y2XsF7dey2uuqmJExx8mYkTEnk5HMmGQy+uURc0vOwZmzspFODgAA8Fv0/m4vO629RsU4jmOMcRxHjIgjYvQ/imhb6EFlp8+/BQCA30V6erqPj4+Hh8drr73W1tYm7dbrKe3TTz81MTHJyckhoubm5qVLl3p5efn4+GzatKljcD/q/bLH1zC+ejHG+Hr2KyKdP/UdAAB+BxqNJj4+PjU1tbCw0NjYeMeOHdIMIuo5rampKSUlJTY2lr9ZXl4eHBxcUFBw8eLFv//97/fu3euYor/0ftkLiJQNGMw9ZCf3SyLGJmQ/hJNODQAA/SsjIyMwMNDFxUWr1SYkJBw5ckSaQUQ9p3388ceJiYlmZmb8TXd39/j4eCKqq6uzs7MbMGCAMEO/6f3X9oZ6yYZ6/ZZqimoHAPCoKCsrk8vlKSkpGzdu/P7770tKSqQZRNRDWlVV1bfffpuWlnb06FEh89atW88880xJScm+ffusrKw6pugvvV/2AADgicEYI6K5c+cGBwcTEcd1vzPpNu29995btWqVTNZpI+Tp6XnlypWKioopU6acPHly8ODB4t5+8Fu2ZQAA8IRzcnJSqVTW1tbe3t4VFRXOzs7SDCLqLu3u3bvXr1+fNm2aNJWIiORyua+v78WLF6UdfQ9lDwAAeqRUKrOyskpLS4lo+/bts2bNErqSk5P592d2m3b+/PmSkhKFQqFQKE6fPh0XF3fmzJlNmzZ9+eWXRFRbW3vp0iUfHx9htn7D8TtTQUOj2trKQhwBAABDlpaWlpiYqNVqo6KiNm/ebGxsTERardbNzS0pKWnZsmUPSePNnTs3KSnJ39+/rKxs6dKld+7cMTExWbly5Ysvvijk9BuUPQAAMCC4yAkAAAbkUSx7c+bMKSgokEZ/zqxZsxQKhVwul3YAAADoPXK/wMAYq6+vl0Z/Af73Qtzd3aUdAAAAen2y2/v444/j4+OjoqK8vb2PHTvGB9PT0yMjIydPnrxw4UK+sCUlJe3evZvv9fLyIqIPP/wwODj40qVL8+bNUygUa9asIaLY2NhRo0a98sorc+bMcXR0PHXqVE1NzfPPPx8cHBwcHPzdd9/pjgoAAPCzWGf1DU2SyK/w0Ucfvf7664yxu3fvent7M8bUanVUVFRTUxNjbMeOHatXr2aMvf3227t27eKHeHp6CsOnTJmSn58v3GSMHTt2bMyYMYWFhfzNmpqaK1eu8I3x48eLM93c3MQ3AQAAxPrqIueoUaOIyNXVVa1WE1Fubu6NGzeeeeYZItJoNG5ubpL8nxUcHDx8+HC+bWZmtm3btuLiYsZYeXl550QAAIAe9VXZEwh/ySY4OPiLL76QdLW3t4sjDyH8JVMi+uyzz2xtbQ8dOkT6q6MAAAC/RJ+8ttfV2LFj8/LyiouLiWjLli2HDx8mIldX1wsXLhDRnTt3mpubhWQjIyOh3S21Wm1jY0NEeXl5jY2N0m4AAIAe9Pluj2dubv7pp58+//zzbW1tQ4cOTUlJIaIXX3zxwIEDfn5+s2bNMjc3F5KnTZsWFxcnk8kiIyPXr18/Y8aM27dvP3jwICcnJzAw8KOPPnrppZeee+65o0ePBgUFRURErFixYtOmTc8++6xKpSovLw8JCXFxcdm7d68wIQAAAA9/pQUAAAxIP13kBAAAeBSg7AEAgAFB2QMAAAOCsgcAAAYEZQ8AAAwIyh4AABgQlD0AADAgKHsAAGBAUPYAAMCAoOwBAIABQdkDAAADgrIHAAAGBGUPAAAMCMoeAAAYEJQ9AAAwIL3/MbOlhe13rrO2VhroyLmN4mzsOGkGAADA76T3d3tX0tvrqhhxpFFTVbm0FwAAHi/p6ek+Pj4eHh6vvfZaW1ubtFuva1ppaWloaKiXl1dkZGRlZaWQqdFoRo4cOWfOnI7B/aj3yx5jRBwRI+JI8tHtAADweNFoNPHx8ampqYWFhcbGxjt27JBmEFEPaYmJiWvWrCkoKHjjjTeWL18uJP/Xf/1XTExMx+D+1ftlzy/MyGYARxyZmpGDU+/PDwAA/SYjIyMwMNDFxUWr1SYkJBw5ckSaQUTdpWm12tzc3KlTp2q12ueee+7cuXN8Zl1d3YEDB+Lj4ztP0H96vyw5e3DKmUahs4x8FTJrO2kvAAA8RsrKyuRyeUpKip+fn1wuLykpkWYQUXdp9+/ft7e3v337tqWlZXl5uZWVVU1NDRFt3LjxzTffNDbu/XeW/EK9X/YAAOCJwb9WNXfu3EOHDhERx3X/LsWuaXzEzc0tKyvL0dGRD5aVlWVkZMydO7fz6H6FsgcAAD1ycnJSqVTW1tbe3t4VFRXOzs7SDCLqLs3BwaG6ulomkwUEBBBRY2OjnZ3d2rVr3333Xeng/oWyBwAAPVIqlVlZWaWlpUS0ffv2WbNmCV3Jyck5OTk9pZmYmPj6+p44cYKIDh8+HBwcTETZ2dmrVq1SKBQLFy48c+ZMXFycMFu/0e1DBQ2NamsrC3EEAAAMWVpaWmJiolarjYqK2rx5M/+ynFardXNzS0pKWrZsWU9pJSUl8+fPV6lUzs7O+/fvl8vlwpxXr15NSko6fPiwEOk3KHsAAGBAfueLnDk5OYsWLZIEm5qahgwZkpubK4lLpKamjhs3TqFQbNiwQQjGxMRs3bpVlNWjq1ev/l6/LAkAAL+X3+0tpLza2lppiMjS0nL//v3e3t7Sjs6ysrL++Mc/Sn75Y+PGjT294goAANAnu72amprZs2dHRUVNnjx506ZNfHD16tUKhSIoKOijjz4iotraWoVC8frrrx8/flyhUCgUisLCQiKaN2+eQqGIjY2VyXRry87OjoyMnDx5cnR09PHjx4lo165dCoViy5YtGzZsEHZ7fDsmJubKlSv8QCL6xz/+ERERER4evnbtWj6Snp7u6+sbGhp68OBBIQ0AAAwF66y+oUkS+RW2bt36t7/9jTHW3t5+9uxZxlhbW9u5c+f43okTJ1ZVVfHt8+fPL1y4UD+ug6enp1ar5dsLFizIyMhgjDU0NFy6dEnIWb9+/bZt24SbvKVLl6ampvLtH3/88eWXX+bbcXFxx48fZ4yNHTv22rVrjLHPP/989uzZ+nEAAGAQ+uQiZ2Bg4JIlS9ra2saMGfP0008TkUwmy87OTk5ONjIyunv3rkqlsre3lw7rQVhYWFJS0vTp0/39/SMiIqTdPTt58mRWVlZsbCwR1dbWFhYW1tfXazQaHx8fIvL19ZUOAACAJ12flL2AgIBTp05lZmZevnx53bp1Fy5cuHnz5v79+0+fPi2TyWbMmCEd8FCJiYlTpky5ePHioUOHdu7cuXPnTmlGzxISEt544w3hZn19vagTAAAMTp+8tnfgwIGysrJp06atWrWK47gHDx6o1WpLS0uZTNbY2JiXlydkGhkZicZ175NPPhk4cOC8efPWrl2bnZ0t7e5ZdHT03r17W1paGGMJCQlFRUU2Njampqb8Aq5evSodAAAAT7q+2u0lJiZWV1e3trYuXrzYwcHBwcHByckpKCjI0dExPj4+Pj7+7NmzROTj43Pnzp2QkBAi2rVr1/Dhw1944YWSkpJ79+5FREQMGTLk4MGDI0eOjIqKMjIyam9v/+CDD4joxIkT69atKy4uNjU1TUlJeffdd2NiYt5///1jx44VFBT88MMPdnZ2+/btCwgIWLBggVKpbGtrmzp1qqurKxH985//nDdvnp2d3bRp0zqvGgAAnnz4dXUAADAgfXKREwAA4NGEsgcAAAYEZQ8AAAwIyh4AABgQlD0AADAgKHsAAGBAUPYAAMCAoOwBAIABQdkDAAADgrIHAAAGBGUPAAAMCMoeAAAYEJQ9AAAwICh7AABgQFD2AADAgKDsAQCAAen9T1e/9kNb2R1mas5ZDeCGenJyF06aAQAA8Dvp/d3ejQtM20LEETFqrJP2AgDA4yU9Pd3Hx8fDw+O1115ra2uTdut1TSstLQ0NDfXy8oqMjKysrOTTzp49O2zYsOjo6E6D+1Hvlz3PcZyxKREj4sh2oLQXAAAeIxqNJj4+PjU1tbCw0NjYeMeOHdIMIuohLTExcc2aNQUFBW+88cby5cuJ6OzZs8uXL//ss8+k4/tR75c9vzCj6BeMQ2cbPRUuG+yEK5wAAI+xjIyMwMBAFxcXrVabkJBw5MgRaQYRdZem1Wpzc3OnTp2q1Wqfe+65c+fOERHHcceOHXN2dpaO70e9X/YAAOCJUVZWJpfLU1JS/Pz85HJ5SUmJNIOIuku7f/++vb397du3LS0ty8vLraysampqlEqlk5OTdHD/6v23tAAAwBODMUZEc+fODQ4OJiKO6/4aXtc0PuLm5paVleXo6MgHOw/6fWC3BwAAPXJyclKpVNbW1t7e3hUVFT1dn+ya5uDgUF1dLZPJAgICiKixsdHOzk467PeAsgcAAD1SKpVZWVmlpaVEtH379lmzZgldycnJOTk5PaWZmJj4+vqeOHGCiA4fPszvAh8Fun2ooKFRbW1lIY4AAIAhS0tLS0xM1Gq1UVFRmzdvNjY2JiKtVuvm5paUlLRs2bKe0kpKSubPn69SqZydnffv3y+Xy1NTU998882WlpaysjJ3d/eQkJAtW7Z0OljfQ9kDAAAD8uRf5PT19ZWGiGJiYrZu3SqNdjFjxoxJkyYpFArhFy3T0tLc3d07JfXMy8tLGgIAgN/VE/5OzubmZo1GI40Sbdy4sacXZsXy8vJu374tjgQGBu7Zs0ccAQCAx0if7PZefPHFzMxMvh0dHV1UVERE6enpkZGRkydPXrhwYX19PRGtWbNm4sSJvr6+69atc3Z2Xr9+fUZGxsKFC/mB165dmzNnjn5KqRUrVoSFhUVHRyckJDQ0NBDRvn37goODQ0JCFi9e3NraSkQLFy6MiIgoKipSKBQKheKLL74gog0bNigUipiYmCtXrjxktvDwcIVCUVpayo+trKysrKxUKBShoaErV64UBna9U2VlZWFhYePHj1+7dq2QBgAAjwrWWX1DkyTyKxw6dOitt95ijFVVVYWEhDDG1Gp1VFRUU1MTY2zHjh2rV6/mM+vr6+3t7b/55hth7IQJEx48eMAY+9Of/nT06FEhLnbz5s2YmBi+nZmZqdFoxI0VK1YcPHhQSPb09BTagqVLl6ampvLtbmfjubm5CW1ecXGxUqnk293eqcTExG3btjHG7t696+DgIBoKAAC/vz65yBkbG7t+/XoiOnbs2DPPPENEubm5N27c4NsajcbNzU1Itre3j42NFW4uWrRoz549r7766qlTpzZu3CjExVxcXFQq1cqVK319faOiokxMTIiosbFx3rx5HMfl5+d7enpKx/Ss29l+iW7vVFZW1l/+8hcicnV1tbW1lQwBAIDfV5+UPXNzc29v77y8vMOHD3/yySd8MDg4mL/MKGFmZia+GRcXN3PmTCcnp+nTpxsZGYm7BJaWlpmZmZmZmXl5eTNnzty9e/fo0aPj4+MvXbo0cODATZs2SQc8VLezSZN60PVOsc7vjAUAgEdKn7y2R0TPP//8rl27amtrXV1diWjs2LF5eXnFxcVEtGXLlsOHD0sH6A0cOHD48OHvvPPO0qVLpX16OTk5X3/9dXh4+LJly8LCwvLy8hhjra2tlpaWRJSVlSVO7ql2CrrOJs3oQbd3Kigo6OjRo0RUVFRUV4cPXgIAeMRILnr2ymt7jDG1Wj1o0KAPP/xQiJw5c0ahUAQGBs6ZM6empoYxlpSUFBwcbGlpqVQqw8PDhcyvvvoqOjpauNlVfX39kiVLfH19AwICFi1a1NzczBjbvHmzn59feHj49u3bvb29b968ySfHxcWFhoYqlcovv/ySMZacnKxUKuVyua+vr1KpLCkp6Xa2+fPnK5VKMzMzpVI5f/58xphKpVIqlYGBgba2tkqlct26day7O1VaWhoSEuLn57d27dpuX1YEAIDf0aP46+qrV6/29/efP3++tAMAAOC36auLnL9OZWXlpEmTcnJynn32WWkfAADAb/Yo7vYAAAD6yK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], "metadata": { "id": "MkdlkccWNqf9" } }, { "cell_type": "markdown", "source": [ "It does seem to generalize quite well to real-world data 🔥" ], "metadata": { "id": "lDmOHbYVPIKe" } }, { "cell_type": "markdown", "source": [ "## Optimization\n", "\n", "As soon as you think the model's performance is good enough for production it's all about making the model as memory efficient and fast as possible.\n", "\n", "There are some obvious solutions to this like choosing the best suited accelerated hardware, *e.g.* better GPUs, making sure no gradients are computed during the forward pass, or lowering the precision, *e.g.* to float16. \n", "\n", "More advanced optimization methods include using open-source accelerator libraries such as [ONNX Runtime](https://onnxruntime.ai/index.html), [quantization](https://pytorch.org/docs/stable/quantization.html), and inference servers like [Triton](https://developer.nvidia.com/nvidia-triton-inference-server).\n", "\n", "At Hugging Face, we have been working a lot to facilitate the optimization of models, especially with our open-source [Optimum library](https://huggingface.co/hardware). Optimum makes it extremely simple to optimize most 🤗 Transformers models.\n", "\n", "If you're looking for **highly optimized** solutions which don't require any technical knowledge, you might be interested in one of Hugging Face's paid inference services:\n", "\n", "- [Inference API](https://huggingface.co/inference-api)\n", "- [Infinity](https://huggingface.co/infinity)" ], "metadata": { "id": "JAmfpV6GRzHk" } } ], "metadata": { "colab": { "name": "ODSC 2022 - 🤗 Transformers Workshop", "provenance": [], "collapsed_sections": [], "toc_visible": true, "include_colab_link": true }, "language_info": { "name": "python" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "ecf82f9bb19a43b0b0f85ba677d7fdaf": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [ "IPY_MODEL_b0b24ae83353474b84ac532037b0ec74", "IPY_MODEL_5f9c324715554952bd0a04aa9c7ec62b", "IPY_MODEL_1b4c3fb1903340e39a65dad195140d2e", "IPY_MODEL_f0cb87644ad84421871f0d413fea8c9d", "IPY_MODEL_8c778cf5552749898775ee3e43f46bc6" ], "layout": "IPY_MODEL_daf8b0e163f148fe84d9e98ba54cb558" } }, "b0b24ae83353474b84ac532037b0ec74": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4585585a9717423aacd8df5d57e0edd7", "placeholder": "​", "style": "IPY_MODEL_efdc4418401e4b1dbce2b31b6ef854fd", "value": "
\nHugging Face\n
\nCopy a token from your Hugging Face tokens page and paste it below.\n
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"\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "u8Mm4_MWEpL9", "colab_type": "text" }, "source": [ "## Reformer - Pushing the Limits of Language Modeling\n", "\n", "Earlier this year, Nikita Kitaev, Łukasz Kaiser and Anselm Levskaya published the [**Reformer**](https://arxiv.org/abs/2001.04451), a transformer model variant with astounishing low memory consumption.\n", "\n", "In this notebook, we will show how Reformer can be used in [`transformers`](https://github.com/huggingface/transformers). \n", "To highlight its low memory consumption, we reduce the novel [**Crime and Punishment**](https://en.wikipedia.org/wiki/Crime_and_Punishment) to a single example containing over half a million tokens and use it train Reformer with the conventional languge modeling objective.\n", "\n", "Thanks to the recent releases of [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) and [`nlp`](https://github.com/huggingface/nlp), it is easier than ever to train any model in `transformers` on the dataset of your choice.\n", "\n", "\n", "### ***Disclaimer***:\n", "\n", "This notebook is essentially a translation of the official reformer notebook from `trax` to `pytorch`: https://colab.research.google.com/github/google/trax/blob/master/trax/models/reformer/text_generation.ipynb#scrollTo=bzQ7G9uGSga5 " ] }, { "cell_type": "markdown", "metadata": { "id": "SrMh3Y_T_JQj", "colab_type": "text" }, "source": [ "First, let's check whether we are given the full portion of the GPU. " ] }, { "cell_type": "code", "metadata": { "id": "M7cQmgM5TvlO", "colab_type": "code", "outputId": "97a5311a-4af9-4f2e-8140-55e54f8ad799", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 51 } }, "source": [ "#@title Check availble memory of GPU\n", "# Check that we are using 100% of GPU\n", "# memory footprint support libraries/code\n", "!ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi\n", "!pip -q install gputil\n", "!pip -q install psutil\n", "!pip -q install humanize\n", "import psutil\n", "import humanize\n", "import os\n", "import GPUtil as GPU\n", "GPUs = GPU.getGPUs()\n", "# XXX: only one GPU on Colab and isn’t guaranteed\n", "gpu = GPUs[0]\n", "def printm():\n", " process = psutil.Process(os.getpid())\n", " print(\"Gen RAM Free: \" + humanize.naturalsize( psutil.virtual_memory().available ), \" | Proc size: \" + humanize.naturalsize( process.memory_info().rss))\n", " print(\"GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB\".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))\n", "printm()" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "Gen RAM Free: 12.7 GB | Proc size: 138.1 MB\n", "GPU RAM Free: 16280MB | Used: 0MB | Util 0% | Total 16280MB\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "vRMY7ULm_XOC", "colab_type": "text" }, "source": [ "In case GPU utilisation (`Util`) is not at 0%, you can uncomment and run the following line to kill all processes to get the full GPU afterwards. \n", "Make sure to comment out the line again to not constantly crash the notebook on purpose. " ] }, { "cell_type": "code", "metadata": { "id": "kTZtozYEUzWd", "colab_type": "code", "colab": {} }, "source": [ "# !kill -9 -1" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "YqkAdaKmKbyg", "colab_type": "text" }, "source": [ "Let's install `nlp` and `transformers` and import the necessary classes from Reformer and Trainer. " ] }, { "cell_type": "code", "metadata": { "id": "2eEDmjb7n6dD", "colab_type": "code", "colab": {} }, "source": [ "# install nlp\n", "!pip install -qq nlp==0.2.0\n", "\n", "# Make sure that we have a recent version of pyarrow in the session before we continue - otherwise reboot Colab to activate it\n", "import pyarrow\n", "if int(pyarrow.__version__.split('.')[1]) < 16:\n", " import os\n", " os.kill(os.getpid(), 9)\n", "\n", "!pip install -qq transformers==2.10.0" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "jT3b07SpL1E9", "colab_type": "text" }, "source": [ "In case the notebook crashesh the wrong version of `pyarrow` was installed here. Simply rerun the cell to install the correct version." ] }, { "cell_type": "code", "metadata": { "id": "Ci_32Hd0iQ5X", "colab_type": "code", "colab": {} }, "source": [ "# imports\n", "from transformers import (\n", " ReformerModelWithLMHead,\n", " ReformerTokenizer,\n", " ReformerConfig,\n", " Trainer,\n", " DataCollator,\n", " TrainingArguments,\n", ")\n", "import nlp\n", "import torch" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "c4t6-YITkqYP", "colab_type": "text" }, "source": [ "First we download *Crime and Punish* which contains the content of a 800 page book using the convenient `nlp` library." ] }, { "cell_type": "code", "metadata": { "id": "dt1RQgR7k81u", "colab_type": "code", "colab": {} }, "source": [ "# load the dataset\n", "dataset = nlp.load_dataset(\"crime_and_punish\", split=\"train\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ohDfNT8alD1J", "colab_type": "text" }, "source": [ "Now let's get a pretrained sentence piece tokenizer that was trained on the *Crime and Punishment* dataset." ] }, { "cell_type": "code", "metadata": { "id": "3aliBYQXkNQ1", "colab_type": "code", "colab": {} }, "source": [ "# get a pretrained tokenizer\n", "tokenizer = ReformerTokenizer.from_pretrained(\"google/reformer-crime-and-punishment\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "VDjPXLnElOA1", "colab_type": "text" }, "source": [ "Because want to pack all data into a **single** sample, we use the handy `map()` function to reduce the dataset into one sample and pad the sample to a length of 524288. We then expand the same sample to 8 training samples so that we can accumulate gradients during training. Finally, we make the dataset ready for training, by only keeping the columns needed for training." ] }, { "cell_type": "code", "metadata": { "id": "EgNQIUo9lcUr", "colab_type": "code", "colab": {} }, "source": [ "sequence_length = 2 ** 19 # 524288\n", "\n", "# define our map function to reduce the dataset to one sample\n", "def flatten_and_tokenize(batch):\n", " all_input_text = [\"\".join(batch[\"line\"])]\n", " input_ids_dict = tokenizer.batch_encode_plus(\n", " all_input_text, pad_to_max_length=True, max_length=sequence_length\n", " )\n", "\n", " # duplicate data 8 times to have have 8 examples in dataset\n", " for key in input_ids_dict.keys():\n", " input_ids_dict[key] = [8 * [x] for x in input_ids_dict[key]][0]\n", "\n", " return input_ids_dict\n", "\n", "# reduce the dataset and set batch_size to all inputs\n", "dataset = dataset.map(\n", " flatten_and_tokenize, batched=True, batch_size=-1, remove_columns=[\"line\"]\n", ")\n", "\n", "# prepare dataset to be in torch format\n", "dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\"])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "5Nhhcj2_NF5J", "colab_type": "text" }, "source": [ "Let's do a quick check that our dataset contains 8 examples" ] }, { "cell_type": "code", "metadata": { "id": "sftN9BW8OmJX", "colab_type": "code", "outputId": "4e7f4d8d-f7db-4dd2-b04e-6d030c763067", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "dataset" ], "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Dataset(schema: {'input_ids': 'list', 'token_type_ids': 'list', 'attention_mask': 'list'}, num_rows: 8)" ] }, "metadata": { "tags": [] }, "execution_count": 8 } ] }, { "cell_type": "markdown", "metadata": { "id": "N8f1nEKtNak9", "colab_type": "text" }, "source": [ "Great! We can now move to designing the training." ] }, { "cell_type": "markdown", "metadata": { "id": "uxZBWnNllwnX", "colab_type": "text" }, "source": [ "As the official trax notebook says: *We don't want the model to just memorize the dataset (the single example) by encoding the words in its position embeddings. Thus, at each training iteration we will randomly select how much padding to put before the text vs. after it*." ] }, { "cell_type": "markdown", "metadata": { "id": "Pz4l4IAsOn8b", "colab_type": "text" }, "source": [ "With the `Trainer` framework of `transformers`, we can implement by using a *Reformer* specific `DataCollator` that randomely *shifts* the `input_ids` to the right and sets the `labels` correctly." ] }, { "cell_type": "code", "metadata": { "id": "TldJpBXkmF7W", "colab_type": "code", "colab": {} }, "source": [ "class ReformerCollator(DataCollator):\n", " def __init__(self, max_roll_length):\n", " self.max_roll_length = max_roll_length\n", "\n", " # From the official notebook: \"Normally we would have a dataset with many examples, but for this demonstration we fit a language model on the single novel only. We don't want the model to just memorize the dataset by encoding the words in its position embeddings, so at each training iteration we will randomly select how much padding to put before the text vs. after it\"\n", " def collate_batch(self, features):\n", " # get random shift int\n", " random_shift_length = torch.randint(self.max_roll_length, (1,)).item()\n", "\n", " # shift input and mask\n", " rolled_input_ids = torch.roll(\n", " features[0][\"input_ids\"], random_shift_length\n", " ).unsqueeze(0)\n", " rolled_attention_mask = torch.roll(\n", " features[0][\"attention_mask\"], random_shift_length\n", " ).unsqueeze(0)\n", "\n", " # return dict having the correct argument naming\n", " return {\n", " \"input_ids\": rolled_input_ids, # BS x SEQ_LEN\n", " \"labels\": rolled_input_ids, # BS x SEQ_LEN\n", " \"attention_mask\": rolled_attention_mask, # BS x SEQ_LEN\n", " }" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "lDoEmgXXmQPf", "colab_type": "text" }, "source": [ "To instantiate the data collator the length of padded `input_ids` needs to be calculated. " ] }, { "cell_type": "code", "metadata": { "id": "GP90f1xLmwQ6", "colab_type": "code", "colab": {} }, "source": [ "# the non_padded_sequence_length defines the max shift for our data collator\n", "non_padded_sequence_length = sequence_length - sum(\n", " dataset[\"attention_mask\"][0]\n", ")\n", "\n", "# get the data collator\n", "data_collator = ReformerCollator(non_padded_sequence_length)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "pVuOoH63jgKF", "colab_type": "text" }, "source": [ "Next, we will define our reformer model by defining the `ReformerConfig`. \n", "As can be seen we alternate between *local attention* layers and *lsh attention* layers to have a total of 6 layers. Also note that we factorize the `num_buckets` and use **Axial Position Embeddings**. For more insight on how the bucketing and Axial Position Embeddings work please refer to the Reformer [docs](https://huggingface.co/transformers/model_doc/reformer.html). " ] }, { "cell_type": "code", "metadata": { "id": "qS_3Sl8hjk-l", "colab_type": "code", "colab": {} }, "source": [ "config = {\n", " \"attention_head_size\": 64,\n", " \"attn_layers\": [\"local\", \"lsh\", \"local\", \"lsh\", \"local\", \"lsh\"],\n", " \"axial_pos_embds\": True,\n", " \"sinusoidal_pos_embds\": False,\n", " \"axial_pos_embds_dim\": [64, 192],\n", " \"axial_pos_shape\": [512, 1024],\n", " \"lsh_attn_chunk_length\": 64,\n", " \"local_attn_chunk_length\": 64,\n", " \"feed_forward_size\": 512,\n", " \"hidden_act\": \"relu\",\n", " \"hidden_size\": 256,\n", " \"is_decoder\": True,\n", " \"max_position_embeddings\": 524288,\n", " \"num_attention_heads\": 2,\n", " \"num_buckets\": [64, 128],\n", " \"num_hashes\": 1,\n", " \"vocab_size\": 320,\n", " \"lsh_attention_probs_dropout_prob\": 0.0,\n", " \"lsh_num_chunks_before\": 1,\n", " \"lsh_num_chunks_after\": 0,\n", " \"local_num_chunks_before\": 1,\n", " \"local_num_chunks_after\": 0,\n", " \"local_attention_probs_dropout_prob\": 0.025,\n", " \"hidden_dropout_prob\": 0.025,\n", "}\n", "\n", "config = ReformerConfig(**config)\n", "model = ReformerModelWithLMHead(config)\n", "model = model.train()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "7GN6_Lx0jz9H", "colab_type": "text" }, "source": [ "Lastly, let's set up the training args. **Note**: *these training settings have not throughly been tested and might be tuned for better results*." ] }, { "cell_type": "code", "metadata": { "id": "0QPBj-s0juaY", "colab_type": "code", "colab": {} }, "source": [ "# define the training args\n", "training_args = {\n", " \"learning_rate\": 1e-3,\n", " \"max_steps\": 2000,\n", " \"do_train\": True,\n", " \"evaluate_during_training\": True,\n", " \"gradient_accumulation_steps\": 8,\n", " \"logging_steps\": 50,\n", " \"warmup_steps\": 500,\n", " \"weight_decay\": 0.001,\n", " \"fp16\": True,\n", " \"per_gpu_train_batch_size\": 1,\n", " \"per_gpu_eval_batch_size\": 1,\n", " \"save_steps\": 50,\n", " \"output_dir\": \"./\"\n", "}\n", "\n", "training_args = TrainingArguments(**training_args)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "MpYDrBJhtZ4a", "colab_type": "text" }, "source": [ "We define a simple \"accuracy\" metric to keep track of how many samples are correctly predicted." ] }, { "cell_type": "code", "metadata": { "id": "to-SM2HftkO-", "colab_type": "code", "colab": {} }, "source": [ "def compute_metrics(pred):\n", " non_padded_indices = (pred.label_ids != -100)\n", "\n", " # correctly shift labels and pred as it's done in forward()\n", " labels = pred.label_ids[..., 1:][non_padded_indices[..., 1:]]\n", " pred = np.argmax(pred.predictions[:, :-1], axis=-1)[non_padded_indices[..., :-1]]\n", "\n", " acc = np.mean(np.asarray(pred == labels), dtype=np.float)\n", " return {\"accuracy\": acc}\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "EgNTZL0EAO6v", "colab_type": "text" }, "source": [ "To enable training with mixed precision, we need to download the apex package and connect it to CUDA. The following command does all that, but might take a while to finish.\n", "\n", "Let's first write a setup file" ] }, { "cell_type": "code", "metadata": { "id": "QCWVZsAXJF0_", "colab_type": "code", "cellView": "both", "outputId": "2a01b4a1-7a8b-4cf0-c65f-e5f090f4cb34", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "%%writefile setup.sh \n", "\n", "# install apex to be able to use mix precision\n", "export CUDA_HOME=/usr/local/cuda-10.1\n", "git clone https://github.com/NVIDIA/apex\n", "pip install -v -q --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" ./apex" ], "execution_count": 14, "outputs": [ { "output_type": "stream", "text": [ "Overwriting setup.sh\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "0PCHnADtAZNm", "colab_type": "text" }, "source": [ "And execute it afterward. " ] }, { "cell_type": "code", "metadata": { "id": "3d9M8Kwl_fdV", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "outputId": "07f9a37a-a3e5-4a49-9b40-7421773091d3" }, "source": [ "!sh setup.sh" ], "execution_count": 15, "outputs": [ { "output_type": "stream", "text": [ "fatal: destination path 'apex' already exists and is not an empty directory.\n", "/usr/local/lib/python3.6/dist-packages/pip/_internal/commands/install.py:283: UserWarning: Disabling all use of wheels due to the use of --build-options / --global-options / --install-options.\n", " cmdoptions.check_install_build_global(options)\n", "Processing ./apex\n", "Skipping wheel build for apex, due to binaries being disabled for it.\n", "Installing collected packages: apex\n", " Found existing installation: apex 0.1\n", " Uninstalling apex-0.1:\n", " Successfully uninstalled apex-0.1\n", " Running setup.py install for apex ... \u001b[?25l\u001b[?25hdone\n", "Successfully installed apex-0.1\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "KhYTZi3ZnTb9", "colab_type": "text" }, "source": [ "Finally we can start training. Since Google Colab only gives us a single GPU, this might take quite some time.\n", "\n", "\n", "**Note** it might happen that the training step afterward will throw an `fp16` not-installed error. In this case you will have to restart the runtime and run the notebook again." ] }, { "cell_type": "code", "metadata": { "id": "WskGtnXsnWdu", "colab_type": "code", "outputId": "078d3556-0436-4c0f-b773-f2ad5006015b", "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "112513edca074c8090eff7e9367ba945", "5be12d1052b64947ae423d7be17f7ecf", "f61b9a89144c44aba06e9c7eef3ddfae", "534129f53a6c45d4b9b14b93cf2afeb4", "91dc7f31d2c04e888ed4ea09544a235e", "472aa637d06c4b2f8d59b1aea31396c7", "7f770ae1db3543ed865393b736f21f22", "dfdeaf54bca94bf19dd52fe47e80ccb5", "0177dca88d114fb49651b1fb0c183874", "1c1f396da4eb43d1a9c9a484b43b49c9", "dbcfffc70647440ea4c7b110daec210f", "836671af41264baa9406e50e19f600e1", "343faafec93c439eafab303b968226cb", "e24f95a80cb54de2846eaf0c52720be2", "f6f2bc5d7a1c4818a1e65286dad1cef3", "5c111e5b7e8b49d6a0e36088ce2d4711", "eb7cd9cff68848af940a10935afd812c", "0703c39ac0124183ab8ec474aee68f15", "2e2abc176a5f4df4aa6ae85458d936c2", 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"f50b926f80ab40b1a92afc13f77d299a", "ce867212180048ebac7f8f6cc8f68fa3", "361f439840db49a784c76a66fbfacacd", "c16780fd83db4274b5525e71b4c9d715", "385ff11e8de64a439f89bb7d004f61a2", "efe0c41503034a75bf81626970f89e41", "1d8efb00be404836a791bc22b94d62bd", "c90088122fcb46588b7eaecb01b0bc79", "f02864ee506746e98d9c2fcbf7e5eccf", "2065825a42fb4116906c72410f338668", "c3f1cd7c67e14345b33eca8063af1e21", "a8c1e962141242f5a81157fabb2ae2dc", "1104fb715a6e4e87b5ce6708e56a1e8b", "dcdbba0c22ba4c83a62d58243ce9bbea", "283586c399bb40ce93b93cd588a7e079", "3ec052e03e944d478e3aa00d7679a667", "19279705c5984b638920c50b964ac8a1", "3fc6b5949aa148b38c5445f8b48b6c0b", "7d3589b7447a432581838ee252ba276c", "32938d636bb04bcb8ed046fd922fbc09", "a9ace6fc818b4b029d7eeb43cc3e53a6", "83d8a26e03114663b982e2aa8dd87fa8", "d7aa877afb64485988bd4ebb77646704", "8b7f04e69aaf4660bb0d16cefa9375b5", "088df58baa7847b3bda9dc2aefb8292a", "753d994fbb2547b7bb2a5edbe0a9ca41", "413e034d75b5492dbfc75ec8143c3251", "fa8a20fe79f9439d87743c8d23322971", "dbe2dc4cf57e45a4a236c3081ff4f14d", "b1555f89566c46d1a6180a122d01152d", "01b78ab5972448fe96ff18d425be77f9", "4b06431fe9b7417ca5f1b8135c9cecfe", "fdb8dd53b5cb486b91569bbb11ee2e01", "f074fc8f37d34a8495960197881de02d", "e8312257ea8744b7a6e3508569aeadd8", "0c1a996b0a524a34af21e3cc8769e0f7", "5d74e4c78883421886b5bdb6158c3734", "6808c48f30ee46829b27850336f647be", "8ef23cee027b45ab9fad12060a05da64", "c4013d25d08047eb88fa4ab7af75e6e9", "6c1eca189af04dc4b07480da72d38cf9" ] } }, "source": [ "# create the trainer\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " data_collator=data_collator,\n", " train_dataset=dataset,\n", " eval_dataset=dataset,\n", " prediction_loss_only=True,\n", ")\n", "\n", "# train\n", "trainer.train()" ], "execution_count": 16, "outputs": [ { "output_type": "stream", "text": [ "Selected optimization level O1: Insert automatic casts around Pytorch functions and Tensor methods.\n", "\n", "Defaults for this optimization level are:\n", "enabled : True\n", "opt_level : O1\n", "cast_model_type : None\n", "patch_torch_functions : True\n", "keep_batchnorm_fp32 : None\n", "master_weights : None\n", "loss_scale : dynamic\n", "Processing user overrides (additional kwargs that are not None)...\n", "After processing overrides, optimization options are:\n", "enabled : True\n", "opt_level : O1\n", "cast_model_type : None\n", "patch_torch_functions : True\n", "keep_batchnorm_fp32 : None\n", "master_weights : None\n", "loss_scale : dynamic\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "112513edca074c8090eff7e9367ba945", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Epoch', max=2001.0, style=ProgressStyle(description_width…" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0177dca88d114fb49651b1fb0c183874", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Iteration', max=8.0, style=ProgressStyle(description_widt…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:114: UserWarning: Seems like `optimizer.step()` has been overridden after learning rate scheduler initialization. Please, make sure to call `optimizer.step()` before `lr_scheduler.step()`. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", " \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "eb7cd9cff68848af940a10935afd812c", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Iteration', max=8.0, style=ProgressStyle(description_widt…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b0badcbf3d7c4e12bb1f472c02b0a630", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Iteration', max=8.0, style=ProgressStyle(description_widt…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": 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"ename": "KeyboardInterrupt", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m# train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, model_path)\u001b[0m\n\u001b[1;32m 468\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 469\u001b[0m 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gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m 196\u001b[0m \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 197\u001b[0m \"\"\"\n\u001b[0;32m--> 198\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m 98\u001b[0m Variable._execution_engine.run_backward(\n\u001b[1;32m 99\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m allow_unreachable=True) # allow_unreachable flag\n\u001b[0m\u001b[1;32m 101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ] }, { "cell_type": "markdown", "metadata": { "id": "WTgC9Ukl0J0d", "colab_type": "text" }, "source": [ "Let's see what the model learned to generate." ] }, { "cell_type": "code", "metadata": { "id": "ldi9UC_M0JIZ", "colab_type": "code", "colab": {} }, "source": [ "print(tokenizer.decode(model.generate(tokenizer.encode(\"Later that day, he\", return_tensors=\"pt\").to(model.device))[0]))" ], "execution_count": 0, "outputs": [] } ] } ================================================ FILE: README.md ================================================ A collection of google colab notebooks. ================================================ FILE: Reformer_1_4.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Reformer 1/4.ipynb", "provenance": [], "collapsed_sections": [], "authorship_tag": "ABX9TyN903RzmZ6tjCmIXftehEY7", "include_colab_link": true }, "kernelspec": { 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"1.2.0", "_model_module": "@jupyter-widgets/controls" } }, "c392d39d705843f7b10a97e7090e2d6a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { "_view_name": "LayoutView", "grid_template_rows": null, "right": null, "justify_content": null, "_view_module": "@jupyter-widgets/base", "overflow": null, "_model_module_version": "1.2.0", "_view_count": null, "flex_flow": null, "width": null, "min_width": null, "border": null, "align_items": null, "bottom": null, "_model_module": "@jupyter-widgets/base", "top": null, "grid_column": null, "overflow_y": null, "overflow_x": null, "grid_auto_flow": null, "grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "MJOlM8rEC2SA", "colab_type": "text" }, "source": [ "# **The Reformer - Pushing the limits of language modeling**\n", "\n", "***How the Reformer uses less than 8GB of RAM to train on sequences of half a million tokens***" ] }, { "cell_type": "markdown", "metadata": { "id": "mk1ETLlfEMGA", "colab_type": "text" }, "source": [ "The Reformer model as introduced by [Kitaev, Kaiser et al. (2020)](https://arxiv.org/pdf/2001.04451.pdf) is one of the most memory-efficient transformer models for long sequence modeling as of today.\n", "\n", "Recently, long sequence modeling has experienced a surge of interest as can be seen by the many submissions from this year alone - [Beltagy et al. (2020)](https://arxiv.org/abs/2004.05150), [Roy et al. (2020)](https://arxiv.org/abs/2003.05997), [Tay et al.](https://arxiv.org/abs/2002.11296), [Wang et al.](https://arxiv.org/abs/2006.04768) to name a few. \n", "The motivation behind long sequence modeling is that many tasks in NLP, *e.g.* summarization, question answering, require the model to process longer input sequences than models, such as BERT, are able to handle. In tasks that require the model to process a large input sequence, long sequence models do not have to cut the input sequence to avoid memory overflow and thus have been shown to outperform standard \"BERT\"-like models *cf.* [Beltagy et al. (2020)](https://arxiv.org/abs/2004.05150). \n", "\n", "The Reformer pushes the limit of longe sequence modeling by its ability to process up to half a million tokens at once as shown in this [demo](https://github.com/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb). As a comparison, a conventional `bert-base-uncased` model limits the input length to only 512 tokens. In Reformer, each part of the standard transformer architecture is re-engineered to optimize for minimal memory requirement without a significant drop in performance.\n", "\n", "The memory improvements can be attributed to **4** features which the Reformer authors introduced to the transformer world:\n", "\n", "1. **Reformer Self-Attention Layer** - *How to efficiently implement self-attention without being restricted to a local context?*\n", "2. **Chunked Feed Forward Layers** - *How to get a better time-memory trade-off for large feed forward layers?*\n", "3. **Reversible Residual Layers** - *How to drastically reduce memory consumption in training by a smart residual architecture?*\n", "4. **Axial Positional Encodings** - *How to make positional encodings usable for extremely large input sequences?*\n", "\n", "The goal of this blog post is to give the reader an **in-depth** understanding of each of the four Reformer features mentioned above. While the explanations are focussed on the Reformer, the reader should get a better intuition under which circumstances each of the four features can be effective for other transformer models as well. \n", "The four sections are only loosely connected, so they can very well be read individually.\n", "\n", "Reformer is part of the 🤗Transformers library. For all users of the Reformer, it is advised to go through this very detailed blog post to better understand how the model works and how to correctly set its configuration. All equations are accompanied by their equivalent name for the Reformer config, *e.g.* `config.`, so that the reader can quickly relate to the official docs and configuration file.\n", "\n", "**Note**: *Axial Positional Encodings* are not explained in the official Reformer paper, but are extensively used in the official codebase. This blog post gives the first in-depth explanation of Axial Positional Encodings." ] }, { "cell_type": "markdown", "metadata": { "id": "0T0PZLbKMelj", "colab_type": "text" }, "source": [ "## **1. Reformer Self-Attention Layer**\n", "\n", "Reformer uses two kinds of special self-attention layers: *local* self-attention layers and Locality Sensitive Hashing (*LSH*) self-attention layers.\n", "\n", "To better introduce these new self-attention layers, we will briefly recap \n", "conventional self-attention as introduced in [Vaswani et al. 2017](https://arxiv.org/abs/1706.03762).\n", "\n", "This blog post uses the same notation and coloring as the popular blog post [The illustrated transformer](http://jalammar.github.io/illustrated-transformer/), so the reader is strongly advised to read this blog first. \n", "\n", "**Important**: While Reformer was originally introduced for causal self-attention, it can very well be used for bi-directional self-attention as well. In this post, Reformer's self-attention is presented for *bidirectional* self-attention." ] }, { "cell_type": "markdown", "metadata": { "id": "txlnWJ-uzHoY", "colab_type": "text" }, "source": [ "### **Recap Global Self-Attention**\n", "\n", "The core of every Transformer model is the **self-attention** layer. To recap the conventional self-attention layer, which we refer to here as the **global self-attention** layer, let us assume we apply a transformer layer on the embedding vector sequence $\\mathbf{X} = \\mathbf{x}_1, \\ldots, \\mathbf{x}_n$ where each vector $\\mathbf{x}_i$ is of size `config.hidden_size`, *i.e.* $d_h$. \n", "\n", "In short, a global self-attention layer projects $\\mathbf{X}$ to the query, key and value matrices $\\mathbf{Q}, \\mathbf{K}, \\mathbf{V}$ and computes the output $\\mathbf{Z}$ using the *softmax* operation as follows:\n", "$\\mathbf{Z} = \\text{SelfAttn}(\\mathbf{X}) = \\text{softmax}(\\mathbf{Q}\\mathbf{K}^T) \\mathbf{V}$ with $\\mathbf{Z}$ being of dimension $d_h \\times n$ (leaving out the key normalization factor and self-attention weights $\\mathbf{W}^{O}$ for simplicity). For more detail on the complete transformer operation, see [the illustrated transformer](http://jalammar.github.io/illustrated-transformer/).\n", "\n", "Visually, we can illustrate this operation as follows for $n=16, d_h=3$:\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/conventional_attention.png)\n", "\n", "*Note*: For all visualizations `batch_size` and `config.num_attention_heads` is assumed to be 1. Some vectors, *e.g.* $\\mathbf{x_3}$ and its corresponding output vector $\\mathbf{z_3}$ are marked so that *LSH self-attention* can later be better explained. The presented logic can effortlessly be extended for multi-head self-attention (`config.num_attention_heads` > 1). The reader is advised to read [the illustrated transformer](http://jalammar.github.io/illustrated-transformer/) as a reference for multi-head self-attention.\n", "\n", "Important to remember is that for each output vector $\\mathbf{z}_i$, the whole input sequence $\\mathbf{X}$ is processed. The tensor of the inner dot-product $\\mathbf{Q}\\mathbf{K}^T$ has an asymptotic memory complexity of $\\mathcal{O}(n^2)$ which usually represents the memory bottleneck in a transformer model. \n", "\n", "This is also the reason why `bert-base-cased` has a `config.max_position_embedding_size` of only 512." ] }, { "cell_type": "markdown", "metadata": { "id": "sFgRgcTczHFS", "colab_type": "text" }, "source": [ "### **Local Self-Attention**\n", "\n", "**Local self-attention** is the obvious solution to reducing the $\\mathcal{O}(n^2)$ memory bottleneck, allowing us to model longer sequences with a reduced computational cost. In local self-attention the input $\\mathbf{X} = \\mathbf{X}_{1:n} = \\mathbf{x}_1, ... \\mathbf{x}_n$ is cut into $n_c$ chunks: $\\mathbf{X} = \\left[\\mathbf{X}_{1:l_c}, \\ldots, \\mathbf{X}_{(n_c - 1) * l_c : n_c * l_c}\\right]$ each of length `config.local_chunk_length`, *i.e.* $l_c$, and subsequently global self-attention is applied on each chunk separately.\n", "\n", "Let's take our input sequence for $n=16, d_h=3$ again for visualization:\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/input.png)\n", "\n", "Assuming $l_c = 4, n_c = 4$, chunked attention can be illustrated as follows:\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/chunked_attention_1.png)\n", "\n", "As can be seen, the attention operation is applied for each chunk $\\mathbf{X}_{1:4}, \\mathbf{X}_{5:8}, \\mathbf{X}_{9:12}, \\mathbf{X}_{13:16}$ individually.\n", "The first drawback of this architecture becomes obvious: Some input vectors have no access to their immediate context, *e.g.* $\\mathbf{x}_9$ has no access to $\\mathbf{x}_8$ and vice-versa in our example. This is problematic because these tokens are not able to learn word representations that take their immediate context into account.\n", "\n", "A simple remedy is to augment each chunk with `config.local_num_chunks_before`, *i.e.* $n_p$, chunks and `config.local_num_chunks_after`, *i.e.* $n_a$, so that every input vector has at least access to $n_p$ previous input vectors and $n_a$ following input vectors. This can also be understood as chunking with overlap whereas $n_p$ and $n_a$ define the amount of overlap each chunk has with all previous chunks and following chunks. We denote this extended local self-attention as follows: \n", "\n", "$\\mathbf{Z}^{\\text{loc}} = \\left[\\mathbf{Z}_{1:l_c}^{\\text{loc}}, \\ldots, \\mathbf{Z}_{(n_c - 1) * l_c : n_c * l_c}^{\\text{loc}}\\right]$ with $\\mathbf{Z}_{l_c * (i - 1) + 1 : l_c * i}^{\\text{loc}} = \\text{SelfAttn}(\\mathbf{X}_{l_c * (i - 1 - n_p) + 1: l_c * (i + n_a)})\\left[n_p * l_c: -n_a * l_c\\right], \\forall i \\in \\{1, \\ldots, n_c \\}$.\n", "\n", "Okay, this formula looks quite complicated. Let's make it easier.\n", "In Reformer's self-attention layers $n_a$ is usually set to 0 and $n_p$ is set to 1, so let's write down the formula again for $i = 1$:\n", "\n", "$\\mathbf{Z}_{1:l_c}^{\\text{loc}} = \\text{SelfAttn}(\\mathbf{X}_{-l_c + 1: l_c})\\left[l_c:\\right]$\n", "\n", "We notice that we have a circular relationship so that the first segment can attend the last segment as well. Let's illustrate this slightly enhanced local attention again. First, we apply self-attention within each windowed segment and keep only the central output segment.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/local_attention_2.png)\n", "\n", "Finally, the relevant output is concatenated to $\\mathbf{Z}^{\\text{loc}}$ and looks as follows.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/local_attention_3.png)\n", "\n", "*Note that local self-attention is implemented efficiently way so that no output is computed and subsequently \"thrown-out\" as shown here for illustration purposes by the red cross.*\n", "\n", "It's important to note here that extending the input vectors for each chunked self-attention function allows *each* single output vector $\\mathbf{z}_i$ of this self-attention function to learn better vector representations. *E.g.* each of the output vectors $\\mathbf{z}_{5}^{\\text{loc}}, \\mathbf{z}_{6}^{\\text{loc}}, \\mathbf{z}_{7}^{\\text{loc}}, \\mathbf{z}_{8}^{\\text{loc}}$ can take into account all of the input vectors $\\mathbf{X}_{1:8}$ to learn better representations.\n", "\n", "The gain in memory consumption is quite obvious: The $\\mathcal{O}(n^2)$ memory complexity is broken down for each segment individually so that the total asymptotic memory consumption is reduced to $\\mathcal{O}(n_c * l_c^2) = \\mathcal{O}(n * l_c)$.\n", "\n", "This enhanced local self-attention is better than the vanilla local self-attention architecture but still has a major drawback in that every input vector can only attend to a local context of predefined size. For NLP tasks that do not require the transformer model to learn long-range dependencies between the input vectors, which include arguably *e.g.* speech recognition, named entity recognition and causal language modeling of short sentences, this might not be a big issue. Many NLP tasks do require the model to learn long-range dependencies, so that local self-attention could lead to significant performance degradation, *e.g.* \n", "* *Question-answering*: the model has to learn the relationship between the question tokens and relevant answer tokens which will most likely not be in the same local range\n", "* *Multiple-Choice*: the model has to compare multiple answer token segments to each other which are usually separated by a significant length\n", "* *Summarization*: the model has to learn the relationship between a long sequence of context tokens and a shorter sequence of summary tokens, whereas the relevant relationships between context and summary can most likely not be captured by local self-attention\n", "* etc...\n", "\n", "Local self-attention on its own is most likely not sufficient for the transformer model to learn the relevant relationships of input vectors (tokens) to each other.\n", "\n", "Therefore, Reformer additionally employs an efficient self-attention layer that approximates global self-attention, called *LSH self-attention*." ] }, { "cell_type": "markdown", "metadata": { "id": "C9t3UmT9M706", "colab_type": "text" }, "source": [ "### **LSH Self-Attention**\n", "\n", "Alright, now that we have understood how local self-attention works, we can take a stab at the probably most innovative piece of Reformer: **Locality sensitive hashing (LSH) Self-Attention**. \n", "\n", "The premise of LSH self-attention is to be more or less as efficient as local self-attention while approximating global self-attention.\n", "\n", "LSH self-attention relies on the LSH algorithm as presented in [Andoni et al (2015)](https://arxiv.org/abs/1509.02897), hence its name.\n", "\n", "The idea behind LSH self-attention is based on the insight that if $n$ is large, the softmax applied on the $\\mathbf{Q}\\mathbf{K}^T$ attention dot-product weights only very few value vectors with values significantly larger than 0 for each query vector. \n", "\n", "Let's explain this in more detail.\n", "Let $\\mathbf{k}_i \\in \\mathbf{K} = \\left[\\mathbf{k}_1, \\ldots, \\mathbf{k}_n \\right]^T$ and $\\mathbf{q}_i \\in \\mathbf{Q} = \\left[\\mathbf{q}_1, \\ldots, \\mathbf{q}_n\\right]^T$ be the key and query vectors. For each $\\mathbf{q}_i$, the computation $\\text{softmax}(\\mathbf{q}_i^T \\mathbf{K}^T)$ can be approximated by using only those key vectors of $\\mathbf{k}_j$ that have a high cosine similarity with $\\mathbf{q}_i$. This owes to the fact that the softmax function puts exponentially more weight on larger input values.\n", "So far so good, the next problem is to efficiently find the vectors that have a\n", "high cosine similarity with $\\mathbf{q}_i$ for all $i$.\n", "\n", "First, the authors of Reformer notice that sharing the query and key projections: $\\mathbf{Q} = \\mathbf{K}$ does not impact the performance of a transformer model${}^1$. Now, instead of having to find the key vectors of high cosine similarity for each query vector $q_i$, only the cosine similarity of query vectors to each other has to be found. \n", "This is important because there is a transitive property to the query-query vector dot product approximation: If $\\mathbf{q}_i$ has a high cosine similarity to the query vectors $\\mathbf{q}_j$ and $\\mathbf{q}_k$, then $\\mathbf{q}_j$ also has a high cosine similarity to $\\mathbf{q}_k$. Therefore, the query vectors can be clustered into buckets, such that all query vectors that belong to the same bucket have a high cosine similarity to each other. Let's define $C_m$ as the *mth* set of position indices, such that their corresponding query vectors are in the same bucket: $C_m = \\{ i | \\text{ s.t. } \\mathbf{q}_i \\in \\text{mth cluster}\\}$ and `config.num_buckets`, *i.e.* $n_b$, as the number of buckets.\n", "\n", "For each set of indices $C_m$, the softmax function on the corresponding bucket of query vectors $\\text{softmax}(\\mathbf{Q}_{i \\in C_m} \\mathbf{Q}^T_{i \\in C_m})$ approximates the softmax function of global self-attention with shared query and key projections $\\text{softmax}(\\mathbf{q}_i^T \\mathbf{Q}^T)$ for all position indices $i$ in $C_m$.\n", "\n", "Second, the authors make use of the **LSH** algorithm to cluster the query vectors into a predefined number of buckets $n_b$. The LSH algorithm is an ideal choice here because it is very efficient and is an approximation of the nearest neighbor algorithm for cosine similarity. Explaining the LSH scheme is out-of-scope for this notebook, so let's just keep in mind that for each vector $\\mathbf{q}_i$ the LSH algorithm attributes its position index $i$ to one of $n_b$ predefined buckets, *i.e.* $\\text{LSH}(\\mathbf{q}_i) = m$ with $i \\in \\{1, \\ldots, n\\}$ and $m \\in \\{1, \\ldots, n_b\\}$.\n", "\n", "Visually, we can illustrate this as follows for our original example:\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/lsh_hashing.png)\n", "\n", "Third, it can be noted that having clustered all query vectors in $n_b$ buckets, the corresponding set of indices $C_m$ can be used to permute the input vectors $\\mathbf{x}_1, \\ldots, \\mathbf{x}_n$ accordingly${}^2$ so that shared query-key self-attention can be applied piecewise similar to local attention. \n", "\n", "Let's clarify with our example input vectors $\\mathbf{X} = \\mathbf{x}_1, ..., \\mathbf{x}_{16}$ and assume `config.num_buckets=4` and `config.lsh_chunk_length = 4`. Looking at the graphic above we can see that we have assigned each query vector $\\mathbf{q}_1, \\ldots, \\mathbf{q}_{16}$ to one of the clusters $\\mathcal{C}_1, \\mathcal{C}_2, \\mathcal{C}_3, \\mathcal{C}_4$. If we now sort the corresponding input vectors $\\mathbf{x}_1, \\ldots, \\mathbf{x}_{16}$ accordingly, we get the following permuted input $\\mathbf{X'}$:\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/lsh_perm.png)\n", "\n", "The self-attention mechanism should be applied for each cluster individually so that for each cluster $\\mathcal{C}_m$ the corresponding output is calculated as follows: $\\mathbf{Z}^{\\text{LSH}}_{i \\in \\mathcal{C}_m} = \\text{SelfAttn}_{\\mathbf{Q}=\\mathbf{K}}(\\mathbf{X}_{i \\in \\mathcal{C}_m})$.\n", "\n", "Let's illustrate this again for our example.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/lsh_cluster_attn.png)\n", "\n", "As can be seen, the self-attention function operates on different sizes of matrices, which is suboptimal for efficient batching in GPU and TPU. \n", "\n", "To overcome this problem, the permuted input can be chunked the same way it is done for local attention so that each chunk is of size `config.lsh_chunk_length`. By chunking the permuted input, a bucket might be split into two different chunks. To remedy this problem, in LSH self-attention each chunk attends to its previous chunk `config.lsh_num_chunks_before=1` in addition to itself, the same way local self-attention does (`config.lsh_num_chunks_after` is usually set to 0). This way, we can be assured that all vectors in a bucket attend to each other with a high probability${}^3$.\n", "\n", "All in all for all chunks $k \\in \\{1, \\ldots, n_c\\}$, LSH self-attention can be noted down as follows:\n", "\n", "$\\mathbf{Z'}_{l_c * k + 1:l_c * (k + 1)}^{\\text{LSH}} = \\text{SelfAttn}_{\\mathbf{Q} = \\mathbf{K}}(\\mathbf{X'}_{l_c * k + 1): l_c * (k + 1)})\\left[l_c:\\right]$\n", "\n", "with $\\mathbf{X'}$ and $\\mathbf{Z'}$ being the input and output vectors permuted according to the LSH algorithm.\n", "Enough complicated formulas, let's illustrate LSH self-attention.\n", "\n", "The permuted vectors $\\mathbf{X'}$ as shown above are chunked and shared query key self-attention is applied to each chunk.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/lsh_attention_2.png)\n", "\n", "Finally, the output $\\mathbf{Z'}^{\\text{LSH}}$ is reordered to its original permutation.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/lsh_attention_3.png)\n", "\n", "One important feature to mention here as well is that the accuracy of LSH self-attention can be improved by running LSH self-attention `config.num_hashes`, *e.g.* $n_h$ times in parallel, each with a different random LSH hash. \n", "By setting `config.num_hashes > 1`, for each output position $i$, multiple output vectors $\\mathbf{z}^{\\text{LSH}, 1}_i, \\ldots, \\mathbf{z}^{\\text{LSH}, n_h}_i$ are computed and subsequently merged: $\\mathbf{z}^{\\text{LSH}}_i = \\sum_k^{n_h} \\mathbf{Z}^{\\text{LSH}, k}_i * \\text{weight}^k_i$. The $\\text{weight}^k_i$ represents the importance of the output vectors $\\mathbf{z}^{\\text{LSH}, k}_i$ of hashing round $k$ in comparison to the other hashing rounds, and is exponentially proportional to the normalization term of their softmax computation. The intuition behind this is that if the corresponding query vector $\\mathbf{q}_i^{k}$ have a high cosine similarity with all other query vectors in its respective chunk, then the softmax normalization term of this chunk tends to be high, so that the corresponding output vectors $\\mathbf{q}_i^{k}$ should be a better approximation to global attention and thus receive more weight than output vectors of hashing rounds with a lower softmax normalization term. For more detail see Appendix A of the [paper](https://arxiv.org/pdf/2001.04451.pdf). For our example, multi-round LSH self-attention can be illustrated as follows.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/lsh_attention_4.png)\n", "\n", "Great. That's it. Now we know how LSH self-attention works in Reformer. \n", "\n", "Regarding the memory complexity, we now have two terms that compete which each other to be the memory bottleneck: the dot-product: $\\mathcal{O}(n_h * n_c * l_c^2) = \\mathcal{O}(n * n_h * l_c)$ and the required memory for LSH bucketing: $\\mathcal{O}(n * n_h * \\frac{n_b}{2})$ with $l_c$ being the chunk length. Because for large $n$, the number of buckets $\\frac{n_b}{2}$ grows much faster than the chunk length $l_c$, the user can again factorize the number of buckets `config.num_buckets` as explained [here](https://huggingface.co/transformers/model_doc/reformer.html#lsh-self-attention).\n", "\n", "Let's recap quickly what we have gone through above:\n", "\n", "1. We want to approximate global attention using the knowledge that the softmax operation only puts significant weights on very few key vectors.\n", "2. If key vectors are equal to query vectors this means that *for each* query vector $\\mathbf{q}_i$, the softmax only puts significant weight on other query vectors that are similar in terms of cosine similarity.\n", "3. This relationship works in both ways, meaning if $\\mathbf{q}_j$ is similar to $\\mathbf{q}_i$ than $\\mathbf{q}_j$ is also similar to $\\mathbf{q}_i$, so that we can do a global clustering before applying self-attention on a permuted input.\n", "4. We apply local self-attention on the permuted input and re-order the output to its original permutation.\n", "\n", "---\n", "${}^{1}$ The authors run some preliminary experiments confirming that shared query key self-attention performs more or less as well as standard self-attention.\n", "\n", "${}^{2}$ To be more exact the query vectors within a bucket are sorted according to their original order. This means if, *e.g.* the vectors $\\mathbf{q}_1, \\mathbf{q}_3, \\mathbf{q}_7$ are all hashed to bucket 2, the order of the vectors in bucket 2 would still be $\\mathbf{q}_1$, followed by $\\mathbf{q}_3$ and $\\mathbf{q}_7$.\n", "\n", "${}^3$ On a side note, it is to mention the authors put a mask on the query vector $\\mathbf{q}_i$ to prevent the vector from attending to itself. Because the cosine similarity of a vector to itself will always be as high or higher than the cosine similarity to other vectors, the query vectors in shared query key self-attention are strongly discouraged to attend to themselves.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "wt_LzqLFjq0u", "colab_type": "text" }, "source": [ "### **Benchmark**\n", "\n", "Benchmark tools were recently added to Transformers - see [here](https://github.com/huggingface/transformers/blob/master/notebooks/05-benchmark.ipynb) for a more detailed explanation.\n", "\n", "To show how much memory can be saved using \"local\" + \"LSH\" self-attention, the Reformer model `google/reformer-enwik8` is benchmarked for different `local_attn_chunk_length` and `lsh_attn_chunk_length`. The default configuration and usage of the `google/reformer-enwik8` model can be checked in more detail [here](https://huggingface.co/google/reformer-enwik8).\n", "\n", "Let's first do some necessary imports and installs." ] }, { "cell_type": "code", "metadata": { "id": "tzpvJ1KgzwY-", "colab_type": "code", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 119 }, "outputId": "15c399ee-07a5-4b06-8094-758c7750be88" }, "source": [ "#@title Installs and Imports\n", "# pip installs\n", "!pip -qq install git+https://github.com/huggingface/transformers.git\n", "!pip install -qq py3nvml\n", "\n", "from transformers import ReformerConfig, PyTorchBenchmark, PyTorchBenchmarkArguments" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "\u001b[K |████████████████████████████████| 3.0MB 3.5MB/s \n", "\u001b[K |████████████████████████████████| 1.1MB 16.6MB/s \n", "\u001b[K |████████████████████████████████| 890kB 32.9MB/s \n", "\u001b[?25h Building wheel for transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[K |████████████████████████████████| 61kB 2.1MB/s \n", "\u001b[?25h" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "As0zSF5AcgdK", "colab_type": "text" }, "source": [ "First, let's benchmark the memory usage of the Reformer model using *global* self-attention. This can be achieved by setting `lsh_attn_chunk_length` = `local_attn_chunk_length` = 8192 so that for all input sequences smaller or equal to 8192, the model automatically switches to global self-attention." ] }, { "cell_type": "code", "metadata": { "id": "14DB0VQCO8pP", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 270, "referenced_widgets": [ "6004ee3d5da74020bb526ac9c5b109d8", "cc6dc34e670040858032ce3bc7f481f5", "cc5b597b9e39493b88268b44e50860df", "ce77e89b68bc4ca29c4dcd38895c6c4d", "8ae3ca9d8f174c208b36e6e3a123d5b0", "42fbc5957cdc44778dcd2dd29c59cbab", "addb7dfd77614baa8dda60a34a734169", "c392d39d705843f7b10a97e7090e2d6a" ] }, "outputId": "fdacc9ba-1ecb-43e2-abf5-8d15185ec984" }, "source": [ "config = ReformerConfig.from_pretrained(\"google/reformer-enwik8\", lsh_attn_chunk_length=16386, local_attn_chunk_length=16386, lsh_num_chunks_before=0, local_num_chunks_before=0)\n", "benchmark_args = PyTorchBenchmarkArguments(sequence_lengths=[2048, 4096, 8192, 16386], batch_sizes=[1], models=[\"Reformer\"], no_speed=True, no_env_print=True)\n", "benchmark = PyTorchBenchmark(configs=[config], args=benchmark_args)\n", "result = benchmark.run()" ], "execution_count": 2, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6004ee3d5da74020bb526ac9c5b109d8", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1279.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "1 / 1\n", "Doesn't fit on GPU. CUDA out of memory. Tried to allocate 2.00 GiB (GPU 0; 11.17 GiB total capacity; 8.87 GiB already allocated; 1.92 GiB free; 8.88 GiB reserved in total by PyTorch)\n", "\n", "==================== INFERENCE - MEMORY - RESULT ====================\n", "--------------------------------------------------------------------------------\n", " Model Name Batch Size Seq Length Memory in MB \n", "--------------------------------------------------------------------------------\n", " Reformer 1 2048 1465 \n", " Reformer 1 4096 2757 \n", " Reformer 1 8192 7893 \n", " Reformer 1 16386 N/A \n", "--------------------------------------------------------------------------------\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "c2ilA44YdOHj", "colab_type": "text" }, "source": [ "The longer the input sequence, the more visible is the quadratic relationship $\\mathcal{O}(n^2)$ between input sequence and peak memory usage. As can be seen, in practice it would require a much longer input sequence to clearly observe that doubling the input sequence quadruples the peak memory usage.\n", "\n", "For this a `google/reformer-enwik8` model using global attention, a sequence length of over 16K results in a memory overflow.\n", "\n", "Now, let's activate *local* and *LSH* self-attention by using the model's default parameters." ] }, { "cell_type": "code", "metadata": { "id": "UonmNfQAZLef", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 272 }, "outputId": "1cc08f5c-feb6-4170-ed54-9d0b273f6055" }, "source": [ " config = ReformerConfig.from_pretrained(\"google/reformer-enwik8\")\n", " benchmark_args = PyTorchBenchmarkArguments(sequence_lengths=[2048, 4096, 8192, 16384, 32768, 65436], batch_sizes=[1], models=[\"Reformer\"], no_speed=True, no_env_print=True)\n", " benchmark = PyTorchBenchmark(configs=[config], args=benchmark_args)\n", " result = benchmark.run()" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ "1 / 1\n", "Doesn't fit on GPU. CUDA out of memory. Tried to allocate 2.00 GiB (GPU 0; 11.17 GiB total capacity; 7.85 GiB already allocated; 1.74 GiB free; 9.06 GiB reserved in total by PyTorch)\n", "Doesn't fit on GPU. CUDA out of memory. Tried to allocate 4.00 GiB (GPU 0; 11.17 GiB total capacity; 6.56 GiB already allocated; 3.99 GiB free; 6.81 GiB reserved in total by PyTorch)\n", "\n", "==================== INFERENCE - MEMORY - RESULT ====================\n", "--------------------------------------------------------------------------------\n", " Model Name Batch Size Seq Length Memory in MB \n", "--------------------------------------------------------------------------------\n", " Reformer 1 2048 1785 \n", " Reformer 1 4096 2621 \n", " Reformer 1 8192 4281 \n", " Reformer 1 16384 7607 \n", " Reformer 1 32768 N/A \n", " Reformer 1 65436 N/A \n", "--------------------------------------------------------------------------------\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "e24Al5h6kVFe", "colab_type": "text" }, "source": [ "As expected using local and LSH self-attention is much more memory efficient for longer input sequences, so that the model runs out of memory only at 32K tokens (on this 11 GB GPU). 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"grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "MJOlM8rEC2SA", "colab_type": "text" }, "source": [ "# **The Reformer - Pushing the limits of language modeling**\n", "\n", "***How the Reformer uses less than 8GB of RAM to train on sequences of half a million tokens***" ] }, { "cell_type": "markdown", "metadata": { "id": "mk1ETLlfEMGA", "colab_type": "text" }, "source": [ "The Reformer model as introduced by [Kitaev, Kaiser et al. (2020)](https://arxiv.org/pdf/2001.04451.pdf) is one of the most memory-efficient transformer models for long sequence modeling as of today.\n", "\n", "Recently, long sequence modeling has experienced a surge of interest as can be seen by the many submissions from this year alone - [Beltagy et al. (2020)](https://arxiv.org/abs/2004.05150), [Roy et al. (2020)](https://arxiv.org/abs/2003.05997), [Tay et al.](https://arxiv.org/abs/2002.11296), [Wang et al.](https://arxiv.org/abs/2006.04768) to name a few. \n", "The motivation behind long sequence modeling is that many tasks in NLP, *e.g.* summarization, question answering, require the model to process longer input sequences than models, such as BERT, are able to handle. In tasks that require the model to process a large input sequence, long sequence models do not have to cut the input sequence to avoid memory overflow and thus have been shown to outperform standard \"BERT\"-like models *cf.* [Beltagy et al. (2020)](https://arxiv.org/abs/2004.05150). \n", "\n", "The Reformer pushes the limit of longe sequence modeling by its ability to process up to half a million tokens at once as shown in this [demo](https://github.com/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb). As a comparison, a conventional `bert-base-uncased` model limits the input length to only 512 tokens. In Reformer, each part of the standard transformer architecture is re-engineered to optimize for minimal memory requirement without a significant drop in performance.\n", "\n", "The memory improvements can be attributed to **4** features which the Reformer authors introduced to the transformer world:\n", "\n", "1. **Reformer Self-Attention Layer** - *How to efficiently implement self-attention without being restricted to a local context?*\n", "=> see [this colab](https://colab.research.google.com/drive/15oP52_7W5dRcAnbgX3tYADsu4R3cjMIf?usp=sharing).\n", "2. **Chunked Feed Forward Layers** - *How to get a better time-memory trade-off for large feed forward layers?*\n", "3. **Reversible Residual Layers** - *How to drastically reduce memory consumption in training by a smart residual architecture?*\n", "4. **Axial Positional Encodings** - *How to make positional encodings usable for extremely large input sequences?*\n", "\n", "The goal of this blog post is to give the reader an **in-depth** understanding of each of the four Reformer features mentioned above. While the explanations are focussed on the Reformer, the reader should get a better intuition under which circumstances each of the four features can be effective for other transformer models as well. \n", "The four sections are only loosely connected, so they can very well be read individually.\n", "\n", "Reformer is part of the 🤗Transformers library. For all users of the Reformer, it is advised to go through this very detailed blog post to better understand how the model works and how to correctly set its configuration. All equations are accompanied by their equivalent name for the Reformer config, *e.g.* `config.`, so that the reader can quickly relate to the official docs and configuration file.\n", "\n", "**Note**: *Axial Positional Encodings* are not explained in the official Reformer paper, but are extensively used in the official codebase. This blog post gives the first in-depth explanation of Axial Positional Encodings." ] }, { "cell_type": "markdown", "metadata": { "id": "GNs6JrxtglSz", "colab_type": "text" }, "source": [ "## **2. Chunked Feed Forward Layers**\n", "\n", "Transformer-based models often employ very large feed forward layers after the self-attention layer in parallel. Thereby, this layer can take up a significant amount of the overall memory and sometimes even represent the memory bottleneck of a model.\n", "First introduced in the Reformer paper, feed forward chunking is a technique that allows to effectively trade better memory consumption for increased time consumption.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "uOL5A2FlD-r4", "colab_type": "text" }, "source": [ "### **Chunked Feed Forward Layer in Reformer**\n", "\n", "In Reformer, the *LSH*- or *local* self-attention layer (review part 1 [here](https://colab.research.google.com/drive/15oP52_7W5dRcAnbgX3tYADsu4R3cjMIf?usp=sharing)) is usually followed by a residual connection, which then defines the first part in a *transformer block*. For more detail on this please refer to this [blog](http://jalammar.github.io/illustrated-transformer/). \n", "\n", "The output of the first part of the *transformer block*, called *normed self-attention* output can be written as $\\mathbf{\\overline{Z}} = \\mathbf{Z} + \\mathbf{X}$, with $\\mathbf{Z}$ being either $\\mathbf{Z}^{\\text{LSH}}$ or $\\mathbf{Z}^\\text{loc}$ in Reformer.\n", "\n", "For our example input $\\mathbf{x}_1, \\ldots, \\mathbf{x}_{16}$, we illustrate the normed self-attention output as follows.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/layer_normed_output.png)\n", "\n", "Now, the second part of a *transformer block* usually consists of two feed forward layers$^{1}$, defined as $\\text{Linear}_{\\text{int}}(\\ldots)$ that processes $\\mathbf{\\overline{Z}}$, to an intermediate output $\\mathbf{Y}_{\\text{int}}$ and $\\text{Linear}_{\\text{out}}(\\ldots)$ that processes the intermediate output to the output $\\mathbf{Y}_{\\text{out}}$. The two feed forward layers can be defined by $\\mathbf{Y}_{\\text{out}} = \\text{Linear}_{\\text{out}}(\\mathbf{Y}_\\text{int}) = \n", "\\text{Linear}_{\\text{out}}(\\text{Linear}_{\\text{int}}(\\mathbf{\\overline{Z}}))$.\n", "\n", "It is important to remember at this point that mathematically the output of a feed forward layer at position $\\mathbf{y}_{\\text{out}, i}$ only depends on the input at this position $\\mathbf{\\overline{y}}_i$. In contrast to the self-attention layer, every output $\\mathbf{y}_{\\text{out}, i}$ is therefore completely independent of all inputs $\\mathbf{\\overline{y}}_{j \\ne i}$ of different positions. \n", "\n", "Let's illustrate the feed forward layers for $\\mathbf{\\overline{z}}_1, \\ldots, \\mathbf{\\overline{z}}_{16}$.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/feed_forward.png)\n", "\n", "As can be depicted from the illustration, all input vectors $\\mathbf{\\overline{z}}_i$ are processed by the same feed forward layer in parallel.\n", "\n", "It becomes interesting when one takes a look at the output dimensions of the feed forward layers. In Reformer, the output dimension of $\\text{Linear}_{\\text{int}}$ is defined as `config.feed_forward_size`, *e.g.* $d_f$, and the output dimension of $\\text{Linear}_{\\text{int}}$ is defined as `config.hidden_size`, *i.e.* $d_h$. \n", "\n", "The Reformer authors observed that in a transformer model the intermediate dimension $d_f$ usually tends to be much larger than the output dimension$^{2}$ $d_h$. This means that the tensor $\\mathbf{\\mathbf{Y}}_\\text{int}$ of dimension $d_f \\times n$ allocates a significant amount of the total memory and can even become the memory bottleneck.\n", "\n", "To get a better feeling for the differences in dimensions let's picture the matrices $\\mathbf{Y}_\\text{int}$ and $\\mathbf{Y}_\\text{out}$ for our example.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/feed_forward_matrix.png)\n", "\n", "It is becoming quite obvious that the tensor $\\mathbf{Y}_\\text{int}$ holds much more memory ($\\frac{d_f}{d_h} \\times n$ as much to be exact) than $\\mathbf{Y}_{\\text{out}}$. But, is it even necessary to compute the full intermediate matrix $\\mathbf{Y}_\\text{int}$ ? Not really, because relevant is only the output matrix $\\mathbf{Y}_\\text{out}$. \n", "To trade memory for speed, one can thus chunk the linear layers computation to only process one chunk at the time. Defining `config.chunk_size_feed_forward` as $c_f$, chunked linear layers are defined as $\\mathbf{Y}_{\\text{out}} = \\left[\\mathbf{Y}_{\\text{out}, 1: c_f}, \\ldots, \\mathbf{Y}_{\\text{out}, (n - c_f): n}\\right]$ with $\\mathbf{Y}_{\\text{out}, (c_f * i): (i * c_f + i)} = \\text{Linear}_{\\text{out}}(\\text{Linear}_{\\text{int}}(\\mathbf{\\overline{Z}}_{(c_f * i): (i * c_f + i)}))$. \n", "In practice, it just means that the output is incrementally computed and concatenated to avoid having to store the whole intermediate tensor $\\mathbf{Y}_{\\text{int}}$ in memory.\n", "\n", "Assuming $c_f=1$ for our example we can illustrate the incremental computation of the output for position $i=9$ as follows. \n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/chunked_feed_forward.png)\n", "\n", "By processing the inputs in chunks of size 1, the only tensors that have to be stored in memory at the same time are $\\mathbf{Y}_\\text{out}$ of a maximum size of $16 \\times d_h$, $\\mathbf{y}_{\\text{int}, i}$ of size $d_f$ and the input $\\mathbf{\\overline{Z}}$ of size $16 \\times d_h$, with $d_h$ being `config.hidden_size`$^{3}$.\n", "\n", "Finally, it is important to remember that *chunked linear layers* yield a mathematically equivalent output to conventional linear layers and can therefore be applied to all transformer linear layers. Making use of `config.chunk_size_feed_forward` therefore allows a better trade-off between memory and speed in certain use cases.\n", "\n", "---\n", "${}^1$ For a simpler explanation, the layer norm layer which is normally applied to $\\mathbf{\\overline{Z}}$ before being processed by the feed forward layers is omitted for now.\n", "\n", "${}^2$ In `bert-base-uncased`, *e.g.* the intermediate dimension $d_f$ is with 3072 four times larger than the output dimension $d_h$.\n", "\n", "${}^3$ As a reminder, the output `config.num_attention_heads` is assumed to be 1 for the sake of clarity and illustration in this notebook, so that the output of the self-attention layers can be assumed to be of size `config.hidden_size`.\n", "\n", "More information on chunked linear / feed forward layers can also be found [here](https://huggingface.co/transformers/glossary.html#feed-forward-chunking) on the 🤗Transformers docs.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "l8H6l0nwguG6", "colab_type": "text" }, "source": [ "### **Benchmark**\n", "\n", "Let's test how much memory can be saved by using chunked feed forward layers. Check out this [notebook](https://github.com/huggingface/transformers/blob/master/notebooks/05-benchmark.ipynb) for more detail on benchmarking in Transformers." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "cellView": "form", "id": "uruiPZSvnTr3", "colab": { "base_uri": "https://localhost:8080/", "height": 119 }, "outputId": "0fea9ec2-b16d-46e1-f6a0-0f1b28c38aa1" }, "source": [ "#@title Installs and Imports\n", "# pip installs\n", "!pip -qq install git+https://github.com/huggingface/transformers.git\n", "!pip install -qq py3nvml\n", "\n", "from transformers import ReformerConfig, PyTorchBenchmark, PyTorchBenchmarkArguments" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "\u001b[K |████████████████████████████████| 3.0MB 6.4MB/s \n", "\u001b[K |████████████████████████████████| 1.1MB 40.7MB/s \n", "\u001b[K |████████████████████████████████| 890kB 39.3MB/s \n", "\u001b[?25h Building wheel for transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[K |████████████████████████████████| 61kB 3.3MB/s \n", "\u001b[?25h" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Fa6nMxpCvIFh", "colab_type": "text" }, "source": [ "First, let's compare the default `google/reformer-enwik8` model without chunked feed forward layers to the one with chunked feed forward layers." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "phw_UOkDnWkC", "colab": { "base_uri": "https://localhost:8080/", "height": 338, "referenced_widgets": [ "d5f4343ae72d49ddbfa3f09d40356fa1", "afc7db77344c4a74b2be9deebaf193e1", "0763a5da0d4441119d5d7efbdf7d0344", "2eb9ca3bbf5c47cf916db94b60cd1306", "0f46f57e27c34e44812af5477092664e", "6e0260d875ad4af8a138797d620fd55b", "d0da1f7cfec54416a10a3b9886bab742", "9a1365461f8a4cae8660ec0ff2400bbf" ] }, "outputId": "40fe6f1f-39eb-44d6-bd2c-4452470c4664" }, "source": [ "config_no_chunk = ReformerConfig.from_pretrained(\"google/reformer-enwik8\") # no chunk\n", "config_chunk = ReformerConfig.from_pretrained(\"google/reformer-enwik8\", chunk_size_feed_forward=1) # feed forward chunk\n", "benchmark_args = PyTorchBenchmarkArguments(sequence_lengths=[1024, 2048, 4096], batch_sizes=[8], models=[\"Reformer-No-Chunk\", \"Reformer-Chunk\"], no_speed=True, no_env_print=True)\n", "benchmark = PyTorchBenchmark(configs=[config_no_chunk, config_chunk], args=benchmark_args)\n", "result = benchmark.run()" ], "execution_count": 2, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d5f4343ae72d49ddbfa3f09d40356fa1", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1279.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "1 / 2\n", "Doesn't fit on GPU. CUDA out of memory. Tried to allocate 2.00 GiB (GPU 0; 11.17 GiB total capacity; 7.85 GiB already allocated; 1.74 GiB free; 9.06 GiB reserved in total by PyTorch)\n", "2 / 2\n", "Doesn't fit on GPU. CUDA out of memory. Tried to allocate 2.00 GiB (GPU 0; 11.17 GiB total capacity; 7.85 GiB already allocated; 1.24 GiB free; 9.56 GiB reserved in total by PyTorch)\n", "\n", "==================== INFERENCE - MEMORY - RESULT ====================\n", "--------------------------------------------------------------------------------\n", " Model Name Batch Size Seq Length Memory in MB \n", "--------------------------------------------------------------------------------\n", " Reformer-No-Chunk 8 1024 4281 \n", " Reformer-No-Chunk 8 2048 7607 \n", " Reformer-No-Chunk 8 4096 N/A \n", " Reformer-Chunk 8 1024 4309 \n", " Reformer-Chunk 8 2048 7669 \n", " Reformer-Chunk 8 4096 N/A \n", "--------------------------------------------------------------------------------\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "VDWUt2VZvuHL", "colab_type": "text" }, "source": [ "Interesting, chunked feed forward layers do not seem to help here at all! The reason is that `config.feed_forward_size` is not sufficiently large to become the memory bottleneck. \n", "\n", "Let's see what happens to the memory peak usage if we increase the size of the feed forward layer by a factor of 4 and reduce the number of attention heads also by a factor of 4 so that the feed forward layer becomes the memory bottleneck." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "kKsvtutaovYp", "colab": { "base_uri": "https://localhost:8080/", "height": 255 }, "outputId": "263ba73c-21b5-407c-f26a-f236d4989aff" }, "source": [ "config_no_chunk = ReformerConfig.from_pretrained(\"google/reformer-enwik8\", chunk_size_feed_forward=0, num_attention_heads=2, feed_forward_size=16384) # no chuck\n", "config_chunk = ReformerConfig.from_pretrained(\"google/reformer-enwik8\", chunk_size_feed_forward=1, num_attention_heads=2, feed_forward_size=16384) # feed forward chunk\n", "benchmark_args = PyTorchBenchmarkArguments(sequence_lengths=[1024, 2048, 4096], batch_sizes=[8], models=[\"Reformer-No-Chunk\", \"Reformer-Chunk\"], no_speed=True, no_env_print=True)\n", "benchmark = PyTorchBenchmark(configs=[config_no_chunk, config_chunk], args=benchmark_args)\n", "result = benchmark.run()" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ "1 / 2\n", "2 / 2\n", "\n", "==================== INFERENCE - MEMORY - RESULT ====================\n", "--------------------------------------------------------------------------------\n", " Model Name Batch Size Seq Length Memory in MB \n", "--------------------------------------------------------------------------------\n", " Reformer-No-Chunk 8 1024 3743 \n", " Reformer-No-Chunk 8 2048 5539 \n", " Reformer-No-Chunk 8 4096 9087 \n", " Reformer-Chunk 8 1024 2973 \n", " Reformer-Chunk 8 2048 3999 \n", " Reformer-Chunk 8 4096 6011 \n", "--------------------------------------------------------------------------------\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "36IOAWbJxgFG", "colab_type": "text" }, "source": [ "Now a clear decrease in peak memory usage can be seen for longer input sequences. \n", "As a conclusion, it should be noted that chunked feed forward layers only make sense for models having few attention heads and large feed forward layers." ] } ] } ================================================ FILE: Reformer_3_4.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Reformer 3/4.ipynb", "provenance": [], "collapsed_sections": [], "authorship_tag": "ABX9TyP6Nic4j60p6gjdBhqkUkCF", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "e3cb4123ed1044e3b2bb3c154527d3d2": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "state": { "_view_name": "HBoxView", "_dom_classes": [], "_model_name": "HBoxModel", 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"metadata": { "id": "MJOlM8rEC2SA", "colab_type": "text" }, "source": [ "# **The Reformer - Pushing the limits of language modeling**\n", "\n", "***How the Reformer uses less than 8GB of RAM to train on sequences of half a million tokens***" ] }, { "cell_type": "markdown", "metadata": { "id": "mk1ETLlfEMGA", "colab_type": "text" }, "source": [ "The Reformer model as introduced by [Kitaev, Kaiser et al. (2020)](https://arxiv.org/pdf/2001.04451.pdf) is one of the most memory-efficient transformer models for long sequence modeling as of today.\n", "\n", "Recently, long sequence modeling has experienced a surge of interest as can be seen by the many submissions from this year alone - [Beltagy et al. (2020)](https://arxiv.org/abs/2004.05150), [Roy et al. (2020)](https://arxiv.org/abs/2003.05997), [Tay et al.](https://arxiv.org/abs/2002.11296), [Wang et al.](https://arxiv.org/abs/2006.04768) to name a few. \n", "The motivation behind long sequence modeling is that many tasks in NLP, *e.g.* summarization, question answering, require the model to process longer input sequences than models, such as BERT, are able to handle. In tasks that require the model to process a large input sequence, long sequence models do not have to cut the input sequence to avoid memory overflow and thus have been shown to outperform standard \"BERT\"-like models *cf.* [Beltagy et al. (2020)](https://arxiv.org/abs/2004.05150). \n", "\n", "The Reformer pushes the limit of longe sequence modeling by its ability to process up to half a million tokens at once as shown in this [demo](https://github.com/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb). As a comparison, a conventional `bert-base-uncased` model limits the input length to only 512 tokens. In Reformer, each part of the standard transformer architecture is re-engineered to optimize for minimal memory requirement without a significant drop in performance.\n", "\n", "The memory improvements can be attributed to **4** features which the Reformer authors introduced to the transformer world:\n", "\n", "1. **Reformer Self-Attention Layer** - *How to efficiently implement self-attention without being restricted to a local context?* => see [this colab](https://colab.research.google.com/drive/15oP52_7W5dRcAnbgX3tYADsu4R3cjMIf?usp=sharing)\n", "2. **Chunked Feed Forward Layers** - *How to get a better time-memory trade-off for large feed forward layers?* => see [this colab](https://colab.research.google.com/drive/1xKK32Yhda-iYgtoA3eCrnCVuy_lraQR9?usp=sharing)\n", "3. **Reversible Residual Layers** - *How to drastically reduce memory consumption in training by a smart residual architecture?*\n", "4. **Axial Positional Encodings** - *How to make positional encodings usable for extremely large input sequences?*\n", "\n", "The goal of this blog post is to give the reader an **in-depth** understanding of each of the four Reformer features mentioned above. While the explanations are focussed on the Reformer, the reader should get a better intuition under which circumstances each of the four features can be effective for other transformer models as well. \n", "The four sections are only loosely connected, so they can very well be read individually.\n", "\n", "Reformer is part of the 🤗Transformers library. For all users of the Reformer, it is advised to go through this very detailed blog post to better understand how the model works and how to correctly set its configuration. All equations are accompanied by their equivalent name for the Reformer config, *e.g.* `config.`, so that the reader can quickly relate to the official docs and configuration file.\n", "\n", "**Note**: *Axial Positional Encodings* are not explained in the official Reformer paper, but are extensively used in the official codebase. This blog post gives the first in-depth explanation of Axial Positional Encodings." ] }, { "cell_type": "markdown", "metadata": { "id": "x8UDe-tVhHSz", "colab_type": "text" }, "source": [ "## **3. Reversible Residual Layers**\n", "\n", "Reversible residual layers were first introduced in [N. Gomez et al](https://arxiv.org/abs/1707.04585) and used to reduce memory consumption when training the popular *ResNet* model. Mathematically, reversible residual layers are slightly different \n", "to \"real\" residual layers but do not require the activations to be saved during the forward pass, which can drastically reduce memory consumption for training." ] }, { "cell_type": "markdown", "metadata": { "id": "FYDqvqMjoMNE", "colab_type": "text" }, "source": [ "### **Reversible Residual Layers in Reformer**\n", "\n", "Let's start by investigating why training a model requires \n", "much more memory than the inference of the model.\n", "\n", "When running a model in inference, the required memory equals more or less the memory it takes to compute the **single** largest tensor in the model.\n", "On the other hand, when training a model, the required memory equals more or less the **sum** of all differentiable tensors.\n", "\n", "This is not surprising when considering how auto differentiation works in deep learning frameworks. These lecture [slides](https://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slides/lec10.pdf) by Roger Grosse of the University of Toronto are great to better understand auto differentiation.\n", "\n", "In a nutshell, in order to calculate the gradient of a differentiable function (*e.g.* a layer), auto differentiation requires the gradient of the function's output and the function's input and output tensor. While the gradients are dynamically computed and subsequently discarded, the input and output tensors (*a.k.a* activations) of a function are stored during the forward pass.\n", "\n", "Alright, let's apply this to a transformer model. A transformer model includes a stack of multiple so-called transformer layers. Each additional transformer layer forces the model to store more activations during the forward pass and thus increases the required memory for training. \n", "Let's take a more detailed look. A transformer layer essentially consists of two residual layers. The first residual layer represents the *self-attention* mechanism as explained in section 1) and the second residual layer represents the *linear* or feed-forward layers as explained in section 2).\n", "\n", "Using the same notation in the previous notebooks [here](https://colab.research.google.com/drive/15oP52_7W5dRcAnbgX3tYADsu4R3cjMIf?usp=sharing) and [here](https://colab.research.google.com/drive/1xKK32Yhda-iYgtoA3eCrnCVuy_lraQR9#scrollTo=GNs6JrxtglSz), the input of a transformer layer *i.e.* $\\mathbf{X}$ is first normalized$^{1}$ and subsequently processed by the self-attention layer to get the output $\\mathbf{Z} = \\text{SelfAttn}(\\text{LayerNorm}(\\mathbf{X}))$. We will abbreviate these two layers with $G$ so that $\\mathbf{Z} = G(\\mathbf{X})$. \n", "Next, the residual $\\mathbf{Z}$ is added to the input $\\mathbf{\\overline{Z}} = \\mathbf{Z} + \\mathbf{X}$ and the sum is fed into the second residual layer - the two linear layers. $\\mathbf{\\overline{Z}}$ is processed by a second normalization layer, followed by the two linear layers to get $\\mathbf{Y} = \\text{Linear}(\\text{LayerNorm}(\\mathbf{Z} + \\mathbf{X}))$. We will abbreviate the second normalization layer and the two linear layers with $F$ yielding $\\mathbf{Y} = F(\\mathbf{\\overline{Z}})$. \n", "Finally, the residual $\\mathbf{Y}$ is added to $\\mathbf{\\overline{Z}}$ to give the output of the transformer layer $\\mathbf{\\overline{Y}} = \\mathbf{Y} + \\mathbf{\\overline{Z}}$.\n", "\n", "Let's illustrate a complete transformer layer using the example of $\\mathbf{x}_1, \\ldots, \\mathbf{x}_{16}$.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/normal_trans_resnet.png)\n", "\n", "To calculate the gradient of *e.g.* the self-attention block $G$, three tensors have to be known beforehand: the gradient $\\partial \\mathbf{Z}$, the output $\\mathbf{Z}$, and the input $\\mathbf{X}$. While $\\partial \\mathbf{Z}$ can be calculated on-the-fly and discarded afterward, the values for $\\mathbf{Z}$ and $\\mathbf{X}$ have to be calculated and stored during the forward pass since it is not possible to recalculate them easily on-the-fly during backpropagation. Therefore, during the forward pass, large tensor outputs, such as the query-key dot product matrix $\\mathbf{Q}\\mathbf{K}^T$ or the intermediate output of the linear layers $\\mathbf{Y}^{\\text{int}}$, have to be stored in memory $^{2}$.\n", "\n", "Here, reversible residual layers come to our help. The idea is relatively straight-forward. The residual block is designed in a way so that instead of having to store the input and output tensor of a function, both can easily be recalculated during the backward pass so that no tensor has to be stored in memory during the forward pass. \n", "This is achieved by using two input streams $\\mathbf{X}^{(1)}, \\mathbf{X}^{(2)}$, and two output streams $\\mathbf{\\overline{Y}}^{(1)}, \\mathbf{\\overline{Y}}^{(2)}$. The first residual $\\mathbf{Z}$ is computed by the first output stream $\\mathbf{Z} = G(\\mathbf{X}^{(1)})$ and subsequently added to the input of the second input stream, so that $\\mathbf{\\overline{Z}} = \\mathbf{Z} + \\mathbf{X}^{(2)}$. \n", "Similarly, the residual $\\mathbf{Y} = F(\\mathbf{\\overline{Z}})$ is added to the first input stream again, so that the two output streams are defined by $\\mathbf{Y}^{(1)} = \\mathbf{Y} + \\mathbf{X}^{(1)}$ and $\\mathbf{Y}^{(2)} = \\mathbf{X}^{(2)} + \\mathbf{Z} = \\mathbf{\\overline{Z}}$.\n", "\n", "The reversible transformer layer can be visualized for $\\mathbf{x}_1, \\ldots, \\mathbf{x}_{16}$ as follows.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/rev_trans_resnet.png)\n", "\n", "As can be seen, the outputs $\\mathbf{\\overline{Y}}^{(1)}, \\mathbf{\\overline{Y}}^{(2)}$ are calculated in a very similar way than $\\mathbf{\\overline{Y}}$ of the non-reversible layer, but they are mathematically different. The authors of Reformer observe in some initial experiments that the performance of a reversible transformer model matches the performance of a standard transformer model. \n", "The first visible difference to the standard transformer layer is that there are two input streams and output streams $^{3}$, which at first slightly increases the required memory for both the forward pass.\n", "The two-stream architecture is crucial though for not having to save any activations during the forward pass. Let's explain. For backpropagation, the reversible transformer layer has to calculate the gradients $\\partial G$ and $\\partial F$. In addition to the gradients $\\partial \\mathbf{Y}$ and $\\partial \\mathbf{Z}$ which can be calculated on-the-fly, the tensor values $\\mathbf{Y}$, $\\mathbf{\\overline{Z}}$ have to be known for $\\partial F$ and the tensor values $\\mathbf{Z}$ and $\\mathbf{X}^{(1)}$ for $\\partial G$ to make auto-differentiation work.\n", "\n", "If we assume to know $\\mathbf{\\overline{Y}}^{(1)}, \\mathbf{\\overline{Y}}^{(2)}$, it can easily be depicted from the graph that one can calculate $\\mathbf{X}^{(1)}, \\mathbf{X}^{(2)}$ as follows. $\\mathbf{X}^{(1)} = F(\\mathbf{\\overline{Y}}^{(1)}) - \\mathbf{\\overline{Y}}^{(1)}$. Great, now that $\\mathbf{X}^{(1)}$ is known, $\\mathbf{X}^{(2)}$ can be computed by $\\mathbf{X}^{(2)} = \\mathbf{\\overline{Y}}^{(1)} - G(\\mathbf{X}^{(1)})$. Alright now, $\\mathbf{Z}$ and $\\mathbf{Y}$ are trivial to compute via $\\mathbf{Y} = \\mathbf{\\overline{Y}}^{(1)} - \\mathbf{X}^{(1)}$ and $\\mathbf{Z} = \\mathbf{\\overline{Y}}^{(2)} - \\mathbf{X}^{(2)}$. So as a conclusion, if only the outputs $\\mathbf{\\overline{Y}}^{(1)}, \\mathbf{\\overline{Y}}^{(2)}$ of the **last** reversible transformer layer are stored during the forward pass, all other relevant activations can be derived by making use of $G$ and $F$ during the backward pass and passing $\\mathbf{X}^{(1)}$ and $\\mathbf{X}^{(2)}$. The overhead of two forward passes of $G$ and $F$ per reversible transformer layer during the backpropagation is traded against not having to store any activations during the forward pass. Not a bad deal!\n", "\n", "**Note**: Since recently, major deep learning frameworks have released code that allows to store only certain activations and recompute larger ones during the backward propagation (Tensoflow [here](https://www.tensorflow.org/api_docs/python/tf/recompute_grad) and PyTorch [here](https://pytorch.org/docs/stable/checkpoint.html)). For standard reversible layers, this still means that at least one activation has to be stored for each transformer layer, but by defining which activations can dynamically be recomputed a lot of memory can be saved.\n", "\n", "---\n", "$^{1}$ In the previous two sections, we have omitted the layer norm layers preceding both the self-attention layer and the linear layers. The reader should know that both $\\mathbf{X}$ and $\\mathbf{\\overline{Z}}$ are both processed by layer normalization before being fed into self-attention and the linear layers respectively.\n", "$^{2}$ While in the design the dimension of $\\mathbf{Q}\\mathbf{K}$ is written as $n \\times n$, in a *LSH self-attention* or *local self-attention* layer the dimension would only be $n \\times l_c \\times n_h$ or $n \\times l_c$ respectively with $l_c$ being the chunk length and $n_h$ the number of hashes\n", "$^{3}$ In the first reversible transformer layer $\\mathbf{X}^{(2)}$ is set to be equal to $\\mathbf{X}^{(1)}$.\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "WgFnJ7E2DoUx" }, "source": [ "### **Benchmark**\n", "\n", "In order to measure the effect of reversible residual layers, we will compare the memory consumption of BERT with Reformer in training for an increasing number of layers." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "cellView": "form", "id": "o0yrOhyTDoVG", "colab": { "base_uri": "https://localhost:8080/", "height": 118 }, "outputId": "15f97b11-4f7e-4767-c830-f6008bb87a92" }, "source": [ "#@title Installs and Imports\n", "# pip installs\n", "!pip -qq install git+https://github.com/huggingface/transformers.git\n", "!pip install -qq py3nvml\n", "\n", "from transformers import ReformerConfig, BertConfig, PyTorchBenchmark, PyTorchBenchmarkArguments" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "\u001b[K |████████████████████████████████| 3.0MB 3.5MB/s \n", "\u001b[K |████████████████████████████████| 1.1MB 35.7MB/s \n", "\u001b[K |████████████████████████████████| 890kB 36.9MB/s \n", "\u001b[?25h Building wheel for transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[K |████████████████████████████████| 61kB 2.2MB/s \n", "\u001b[?25h" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "tff-yj23LHgz", "colab_type": "text" }, "source": [ "Let's measure the required memory for the standard `bert-base-uncased` BERT model by increasing the number of layers from 4 to 12." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "a5SviIEEITrV", "colab": { "base_uri": "https://localhost:8080/", "height": 267, "referenced_widgets": [ "e3cb4123ed1044e3b2bb3c154527d3d2", "2ddd3f160cf34364acb649b604fc6ebc", "f021349b4d4c4c0eb207d73464ae20d2", "e8e64ca107a24d72bf4a6b90df996292", "ab61967930554898b24aba55e55c66b2", "f0af45fe41754b48afb8655361b840a9", "a259f9768c494b83a01f329e31def43d", "25922977db3f495ea67cd33fa458266a" ] }, "outputId": "7e8128d2-9151-4635-d010-d27d054d9fd5" }, "source": [ "config_4_layers_bert = BertConfig.from_pretrained(\"bert-base-uncased\", num_hidden_layers=4)\n", "config_8_layers_bert = BertConfig.from_pretrained(\"bert-base-uncased\", num_hidden_layers=8)\n", "config_12_layers_bert = BertConfig.from_pretrained(\"bert-base-uncased\", num_hidden_layers=12)\n", "benchmark_args = PyTorchBenchmarkArguments(sequence_lengths=[512], batch_sizes=[8], models=[\"Bert-4-Layers\", \"Bert-8-Layers\", \"Bert-12-Layers\"], training=True, no_inference=True, no_speed=True, no_env_print=True)\n", "benchmark = PyTorchBenchmark(configs=[config_4_layers_bert, config_8_layers_bert, config_12_layers_bert], args=benchmark_args)\n", "result = benchmark.run()" ], "execution_count": 2, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e3cb4123ed1044e3b2bb3c154527d3d2", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "1 / 3\n", "2 / 3\n", "3 / 3\n", "\n", "==================== TRAIN - MEMORY - RESULTS ====================\n", "--------------------------------------------------------------------------------\n", " Model Name Batch Size Seq Length Memory in MB \n", "--------------------------------------------------------------------------------\n", " Bert-4-Layers 8 512 4103 \n", " Bert-8-Layers 8 512 5759 \n", " Bert-12-Layers 8 512 7415 \n", "--------------------------------------------------------------------------------\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "wjuVFnq-LldT", "colab_type": "text" }, "source": [ "It can be seen that adding a single layer of BERT linearly increases the required memory by ca. 400MB." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "1ZHeLsY4DoVV", "colab": { "base_uri": "https://localhost:8080/", "height": 267, "referenced_widgets": [ "fdf61e6ed5cd4ad6994fe1dc1b33f043", "1b2af7b1525e4f848e47de1cfc8c9eca", "f4b97d79982f4af2b94d1ea73c97fb03", "499bd46fba22474486fee028ed5e2cd5", "74eed5249643447d911efe4c0e49ec1c", "06718d965f74437e8f7a4fa271172415", "63ab253bc59147bca3c3208cc17d6007", "d4b907b9de5f4a5495904ba3c3a8c592" ] }, "outputId": "c60b4c5e-d101-4d9f-f04d-4d9cb867fb5c" }, "source": [ "config_4_layers_reformer = ReformerConfig.from_pretrained(\"google/reformer-enwik8\", num_hidden_layers=4, num_hashes=1)\n", "config_8_layers_reformer = ReformerConfig.from_pretrained(\"google/reformer-enwik8\", num_hidden_layers=8, num_hashes=1)\n", "config_12_layers_reformer = ReformerConfig.from_pretrained(\"google/reformer-enwik8\", num_hidden_layers=12, num_hashes=1)\n", "benchmark_args = PyTorchBenchmarkArguments(sequence_lengths=[512], batch_sizes=[8], models=[\"Reformer-4-Layers\", \"Reformer-8-Layers\", \"Reformer-12-Layers\"], training=True, no_inference=True, no_speed=True, no_env_print=True)\n", "benchmark = PyTorchBenchmark(configs=[config_4_layers_reformer, config_8_layers_reformer, config_12_layers_reformer], args=benchmark_args)\n", "result = benchmark.run()" ], "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fdf61e6ed5cd4ad6994fe1dc1b33f043", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1279.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "1 / 3\n", "2 / 3\n", "3 / 3\n", "\n", "==================== TRAIN - MEMORY - RESULTS ====================\n", "--------------------------------------------------------------------------------\n", " Model Name Batch Size Seq Length Memory in MB \n", "--------------------------------------------------------------------------------\n", " Reformer-4-Layers 8 512 4607 \n", " Reformer-8-Layers 8 512 4987 \n", " Reformer-12-Layers 8 512 5367 \n", "--------------------------------------------------------------------------------\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "69ZaU3jhDoVa" }, "source": [ "For Reformer, on the other hand, adding a layer adds significantly less memory in practice. Adding a single layer increases the required memory on average by less than 100MB so that a much larger 12-Layer `reformer-enwik8` model requires less memory than a 12-Layer `bert-base-uncased` model." ] } ] } ================================================ FILE: Reformer_4_4.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Reformer 4/4.ipynb", "provenance": [], "collapsed_sections": [], "authorship_tag": "ABX9TyM+1sgDFnZcLUNTEYhp1sQG", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "ba1341e2776c4c77ba3ef1cb678ca276": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "state": { "_view_name": "HBoxView", "_dom_classes": [], "_model_name": "HBoxModel", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_view_count": null, 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"align_items": null, "bottom": null, "_model_module": "@jupyter-widgets/base", "top": null, "grid_column": null, "overflow_y": null, "overflow_x": null, "grid_auto_flow": null, "grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "MJOlM8rEC2SA", "colab_type": "text" }, "source": [ "# **The Reformer - Pushing the limits of language modeling**\n", "\n", "***How the Reformer uses less than 8GB of RAM to train on sequences of half a million tokens***" ] }, { "cell_type": "markdown", "metadata": { "id": "mk1ETLlfEMGA", "colab_type": "text" }, "source": [ "The Reformer model as introduced by [Kitaev, Kaiser et al. (2020)](https://arxiv.org/pdf/2001.04451.pdf) is one of the most memory-efficient transformer models for long sequence modeling as of today.\n", "\n", "Recently, long sequence modeling has experienced a surge of interest as can be seen by the many submissions from this year alone - [Beltagy et al. (2020)](https://arxiv.org/abs/2004.05150), [Roy et al. (2020)](https://arxiv.org/abs/2003.05997), [Tay et al.](https://arxiv.org/abs/2002.11296), [Wang et al.](https://arxiv.org/abs/2006.04768) to name a few. \n", "The motivation behind long sequence modeling is that many tasks in NLP, *e.g.* summarization, question answering, require the model to process longer input sequences than models, such as BERT, are able to handle. In tasks that require the model to process a large input sequence, long sequence models do not have to cut the input sequence to avoid memory overflow and thus have been shown to outperform standard \"BERT\"-like models *cf.* [Beltagy et al. (2020)](https://arxiv.org/abs/2004.05150). \n", "\n", "The Reformer pushes the limit of longe sequence modeling by its ability to process up to half a million tokens at once as shown in this [demo](https://github.com/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb). As a comparison, a conventional `bert-base-uncased` model limits the input length to only 512 tokens. In Reformer, each part of the standard transformer architecture is re-engineered to optimize for minimal memory requirement without a significant drop in performance.\n", "\n", "The memory improvements can be attributed to **4** features which the Reformer authors introduced to the transformer world:\n", "\n", "1. **Reformer Self-Attention Layer** - *How to efficiently implement self-attention without being restricted to a local context?* => see [this colab](https://colab.research.google.com/drive/15oP52_7W5dRcAnbgX3tYADsu4R3cjMIf?usp=sharing)\n", "2. **Chunked Feed Forward Layers** - *How to get a better time-memory trade-off for large feed forward layers?* => see [this colab](https://colab.research.google.com/drive/1xKK32Yhda-iYgtoA3eCrnCVuy_lraQR9?usp=sharing)\n", "3. **Reversible Residual Layers** - *How to drastically reduce memory consumption in training by a smart residual architecture?* => see [this colab](https://colab.research.google.com/drive/1BLffcRt9LXmM7nKU2UXhtm0PqAG0UE7J#scrollTo=mk1ETLlfEMGA)\n", "4. **Axial Positional Encodings** - *How to make positional encodings usable for extremely large input sequences?*\n", "\n", "The goal of this blog post is to give the reader an **in-depth** understanding of each of the four Reformer features mentioned above. While the explanations are focussed on the Reformer, the reader should get a better intuition under which circumstances each of the four features can be effective for other transformer models as well. \n", "The four sections are only loosely connected, so they can very well be read individually.\n", "\n", "Reformer is part of the 🤗Transformers library. For all users of the Reformer, it is advised to go through this very detailed blog post to better understand how the model works and how to correctly set its configuration. All equations are accompanied by their equivalent name for the Reformer config, *e.g.* `config.`, so that the reader can quickly relate to the official docs and configuration file.\n", "\n", "**Note**: *Axial Positional Encodings* are not explained in the official Reformer paper, but are extensively used in the official codebase. This blog post gives the first in-depth explanation of Axial Positional Encodings." ] }, { "cell_type": "markdown", "metadata": { "id": "m9ox4Pm8Dgil", "colab_type": "text" }, "source": [ "## **4. Axial Positional Encodings**\n", "\n", "Reformer makes it possible to process huge input sequences. However, for such long input sequences standard positional encoding weight matrices alone would use more than 1GB to store its weights.\n", "To prevent such large positional encoding matrices, the official Reformer code makes use of *Axial Position Encodings*.\n", "\n", "**Important:** *Axial Position Encodings were not explained in the official paper, but can be well understood from looking into the code and talking to the authors*\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ogDIps0Zjljs", "colab_type": "text" }, "source": [ "### **Axial Positional Encodings in Reformer**\n", "\n", "Transformers need positional encodings to account for the order of words in the input because self-attention layers have *no notion of order*. \n", "Positional encodings are usually defined by a simple look-up matrix $\\mathbf{E} = \\left[\\mathbf{e}_1, \\ldots, \\mathbf{e}_{n_\\text{max}}\\right]$ The positional encoding vector $\\mathbf{e}_i$ is then simply added to the *ith* input vector $\\mathbf{x}_i + \\mathbf{e}_i$ so that the model can distinguish if an input vector (*a.k.a* token) is at position $i$ or $j$. \n", "For every input position, the model needs to be able to look up the corresponding positional encoding vector so that the dimension of $\\mathbf{E}$ is defined by the maximum length of input vectors the model can process `config.max_position_embeddings`, *i.e.* $n_\\text{max}$, and the `config.hidden_size`, *i.e.* $d_h$ of the input vectors. \n", "\n", "Assuming $d_h=4$ and $n_\\text{max}=49$, such a positional encoding matrix can be visualized as follows:\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/positional_encodings_default.png)\n", "\n", "Here, we showcase only the positional encodings $\\mathbf{e}_1$, $\\mathbf{e}_2$, and $\\mathbf{e}_{49}$ each of dimension, *a.k.a* height 4.\n", "\n", "Let's imagine, we want to train a Reformer model on sequences of a length of up to 0.5M tokens and an input vector `config.hidden_size` of 1024 (see notebook [here](https://github.com/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb)). The corresponding positional embeddings have a size of $0.5M \\times 1024 \\sim 512M$ parameters, which corresponds to a size of 2GB.\n", "\n", "Such positional encodings would use an unnecessarily large amount of memory both when loading the model in memory and when saving the model on a hard drive.\n", "\n", "The Reformer authors managed to drastically shrink the positional encodings in size by cutting the `config.hidden_size` dimension in two and smartly factorizing the $n_\\text{max}$ dimension. In Transformer, the user can decide into which shape $n_\\text{max}$ can be factorized into by setting `config.axial_pos_shape` to an appropriate list of two values $n_\\text{max}^1$ and $n_\\text{max}^2$ so that $n_\\text{max}^1 \\times n_\\text{max}^2 = n_\\text{max}$. By setting `config.axial_pos_embds_dim` to an appropriate list of two values $d_h^1$ and $d_h^2$ so that $d_h^1 + d_h^2 = d_h$, the user can decide how the hidden size dimension should be cut. \n", "Now, let's visualize and explain more intuitively.\n", "\n", "One can think of factorizing $n_\\text{max}$ as folding the dimension into a third axis, which is shown in the following for the factorization `config.axial_pos_shape = [7, 7]`:\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/3d_positional_encoding.png)\n", "\n", "Each of the three standing rectangular prisms corresponds to one of the encoding vectors $\\mathbf{e}_1, \\mathbf{e}_2, \\mathbf{e}_{49}$, but we can see that the 49 encoding vectors are divided into 7 rows of 7 vectors each.\n", "Now the idea is to use only one row of 7 encoding vectors and expand those vectors to the other 6 rows, essentially reusing their values. \n", "Because it is discouraged to have the same values for different encoding vectors, each vector of dimension (*a.k.a* height) `config.hidden_size=4` is cut into the lower encoding vector $\\mathbf{e}_\\text{down}$ of size $1$ and $\\mathbf{e}_\\text{up}$ of size $3$, so that the lower part can be expanded along the row dimension and the upper part can be expanded along the column dimension.\n", "Let's visualize for more clarity.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/3d_positional_encoding_cut.png)\n", "\n", "We can see that we have cut the embedding vectors into $\\mathbf{e}_\\text{down}$ (*in blue*) and $\\mathbf{e}_\\text{up}$ (*in yellow*).\n", "Now for the \"sub\"-vectors $\\mathbf{E}_\\text{down} = \\left[\\mathbf{e}_{\\text{down},1}, \\ldots, \\mathbf{e}_{\\text{down},49}\\right]$ only the first row, *a.k.a.* the width in the graphic, of $7$ is kept and expanded along the column dimension, *a.k.a.* the depth of the graphic. Inversely, for the \"sub\"-vectors $\\mathbf{E}_\\text{up} = \\left[\\mathbf{e}_{\\text{up},1}, \\ldots, \\mathbf{e}_{\\text{up},49}\\right]$ only the first column of $7$ is kept and expanded along the row dimension.\n", "The resulting embedding vectors $\\mathbf{e'}_i$ then correspond to\n", "\n", " \\begin{align}\n", " \\mathbf{e'}_i &= \\begin{bmatrix}\n", " \\mathbf{e}_{\\text{down, } i \\% n_\\text{max}^1} \\\\\n", " \\mathbf{e}_{\\text{up, } \\left \\lfloor{\\frac{i}{n_\\text{max}^2}}\\right \\rceil}\n", " \\end{bmatrix}\n", " \\end{align}\n", "whereas $n_\\text{max}^1 = 7$ and $n_\\text{max}^2 = 7$ in our example.\n", "These new encodings $\\mathbf{E'} = \\left[\\mathbf{e'}_1, \\ldots, \\mathbf{e'}_{n_\\text{max}}\\right]$ are called **Axial Position Encodings**. \n", "\n", "In the following, these axial position encodings are illustrated in more detail for our example.\n", "\n", "![alt text](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/reformer_benchmark/axial_pos_encoding.png)\n", "\n", "Now it should be more understandable how the final positional encoding vectors $\\mathbf{E'}$ are calculated only from $\\mathbf{E}_{\\text{down}}$ of dimension $d_h^1 \\times n_\\text{max}^1$ and $\\mathbf{E}_{\\text{up}}$ of dimension $d_h^2 \\times n_\\text{max}^2$.\n", "\n", "The crucial aspect to see here is that Axial Positional Encodings make sure that none of the vectors $\\left[\\mathbf{e'}_1, \\ldots, \\mathbf{e'}_{n_\\text{max}}\\right]$ are equal to each other by design and that the overall size of the encoding matrix is reduced from $n_\\text{max} \\times d_h$ to $n_\\text{max}^1 \\times d_h^1 + n_\\text{max}^2 \\times d_h^2$.\n", "By allowing each axial positional encoding vector to be different by design the model is given much more flexibility to learn efficient positional representations if axial positional encodings are learned by the model.\n", "\n", "To demonstrate the drastic reduction in size, \n", "let's assume we would have set `config.axial_pos_shape = [1024, 512]` and `config.axial_pos_embds_dim = [512, 512]` for a Reformer model that can process inputs up to a length of 0.5M tokens. The resulting axial positional encoding matrix would have had a size of only $1024 \\times 512 + 512 \\times 512 \\sim 800K$ parameters which corresponds to roughly 3MB. This is a drastic reduction from the 2GB a standard positional encoding matrix would require in this case.\n", "\n", "For a more condensed and math-heavy explanation please refer to the 🤗Transformers docs [here](https://huggingface.co/transformers/model_doc/reformer.html#axial-positional-encodings)." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Zo9ZLuNg8dsz" }, "source": [ "### **Benchmark**\n", "\n", "Lastly, let's also compare the peak memory consumption of conventional positional embeddings to *axial positional embeddings*." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "cellView": "form", "id": "b5whCRc_8ds7", "colab": { "base_uri": "https://localhost:8080/", "height": 118 }, "outputId": "d152a5f5-e1ac-4663-e233-f603f6facf68" }, "source": [ "#@title Installs and Imports\n", "# pip installs\n", "!pip -qq install git+https://github.com/huggingface/transformers.git\n", "!pip install -qq py3nvml\n", "\n", "from transformers import ReformerConfig, PyTorchBenchmark, PyTorchBenchmarkArguments, ReformerModel" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "\u001b[K |████████████████████████████████| 3.0MB 7.2MB/s \n", "\u001b[K |████████████████████████████████| 1.1MB 32.1MB/s \n", "\u001b[K |████████████████████████████████| 890kB 42.5MB/s \n", "\u001b[?25h Building wheel for transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[K |████████████████████████████████| 61kB 3.7MB/s \n", "\u001b[?25h" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "a1xiDAWV8dtK" }, "source": [ "Positional embeddings depend only on two configuration parameters: The maximum allowed length of input sequences `config.max_position_embeddings` and `config.hidden_size`. Let's use a model that pushes the maximum allowed length of input sequences to half a million tokens, called `google/reformer-crime-and-punishment`, to see the effect of using axial positional embeddings." ] }, { "cell_type": "markdown", "metadata": { "id": "BVw3z_12XVS3", "colab_type": "text" }, "source": [ "To begin with, we will compare the shape of axial position encodings with standard positional encodings and the number of parameters in the model." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "mLMgZt_38dtR", "colab": { "base_uri": "https://localhost:8080/", "height": 418, "referenced_widgets": [ "ba1341e2776c4c77ba3ef1cb678ca276", "6162dacc847c429ca2f27ac99a7b560e", "5eaee701f0bb46e0910048a0556a692a", "1a21f7fbff5541369250106bdcc8f1cd", "bbd58baaef314653b0a3b574f2a8fc51", "f6063125c6584f239a1679c5478ccb68", "611dc661229f4675abba110d179bbf98", "53d755610bb1438a84855b5764c91fc5" ] }, "outputId": "27818a5e-6742-4278-e16e-dcd456df9d2d" }, "source": [ "config_no_pos_axial_embeds = ReformerConfig.from_pretrained(\"google/reformer-crime-and-punishment\", axial_pos_embds=False) # disable axial positional embeddings\n", "config_pos_axial_embeds = ReformerConfig.from_pretrained(\"google/reformer-crime-and-punishment\", axial_pos_embds=True, axial_pos_embds_dim=(64, 192), axial_pos_shape=(512, 1024)) # enable axial positional embeddings\n", "\n", "print(\"Default Positional Encodings\")\n", "print(20 * '-')\n", "model = ReformerModel(config_no_pos_axial_embeds)\n", "print(f\"Positional embeddings shape: {model.embeddings.position_embeddings}\")\n", "print(f\"Num parameters of model: {model.num_parameters()}\")\n", "print(20 * '-' + '\\n\\n')\n", "\n", "print(\"Axial Positional Encodings\")\n", "print(20 * '-')\n", "model = ReformerModel(config_pos_axial_embeds)\n", "print(f\"Positional embeddings shape: {model.embeddings.position_embeddings}\")\n", "print(f\"Num parameters of model: {model.num_parameters()}\")\n", "print(20 * '-' + '\\n\\n')" ], "execution_count": 2, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ba1341e2776c4c77ba3ef1cb678ca276", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1151.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "Default Positional Encodings\n", "--------------------\n", "Positional embeddings shape: PositionEmbeddings(\n", " (embedding): Embedding(524288, 256)\n", ")\n", "Num parameters of model: 136572416\n", "--------------------\n", "\n", "\n", "Axial Positional Encodings\n", "--------------------\n", "Positional embeddings shape: AxialPositionEmbeddings(\n", " (weights): ParameterList(\n", " (0): Parameter containing: [torch.FloatTensor of size 512x1x64]\n", " (1): Parameter containing: [torch.FloatTensor of size 1x1024x192]\n", " )\n", ")\n", "Num parameters of model: 2584064\n", "--------------------\n", "\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "JlMqQlINYbAj", "colab_type": "text" }, "source": [ "Having read the theory, the shape of the axial positional encoding weights should not be a surprise to the reader.\n", "\n", "Regarding the results, it can be seen that for models being capable of processing such long input sequences, it is not practical to use default positional encodings. \n", "In the case of `google/reformer-crime-and-punishment`, standard positional encodings alone contain more than 100M parameters. \n", "Axial positional encodings reduce this number to just over 200K.\n", "\n", "Lastly, let's also compare the required memory at inference time." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "0sh2a96d8dtL", "colab": { "base_uri": "https://localhost:8080/", "height": 185 }, "outputId": "5d33e247-b018-408f-f2c4-455a1c81d8db" }, "source": [ "benchmark_args = PyTorchBenchmarkArguments(sequence_lengths=[512], batch_sizes=[8], models=[\"Reformer-No-Axial-Pos-Embeddings\", \"Reformer-Axial-Pos-Embeddings\"], no_speed=True, no_env_print=True)\n", "benchmark = PyTorchBenchmark(configs=[config_no_pos_axial_embeds, config_pos_axial_embeds], args=benchmark_args)\n", "result = benchmark.run()" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ "1 / 2\n", "2 / 2\n", "\n", "==================== INFERENCE - MEMORY - RESULT ====================\n", "--------------------------------------------------------------------------------\n", " Model Name Batch Size Seq Length Memory in MB \n", "--------------------------------------------------------------------------------\n", "Reformer-No-Axial-Pos-Embeddin 8 512 959 \n", "Reformer-Axial-Pos-Embeddings 8 512 447 \n", "--------------------------------------------------------------------------------\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": 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"\n", "Earlier this year, Nikita Kitaev, Łukasz Kaiser and Anselm Levskaya published the [**Reformer**](https://arxiv.org/abs/2001.04451), a transformer model variant with astounishing low memory consumption.\n", "\n", "In this notebook, we will show how Reformer can be used in [`transformers`](https://github.com/huggingface/transformers). \n", "\n", "\n", "### ***Disclaimer***:\n", "\n", "This notebook is derived from https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb and should showcase how Reformer can be leveraged for masked language modeling.\n", "\n", "### ***IMPORTANT***:\n", "\n", "This notebook has by no means fitting configurations for large-scale pretraining. It just showcases *technically* how one can use Reformer for masked language modeling. Before starting a costly pretraining of Reformer, one has to make sure the dataset is correctly processed, the Reformer configuration is carefully designed, the tokenizer is carefully chosen / designed and the training parameters, *e.g.* learning rate, are carefully set. Also, this script has to be adapted if one want to train padded batches on Reformer as explained later." ] }, { "cell_type": "markdown", "metadata": { "id": "SrMh3Y_T_JQj", "colab_type": "text" }, "source": [ "First, let's check whether we are given the full portion of the GPU. " ] }, { "cell_type": "code", "metadata": { "id": "M7cQmgM5TvlO", "colab_type": "code", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 51 }, "outputId": "656ef8be-016e-4065-8de9-1594f9e69aee" }, "source": [ "#@title Check availble memory of GPU\n", "# Check that we are using 100% of GPU\n", "# memory footprint support libraries/code\n", "!ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi\n", "!pip -q install gputil\n", "!pip -q install psutil\n", "!pip -q install humanize\n", "import psutil\n", "import humanize\n", "import os\n", "import GPUtil as GPU\n", "GPUs = GPU.getGPUs()\n", "# XXX: only one GPU on Colab and isn’t guaranteed\n", "gpu = GPUs[0]\n", "def printm():\n", " process = psutil.Process(os.getpid())\n", " print(\"Gen RAM Free: \" + humanize.naturalsize( psutil.virtual_memory().available ), \" | Proc size: \" + humanize.naturalsize( process.memory_info().rss))\n", " print(\"GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB\".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))\n", "printm()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Gen RAM Free: 11.2 GB | Proc size: 3.1 GB\n", "GPU RAM Free: 940MB | Used: 10501MB | Util 92% | Total 11441MB\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "vRMY7ULm_XOC", "colab_type": "text" }, "source": [ "In case GPU utilisation (`Util`) is not at 0%, you can uncomment and run the following line to kill all processes to get the full GPU afterwards. \n", "Make sure to comment out the line again to not constantly crash the notebook on purpose. " ] }, { "cell_type": "code", "metadata": { "id": "kTZtozYEUzWd", "colab_type": "code", "colab": {} }, "source": [ "# !kill -9 -1" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "YqkAdaKmKbyg", "colab_type": "text" }, "source": [ "Let's install `nlp` and `transformers` and import the necessary classes from Reformer and Trainer. " ] }, { "cell_type": "code", "metadata": { "id": "2eEDmjb7n6dD", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "b6cc6354-d519-4452-b868-51e7e77ffe33" }, "source": [ "# install nlp\n", "!pip install -qq nlp==0.2.0\n", "\n", "# Make sure that we have a recent version of pyarrow in the session before we continue - otherwise reboot Colab to activate it\n", "import pyarrow\n", "if int(pyarrow.__version__.split('.')[1]) < 16:\n", " import os\n", " os.kill(os.getpid(), 9)\n", "\n", "!pip install -qq git+git://github.com/huggingface/transformers.git@reformer_masked_lm" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ " Building wheel for transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "jT3b07SpL1E9", "colab_type": "text" }, "source": [ "In case the notebook crashesh the wrong version of `pyarrow` was installed here. Simply rerun the cell to install the correct version." ] }, { "cell_type": "code", "metadata": { "id": "Ci_32Hd0iQ5X", "colab_type": "code", "colab": {} }, "source": [ "# imports\n", "from transformers import (\n", " ReformerForMaskedLM,\n", " ReformerTokenizer,\n", " ReformerConfig,\n", " Trainer,\n", " DataCollatorForLanguageModeling,\n", " TrainingArguments,\n", ")\n", "import nlp\n", "import torch\n", "from torch.utils.data.dataset import Dataset" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "c4t6-YITkqYP", "colab_type": "text" }, "source": [ "First we download *Crime and Punish* which contains the content of a 800 page book using the convenient `nlp` library." ] }, { "cell_type": "code", "metadata": { "id": "dt1RQgR7k81u", "colab_type": "code", "colab": {} }, "source": [ "# load the dataset\n", "dataset = nlp.load_dataset(\"crime_and_punish\", split=\"train\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ohDfNT8alD1J", "colab_type": "text" }, "source": [ "Now let's get a pretrained sentence piece tokenizer that was trained on the *Crime and Punishment* dataset." ] }, { "cell_type": "code", "metadata": { "id": "3aliBYQXkNQ1", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "fa6cae53-a330-445e-bb11-a6795115f2b2" }, "source": [ "# get a pretrained tokenizer\n", "tokenizer = ReformerTokenizer.from_pretrained(\"google/reformer-crime-and-punishment\")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Special tokens have been added in the vocabulary, make sure the associated word emebedding are fine-tuned or trained.\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "EVIFks-pkjGu", "colab_type": "text" }, "source": [ "Because we want to do masked language modeling, let's add a [MASK] token to the tokenizer." ] }, { "cell_type": "code", "metadata": { "id": "udIaESVzkigQ", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "7ae36d31-e629-454c-b10c-4cc3cc35dcfe" }, "source": [ "tokenizer.add_special_tokens({\"mask_token\": '[MASK]'})" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "1" ] }, "metadata": { "tags": [] }, "execution_count": 34 } ] }, { "cell_type": "markdown", "metadata": { "id": "REhI_t-Glkdl", "colab_type": "text" }, "source": [ "Alright, let's check that the tokenizer has a mask token and see how many word embeddings are needed." ] }, { "cell_type": "code", "metadata": { "id": "yMp2bkeFlxX_", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 51 }, "outputId": "0810aadb-1a40-49f1-c5a3-f63eb1583232" }, "source": [ "print(tokenizer.mask_token_id)\n", "len(tokenizer)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "321\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "322" ] }, "metadata": { "tags": [] }, "execution_count": 35 } ] }, { "cell_type": "markdown", "metadata": { "id": "VDjPXLnElOA1", "colab_type": "text" }, "source": [ "In this notebook, we will use the first 16384 tokens to showcase how masked language modeling can be done.\n", "We can use the handy `map()` function to reduce the dataset into one sample." ] }, { "cell_type": "code", "metadata": { "id": "EgNQIUo9lcUr", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 71 }, "outputId": "583fa0b9-2bf6-47e9-9303-e36c92956e9e" }, "source": [ "sequence_length = 2 ** 14 # 16384\n", "\n", "# define our map function to reduce the dataset to one sample\n", "def flatten_and_tokenize(batch):\n", " all_input_text = [\"\".join(batch[\"line\"])]\n", " input_ids_dict = tokenizer(all_input_text, pad_to_max_length=True, max_length=sequence_length)\n", "\n", " # duplicate data 8 times to have have 8 examples in dataset\n", " for key in input_ids_dict.keys():\n", " input_ids_dict[key] = [4 * [x] for x in input_ids_dict[key]][0]\n", "\n", " return input_ids_dict\n", "\n", "# reduce the dataset and set batch_size to all inputs\n", "dataset = dataset.map(\n", " flatten_and_tokenize, batched=True, batch_size=-1, remove_columns=[\"line\"]\n", ")\n", "\n", "# prepare dataset to be in torch format\n", "dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\"])" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Truncation was not explicitely activated but `max_length` is provided a specific value, please use `truncation=True` to explicitely truncate examples to max length. Defaulting to 'only_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you may want to check this is the right behavior.\n", "Truncation was not explicitely activated but `max_length` is provided a specific value, please use `truncation=True` to explicitely truncate examples to max length. Defaulting to 'only_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you may want to check this is the right behavior.\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "5Nhhcj2_NF5J", "colab_type": "text" }, "source": [ "Let's do a quick check that our dataset samples have a length of 16384 and that there are 4 samples." ] }, { "cell_type": "code", "metadata": { "id": "sftN9BW8OmJX", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "d9cb8572-0188-45e1-ba42-de32abff907b" }, "source": [ "print(dataset['input_ids'].shape)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "torch.Size([4, 16384])\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Pz4l4IAsOn8b", "colab_type": "text" }, "source": [ "With the `Trainer` framework of `transformers`, we will use the language model data collator." ] }, { "cell_type": "code", "metadata": { "id": "TldJpBXkmF7W", "colab_type": "code", "colab": {} }, "source": [ "# copy 0.15 from run language modeling script\n", "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "N8f1nEKtNak9", "colab_type": "text" }, "source": [ "Great! We can now move on to wrap the dataset into a dataset class as expected by the language data collator.\n", "\n", "**Note**: This data collator currently assumes that no attention mask is needed. Thus it will not work for batch training that includes padded `input_ids`. In order to allow for training with padding one will have to write his own language model data collator." ] }, { "cell_type": "code", "metadata": { "id": "vnZupc3Aot3Z", "colab_type": "code", "colab": {} }, "source": [ "class MLMReformerDataset(Dataset):\n", "\n", " def __init__(self, dataset):\n", " self.dataset = dataset\n", "\n", " def __len__(self):\n", " return len(self.dataset)\n", "\n", " def __getitem__(self, i):\n", " return self.dataset['input_ids'][i]\n", "\n", "mlm_dataset = MLMReformerDataset(dataset)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "pVuOoH63jgKF", "colab_type": "text" }, "source": [ "Next, we will define our reformer model by defining the `ReformerConfig`. \n", "For the sake of this notebook, the `google/reformer-enwik8` config is taken whereas the vocabulary size is adapted to be used with our tokenizer." ] }, { "cell_type": "code", "metadata": { "id": "qS_3Sl8hjk-l", "colab_type": "code", "colab": {} }, "source": [ "config = {\n", " \"attention_head_size\": 128,\n", " \"attn_layers\": [\n", " \"local\",\n", " \"local\",\n", " \"lsh\",\n", " \"local\",\n", " \"local\",\n", " \"local\",\n", " \"lsh\",\n", " \"local\",\n", " \"local\",\n", " \"local\",\n", " \"lsh\",\n", " \"local\"\n", " ],\n", " \"axial_norm_std\": 1.0,\n", " \"axial_pos_embds\": True,\n", " \"axial_pos_embds_dim\": [\n", " 256,\n", " 768\n", " ],\n", " \"axial_pos_shape\": [\n", " 128,\n", " 128\n", " ],\n", " \"chunk_size_feed_forward\": 0,\n", " \"chunk_size_lm_head\": 0,\n", " \"eos_token_id\": 2,\n", " \"feed_forward_size\": 4096,\n", " \"hidden_act\": \"relu\",\n", " \"hidden_dropout_prob\": 0.2,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"is_decoder\": False,\n", " \"layer_norm_eps\": 1e-12,\n", " \"local_attention_probs_dropout_prob\": 0.2,\n", " \"local_attn_chunk_length\": 128,\n", " \"local_num_chunks_after\": 0,\n", " \"local_num_chunks_before\": 1,\n", " \"lsh_attention_probs_dropout_prob\": 0.1,\n", " \"lsh_attn_chunk_length\": 256,\n", " \"lsh_num_chunks_after\": 0,\n", " \"lsh_num_chunks_before\": 1,\n", " \"max_position_embeddings\": 16384,\n", " \"model_type\": \"reformer\",\n", " \"num_attention_heads\": 8,\n", " \"num_buckets\": 512,\n", " \"num_hashes\": 1,\n", " \"pad_token_id\": 0,\n", " \"vocab_size\": 322 # +1 for [MASK] token\n", "}\n", "\n", "config = ReformerConfig(**config)\n", "model = ReformerForMaskedLM(config)\n", "model = model.train()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "7GN6_Lx0jz9H", "colab_type": "text" }, "source": [ "Lastly, let's set up the training args. **Note**: *these training settings have not throughly been tested and might be tuned for better results*." ] }, { "cell_type": "code", "metadata": { "id": "0QPBj-s0juaY", "colab_type": "code", "colab": {} }, "source": [ "# define the training args\n", "training_args = {\n", " \"learning_rate\": 1e-3,\n", " \"max_steps\": 20,\n", " \"do_train\": True,\n", " \"gradient_accumulation_steps\": 4,\n", " \"logging_steps\": 4,\n", " \"warmup_steps\": 0,\n", " \"weight_decay\": 0.001,\n", " \"per_gpu_train_batch_size\": 1,\n", " \"per_gpu_eval_batch_size\": 1,\n", " \"save_steps\": 20,\n", " \"output_dir\": \"./\"\n", "}\n", "\n", "training_args = TrainingArguments(**training_args)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "KhYTZi3ZnTb9", "colab_type": "text" }, "source": [ "Finally we can start training. Since Google Colab only gives us a single GPU, this might take quite some time." ] }, { "cell_type": "code", "metadata": { "id": "WskGtnXsnWdu", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "0a81bfdfb2db49a88345a4e12effa0c6", "59731901896b44a4a1bc8d2e493aab05", "f91c6da0320b47ed9927b36cdf3720d3", "1364911f05b54a628ab7b4bbc9a14285", "db6b07e6bbc4491ba6ca46b04740d747", "679509639e9c46baa0a936381dff562c", "eebb7f933ecf4e5fb82a3c7c52f4c5f5", "e5fc9c1f3ce74e258bae0ac71d1c0939", "47168b71f2a54b2c89f7211006036fe8", "bc8e4e3196724a11ae0715ba7064109d", "16c02c6363e84ee09921a2ea0f147610", "408cb0575817492882e7f6a83ad3b00c", "b01a3e4f2e6340c3aadb2055ff8056e6", "a8013247f40a4448866854f19bc5c0ab", "40db9308acde4dec83bb2ce497cf4c91", "78f85c137da54b7d86f680faececf62d", "b4b72785b6ae40129fdb3b3fc86cbfb2", "ee6c3cff73654e87a2bce9de286fe885", "25732ce8485748528995e58e75ff361a", "bb12bd3fa1fb4dbc8ec078b721e5afa3", 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"7dc04dd3079d46eaac08f48277436350", "efdd5acc9668457e9b973675af3d7c55", "da6610ee7e334797b676c7a370c1f209", "9469df8d7fae428da4f5b2c9c09e1d01", "ae2722fda7f14d5a9515747a728941fb", "1fab5273a98f44eeaa6a9cf113ad508e", "837d8aea960b4983ac7ebb0fd2016cd3", "023cd283dd654d8ab1ae86f6e0088f86", "b111e9dc42b648d4b126061eb3b4f1c8", "2aeffa37c61c4c689634ada634ac97a4", "cb27f256804c465a86576f1a7fc71284", "bcabed650a214c36a583333ae510e4f1", "6245be237c274151b7866cc8728f0a15", "0509d2059b0146c5b8fc2aaed6fa1b0a", "2de4c0b820f64187ae094b58a36b43c9", "674533b8c7e5492586fbde5e7dd64b06", "1e7818aed5764a8d9940791b90bf2f59", "d314b0b9316e4f18b4ef02abfb0ea762", "77085c55bf9b40edbd7eee120122b2c6", "9f045bab427a4df484920c554464c848", "81dfb39902e54a29a2124f2126c2035b" ] }, "outputId": "699a58d1-c9a4-4938-90ed-b1fbe63a206f" }, "source": [ "# create the trainer\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " data_collator=data_collator,\n", " train_dataset=mlm_dataset\n", ")\n", "\n", "# train\n", "trainer.train()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future version. Using `--per_device_train_batch_size` is preferred.\n", "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future version. Using `--per_device_train_batch_size` is preferred.\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0a81bfdfb2db49a88345a4e12effa0c6", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Epoch', max=21.0, style=ProgressStyle(description_width='…" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "47168b71f2a54b2c89f7211006036fe8", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Iteration', max=4.0, style=ProgressStyle(description_widt…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b4b72785b6ae40129fdb3b3fc86cbfb2", "version_minor": 0, 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"@jupyter-widgets/base", "_model_module_version": "1.5.0", "_view_count": null, "_view_module_version": "1.2.0", "_model_module": "@jupyter-widgets/controls" } }, "63fffc66b54248c98edac042f5b3e441": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { "_view_name": "LayoutView", "grid_template_rows": null, "right": null, "justify_content": null, "_view_module": "@jupyter-widgets/base", "overflow": null, "_model_module_version": "1.2.0", "_view_count": null, "flex_flow": null, "width": null, "min_width": null, "border": null, "align_items": null, "bottom": null, "_model_module": "@jupyter-widgets/base", "top": null, "grid_column": null, "overflow_y": null, "overflow_x": null, "grid_auto_flow": null, "grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "H1F58j028eTV" }, "source": [ "## **Warm-starting RoBERTaShared for BBC XSum**\n", "\n", "***Note***: This notebook only uses a few training, validation, and test data samples for demonstration purposes. To fine-tune an encoder-decoder model on the full training data, the user should change the training and data preprocessing parameters accordingly as highlighted by the comments.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "3FO5ESocXvlK" }, "source": [ "### **Data Preprocessing**\n" ] }, { "cell_type": "code", "metadata": { "id": "w67vkz3KP9eZ" }, "source": [ "%%capture\n", "!pip install datasets\n", "!pip install transformers\n", "\n", "import datasets==1.0.2\n", "import transformers" ], "execution_count": 2, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "sgTiC0rhMb7C" }, "source": [ "from transformers import RobertaTokenizerFast\n", "\n", "tokenizer = RobertaTokenizerFast.from_pretrained(\"roberta-base\")\n", "\n", "train_data = datasets.load_dataset(\"xsum\", split=\"train\")\n", "val_data = datasets.load_dataset(\"xsum\", split=\"validation[:10%]\")" ], "execution_count": 6, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "yoN2q0hZUbXN", "outputId": "40e31f0d-1a10-42ee-fc68-c2155c2caba6", "colab": { "base_uri": "https://localhost:8080/", "height": 117, "referenced_widgets": [ "675630bf7b35483f9a424eb7433ab76a", "aee6f9cdac9941299d2ec1c78536191f", "ab3d9f6d932e47379096af656cfeb2d1", "a01bbe8936cb4dda8daa77bb85ca246e", "cdb47e05cd6b494995697adb8ba8bebc", "4be7e7d7455640399ed4d89c245cba0d", "6e500039037e4125ac64ba79a84fde4b", "10d017c0613941f9a7ca10d8c96443f8", "4c9414715ca14a59b035e93da306ecac", "8ac022b26f2b41d0bd78bac70a5f807c", "01c67a4bd8d54b9b9b6123610bfb0129", "08f1fa9570c94c259cddf5bd2fdf2e2f", "4e41b1eb73c24045a1e6a279b0edab2b", "ee2d9d9d707b4df5bde4ab4d0fa76323", "6e1a5d906c1c4008857013975bc3aa43", "028ae6ff753d42998352539915e4211e" ] } }, "source": [ "batch_size=4 # change to 16 for full training\n", "encoder_max_length=512\n", "decoder_max_length=64\n", "\n", "def process_data_to_model_inputs(batch): \n", " # Tokenizer will automatically set [BOS] [EOS] \n", " inputs = tokenizer(batch[\"document\"], padding=\"max_length\", truncation=True, max_length=encoder_max_length)\n", " outputs = tokenizer(batch[\"summary\"], padding=\"max_length\", truncation=True, max_length=decoder_max_length)\n", " \n", " batch[\"input_ids\"] = inputs.input_ids \n", " batch[\"attention_mask\"] = inputs.attention_mask \n", " batch[\"decoder_input_ids\"] = outputs.input_ids \n", " batch[\"labels\"] = outputs.input_ids.copy() \n", " # mask loss for padding \n", " batch[\"labels\"] = [ \n", " [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch[\"labels\"]\n", " ] \n", " batch[\"decoder_attention_mask\"] = outputs.attention_mask \n", " \n", " return batch \n", "\n", "# only use 32 training examples for notebook - DELETE LINE FOR FULL TRAINING\n", "train_data = train_data.select(range(32))\n", "\n", "train_data = train_data.map(\n", " process_data_to_model_inputs, \n", " batched=True, \n", " batch_size=batch_size, \n", " remove_columns=[\"document\", \"summary\"],\n", ")\n", "train_data.set_format(\n", " type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\n", ")\n", "\n", "\n", "# only use 16 training examples for notebook - DELETE LINE FOR FULL TRAINING\n", "val_data = val_data.select(range(16))\n", "\n", "val_data = val_data.map(\n", " process_data_to_model_inputs, \n", " batched=True, \n", " batch_size=batch_size, \n", " remove_columns=[\"document\", \"summary\"],\n", ")\n", "val_data.set_format(\n", " type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\n", ")" ], "execution_count": 7, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "675630bf7b35483f9a424eb7433ab76a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=8.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4c9414715ca14a59b035e93da306ecac", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "aEjb026cNC38" }, "source": [ "### **Warm-starting the Encoder-Decoder Model**" ] }, { "cell_type": "code", "metadata": { "id": "tS0UndNoQh8t", "outputId": "ddacb8cb-ca55-4776-d8d3-2ccf41378dd7", "colab": { "base_uri": "https://localhost:8080/", "height": 117, "referenced_widgets": [ "cd12b738e04245a89e31d47cad8a4a3f", "2f9748d8618f4ea8b3fe0af268992a7d", "e60ba26de4d34f539d66554e6cb90b7d", "473ce4be8e344adbac52144032dbe914", "a158d46733744cd6be61b3b0abeb298e", "a838ab3ce8cc4f0dac08af3f88cc02b6", "b465bedd3d314c9788c224d2cdd4f6e0", "08fdb94b2c9044d0a69b72f81001fce8", "36614fcee27140d7b9d7ee01c618cba2", "6d6b9e95a2724fd694d45da7f4ce7faa", "4df13140a29842a0b86f3f6c3101f1f5", "c5f561a4f69b43f996e6f8a8fc662278", "1e04a02599d7491ca824bba034fcf774", "33c61acc8f17408d975d25dc75684118", "91048428e64a4a1fb05339c34e0caba7", "d9d9008812464965a311f0eed5085d32" ] } }, "source": [ "from transformers import EncoderDecoderModel\n", "\n", "# set encoder decoder tying to True\n", "roberta_shared = EncoderDecoderModel.from_encoder_decoder_pretrained(\"roberta-base\", \"roberta-base\", tie_encoder_decoder=True)" ], "execution_count": 9, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cd12b738e04245a89e31d47cad8a4a3f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=481.0, style=ProgressStyle(description_…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "36614fcee27140d7b9d7ee01c618cba2", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=501200538.0, style=ProgressStyle(descri…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "JD2jv3GkyjR-" }, "source": [ "# set special tokens\n", "roberta_shared.config.decoder_start_token_id = tokenizer.bos_token_id \n", "roberta_shared.config.eos_token_id = tokenizer.eos_token_id\n", "\n", "# sensible parameters for beam search\n", "# set decoding params \n", "roberta_shared.config.max_length = 64\n", "roberta_shared.config.early_stopping = True\n", "roberta_shared.config.no_repeat_ngram_size = 3\n", "roberta_shared.config.length_penalty = 2.0\n", "roberta_shared.config.num_beams = 4\n", "roberta_shared.config.vocab_size = roberta_shared.config.encoder.vocab_size " ], "execution_count": 12, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "u98CLZiTkgzv" }, "source": [ "### **Fine-Tuning Warm-Started Encoder-Decoder Models**" ] }, { "cell_type": "markdown", "metadata": { "id": "-gYzA-w96wCt" }, "source": [ "The `Seq2SeqTrainer` that can be found under [examples/seq2seq/seq2seq_trainer.py](https://github.com/huggingface/transformers/blob/master/examples/seq2seq/seq2seq_trainer.py) will be used to fine-tune a warm-started encoder-decoder model.\n", "\n", "Let's download the `Seq2SeqTrainer` code and import the module along with `TrainingArguments`." ] }, { "cell_type": "code", "metadata": { "id": "pyiwaF0noA5c" }, "source": [ "%%capture\n", "!rm seq2seq_trainer.py\n", "!wget https://raw.githubusercontent.com/huggingface/transformers/master/examples/seq2seq/seq2seq_trainer.py\n", "\n", "!pip install git-python==1.0.3\n", "!pip install sacrebleu==1.4.12\n", "!pip install rouge_score\n", "\n", "from seq2seq_trainer import Seq2SeqTrainer\n", "from transformers import TrainingArguments\n", "from dataclasses import dataclass, field\n", "from typing import Optional" ], "execution_count": 13, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "5nmQRT3XuHHz" }, "source": [ "We need to add some additional parameters to make `TrainingArguments` compatible with the `Seq2SeqTrainer`. Let's just copy the `dataclass` arguments as defined in [this file](https://github.com/patrickvonplaten/transformers/blob/make_seq2seq_trainer_self_contained/examples/seq2seq/finetune_trainer.py)." ] }, { "cell_type": "code", "metadata": { "id": "-zkkd66rtsnA" }, "source": [ "@dataclass\n", "class Seq2SeqTrainingArguments(TrainingArguments):\n", " label_smoothing: Optional[float] = field(\n", " default=0.0, metadata={\"help\": \"The label smoothing epsilon to apply (if not zero).\"}\n", " )\n", " sortish_sampler: bool = field(default=False, metadata={\"help\": \"Whether to SortishSamler or not.\"})\n", " predict_with_generate: bool = field(\n", " default=False, metadata={\"help\": \"Whether to use generate to calculate generative metrics (ROUGE, BLEU).\"}\n", " )\n", " adafactor: bool = field(default=False, metadata={\"help\": \"whether to use adafactor\"})\n", " encoder_layerdrop: Optional[float] = field(\n", " default=None, metadata={\"help\": \"Encoder layer dropout probability. Goes into model.config.\"}\n", " )\n", " decoder_layerdrop: Optional[float] = field(\n", " default=None, metadata={\"help\": \"Decoder layer dropout probability. Goes into model.config.\"}\n", " )\n", " dropout: Optional[float] = field(default=None, metadata={\"help\": \"Dropout probability. Goes into model.config.\"})\n", " attention_dropout: Optional[float] = field(\n", " default=None, metadata={\"help\": \"Attention dropout probability. Goes into model.config.\"}\n", " )\n", " lr_scheduler: Optional[str] = field(\n", " default=\"linear\", metadata={\"help\": f\"Which lr scheduler to use.\"}\n", " )" ], "execution_count": 14, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "dPUAgo7pxH24" }, "source": [ "Also, we need to define a function to correctly compute the ROUGE score during validation. ROUGE is a much better metric to track during training than only language modeling loss." ] }, { "cell_type": "code", "metadata": { "id": "68IHmFYLx09W", "outputId": "c46a4823-7776-4811-99f5-45d811121e5d", "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "ab402c1f5e504e95b874154fe6b989c6", "62ffcf0e5e704a3e9c18ebf1f95d066c", "e53e70f6060e4ec39be4d08200e12103", "5e3e2a4d489a4116a563facae89a5df8", "94061516d092473fa12dc620f73742b2", "08c95f04c4c84950be216d8b2f802a86", "89320ef633f945668e19033366c6d5cc", "b8e18163e2c741a298bd0060899e8eb4" ] } }, "source": [ "# load rouge for validation\n", "rouge = datasets.load_metric(\"rouge\")\n", "\n", "def compute_metrics(pred):\n", " labels_ids = pred.label_ids\n", " pred_ids = pred.predictions\n", "\n", " # all unnecessary tokens are removed\n", " pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n", " labels_ids[labels_ids == -100] = tokenizer.pad_token_id\n", " label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\n", "\n", " rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\n", "\n", " return {\n", " \"rouge2_precision\": round(rouge_output.precision, 4),\n", " \"rouge2_recall\": round(rouge_output.recall, 4),\n", " \"rouge2_fmeasure\": round(rouge_output.fmeasure, 4),\n", " }" ], "execution_count": 15, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ab402c1f5e504e95b874154fe6b989c6", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1895.0, style=ProgressStyle(description…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "1ik4hZb2yV-b" }, "source": [ "Cool! Finally, we start training." ] }, { "cell_type": "code", "metadata": { "id": "LAaTxUpdzshF", "outputId": "dcc6348b-b99a-43ca-dabb-9559645c2461", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "source": [ "# set training arguments - these params are not really tuned, feel free to change\n", "training_args = Seq2SeqTrainingArguments(\n", " output_dir=\"./\",\n", " per_device_train_batch_size=batch_size,\n", " per_device_eval_batch_size=batch_size,\n", " predict_with_generate=True,\n", " evaluate_during_training=True,\n", " do_train=True,\n", " do_eval=True,\n", " logging_steps=2, # set to 2000 for full training\n", " save_steps=16, # set to 500 for full training\n", " eval_steps=4, # set to 7500 for full training\n", " warmup_steps=1, # set to 3000 for full training\n", " max_steps=16, # delete for full training\n", " # num_train_epochs=5, uncomment for full training\n", " overwrite_output_dir=True,\n", " save_total_limit=3,\n", " fp16=True, \n", ")\n", "\n", "# instantiate trainer\n", "trainer = Seq2SeqTrainer(\n", " model=roberta_shared,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " train_dataset=train_data,\n", " eval_dataset=val_data,\n", ")\n", "trainer.train()" ], "execution_count": 16, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/transformers/training_args.py:339: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n", " FutureWarning,\n", "The `config.pad_token_id` is `None`. Using `config.eos_token_id` = 2 for padding..\n", "/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:847: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n", " return torch.tensor(x, **format_kwargs)\n", "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", " \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "text/html": [ "\n", "
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StepTraining LossValidation LossRouge2 PrecisionRouge2 RecallRouge2 Fmeasure
410.89423010.5224190.0000000.0000000.000000
89.0843248.7287830.0048000.0069000.005700
128.3326808.4578290.0049000.0090000.005900
168.2426768.3911000.0033000.0047000.003800

" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=16, training_loss=9.3665771484375)" ] }, "metadata": { "tags": [] }, "execution_count": 16 } ] }, { "cell_type": "markdown", "metadata": { "id": "ZwQIEhKOrJpl" }, "source": [ "### **Evaluation**\n", "\n", "Awesome, we finished training our dummy model. Let's now evaluated the model on the test data. We make use of the dataset's handy `.map()` function to generate a summary of each sample of the test data." ] }, { "cell_type": "code", "metadata": { "id": "oOoSrwWarJAC", "outputId": "1e3958ba-ff07-479c-d0a7-52432dbc4d74", "colab": { "base_uri": "https://localhost:8080/", "height": 85, "referenced_widgets": [ "503abb1636b54237af26b2fad4cdef88", "efb61f3ab94c4bfe9cd6edda4f6fd49b", "e5492b5dc753424fbcde8de8d61adf7d", "2b353515e516441ba627d0c341486958", "47a9011d4e5e4ed9b8b340c9360704ad", "1d1a89bb6ff1425d922d22690b63ebe3", "acd4718707eb4e5e964a47e69e94d57b", "63fffc66b54248c98edac042f5b3e441" ] } }, "source": [ "import datasets\n", "from transformers import RobertaTokenizer, EncoderDecoderModel\n", "\n", "tokenizer = RobertaTokenizer.from_pretrained(\"roberta-base\")\n", "model = EncoderDecoderModel.from_pretrained(\"./checkpoint-16\")\n", "model.to(\"cuda\")\n", "\n", "test_data = datasets.load_dataset(\"xsum\", split=\"test\")\n", "\n", "# only use 16 training examples for notebook - DELETE LINE FOR FULL TRAINING\n", "test_data = test_data.select(range(16))\n", "\n", "batch_size = 16 # change to 64 for full evaluation\n", "\n", "# map data correctly\n", "def generate_summary(batch):\n", " # Tokenizer will automatically set [BOS] [EOS]\n", " inputs = tokenizer(batch[\"document\"], padding=\"max_length\", truncation=True, max_length=512, return_tensors=\"pt\")\n", " input_ids = inputs.input_ids.to(\"cuda\")\n", " attention_mask = inputs.attention_mask.to(\"cuda\")\n", "\n", " outputs = model.generate(input_ids, attention_mask=attention_mask)\n", "\n", " # all special tokens including will be removed\n", " output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n", "\n", " batch[\"pred\"] = output_str\n", "\n", " return batch\n", "\n", "results = test_data.map(generate_summary, batched=True, batch_size=batch_size, remove_columns=[\"document\"])\n", "\n", "pred_str = results[\"pred\"]\n", "label_str = results[\"summary\"]\n", "\n", "rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\n", "\n", "print(rouge_output)" ], "execution_count": 22, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "503abb1636b54237af26b2fad4cdef88", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "Score(precision=0.013238526341423976, recall=0.024268890670303207, fmeasure=0.01710138826716159)\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "7zdm50ZotZqb" }, "source": [ "The fully trained *RoBERTaShared* model is uploaded to the 🤗model hub under [patrickvonplaten/roberta_shared_bbc_xsum](https://huggingface.co/patrickvonplaten/roberta_shared_bbc_xsum). \n", "\n", "The model achieves a ROUGE-2 score of **16.89**, which is a bit worse than reported in the paper. Training the model for a bit longer will most likely close this performance gap.\n", "\n", "For some summarization examples, the reader is advised to use the online inference API of the model, [here](https://huggingface.co/patrickvonplaten/roberta_shared_bbc_xsum)." ] } ] } ================================================ FILE: Text_Classification_on_GLUE_on_TPU_using_Jax_Flax.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Text Classification on GLUE on TPU using Jax/Flax", "provenance": [], "collapsed_sections": [], "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" }, "widgets": { 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"grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } }, "accelerator": "TPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "rEJBSTyZIrIb" }, "source": [ "# Fine-tuning a 🤗 Transformers model on TPU with **Flax/JAX**" ] }, { "cell_type": "markdown", "metadata": { "id": "kTCFado4IrIc" }, "source": [ "In this notebook, we will see how to fine-tune one of the [🤗 Transformers](https://github.com/huggingface/transformers) models on TPU using [**Flax**](https://flax.readthedocs.io/en/latest/index.html). As can be seen on [this benchmark](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification#runtime-evaluation) using Flax/JAX on GPU/TPU is often much faster and can also be considerably cheaper than using PyTorch on GPU/TPU.\n", "\n", "[**Flax**](https://flax.readthedocs.io/en/latest/index.html) is a high-performance neural network library designed for flexibility built on top of JAX (see below). It aims to provide users with full control of their training code and is carefully designed to work well with JAX transformations such as `grad` and `pmap` (see the [Flax philosophy](https://flax.readthedocs.io/en/latest/philosophy.html)). For an introduction to Flax see the [Flax Basic Colab](https://flax.readthedocs.io/en/latest/notebooks/flax_basics.html) or the list of curated [Flax examples](https://flax.readthedocs.io/en/latest/examples.html).\n", "\n", "[**JAX**](https://jax.readthedocs.io/en/latest/index.html) is Autograd and XLA, brought together for high-performance numerical computing and machine learning research. It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more. A great place for getting started with JAX is the [JAX 101 Tutorial](https://jax.readthedocs.io/en/latest/jax-101/index.html)." ] }, { "cell_type": "markdown", "metadata": { "id": "X4cRE8IbIrIV" }, "source": [ "If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets as well as [Flax](https://github.com/google/flax.git) and [Optax](https://github.com/deepmind/optax). Optax is a gradient processing and optimization library for JAX." ] }, { "cell_type": "code", "metadata": { "id": "MOsHUjgdIrIW" }, "source": [ "%%capture\n", "!pip install datasets\n", "!pip install git+https://github.com/huggingface/transformers.git\n", "!pip install flax\n", "!pip install git+https://github.com/deepmind/optax.git" ], "execution_count": 12, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "EwRqMUxqzOKN" }, "source": [ "You also will need to set up the TPU for JAX in this notebook. This can be done by executing the following lines." ] }, { "cell_type": "code", "metadata": { "id": "drej_4loxdCF" }, "source": [ "import jax.tools.colab_tpu\n", "jax.tools.colab_tpu.setup_tpu()" ], "execution_count": 13, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "bAIlBLFXXix7" }, "source": [ "If everything is set up correctly, the following command should return a list of 8 TPU devices." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Kc0dzzrsXktW", "outputId": "9fcd0b30-0176-4b40-cbe7-308f78ed6f03" }, "source": [ "jax.local_devices()" ], "execution_count": 14, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[TpuDevice(id=0, process_index=0, coords=(0,0,0), core_on_chip=0),\n", " TpuDevice(id=1, process_index=0, coords=(0,0,0), core_on_chip=1),\n", " TpuDevice(id=2, process_index=0, coords=(1,0,0), core_on_chip=0),\n", " TpuDevice(id=3, process_index=0, coords=(1,0,0), core_on_chip=1),\n", " TpuDevice(id=4, process_index=0, coords=(0,1,0), core_on_chip=0),\n", " TpuDevice(id=5, process_index=0, coords=(0,1,0), core_on_chip=1),\n", " TpuDevice(id=6, process_index=0, coords=(1,1,0), core_on_chip=0),\n", " TpuDevice(id=7, process_index=0, coords=(1,1,0), core_on_chip=1)]" ] }, "metadata": { "tags": [] }, "execution_count": 14 } ] }, { "cell_type": "markdown", "metadata": { "id": "HFASsisvIrIb" }, "source": [ "If you're opening this notebook locally, make sure your environment has an install from the last version of those libraries.\n", "\n", "You can find a script version of this notebook to fine-tune your model [here](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification)." ] }, { "cell_type": "markdown", "metadata": { "id": "DlpF5MnonmmQ" }, "source": [ "As an example, we will fine-tune a pretrained [*auto-encoding model*](https://huggingface.co/transformers/model_summary.html#autoencoding-models) on a text classification task of the [GLUE Benchmark](https://gluebenchmark.com/). Note that this notebook does not focus so much on data preprocessing, but rather on how to write a training and evaluation loop in **JAX/Flax**. If you want more detailed explanations regarding the data preprocessing, please check out [this](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb#scrollTo=545PP3o8IrJV) notebook.\n", "\n", "The GLUE Benchmark is a group of nine classification tasks on sentences or pairs of sentences which are:\n", "[CoLA](https://nyu-mll.github.io/CoLA/), [MNLI](https://arxiv.org/abs/1704.05426), [MRPC](https://www.microsoft.com/en-us/download/details.aspx?id=52398), [QNLI](https://rajpurkar.github.io/SQuAD-explorer/), [QQP](https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs), [RTE](https://aclweb.org/aclwiki/Recognizing_Textual_Entailment), [SST-2](https://nlp.stanford.edu/sentiment/index.html), [STS-B](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark), [WNLI](https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html).\n", "\n", "We will see how to easily load the dataset for each one of those tasks and how to write a training loop in Flax. Each task is named by its acronym, with `mnli-mm` standing for the mismatched version of MNLI (so same training set as `mnli` but different validation and test sets):" ] }, { "cell_type": "code", "metadata": { "id": "YZbiBDuGIrId" }, "source": [ "GLUE_TASKS = [\"cola\", \"mnli\", \"mnli-mm\", \"mrpc\", \"qnli\", \"qqp\", \"rte\", \"sst2\", \"stsb\", \"wnli\"]" ], "execution_count": 15, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "4RRkXuteIrIh" }, "source": [ "This notebook is built to run on any of the tasks in the list above, with any Flax/JAX model checkpoint from the [Model Hub](https://huggingface.co/models) as long as that model has a version with a classification head. Depending on the model you are using, you might need to adjust the batch size to avoid out-of-memory errors. Set those three parameters, then the rest of the notebook should run smoothly:" ] }, { "cell_type": "code", "metadata": { "id": "zVvslsfMIrIh" }, "source": [ "task = \"cola\"\n", "model_checkpoint = \"roberta-base\"\n", "per_device_batch_size = 4" ], "execution_count": 16, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "whPRbBNbIrIl" }, "source": [ "## Loading the dataset" ] }, { "cell_type": "markdown", "metadata": { "id": "W7QYTpxXIrIl" }, "source": [ "We will use the [🤗 Datasets](https://github.com/huggingface/datasets) library to download the data and get the metric we need to use for evaluation (to compare our model to the benchmark). This can be easily done with the functions `load_dataset` and `load_metric`. " ] }, { "cell_type": "code", "metadata": { "id": "IreSlFmlIrIm" }, "source": [ "from datasets import load_dataset, load_metric" ], "execution_count": 17, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "CKx2zKs5IrIq" }, "source": [ "Apart from `mnli-mm` being a special code, we can directly pass our task name to those functions. `load_dataset` will cache the dataset to avoid downloading it again the next time you run this cell." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "s_AY1ATSIrIq", "outputId": "673c829d-26bf-48ba-8b0b-b1570a3fbf9d" }, "source": [ "actual_task = \"mnli\" if task == \"mnli-mm\" else task\n", "is_regression = task == \"stsb\"\n", "\n", "raw_dataset = load_dataset(\"glue\", actual_task)\n", "metric = load_metric('glue', actual_task)" ], "execution_count": 18, "outputs": [ { "output_type": "stream", "text": [ "Reusing dataset glue (/root/.cache/huggingface/datasets/glue/cola/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "n9qywopnIrJH" }, "source": [ "## Preprocessing the data" ] }, { "cell_type": "markdown", "metadata": { "id": "YVx71GdAIrJH" }, "source": [ "Before we can feed those texts to our model, we need to preprocess them. This is done by a 🤗 Transformers `Tokenizer` which will (as the name indicates) tokenize the inputs. This includes converting the tokens to their corresponding IDs in the pretrained vocabulary and putting them in a format the model expects, as well as generate the other inputs that the model requires.\n", "\n", "To do all of this, we instantiate our tokenizer with the `AutoTokenizer.from_pretrained` method, which will ensure:\n", "\n", "- we get a tokenizer that corresponds to the model architecture we want to use,\n", "- we download the vocabulary used when pretraining this specific checkpoint.\n", "\n", "That vocabulary will be cached, so it's not downloaded again the next time we run the cell." ] }, { "cell_type": "code", "metadata": { "id": "eXNLu_-nIrJI" }, "source": [ "from transformers import AutoTokenizer\n", " \n", "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)" ], "execution_count": 19, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qo_0B1M2IrJM" }, "source": [ "To preprocess our dataset, we will thus need the names of the columns containing the sentence(s). The following dictionary keeps track of the correspondence task to column names:" ] }, { "cell_type": "code", "metadata": { "id": "fyGdtK9oIrJM" }, "source": [ "task_to_keys = {\n", " \"cola\": (\"sentence\", None),\n", " \"mnli\": (\"premise\", \"hypothesis\"),\n", " \"mnli-mm\": (\"premise\", \"hypothesis\"),\n", " \"mrpc\": (\"sentence1\", \"sentence2\"),\n", " \"qnli\": (\"question\", \"sentence\"),\n", " \"qqp\": (\"question1\", \"question2\"),\n", " \"rte\": (\"sentence1\", \"sentence2\"),\n", " \"sst2\": (\"sentence\", None),\n", " \"stsb\": (\"sentence1\", \"sentence2\"),\n", " \"wnli\": (\"sentence1\", \"sentence2\"),\n", "}" ], "execution_count": 20, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "2C0hcmp9IrJQ" }, "source": [ "We can then write the function that will preprocess our samples. We just feed them to the `tokenizer` with the argument `truncation=True`. This will ensure that an input longer than what the model can handle will be truncated to the maximum length accepted by the model." ] }, { "cell_type": "code", "metadata": { "id": "FgjEr0fKMRQ_" }, "source": [ "sentence1_key, sentence2_key = task_to_keys[task]\n", "\n", "def preprocess_function(examples):\n", " texts = (\n", " (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])\n", " )\n", " processed = tokenizer(*texts, padding=\"max_length\", max_length=128, truncation=True)\n", " \n", " processed[\"labels\"] = examples[\"label\"]\n", " return processed" ], "execution_count": 21, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "zS-6iXTkIrJT" }, "source": [ "To apply this function to all the sentences (or pairs of sentences) in our dataset, we just use the `map` method of our dataset object we created earlier. This will apply the function on all the elements of all the splits in the dataset, so our training, validation, and testing data will be preprocessed in one single command.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "DDtsaJeVIrJT", "colab": { "base_uri": "https://localhost:8080/", "height": 165, "referenced_widgets": [ "45feb92a778243dd8a7b7f98648d7b4a", "535be95dadb04e2d8994c85c4da302b5", "d5afbf3d124a4d538a73a0ec505e588c", "413307fb2c6e4954ada40502eacee67b", "c7318c447e69457fa162373a1c6d771b", "ff075df9b76c4f4fbdf6d4b40df13d43", "0ada4ca6dd374decb338a12fbebdab75", "3b56ee5166dd4757afb79d366061ccda", "6ad9bad551af4563ba008ea14832111a", "0a1d8b61e6794142a46076554ffa9ee6", "37a556660e23489f9e0e38f230b7e14d", "80da478f6f5b471face0b35421a73e3a", "e21ce1bf89324a16bf508191191d6423", "c5632265c10e4d818a09aa0fde7f3f23", "88129a9dc5b846d38c04854593941bd7", "406851012e774bc2ae54458d79d65787", "4ac0d9a4a74c481b87b8c14640aa367d", "f063d07acbae46b7ad6cf5209a83c795", "5e3910c2a0284fb5b2a16f133d9f8226", "7ade52cc49e5470b9a796a057b1743a6", "16c866ef556f45b7959449f32e94e8a8", "b1d7b766995046af86ebb9fdf42f4800", "298d70dba33b43e495235c6b20f95cac", "1e115a20d964440fa388b3baec9f712b" ] }, "outputId": "951ecfba-0d77-4f76-b297-98a58b3aaf10" }, "source": [ "tokenized_dataset = raw_dataset.map(preprocess_function, batched=True, remove_columns=raw_dataset[\"train\"].column_names)" ], "execution_count": 22, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "45feb92a778243dd8a7b7f98648d7b4a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=9.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6ad9bad551af4563ba008ea14832111a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4ac0d9a4a74c481b87b8c14640aa367d", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "voWiw8C7IrJV" }, "source": [ "As a final step, we split the dataset into the *train* and *validation* dataset and give each set more explicit names." ] }, { "cell_type": "code", "metadata": { "id": "K03vmCmPqjIP" }, "source": [ "train_dataset = tokenized_dataset[\"train\"]\n", "eval_dataset = tokenized_dataset[\"validation\"]" ], "execution_count": 23, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "545PP3o8IrJV" }, "source": [ "## Fine-tuning the model" ] }, { "cell_type": "markdown", "metadata": { "id": "FBiW8UpKIrJW" }, "source": [ "Now that our data is ready, we can download the pretrained model and fine-tune it. Since all our tasks are about sentence classification, we use the `FlaxAutoModelForSequenceClassification` class. Like with the tokenizer, the `from_pretrained` method will download and cache the model for us. \n", "\n", "All weight parameters that are not found in the pretrained model weights will be randomely initialized upon instantiating the model class. Because the GLUE task contains relatively small training datasets, a different seed for weight initialization might very well lead to significantly different results. For \n", "reproducibility, we set the random seed to *0* in this notebook.\n", "\n", "The only thing we have to specify in the config is the number of labels for our problem (which is always 2, except for STS-B which is a regression problem, and MNLI where we have 3 labels):" ] }, { "cell_type": "code", "metadata": { "id": "TlqNaB8jIrJW", "colab": { "base_uri": "https://localhost:8080/", "height": 174, "referenced_widgets": [ "f0ae6f88c74d46cc8b4739596f624599", "d1e387707c7c441d92e81fc4627029ac", "251a1024319a4961aa67d1fa1c31f86b", "bd9d038cfc4743babadd2687cd40ce9c", "8ed70327573a4700874f6d421a10b8f3", "3700b4e5b86b4831bd65dad93a0e6017", "5395b728f17c45f496452f9a28f76718", "6278211ccbf44368ac285c675adc929a" ] }, "outputId": "701cc52a-ffb1-4be6-8033-9def9053e1bf" }, "source": [ "from transformers import FlaxAutoModelForSequenceClassification, AutoConfig\n", "\n", "num_labels = 3 if task.startswith(\"mnli\") else 1 if task==\"stsb\" else 2\n", "seed = 0\n", "\n", "config = AutoConfig.from_pretrained(model_checkpoint, num_labels=num_labels)\n", "model = FlaxAutoModelForSequenceClassification.from_pretrained(model_checkpoint, config=config, seed=seed)" ], "execution_count": 24, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f0ae6f88c74d46cc8b4739596f624599", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=655572523.0, style=ProgressStyle(descri…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "Some weights of the model checkpoint at roberta-base were not used when initializing FlaxRobertaForSequenceClassification: {('encoder', 'layer', '11', 'attention', 'layer_norm', 'gamma'), ('encoder', 'layer', '8', 'attention', 'self', 'key', 'bias'), ('encoder', 'layer', '3', 'attention', 'self', 'value', 'bias'), ('encoder', 'layer', '0', 'attention', 'self', 'key', 'kernel'), ('encoder', 'layer', '4', 'attention', 'self', 'value', 'kernel'), ('encoder', 'layer', '1', 'attention', 'layer_norm', 'gamma'), ('encoder', 'layer', '4', 'attention', 'self', 'out', 'bias'), ('encoder', 'layer', '9', 'attention', 'self', 'query', 'kernel'), ('encoder', 'layer', '3', 'intermediate', 'dense', 'bias'), ('encoder', 'layer', '5', 'output', 'layer_norm', 'gamma'), ('encoder', 'layer', '0', 'attention', 'self', 'value', 'bias'), ('encoder', 'layer', '1', 'output', 'dense', 'bias'), ('encoder', 'layer', '4', 'attention', 'self', 'key', 'bias'), ('encoder', 'layer', '8', 'intermediate', 'dense', 'bias'), ('encoder', 'layer', '9', 'attention', 'layer_norm', 'gamma'), ('layer_norm', 'weight'), ('encoder', 'layer', '5', 'attention', 'self', 'query', 'kernel'), ('encoder', 'layer', '2', 'attention', 'self', 'value', 'kernel'), ('embeddings', 'position_embeddings', 'weight'), ('encoder', 'layer', '11', 'attention', 'layer_norm', 'beta'), ('encoder', 'layer', '11', 'output', 'layer_norm', 'beta'), ('encoder', 'layer', '1', 'attention', 'layer_norm', 'beta'), ('encoder', 'layer', '10', 'output', 'layer_norm', 'gamma'), ('encoder', 'layer', '2', 'attention', 'self', 'out', 'bias'), ('encoder', 'layer', '8', 'attention', 'self', 'out', 'bias'), ('encoder', 'layer', '10', 'output', 'dense', 'kernel'), ('encoder', 'layer', '6', 'intermediate', 'dense', 'kernel'), ('encoder', 'layer', '7', 'attention', 'self', 'query', 'bias'), ('encoder', 'layer', '11', 'output', 'dense', 'bias'), ('encoder', 'layer', '0', 'intermediate', 'dense', 'kernel'), ('encoder', 'layer', '9', 'attention', 'self', 'out', 'kernel'), ('encoder', 'layer', '7', 'attention', 'layer_norm', 'gamma'), ('encoder', 'layer', '0', 'output', 'dense', 'bias'), ('encoder', 'layer', '0', 'output', 'dense', 'kernel'), ('encoder', 'layer', '9', 'attention', 'self', 'out', 'bias'), ('encoder', 'layer', '1', 'output', 'layer_norm', 'beta'), ('dense', 'kernel'), 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'7', 'output', 'LayerNorm', 'bias'), ('roberta', 'embeddings', 'LayerNorm', 'scale'), ('roberta', 'encoder', 'layer', '6', 'attention', 'self', 'value', 'kernel'), ('roberta', 'encoder', 'layer', '5', 'attention', 'self', 'value', 'bias'), ('roberta', 'encoder', 'layer', '6', 'attention', 'output', 'dense', 'kernel'), ('roberta', 'encoder', 'layer', '2', 'attention', 'self', 'query', 'kernel'), ('roberta', 'encoder', 'layer', '4', 'attention', 'self', 'key', 'kernel'), ('roberta', 'encoder', 'layer', '8', 'attention', 'output', 'LayerNorm', 'bias'), ('roberta', 'encoder', 'layer', '6', 'attention', 'output', 'LayerNorm', 'scale'), ('roberta', 'encoder', 'layer', '8', 'attention', 'self', 'value', 'kernel'), ('roberta', 'encoder', 'layer', '0', 'output', 'LayerNorm', 'bias'), ('roberta', 'encoder', 'layer', '9', 'attention', 'output', 'LayerNorm', 'bias'), ('roberta', 'encoder', 'layer', '7', 'intermediate', 'dense', 'kernel'), ('roberta', 'encoder', 'layer', '4', 'attention', 'output', 'LayerNorm', 'bias'), ('roberta', 'encoder', 'layer', '7', 'output', 'dense', 'kernel'), ('roberta', 'encoder', 'layer', '8', 'attention', 'self', 'key', 'bias'), ('roberta', 'encoder', 'layer', '6', 'intermediate', 'dense', 'bias'), ('roberta', 'encoder', 'layer', '11', 'output', 'LayerNorm', 'scale'), ('roberta', 'encoder', 'layer', '2', 'output', 'dense', 'bias'), ('roberta', 'encoder', 'layer', '5', 'attention', 'output', 'LayerNorm', 'scale'), ('roberta', 'encoder', 'layer', '5', 'output', 'dense', 'kernel'), ('roberta', 'encoder', 'layer', '0', 'attention', 'self', 'key', 'bias'), ('roberta', 'encoder', 'layer', '9', 'attention', 'self', 'query', 'kernel'), ('roberta', 'encoder', 'layer', '8', 'attention', 'self', 'value', 'bias')}\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "CczA5lJlIrJX" }, "source": [ "The warning is telling us we are throwing away some weights (the `vocab_transform` and `vocab_layer_norm` layers) and randomly initializing some others (the `pre_classifier` and `classifier` layers). This is normal in this case because we are removing the head used to pretrain the model on a masked language modeling objective and replacing it with a new head for which we don't have pretrained weights, so the library warns us we should fine-tune this model before using it for inference, which is exactly what we are going to do." ] }, { "cell_type": "markdown", "metadata": { "id": "dnQ43MgRqV-K" }, "source": [ "To write a full training and evaluation loop in Flax, we will need to import a couple of packages." ] }, { "cell_type": "code", "metadata": { "id": "Mn1GdGpipfWK" }, "source": [ "import flax\n", "import jax\n", "import optax\n", "\n", "from itertools import chain\n", "from tqdm.notebook import tqdm\n", "from typing import Callable\n", "\n", "import jax.numpy as jnp\n", "\n", "from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key\n", "from flax.training import train_state" ], "execution_count": 25, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "I3GOlfPQrRdY" }, "source": [ "For all GLUE tasks except `\"wnli\"` and `\"mrpc\"`, it is usually sufficient to train for just 3 epochs. `\"wnli\"` and `\"mrpc\"` are so small that we recommend training on 5 epochs. We use a learning rate of *0.00002*." ] }, { "cell_type": "code", "metadata": { "id": "8zpJOCJhmSno" }, "source": [ "num_train_epochs = 3 if task not in [\"mrpc\", \"wnli\"] else 5\n", "learning_rate = 2e-5" ], "execution_count": 26, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "K7mQfa25tAFb" }, "source": [ "We've already set the batch size per device, but are now interested in the effective total batch_size:" ] }, { "cell_type": "code", "metadata": { "id": "QfP-yW52s-lu", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "814812c4-3286-456e-b0f1-801f5d19c6f5" }, "source": [ "total_batch_size = per_device_batch_size * jax.local_device_count()\n", "print(\"The overall batch size (both for training and eval) is\", total_batch_size)" ], "execution_count": 27, "outputs": [ { "output_type": "stream", "text": [ "The overall batch size (both for training and eval) is 32\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "CDQbpTFvtpyN" }, "source": [ "Next, we define the learning rate schedule. A simple and effective learning rate schedule is the linear decay with warmup (click [here](https://huggingface.co/transformers/main_classes/optimizer_schedules.html#transformers.get_linear_schedule_with_warmup) for more information). Since GLUE datasets are rather small and therefore have few training steps, we set the number of warmup steps simply to 0. The schedule is then fully defined by the number of training steps and the learning rate.\n", "\n", "It is recommended to use the [**optax**](https://github.com/deepmind/optax) library for training utilities, *e.g.* learning rate schedules and optimizers." ] }, { "cell_type": "code", "metadata": { "id": "0vnrOjubxkFw" }, "source": [ "num_train_steps = len(train_dataset) // total_batch_size * num_train_epochs\n", "\n", "learning_rate_function = optax.linear_schedule(init_value=learning_rate, end_value=0, transition_steps=num_train_steps)" ], "execution_count": 28, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xorWAbYWMfEm" }, "source": [ "### Defining the training state" ] }, { "cell_type": "markdown", "metadata": { "id": "ktzA1CJAyrZB" }, "source": [ "Next, we will create the *training state* that includes the optimizer, the loss function, and is responsible for updating the model's parameters during training.\n", "\n", "Most JAX transformations (notably [jax.jit](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)) require functions that are transformed to have no side-effects. This is because any such side-effects will only be executed once, when the Python version of the function is run during compilation (see [Stateful Computations in JAX](https://jax.readthedocs.io/en/latest/jax-101/07-state.html)). As a consequence, Flax models (which can be transformed by JAX transformations) are **immutable**, and the state of the model (i.e., its weight parameters) are stored *outside* of the model instance.\n", "\n", "Models are initialized and updated in a purely functional way: you pass the state to the model when calling it, and the model returns the new (possibly modified) state, leaving the model instance itself unchanged.\n", "\n", "Flax provides a convenience class [`flax.training.train_state.TrainState`](https://github.com/google/flax/blob/9da95cdd12591f42d2cd4c17089861bff7e43cc5/flax/training/train_state.py#L22), which stores things such as the model parameters, the loss function, the optimizer, and exposes an `apply_gradients` function to update the model's weight parameters.\n", "\n", "Alright, let's begin by defining our *training state* class. We create a derived `TrainState` class that additionally stores the model's forward pass as `eval_function` as well as a `loss_function`." ] }, { "cell_type": "code", "metadata": { "id": "7F81TyR3nJrM" }, "source": [ "class TrainState(train_state.TrainState):\n", " logits_function: Callable = flax.struct.field(pytree_node=False)\n", " loss_function: Callable = flax.struct.field(pytree_node=False)" ], "execution_count": 29, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DRF_WotzGOpR" }, "source": [ "We will be using the standard Adam optimizer with weight decay. For more information on AdamW (Adam + weight decay), one can take a look at [this](https://www.fast.ai/2018/07/02/adam-weight-decay/) blog post.\n", "\n", "AdamW can easily be imported from [optax](https://github.com/deepmind/optax):" ] }, { "cell_type": "code", "metadata": { "id": "lku54GQWq_6v" }, "source": [ "def adamw(weight_decay):\n", " return optax.adamw(learning_rate=learning_rate_function, b1=0.9, b2=0.999, eps=1e-6, weight_decay=weight_decay)" ], "execution_count": 30, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "FlYAEe58JPdy" }, "source": [ "Next, the function to post-process each gradient before updating the weight parameters, called `gradient_transformation`, is defined. It essentially states that all weights shall be updated using the Adam optimizer with weight decay 0.01 except *biases* and *LayerNorm* for which the Adam optimizer without a weight decay is used. Regularizing the bias and/or *LayerNorm* has not shown to improve performance and can even be disadvantageous, which is why we disable it here. For more information on this, please check out the following [blog post](https://medium.com/@shrutijadon10104776/why-we-dont-use-bias-in-regularization-5a86905dfcd6) or [paper](https://arxiv.org/abs/1711.05101)." ] }, { "cell_type": "code", "metadata": { "id": "ap-zaOyKJDXM" }, "source": [ "decay_path = lambda p: not any(x in p for x in [\"bias\", \"LayerNorm.weight\"])\n", "\n", "def traverse(function):\n", " def mask(data):\n", " flat = flax.traverse_util.flatten_dict(data)\n", " return flax.traverse_util.unflatten_dict({k: function(k, v) for k, v in flat.items()})\n", " return mask\n", "\n", "gradient_transformation = optax.chain(\n", " optax.masked(adamw(0.0), mask=traverse(lambda path, _: decay_path(path))),\n", " optax.masked(adamw(0.01), mask=traverse(lambda path, _: not decay_path(path))),\n", ")" ], "execution_count": 31, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0vbf4KyrKnDl" }, "source": [ "Now we also need to define the evaluation and loss function given the model's output logits. \n", "For regression tasks, the evaluation function simply takes the first logit element and the mean-square error (mse) loss is used. For classification tasks, the evaluation function uses the `argmax` of the logits, and the cross-entropy loss with `num_labels` is used." ] }, { "cell_type": "code", "metadata": { "id": "4Rj31RyjJ_9T" }, "source": [ "def loss_function(logits, labels):\n", " if is_regression:\n", " return jnp.mean((logits[..., 0] - labels) ** 2)\n", " \n", " logits = flax.linen.log_softmax(logits)\n", " xentropy = optax.softmax_cross_entropy(logits, onehot(labels, num_classes=num_labels))\n", " return jnp.mean(xentropy)\n", " \n", "def eval_function(logits):\n", " return logits[..., 0] if is_regression else logits.argmax(-1)" ], "execution_count": 32, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "XM7LmtIRHWFl" }, "source": [ "Finally, we put the pieces together to instantiate a `TrainState`." ] }, { "cell_type": "code", "metadata": { "id": "pqK0fDCvmxao" }, "source": [ "state = TrainState.create(\n", " apply_fn=model.__call__,\n", " params=model.params,\n", " tx=gradient_transformation,\n", " logits_function=eval_function,\n", " loss_function=loss_function,\n", ")" ], "execution_count": 33, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "HQHbpxNjMpXb" }, "source": [ "### Defining the training and evaluation step\n", "\n", "During fine-tuning, we want to update the model parameters and evaluate the performance after each epoch. \n", "\n", "Let's write the functions `train_step` and `eval_step` accordingly. During training the weight parameters should be updated as follows:\n", "\n", "1. Define a loss function `loss_function` that first runs a forward pass of the model given data input. Remember that Flax models are immutable, and we explicitly pass it the state (in this case the model parameters and the RNG). `loss_function` returns a scalar loss (using the previously defined `state.loss_function`) between the model output and input targets.\n", "2. Differentiate this loss function using [`jax.value_and_grad`](https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html#evaluate-a-function-and-its-gradient-using-value-and-grad). This is a JAX transformation called [automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation), which computes the gradient of `loss_function` given the input to the function (i.e., the parameters of the model), and returns the value and the gradient in a pair `(loss, gradients)`.\n", "3. Compute the mean gradient over all devices using the collective operation [lax.pmean](https://jax.readthedocs.io/en/latest/_autosummary/jax.lax.pmean.html). As we will see below, each device runs `train_step` on a different batch of data, but by taking the mean here we ensure the model parameters are the same on all devices.\n", "4. Use `state.apply_gradients`, which applies the gradients to the weights.\n", "\n", "Below, you can see how each of the described steps above is put into practice." ] }, { "cell_type": "code", "metadata": { "id": "QXDgjdbzn3QA" }, "source": [ "def train_step(state, batch, dropout_rng):\n", " targets = batch.pop(\"labels\")\n", " dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)\n", "\n", " def loss_function(params):\n", " logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]\n", " loss = state.loss_function(logits, targets)\n", " return loss\n", "\n", " grad_function = jax.value_and_grad(loss_function)\n", " loss, grad = grad_function(state.params)\n", " grad = jax.lax.pmean(grad, \"batch\")\n", " new_state = state.apply_gradients(grads=grad)\n", " metrics = jax.lax.pmean({\"loss\": loss, \"learning_rate\": learning_rate_function(state.step)}, axis_name=\"batch\")\n", " return new_state, metrics, new_dropout_rng" ], "execution_count": 34, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "lfiPHXYiSvSk" }, "source": [ "Now, we want to do parallelized training over all TPU devices. To do so, we use [`jax.pmap`](https://jax.readthedocs.io/en/latest/jax.html?highlight=pmap#parallelization-pmap). This will compile the function once and run the same program on each device (it is an [SPMD program](https://en.wikipedia.org/wiki/SPMD)). When calling this pmapped function, all inputs (`\"state\"`, `\"batch\"`, `\"dropout_rng\"`) should be replicated for all devices, which means that the first axis of each argument is used to map over all TPU devices.\n", "\n", "The argument `donate_argnums` is used to tell JAX that the first argument `\"state\"` is \"donated\" to the computation, because it is not needed anymore afterwards. XLA can make use of donated buffers to reduce the memory needed." ] }, { "cell_type": "code", "metadata": { "id": "t6eLQN4Jn6jS" }, "source": [ "parallel_train_step = jax.pmap(train_step, axis_name=\"batch\", donate_argnums=(0,))" ], "execution_count": 35, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "pu5G-8lfVNzt" }, "source": [ "Similarly, we can now define the evaluation step. Here, the function is much easier as it simply needs to stack the model's forward pass with the previously defined `eval_function` (or `logits_function`)." ] }, { "cell_type": "code", "metadata": { "id": "chY-zaUln94G" }, "source": [ "def eval_step(state, batch):\n", " logits = state.apply_fn(**batch, params=state.params, train=False)[0]\n", " return state.logits_function(logits)" ], "execution_count": 36, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "nHdL0Op8Vrdu" }, "source": [ "Similarly, we also apply `jax.pmap` to the evaluation step." ] }, { "cell_type": "code", "metadata": { "id": "9P8DBfk2oA_e" }, "source": [ "parallel_eval_step = jax.pmap(eval_step, axis_name=\"batch\")" ], "execution_count": 37, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "b99v2GVjWCRr" }, "source": [ "### Defining the data collators\n", "\n", "In a final step before we can start training, we need to define the data collators. The data collator is important to shuffle the training data before each epoch and to prepare the batch for each training and evaluation step.\n", "\n", "Let's start with the training collator." ] }, { "cell_type": "markdown", "metadata": { "id": "J7jyyxibXGks" }, "source": [ "The training collator can be defined as a [Python generator](https://wiki.python.org/moin/Generators) that returns a batch model input every time it is called.\n", "\n", "First, a random permutation of the whole dataset is defined. \n", "Then, every time the training data collator is called the next batch of the randomized dataset is extracted, converted to a JAX array and sharded over all local TPU devices." ] }, { "cell_type": "code", "metadata": { "id": "YZ6iVzivl88S" }, "source": [ "def glue_train_data_loader(rng, dataset, batch_size):\n", " steps_per_epoch = len(dataset) // batch_size\n", " perms = jax.random.permutation(rng, len(dataset))\n", " perms = perms[: steps_per_epoch * batch_size] # Skip incomplete batch.\n", " perms = perms.reshape((steps_per_epoch, batch_size))\n", "\n", " for perm in perms:\n", " batch = dataset[perm]\n", " batch = {k: jnp.array(v) for k, v in batch.items()}\n", " batch = shard(batch)\n", "\n", " yield batch" ], "execution_count": 38, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "bOSPOyMYZt29" }, "source": [ "We define the eval data collator in a similar fashion. \n", "\n", "**Note**:\n", "For simplicity, we throw away the last incomplete batch since it can't be easily sharded over all devices. This means that the evaluation results might be slightly incorrect. It can be easily fixed by including [this](https://github.com/huggingface/transformers/blob/f063c56d942737d2c7aac93895cd8310afd9c7a4/examples/flax/text-classification/run_flax_glue.py#L491) part in the training loop after evaluation." ] }, { "cell_type": "code", "metadata": { "id": "cZpbf9pamCEW" }, "source": [ "def glue_eval_data_loader(dataset, batch_size):\n", " for i in range(len(dataset) // batch_size):\n", " batch = dataset[i * batch_size : (i + 1) * batch_size]\n", " batch = {k: jnp.array(v) for k, v in batch.items()}\n", " batch = shard(batch)\n", "\n", " yield batch" ], "execution_count": 39, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Vf_eG7OBbGAJ" }, "source": [ "Next, we replicate/copy the weight parameters on each device, so that we can pass them to our pmapped functions." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r1sA7cjySB2T", "outputId": "82c78177-4bbc-4868-a514-18c2ac044e96" }, "source": [ "state = flax.jax_utils.replicate(state)" ], "execution_count": 40, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/jax/lib/xla_bridge.py:317: UserWarning: jax.host_count has been renamed to jax.process_count. This alias will eventually be removed; please update your code.\n", " \"jax.host_count has been renamed to jax.process_count. This alias \"\n", "/usr/local/lib/python3.7/dist-packages/jax/lib/xla_bridge.py:304: UserWarning: jax.host_id has been renamed to jax.process_index. This alias will eventually be removed; please update your code.\n", " \"jax.host_id has been renamed to jax.process_index. This alias \"\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "I42ZJzpgbo-s" }, "source": [ "### Training\n", "\n", "Finally, we can write down the full training loop." ] }, { "cell_type": "markdown", "metadata": { "id": "bq40T7_Wbw2p" }, "source": [ "Let's start by generating a seeded `PRNGKey` for the dropout layers and dataset shuffling." ] }, { "cell_type": "code", "metadata": { "id": "-Wyaedrnty4-" }, "source": [ "rng = jax.random.PRNGKey(seed)\n", "dropout_rngs = jax.random.split(rng, jax.local_device_count())" ], "execution_count": 41, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xByhGKVUcquw" }, "source": [ "Now we define the full training loop. For each batch in each epoch, we run a training step. Here, we also need to make sure that the PRNGKey is sharded/split over each device. Having completed an epoch, we report the training metrics and can run the evaluation." ] }, { "cell_type": "code", "metadata": { "id": "FBARxJ8soOrs", "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "149b8e9f64e44f04974d6d5496480239", "e10025b5a2114ca795cfb81398829feb", "6e86f1e800ab4a8f9e37e66462cd7242", "992b6178a02c4d259945d51a3cf0bb41", "51158010ae974746a27796b74ebfa89f", "835f3d3ca9554b9f885f0276e085ba83", "1e71ed0a7262419997503e276fd472ff", "0ff7f0bc66094cc6b220505ee8695a74", "4f4a06ea79fb4c0399e846c2331279fb", "0d259d8e43934517a4e687d53f661826", "e644da6330c4468f80dcbf737585258c", "ee81b01cb18b4679a577bce64bc4d8e1", "41a53cb981604e2f89a1cc9ef5fa565d", "98d78f59a188434eb79565aa552a0dc9", "658f8b53637644068e88d6c0724cf90a", "88be72d8a97c442892366bf0e2f37343" ] }, "outputId": "a39ec0c2-fe07-4821-dfa5-2be4af0e3aaa" }, "source": [ "for i, epoch in enumerate(tqdm(range(1, num_train_epochs + 1), desc=f\"Epoch ...\", position=0, leave=True)):\n", " rng, input_rng = jax.random.split(rng)\n", "\n", " # train\n", " with tqdm(total=len(train_dataset) // total_batch_size, desc=\"Training...\", leave=False) as progress_bar_train:\n", " for batch in glue_train_data_loader(input_rng, train_dataset, total_batch_size):\n", " state, train_metrics, dropout_rngs = parallel_train_step(state, batch, dropout_rngs)\n", " progress_bar_train.update(1)\n", "\n", " # evaluate\n", " with tqdm(total=len(eval_dataset) // total_batch_size, desc=\"Evaluating...\", leave=False) as progress_bar_eval:\n", " for batch in glue_eval_data_loader(eval_dataset, total_batch_size):\n", " labels = batch.pop(\"labels\")\n", " predictions = parallel_eval_step(state, batch)\n", " metric.add_batch(predictions=chain(*predictions), references=chain(*labels))\n", " progress_bar_eval.update(1)\n", "\n", " eval_metric = metric.compute()\n", "\n", " loss = round(flax.jax_utils.unreplicate(train_metrics)['loss'].item(), 3)\n", " eval_score = round(list(eval_metric.values())[0], 3)\n", " metric_name = list(eval_metric.keys())[0]\n", "\n", " print(f\"{i+1}/{num_train_epochs} | Train loss: {loss} | Eval {metric_name}: {eval_score}\")" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "149b8e9f64e44f04974d6d5496480239", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Epoch ...', max=3.0, style=ProgressStyle(description_widt…" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4f4a06ea79fb4c0399e846c2331279fb", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Training...', max=267.0, style=ProgressStyle(description_…" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "ffP-VQOyIrJk" }, "source": [ "To see how your model fared you can compare it to the [GLUE Benchmark leaderboard](https://gluebenchmark.com/leaderboard)." ] }, { "cell_type": "markdown", "metadata": { "id": "2UB8pjv8vxYa" }, "source": [ "### Sharing fine-tuned model\n", "\n", "Now that you've succesfully trained a model, you can share it with the community by uploading the fine-tuned model checkpoint and tokenizer to your account on the [hub](https://huggingface.co/models).\n", "\n", "\n", "If you don't have an account yet, you can click [here](https://huggingface.co/join) join the community 🤗." ] }, { "cell_type": "markdown", "metadata": { "id": "8vPLVtkS-JHi" }, "source": [ "In a first step, we install [git-lfs](https://git-lfs.github.com/) to easily upload the model weights." ] }, { "cell_type": "code", "metadata": { "id": "QOHBpZulqSLT" }, "source": [ "%%capture\n", "!sudo apt-get install software-properties-common\n", "!sudo curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash\n", "!sudo apt-get install git-lfs\n", "!git lfs install" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "XJduNRXk_V2c" }, "source": [ "Next, we you will need to store your git credentials so that `git` knows who is uploading the files. You should replace the fields `` and `` with your credentials accordingly." ] }, { "cell_type": "code", "metadata": { "id": "D5ILJ1B-up41" }, "source": [ "!git config --global user.email \"\" # e.g. \"patrick.v.platen@gmail.com\"\n", "!git config --global user.name \"\" # e.g. \"Patrick von Platen\"" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "QwPQyaTOAM5H" }, "source": [ "You will need to pass your user authentification token `hf_auth_token` to allow the 🤗 hub to upload model weights under your username. To find your authentification token, you need to log in [here](https://huggingface.co/login), click on your icon (top right), go to *Settings*, then *API Tokens*. You can copy the *User API token* and replace it with the field `` below." ] }, { "cell_type": "code", "metadata": { "id": "OgET7o-Jv2OR" }, "source": [ "hf_auth_token = \"\" # e.g. api_DaYgaaVnGdRtznIgiNfotCHFUqmOdARmPx" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "FRM1k_8FCMiP" }, "source": [ "Finally, you can give your fine-tuned model a nice `model_id` or leave the default one as noted below. The model will be uploaded under `https://huggingface.co//`, *e.g.*\n" ] }, { "cell_type": "code", "metadata": { "id": "-XGT5eAzBoUK" }, "source": [ "model_id = f\"{model_checkpoint}_fine_tuned_glue_{task}\"" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "FJHgcqvkBsIc" }, "source": [ "Great! Now all that is left to do is to upload your model:" ] }, { "cell_type": "code", "metadata": { "id": "cIT_aQ-OoxvG" }, "source": [ "model.push_to_hub(model_id, use_auth_token=hf_auth_token)\n", "tokenizer.push_to_hub(model_id, use_auth_token=hf_auth_token)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "JlviIJzcC_WD" }, "source": [ "You can now go to your model page to check it out 😊.\n", "\n", "We strongly recommend to add a model card so that the community can actually make use of your fine-tuned model. You can do so by clicking on *Create Model Card* and adding a descriptive text in markdown format. \n", "\n", "A simple description for your fine-tuned model is given by running the following cell. You can simply copy-paste the output to be used as your model card `README.md`." ] }, { "cell_type": "code", "metadata": { "id": "QSKl6rX7D_-N" }, "source": [ "print(f\"\"\"---\n", "language: en\n", "license: apache-2.0\n", "datasets:\n", "- glue\n", "---\n", "# {\" \".join([x.capitalize() for x in model_id.split(\"_\")])}\n", "\n", "This checkpoint was initialized from the pre-trained checkpoint {model_checkpoint} and subsequently fine-tuned on GLUE task: {task} using [this](https://colab.research.google.com/drive/162pW3wonGcMMrGxmA-jdxwy1rhqXd90x?usp=sharing) notebook.\n", "Training was conducted for {num_train_epochs} epochs, using a linear decaying learning rate of {learning_rate}, and a total batch size of {total_batch_size}.\n", "\n", "The model has a final training loss of {loss} and a {metric_name} of {eval_score}.\n", "\"\"\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "OpFZeamCI-FJ" }, "source": [ "An uploaded model would, *e.g.*, look as follows: [patrickvonplaten/bert-base-cased_fine_tuned_glue_mrpc_demo](https://huggingface.co/patrickvonplaten/bert-base-cased_fine_tuned_glue_mrpc_demo)." ] } ] } ================================================ FILE: Using_🤗_Transformers_and_🤗_Datasets_filter_customer_feedback_filtering.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "7-Quw0_gXyDR" }, "source": [ "# **Using 🤗 Transformers and 🤗 Datasets filter customer feedback filtering**" ] }, { "cell_type": "markdown", "source": [ "Before diving into this notebook, we strongly recommend \n", "going through **all** the chapters of the official [🤗 Hugging Face course](https://huggingface.co/course/chapter1/1). This will make it much easier for \n", "you to follow this notebook and transfer the knowledge to **your** tasks." ], "metadata": { "id": "da6TrUsUZzi7" } }, { "cell_type": "markdown", "source": [ "In this notebook, we will simulate a real-world use \n", "case and try to solve it using tools of the Hugging Face ecosystem.\n", "\n", "We strongly recommend using this notebook as a template/example to \n", "solve **your** real-world use case." ], "metadata": { "id": "H4mgysOXZZWD" } }, { "cell_type": "markdown", "source": [ "# **Defining Task, Dataset & Model**\n", "\n", "Before jumping into the actual coding part, it's important to have a clear definition of the use case that you would like to automate or partly automate.\n", "A clear definition of the use case helps in identifying the most suitable task, dataset to use, and model to apply for your use case." ], "metadata": { "id": "B_tTKBgzNNP0" } }, { "cell_type": "markdown", "source": [ "## **Define your NLP task**\n", "\n", "Alright, let's dive into a hypothetical problem we wish to save using models of natural language processing. Let's assume, we are selling a product and our customer support team receives thousands of messages including feedback, complaints, and questions which ideally should all be answered. \n", "\n", "Quickly, it becomes obvious though that customer support is by no means able to reply to every message. Thus, we decide to only reply \n", "to the most unsatisfied custmoers and set the goal of replying to 100% of very unsatisfied messages.\n", "\n", "Assuming that a) messages of very unsatisfied customers represent only a fraction of all messages and b) that we can filter out unsatisfied messages in an automated way, customer support should be able to reach this goal.\n", "\n", "To filter out unsatisfied messages in an automated way, we plan on applying natural language processing technologies. \n", "\n", "\n", "The first step is now to map our use case - *filtering out unsatisfied messages* - to a natural language processing task.\n", "\n", "To do so, it is recommended to go over all available tasks on the Hugging Face Hub [here](https://huggingface.co/tasks). If you are not sure which task applies to your use case, you should click on all of the different tasks to better understand them, *e.g.* \n", "\n", "Automatically replying The task of finding messages of the most unsatisfied customers can be labeled as a text classification task: Classify a message into one of *very unsatisfied*, *unsatisfied*, *neutral*, *satisfied*, or *very satisfied*.\n", "\n" ], "metadata": { "id": "SCP5jic6ZCEG" } }, { "cell_type": "markdown", "source": [ "## **Find suitable datasets**\n", "\n", "Having decided on the task, next we should find the data the model will be trained on. This is usually more important for the downstream performance of your use case than picking the right model architecture.\n", "Keep in mind that a model is **only as good as the data it has been trained on**. Thus, we should be very careful when curating and/or selecting the dataset.\n", "\n", "Since we consider the hypothetical use case of *filtering out unsatisfied messages*, let's look into what datasets are available to us.\n", "\n", "For your real-world use case, it is **very likely** that you have internal data that best represents the actual data your NLP system is supposed to handle. Therefore, you should use such internal data to train your NLP system.\n", "It can nevertheless be helpful to also include publicly available to improve the generalizability of your model.\n", "\n", "Let's take a look at all available Datasets on the [Hugging Face Hub](https://huggingface.co/datasets). On the left side, you can filter the datasets according to *Task Categories* as well as *Tasks* which are more specific. Our use case corresponds to *Text Classification* -> *Sentiment Analysis* so let's select [these filters](https://huggingface.co/datasets?task_categories=task_categories:text-classification&task_ids=task_ids:sentiment-classification&sort=downloads). We are left with *ca.* 80 datasets at the time of writing this notebook. Two aspects should be evaluated when picking a dataset:\n", "\n", "- **Quality**: Is the dataset of high quality? More specifically: Does the data correspond to the data you expect to deal with in your use case? Is the data diverse, unbiased, ...?\n", "- **Size**: How big is the dataset? Usually one can safely say the bigger the dataset, the better.\n", "\n", "It's quite difficult to efficiently evaluate whether a dataset is of high quality and it's even more difficult to know whether and how the dataset is biased.\n", " An efficient and reasonable heuristic for high quality is to look at the download statistics. The more downloads, the more usage, the higher chance that the dataset is of high quality. The size is easy to evaluate as it can usually be quickly read upon. Let's take a look at the most downloaded datasets:\n", "\n", "- [Glue](https://huggingface.co/datasets/glue)\n", "- [Amazon polarity](https://huggingface.co/datasets/amazon_polarity)\n", "- [Tweet eval](https://huggingface.co/datasets/tweet_eval)\n", "- [Yelp review full](https://huggingface.co/datasets/yelp_review_full)\n", "- [Amazon reviews multi](https://huggingface.co/datasets/amazon_reviews_multi)\n", "\n", "Now we can inspect those datasets in more detail by reading through the dataset card which ideally should give all relevant and important information. In addition, the [dataset viewer](https://huggingface.co/datasets/glue/viewer/cola/test) is an incredibly powerful tool to inspect whether the data suits your use case.\n", "\n", "Let's quickly go over the dataset cards of the models above: \n", "- *GLUE* is a collection of small datasets that mostly serves as a means to compare new model architectures for researchers. The datasets are too small and don't correspond enough to our use case.\n", "- *Amazon polarity* is huge and a well-suited dataset for customer feedback since the data deals with customer reviews. However, it only has binary labels (positive/negative) whereas we are looking for more granularity in the sentiment classification. \n", "- *Tweet eval* uses different emojis as labels which cannot that easily be mapped to a scale going from unsatisfied to satisfied.\n", "- *Amazon reviews multi* seems to be the most suited dataset here. We have sentiment labels ranging from 1-5 corresponding to 1-5 stars on Amazon. These labels can very well be mapped to *very unsatisfied, unsatisfied, neutral, satisfied, very satisfied*. Having inspected some examples on [the dataset viewer](https://huggingface.co/datasets/amazon_reviews_multi/viewer/en/train) we can see that the reviews look very similar to how customer feedback reviews would look, so this seems like a very good dataset. In addition, each review has a `product_category` label so we could even go as far as to only use reviews of a product category that corresponds to the one we are working in. The dataset is multi-lingual, but we are just interested in the English version for now.\n", "- *Yelp review full* looks like a very suitable dataset. It's large and contains product reviews and sentiment labels from 1 to 5. Sadly, the dataset viewer is not working here at the moment and the dataset card is also relatively sparse requiring some more time to inspect the dataset. At this point, we should read the paper, but given the time-constraint of this blog post, we'll choose to go for *Amazon reviews multi*.\n", "\n", "As a conclusion, let's focus on the [*Amazon reviews multi*](https://huggingface.co/datasets/amazon_reviews_multi) dataset considering all training examples.\n", "\n", "As a final note, we recommend making use of Hub's dataset functionality even when working with private datasets. The Hugging Face Hub, Transformers, and Datasets are flawlessly integrated, which makes it trivial to use them in combination when training models.\n", "\n", "In addition, the Hugging Face Hub offers:\n", "\n", "- [A dataset viewer for every dataset](https://huggingface.co/datasets/amazon_reviews_multi)\n", "- [Easy demoing of every model using widgets](https://huggingface.co/docs/hub/main#whats-a-widget)\n", "- [Private and Public models](https://huggingface.co/docs/hub/adding-a-model#creating-a-repository)\n", "- [Git version control for repositories](https://huggingface.co/docs/hub/main#whats-a-repository)\n", "- [Highest security mechanisms](https://huggingface.co/docs/hub/security)" ], "metadata": { "id": "k_MrbAHndCAy" } }, { "cell_type": "markdown", "source": [ "## **Find a suitable model**\n", "\n", "Having decided on the task and the dataset that best describes our use case, we can now look into choosing a model to be used.\n", "\n", "Most likely, you will have to fine-tune a pretrained model for your use case, but it is worth checking whether they are already fine-tuned models on the Hub that perform well. In this case, you might reach a higher performance by just continuing to fine-tune such a model on your dataset.\n", "\n", "Let's take a look at all models that have been fine-tuned on Amazon Reviews Multi, you can find the list of models on the bottom right corner - clicking on *Browse models trained on this dataset* you can see [a list of all models fine-tuned on the dataset that are publicly available](https://huggingface.co/models?dataset=dataset:amazon_reviews_multi). Note that we are only interested in the English version of the dataset because our customer feedback will only be in English. It looks like most of the most downloaded models are trained on the multi-lingual version of the dataset and those that don't seem to be multi-lingual have very little information or poor performance. At this point, \n", "it might be more sensible to fine-tune a purely pretrained model instead of using one of the already fine-tuned ones shown in the link above.\n", "\n", "Alright, the next step now is to find a suitable pretrained model to be used for fine-tuning. This is actually more difficult than it seems given the large amount of pretrained and fine-tuned models that are the [Hugging Face Hub](https://huggingface.co/models) . The best option is usually to simply try out a variety of different models to see which one performs best. \n", "We still haven't found the perfect way of comparing different model checkpoints to each other at Hugging Face, but we provide some resources that are worth looking into:\n", "\n", "- The [model summary](https://huggingface.co/docs/transformers/model_summary) gives a short overview of different model architectures.\n", "- A task-specific search on the Hugging Face Hub, *e.g.* [a search on text-classification models](https://huggingface.co/models), shows you the most downloaded checkpoints which is also an indication of how well those checkpoints perform.\n", "\n", "Both of the above resources are currently however a bit suboptimal. The model summary is not always kept up to date. The speed at which new model architectures are released and old model architectures become outdated makes it extremely difficult to have an up-to-date summary of all model architectures.\n", "Similarly, it doesn't necessarily mean that the most downloaded model checkpoint is the best one. E.g. [`bert-base-cased`](https://huggingface.co/bert-base-uncased) is amongst the most downloaded model checkpoints but is not the best performing checkpoint anymore. \n", "\n", "The best is often to try out a variety of different model architectures, stay up to date with new model architectures by following experts in the field and checking well-known leaderboards.\n", "\n", "For text-classification, the important benchmarks to look at are [GLUE](https://gluebenchmark.com/leaderboard) and [SuperGLUE](https://super.gluebenchmark.com/leaderboard). Both benchmarks evaluate pretrained models on a variety of text-classification tasks, such as grammatical correctness, natural language inference, Yes/No question answering, etc..., which are quite similar to our target task of sentiment analysis. Thus, it is reasonable to choose one of the leading models of these benchmarks for our task.\n", "\n", "At the time of writing this notebook, the best performing models are very large models containing more than 10 billion parameters most of which are not open-sourced, *e.g.* *ST-MoE-32B*, *Turing NLR v5*, or\n", "*ERNIE 3.0*. One of the top-ranking models that is easily accessible is [DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta). Because Let's try out DeBERTa's newest base version - *i.e.* [`microsoft/deberta-v3-base`](https://huggingface.co/microsoft/deberta-v3-base)." ], "metadata": { "id": "1IucFk0GQt0Q" } }, { "cell_type": "markdown", "source": [ "# **Training / Fine-tuning a model with 🤗 Transformers and 🤗 Datasets**\n", "\n", "In this section, we will jump into the technical details of how to \n", "fine-tune a model end-to-end to be able to automatically filter out very unsatisfied customer feedback messages.\n", "\n", "Cool, let's start by installing all necessary pip packages and by setting up our code environment, then look into preprocessing the dataset and finally start training the model.\n", "\n", "The following notebook can be run online in a google colab pro with the GPU runtime environment enabled." ], "metadata": { "id": "F4IQ0Bb6IRNQ" } }, { "cell_type": "markdown", "source": [ "## **Install all necessary packages**\n", "\n", "To begin with, let's install [`git-lfs`](https://git-lfs.github.com/) so that we can automatically upload our trained checkpoints to the Hub during training." ], "metadata": { "id": "7ntAgMY2TKRx" } }, { "cell_type": "code", "source": [ "!apt install git-lfs" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kL476PVciBlB", "outputId": "ed09144f-a889-4e26-c7a1-bede57591dda" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Reading package lists... Done\n", "Building dependency tree \n", "Reading state information... Done\n", "The following NEW packages will be installed:\n", " git-lfs\n", "0 upgraded, 1 newly installed, 0 to remove and 39 not upgraded.\n", "Need to get 2,129 kB of archives.\n", "After this operation, 7,662 kB of additional disk space will be used.\n", "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 git-lfs amd64 2.3.4-1 [2,129 kB]\n", "Fetched 2,129 kB in 0s (7,487 kB/s)\n", "Selecting previously unselected package git-lfs.\n", "(Reading database ... 155455 files and directories currently installed.)\n", "Preparing to unpack .../git-lfs_2.3.4-1_amd64.deb ...\n", "Unpacking git-lfs (2.3.4-1) ...\n", "Setting up git-lfs (2.3.4-1) ...\n", "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "B683CTrxXyDY" }, "source": [ "Also, we install the 🤗 Transformers and 🤗 Datasets libraries to run this notebook. Since we will be using [DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta-v2#debertav2) in this notebook, we also need to install the [`sentencepiece`](https://github.com/google/sentencepiece) library for its tokenizer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OyR6R9jQXyDY" }, "outputs": [], "source": [ "%%capture\n", "!pip install datasets transformers[sentencepiece]" ] }, { "cell_type": "markdown", "source": [ "Next, let's login into our [Hugging Face account](https://huggingface.co/join) so that models are uploaded correctly under your name tag." ], "metadata": { "id": "AnukCvT4UG-S" } }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 385, "referenced_widgets": [ "ecf82f9bb19a43b0b0f85ba677d7fdaf", "b0b24ae83353474b84ac532037b0ec74", "5f9c324715554952bd0a04aa9c7ec62b", "1b4c3fb1903340e39a65dad195140d2e", "f0cb87644ad84421871f0d413fea8c9d", "8c778cf5552749898775ee3e43f46bc6", "daf8b0e163f148fe84d9e98ba54cb558", "4585585a9717423aacd8df5d57e0edd7", "efdc4418401e4b1dbce2b31b6ef854fd", "05395208365a42d78491771367ce29b2", "7cb8615b59014c0a83f4c72f2c97e54a", "22c5377c8e1045c0893036b8e087a0a5", "52a831fe62094909abddf7fef6a4ffe3", "f6afe52a6aea40399b0cc100f87947eb", "6b0339a84011471abd1dbd67da679139", "9049522a19ed486aa524c741125974a2", "6ef126eb487a4485a554943777678a9f" ] }, "id": "l8Jbx6h2y9w2", "outputId": "4a107d0b-58e1-4a38-f410-31a3b2eb1d5b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n", "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", "\n", "git config --global credential.helper store\u001b[0m\n" ] } ] }, { "cell_type": "markdown", "source": [ "## **Preprocess the dataset**\n", "\n", "Before we can start training the model, we should bring the dataset in a format \n", "that is understandable by the model.\n", "\n", "Thankfully, the 🤗 Datasets library makes this extremely easy as you will see in the following cells.\n", "\n", "The `load_dataset` function loads the dataset, nicely arranges it into predefined attributes, such as `review_body` and `stars`, and finally saves the newly arranged data using the [arrow format](https://arrow.apache.org/#:~:text=Format,data%20access%20without%20serialization%20overhead.) on disk. \n", "The arrow format allows for fast and memory-efficient data reading and writing.\n", "\n", "Let's load and prepare the English version of the `amazon_reviews_multi` dataset." ], "metadata": { "id": "XPz26fv8VC9k" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UUjKAtEgXyDa", "outputId": "7c79db7e-ce2a-4b94-d93a-5f19b82afde2", "colab": { "base_uri": "https://localhost:8080/", "height": 457, "referenced_widgets": [ "599b3b02cc884b808f4675c539e7c6c6", "c810d5857cb94e7b8defcc28a03293c1", "643b2827becc497ba4f2f8edc75e6c5c", "9e15b7840cbe46a4a2f64277a0d59cd7", "38979bc856aa4786a0302f4261fdbf1a", "21b89528860744609492123f7cfc4bdc", "89eef2ea65214940bac035d8346997e1", "6805f97b712c462abeed5f7e9a29cb55", "27d6f6d186a1470781037fa3b48e2aad", "7091548c8802428a92511382b2318302", "f253f7aa84154daf8f88a727e4487836", "cf07002c1741489db4864a9ff08350c9", "a4870b08b93346099e8e7456afbb3cfb", "4352386bc421483491f78d8e6fd68d5b", "f23a8f3bc79a487d8b7a203134b5aefd", "1f3d8c4e66764a6584975c236edd6cf5", "4f3d864687214961805cd797ee9264c4", "515fde7da4c54e69905340bb284accdb", "19664fb4a3274f82817220dd15455e24", "c6eac7d2ae6e47c1bb6f998d33118c5f", "aa19af312d7c403f91a717c1b31e235f", "d014e9b63bb84a919ac462ffc01a85ca", "8643bc90a4754ac2a6d59bc5bf22be99", "1d3fa80b678c49439227c3446fa0e2ee", "1c04500963dd4823b9072b6993ef4178", "04b9be6e059541839ec9e5c670c8a5bb", "e1b95e1441f54166ad66eea413fe9a4e", "4632fb7a0d094dad95731d3dfe5f3d3b", "c7042a4015fa404cb7f8c44810bc47f5", "ba54f8093ea44524acc948211e930c54", "1a4cc5805bee43ff86bb603bb8bc02e2", "56e4b599c78c46dda2e89379a592d02d", "f781383ed8fd4a8fa34092d18f6e604c", "3a5d870bf21d4751aa022549455c8e60", "a390b8c7e8494e799459991e0596f1c6", "881d5655a9a34f16857bbf95fff9abc4", "524960ddd1d04b288cce7f96f452aecd", "dd3c4674834c4565ad617964e4445bc6", "88aa1f054f674001a8490850c7b361fa", "0554e5c172304cfdbc521fdeb11c3a78", "48b2fd1d719747fc89b2abed973632cd", "a73b55c85c414b208df55729e66518ac", "e8e8c6bffd134d53a978589bddc77230", "39de9bf9d32f432e82cf78204a2d1761", "c3754b13402f49668002ca84e0eced7e", "f30df8faf007400388f0869fa32b0423", "6fbcc1108902464283c8d94406a74d1f", 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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-10000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-10000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-10000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-10000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-10000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-10000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-15000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-15000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-15000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-15000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-15000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-15000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-20000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-20000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-20000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-20000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-20000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-20000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-25000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-25000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-25000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-25000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-25000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-25000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-30000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-30000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-30000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-30000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-30000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-30000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-35000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-35000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-35000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-35000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-35000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-35000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-40000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-40000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-40000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-40000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-40000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-40000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-45000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-45000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-45000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-45000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-45000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-45000/added_tokens.json\n", "***** Running Evaluation *****\n", " Num examples = 5000\n", " Batch size = 8\n", "Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-50000\n", "Configuration saved in deberta_amazon_reviews_v1/checkpoint-50000/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/checkpoint-50000/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-50000/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-50000/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/checkpoint-50000/added_tokens.json\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "Cool, we see that the model seems to learn something! Training loss and validation loss is going down and the accuracy also ends up being well over random chance (20%). Interestingly, we see accuracy of around **58.6 %** already after 5000 steps which doesn't improve that much anymore afterward. Choosing a bigger model or training for longer would have probably given better results here, but that's good enough for our hypothetical use case!\n", "\n", "Alright, finally let's upload the model checkpoint to the Hub." ], "metadata": { "id": "CaLVY2nfmcgS" } }, { "cell_type": "code", "source": [ "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 505, "referenced_widgets": [ "f1b47b308fc44d0db5d5c9c0e220ca20", "4a061a1f37194e6baf6a69af5cb21810", "6a0648b8a0ef4572bb2b40de2c8841eb", "200396843e4946838c4da86b6db63da7", "b485f836368e47b68a6429ef021f6bfe", "fe2303f666f8497ea53a47b031ef666f", "ab931e51f4054133a5fb5c9a966574a0", "325bd958d1fa4301a99d00a2aa6269c7", "a070818e87534bf19d3135b0f12397ca", "4c28fc60b50445d3a0692132aadeb48b", "6ed5123c732448b895de96331c1c3e4c", "07209e50ade04003b6f1a8e0e4c31153", "0ab586f91c1f433d93c0174f31a023bc", "e77e2908f7774ff89d8d604e39e90ff5", "559d95a66cc34e54ae1d8aa47a5d1304", "dee389b6c91142b3b94f07b6eefa4937", "832f027ab6ca4d2bbb59b5c67b83307a", "15f910b522cc4bdd8cae4bed4e5741be", "2753294c27b54c17b3f3fbc80087a748", "a55e8bdb7d7044f7840f44915661cdd1", "629612eaf9324a9ea983a7012a888954", "cb74137376914463866f1dec3ebe68b5" ] }, "id": "HmjV2dhkibLc", "outputId": "b3f1c6e2-2a0e-41b1-d79e-92a9cd0deaf9" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to deberta_amazon_reviews_v1\n", "Configuration saved in deberta_amazon_reviews_v1/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/added_tokens.json\n", "Several commits (2) will be pushed upstream.\n", "The progress bars may be unreliable.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.34k/704M [00:00 main\n", "\n", "remote: tput: No value for $TERM and no -T specified \n", "remote: tput: No value for $TERM and no -T specified \n", "remote: tput: No value for $TERM and no -T specified \n", "remote: tput: No value for $TERM and no -T specified \n", "To https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1\n", " 7f3172e..599b891 main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "'https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1/commit/7f3172e3457e9f20305db66972ae64b21d70d9b1'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 24 } ] }, { "cell_type": "markdown", "source": [ "## **Evaluate / Analyse the model**\n", "\n", "Now that we have fine-tuned the model we need to be very careful about analyzing its performance. It's usually not enough to just look at basic metrics defining the quality of a model purely on a metric, such as *accuracy*.\n", "The better approach is to find a metric that best describes the actual use case of the model.\n", "\n", "Let's dive into evaluating the model 🤿." ], "metadata": { "id": "FDg4g1vxvv4t" } }, { "cell_type": "markdown", "source": [ "The model has been uploaded to the Hub under [`deberta_v3_amazon_reviews`](https://huggingface.co/patrickvonplaten/deberta_v3_amazon_reviews) after training, so in a first step, let's download it from there again. If this notebook is run all at once the following cell will simply load the model from the cache." ], "metadata": { "id": "wvefwWDdwkl4" } }, { "cell_type": "code", "source": [ "from transformers import AutoModelForSequenceClassification\n", "\n", "model = AutoModelForSequenceClassification.from_pretrained(\"patrickvonplaten/deberta_v3_amazon_reviews\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "e6a77953f35e4e9d95a380c31115809e", "8ee7908b2de54c6ab1d5e04b1834f5ef", "0276f3a079954474a0d47abd3e98c215", "d85be80c14664ea69a361f3d94a0d246", "88d90dfccaa2481a926cfb0061ddd9f0", "543addda1f3744428934dee014f1f760", "6c986eaae73c43d7a268c2b6a9849691", "65848249df3a488aaa72963ed93fb7e1", "382bdf1be42a4076b961ff663646f321", "6c3174b1750c402288a13057714f4195", "bc2b0917ed68425db053692a10a1e3a0", "b6cf632bf0cc4798b19e895cd28df0c7", "c92923f5af844bedb2128a90a9b0ae5a", "ff350d87b3fa4ef3af82f1e810913147", "b220417f8e5f409c9d15d27e826710f2", "65f463342fde410a8daf118af2557b4b", "47f91be6ba0347d99cf7f795dc199083", "463d53eaa0aa418b9848f8241a56791d", "693a053d799642dc91ac3ea751cd0f4c", "179a46d90c37483e81706a87da825c3c", "d5266627c61446ab815031e24bf874d4", "ac39e7e4fa39452ab36bb82ef2527cbe" ] }, "id": "HShL8goGqMYF", "outputId": "41d3671f-9aa7-4024-bcb5-e53a9c14e073" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading: 0%| | 0.00/1.10k [00:00" ], "text/html": [ "\n", "

\n", " \n", " \n", " [625/625 00:21]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "{'test_accuracy': 0.608,\n", " 'test_loss': 0.9637690186500549,\n", " 'test_runtime': 21.9574,\n", " 'test_samples_per_second': 227.714,\n", " 'test_steps_per_second': 28.464}" ] }, "metadata": {}, "execution_count": 22 } ] }, { "cell_type": "markdown", "source": [ "The results are very similar to performance on the validation dataset, which is usually a good sign as it shows that the model didn't overfit the test dataset.\n", "\n", "However, 60% accuracy is far from being perfect on a 5-class classification problem, but do we need very high accuracy for all classes?\n", "\n", "Since we are mostly concerned with very negative customer feedback, let's maybe just focus on how well the model performs on classifying reviews of the most unsatisfied customers. Also, we decide to help the model a bit - all feedback classified as either **very unsatisfied** or **unsatisfied** will be handled by us - to catch close to 99% of the **very unsatisfied** messages. At the same time, we also measure how many **unsatisfied** messages we can answer this way and how much unnecessary work we do by answering messages of neutral, satisfied, and very satisfied customers.\n", "\n", "Great, let's write a new `compute_metrics` function." ], "metadata": { "id": "paCsFboh12TI" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "\n", "def compute_metrics(pred):\n", " pred_logits = pred.predictions\n", " pred_classes = np.argmax(pred_logits, axis=-1)\n", " labels = np.asarray(pred.label_ids)\n", "\n", " # First let's compute % of very unsatisfied messages we can catch\n", " very_unsatisfied_label_idx = (labels == 0)\n", " very_unsatisfied_pred = pred_classes[very_unsatisfied_label_idx]\n", "\n", " # now both 0 and 1 labels are 0 labels the rest is > 0\n", " very_unsatisfied_pred = very_unsatisfied_pred * (very_unsatisfied_pred - 1)\n", " \n", " # Let's count how many labels are 0 -> that's the \"very unsatisfied\"-accuracy\n", " true_positives = sum(very_unsatisfied_pred == 0) / len(very_unsatisfied_pred)\n", "\n", " # Second let's compute how many satisfied messages we unnecessarily reply to\n", " satisfied_label_idx = (labels > 1)\n", " satisfied_pred = pred_classes[satisfied_label_idx]\n", "\n", " # how many predictions are labeled as unsatisfied over all satisfied messages?\n", " false_positives = sum(satisfied_pred <= 1) / len(satisfied_pred)\n", "\n", " return {\"%_unsatisfied_replied\": round(true_positives, 2), \"%_satisfied_incorrectly_labels\": round(false_positives, 2)}" ], "metadata": { "id": "Wr0whRk1HsNt" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "We again instantiate the Trainer to easily run the evaluation." ], "metadata": { "id": "Z8FDaPF8LV5v" } }, { "cell_type": "code", "source": [ "trainer = Trainer(\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " model=model,\n", " tokenizer=tokenizer,\n", " data_collator=data_collator,\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "myFZM-YOKjra", "outputId": "b6a31d76-98b1-4d61-ec63-4bb8220d3c24" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/content/deberta_amazon_reviews_v1 is already a clone of https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1. Make sure you pull the latest changes with `repo.git_pull()`.\n" ] } ] }, { "cell_type": "markdown", "source": [ "And let's run the evaluation again with our new metric computation which is better suited for our use case." ], "metadata": { "id": "tG5ejdE8LbSx" } }, { "cell_type": "code", "source": [ "prediction_metrics = trainer.predict(tokenized_datasets[\"test\"]).metrics\n", "prediction_metrics" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 203 }, "id": "7oLwsTAxKnpu", "outputId": "bc80fe4d-6a9e-439d-dd28-776eadc6a686" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "***** Running Prediction *****\n", " Num examples = 5000\n", " Batch size = 8\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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\n", " " ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "{'test_%_satisfied_incorrectly_labels': 0.11733333333333333,\n", " 'test_%_unsatisfied_replied': 0.949,\n", " 'test_loss': 0.9637690186500549,\n", " 'test_runtime': 22.8964,\n", " 'test_samples_per_second': 218.375,\n", " 'test_steps_per_second': 27.297}" ] }, "metadata": {}, "execution_count": 28 } ] }, { "cell_type": "markdown", "source": [ "Cool! This already paints a pretty nice picture. We catch around 95% of **very unsatisfied** customers automatically at a cost of wasting our efforts on 10% of satisfied messages.\n", "\n", "Let's do some quick math. We receive daily around 10,000 messages for which we expect ca. 500 to be very negative. Instead of having to answer to all 10,000 messages, using this automatic filtering, we would only need to look into 500 + 0.12 * 10,000 = 1700 messages and only reply to 475 messages while incorrectly missing 5% of the messages. Pretty nice - a 83% reduction in human effort at missing only 5% of very unsatisfied customers!\n", "\n", "Obviously, the numbers don't represent the gained value of an actual use case, but we could come close to it with enough high-quality training data of your real-world example!" ], "metadata": { "id": "jZMdyrfYLnjN" } }, { "cell_type": "markdown", "source": [ "Let's save the results" ], "metadata": { "id": "MLPzAXRa2Qnp" } }, { "cell_type": "code", "source": [ "trainer.save_metrics(\"prediction\", prediction_metrics)" ], "metadata": { "id": "Jx-akRTgNVI5" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "and again upload everything on the Hub." ], "metadata": { "id": "8Q8yORv7NfTj" } }, { "cell_type": "code", "source": [ "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 294 }, "id": "dklENjigNjCV", "outputId": "c9c324b3-d571-4b93-d4aa-c52b863c97ba" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to deberta_amazon_reviews_v1\n", "Configuration saved in deberta_amazon_reviews_v1/config.json\n", "Model weights saved in deberta_amazon_reviews_v1/pytorch_model.bin\n", "tokenizer config file saved in deberta_amazon_reviews_v1/tokenizer_config.json\n", "Special tokens file saved in deberta_amazon_reviews_v1/special_tokens_map.json\n", "added tokens file saved in deberta_amazon_reviews_v1/added_tokens.json\n", "To https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1\n", " 599b891..ad77e6d main -> main\n", "\n", "Dropping the following result as it does not have all the necessary fields:\n", "{'task': {'name': 'Text Classification', 'type': 'text-classification'}}\n", "To https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1\n", " ad77e6d..13e5ddd main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "'https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1/commit/ad77e6da4fdbd07678a91ac57af6652ecb3d3b83'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 30 } ] }, { "cell_type": "markdown", "source": [ "The data is now saved [here](https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1/blob/main/prediction_results.json).\n", "\n", "That's it for today 😎. As a final step, it would also make a lot of sense to try the model out on actual real-world data. This can be done directly on the inference widget on [the model card](https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1):\n", "\n", "![example.png](data:image/png;base64,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], "metadata": { "id": "MkdlkccWNqf9" } }, { "cell_type": "markdown", "source": [ "It does seem to generalize quite well to real-world data 🔥" ], "metadata": { "id": "lDmOHbYVPIKe" } }, { "cell_type": "markdown", "source": [ "## Optimization\n", "\n", "As soon as you think the model's performance is good enough for production it's all about making the model as memory efficient and fast as possible.\n", "\n", "There are some obvious solutions to this like choosing the best suited accelerated hardware, *e.g.* better GPUs, making sure no gradients are computed during the forward pass, or lowering the precision, *e.g.* to float16. \n", "\n", "More advanced optimization methods include using open-source accelerator libraries such as [ONNX Runtime](https://onnxruntime.ai/index.html), [quantization](https://pytorch.org/docs/stable/quantization.html), and inference servers like [Triton](https://developer.nvidia.com/nvidia-triton-inference-server).\n", "\n", "At Hugging Face, we have been working a lot to facilitate the optimization of models, especially with our open-source [Optimum library](https://huggingface.co/hardware). Optimum makes it extremely simple to optimize most 🤗 Transformers models.\n", "\n", "If you're looking for **highly optimized** solutions which don't require any technical knowledge, you might be interested in one of Hugging Face's paid inference services:\n", "\n", "- [Inference API](https://huggingface.co/inference-api)\n", "- [Infinity](https://huggingface.co/infinity)" ], "metadata": { "id": "JAmfpV6GRzHk" } } ], "metadata": { "colab": { "name": "Using 🤗 Transformers and 🤗 Datasets filter customer feedback filtering", "provenance": [], "collapsed_sections": [], "toc_visible": true, "include_colab_link": true }, "language_info": { "name": "python" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "ecf82f9bb19a43b0b0f85ba677d7fdaf": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [ "IPY_MODEL_b0b24ae83353474b84ac532037b0ec74", "IPY_MODEL_5f9c324715554952bd0a04aa9c7ec62b", "IPY_MODEL_1b4c3fb1903340e39a65dad195140d2e", "IPY_MODEL_f0cb87644ad84421871f0d413fea8c9d", "IPY_MODEL_8c778cf5552749898775ee3e43f46bc6" ], "layout": "IPY_MODEL_daf8b0e163f148fe84d9e98ba54cb558" } }, "b0b24ae83353474b84ac532037b0ec74": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4585585a9717423aacd8df5d57e0edd7", "placeholder": "​", "style": "IPY_MODEL_efdc4418401e4b1dbce2b31b6ef854fd", "value": "
\nHugging Face\n
\nCopy a token from your Hugging Face tokens page and paste it below.\n
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null, "grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "LBSYoWbi-45k" }, "source": [ "# **Fine-tuning Multi-Lingual Speech Model with 🤗 Transformers**" ] }, { "cell_type": "markdown", "metadata": { "id": "nT_QrfWtsxIz" }, "source": [ "This notebook shows how to fine-tune multi-lingual pretrained speech models for Automatic Speech Recognition." ] }, { "cell_type": "markdown", "metadata": { "id": "OK7AOAkwVOrx" }, "source": [ "This notebook is built to run on the [Common Voice dataset](https://huggingface.co/datasets/common_voice) with any multi-lingual speech model checkpoint from the [Model Hub](https://huggingface.co/models?language=multilingual&pipeline_tag=automatic-speech-recognition&sort=downloads) as long as that model has a version with a Connectionist Temporal Classification (CTC) head. Depending on the model and the GPU you are using, you might need to adjust the batch size to avoid out-of-memory errors. Set those two parameters, then the rest of the notebook should run smoothly:" ] }, { "cell_type": "code", "metadata": { "id": "fw_GGnvwVjOl" }, "source": [ "model_checkpoint = \"facebook/wav2vec2-large-xlsr-53\"\n", "batch_size = 16" ], "execution_count": 1, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ZUUqzyKnVpvc" }, "source": [ "For a more in-detail explanation of how multi-lingual pretrained speech models function, please take a look at the [🤗 Blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2)." ] }, { "cell_type": "markdown", "metadata": { "id": "e335hPmdtASZ" }, "source": [ "Before we start, let's install both `datasets` and `transformers` from master. Also, we need the `torchaudio` and `librosa` package to load audio files and the `jiwer` to evaluate our fine-tuned model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric ${}^1$." ] }, { "cell_type": "code", "metadata": { "id": "c8eh87Hoee5d" }, "source": [ "%%capture\n", "!pip install datasets==1.14\n", "!pip install transformers==4.11.3\n", "!pip install torchaudio\n", "!pip install librosa\n", "!pip install jiwer" ], "execution_count": 2, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0xxt_LwxDQlO" }, "source": [ "Next we strongly suggest to upload your training checkpoints directly to the [🤗 Hub](https://huggingface.co/) while training. The [🤗 Hub](https://huggingface.co/) has integrated version control so you can be sure that no model checkpoint is getting lost during training. \n", "\n", "To do so you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!)" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 239, "referenced_widgets": [ "4597fce67897473a8e0dc6604002d3ae", "b449fbbb6b1b471a8d50eb9d6c26af09", "70619a1016d347bd9191267dcd005b56", "c18dfdb314284e2a9d9c07bb4dcf1d49", "09de7a45bdcf43019fac8faf21d37f4d", "68dd5b7aa43e4156b72db64761cf66e4", "32234e5488dc4e958a7b4edf284085b1", "604598e08be24d8daee5193119855567", "0cc8170d04624602b6fcb913046065b9", "d493bfc3e89147c4b423d9001519d62f", "4338db0dd26a4c11828326ca20ee4fde", "17252e60e7c8408588fc6f81ba8d262e", "aac4d40371604e1599d7748e57ecf464", "b18eb056b44e419692f3c08b34a7cc81", "d537ad0980dc41889484f227eba49b51", "cdb1234dfc50408eadeef7bbfb1b388b" ] }, "id": "mlMSH3T3EazV", "outputId": "5fcd694f-80a4-4fd5-e18b-3d23ab8a77f8" }, "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4597fce67897473a8e0dc6604002d3ae", "version_minor": 0, "version_major": 2 }, "text/plain": [ "VBox(children=(HTML(value=\"
\\n\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " Birkaç saat sonra, Belçika da aynı yolu izledi.\n", " \n", " \n", " 1\n", " Veliler öfkeliydi.\n", " \n", " \n", " 2\n", " Şimdi Ankara yeniden denemeye hazır.\n", " \n", " \n", " 3\n", " Görüşmelerde Kosova konusuna da değinildi.\n", " \n", " \n", " 4\n", " \"Arnavutluk'ta ihracat zayıf.\"\n", " \n", " \n", " 5\n", " Kredilerin büyük bölümü özel şirketlere verildi.\n", " \n", " \n", " 6\n", " Ancak iki taraf da konuşma yapmadı.\n", " \n", " \n", " 7\n", " Yeni anayasanın onaylamasına çalışılacak.\n", " \n", " \n", " 8\n", " Uyulmadığı takdirde ülke cezalandırılacak.\n", " \n", " \n", " 9\n", " İlk dersler Eylül ayında başlayacak.\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "fowcOllGNNju" }, "source": [ "Alright! The transcriptions look fairly clean. Having translated the transcribed sentences, it seems that the language corresponds more to written-out text than noisy dialogue. This makes sense considering that [Common Voice](https://huggingface.co/datasets/common_voice) is a crowd-sourced read speech corpus." ] }, { "cell_type": "markdown", "metadata": { "id": "vq7OR50LN49m" }, "source": [ "We can see that the transcriptions contain some special characters, such as `,.?!;:`. Without a language model, it is much harder to classify speech chunks to such special characters because they don't really correspond to a characteristic sound unit. *E.g.*, the letter `\"s\"` has a more or less clear sound, whereas the special character `\".\"` does not.\n", "Also in order to understand the meaning of a speech signal, it is usually not necessary to include special characters in the transcription.\n", "\n", "In addition, we normalize the text to only have lower case letters and append a word separator token at the end." ] }, { "cell_type": "code", "metadata": { "id": "svKzVJ_hQGK6" }, "source": [ "import re\n", "chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�]'\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower() + \" \"\n", " return batch" ], "execution_count": 5, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "e3a3083b43784898acb579cea2fc0c9b", "d22042dd9c614f249d4177cd162edb9d", "ce11a521ee08472580ec9873f6f2c8ff", "0945eebd6624416a82b94c11d1f46e75", "f6d66815620f44e98305e0c3eb5a10b4", "980fc9d1dad044568f2cf09c5aabe851", "7716e1bdb2fa48caa0e5b747d8417ed9", "05af76b0c4fc410685102102cf39c2af", "6c0b90a363434052999ec37f4590f355", "3ccfaa3ba3ad4baaa68b4b2ca3f396a7", "28e0bf7007664d528ab282d9dce4ca08", "3f1d57e62b8c45bf83f2107e5803c430", "a52c9d7c77154b558535321558a99f82", "719906e201484ded9b781b9dad38f934", "204723b0f09d4bf9ab57fd77c764eb18", "98b226199dc54316b771f143c3009cc9", "d258296f72f541c291db35ea264527de", "a0f19ef147eb458b9e8b31e080acf150", "9658824e0065469f8cf73d8c4d0fa93b", "6a86593225b145f4b8116df9e6b0888e", "562b1a7721654e5190079e152a25f900", "0c219edc78ea47ee86d100109107f9e6" ] }, "id": "XIHocAuTQbBR", "outputId": "ba4bbeb2-7adf-452b-8b43-a1ce60654529" }, "source": [ "common_voice_train = common_voice_train.map(remove_special_characters)\n", "common_voice_test = common_voice_test.map(remove_special_characters)" ], "execution_count": 6, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e3a3083b43784898acb579cea2fc0c9b", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/3478 [00:00\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " diğer partiler gereken baraja ulaşamadılar\n", " \n", " \n", " 1\n", " yatırımlar üç virgül iki milyon avroyu buldu\n", " \n", " \n", " 2\n", " muhalefet bu sava inanmıyor\n", " \n", " \n", " 3\n", " bende mücadele edeceğime bu yolu seçtim\n", " \n", " \n", " 4\n", " ancak jurciç teklifi reddetmişti\n", " \n", " \n", " 5\n", " operasyon olaysız geçti\n", " \n", " \n", " 6\n", " birkaç günde öğrenmek için çok fazla şey vardı\n", " \n", " \n", " 7\n", " bu kış ziyaretine gittim\n", " \n", " \n", " 8\n", " ancak yetkililer nuh deyip peygamber demiyorlar\n", " \n", " \n", " 9\n", " ve tabii ki banyoda kaçak var\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "jwfaptH5RJwA" }, "source": [ "Good! This looks better. We have removed most special characters from transcriptions and normalized them to lower-case only.\n", "\n", "In CTC, it is common to classify speech chunks into letters, so we will do the same here. \n", "Let's extract all distinct letters of the training and test data and build our vocabulary from this set of letters.\n", "\n", "We write a mapping function that concatenates all transcriptions into one long transcription and then transforms the string into a set of chars. \n", "It is important to pass the argument `batched=True` to the `map(...)` function so that the mapping function has access to all transcriptions at once." ] }, { "cell_type": "code", "metadata": { "id": "LwCshNbbeRZR" }, "source": [ "def extract_all_chars(batch):\n", " all_text = \" \".join(batch[\"sentence\"])\n", " vocab = list(set(all_text))\n", " return {\"vocab\": [vocab], \"all_text\": [all_text]}" ], "execution_count": 7, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "bb7cca1dbee0473f9ffa601f26de2bff", "fcb420f307994c76bf477fe2007a2cfc", "ec0643d015f0466a8f6178a191d78978", "cd98ac18cda14f1191d8be051b0b99f9", "866bdbdda70f4377a1d41de89e02e685", "90ba1f42d4f14030978d5cb801ed7aee", "29a58c0cbf4d4dc3943bbd22c4b74881", "44f30bfe15914a0991c82652cb7ba884", "b115da2b43d0491fb33da0fea4d749e4", "ee9d0fc762d94aa4b2725b9c2826034b", "0d17008b61f043adba012a654eab9c73", "b25d7d91d7a6424b95cf52c88c331853", "f2479f0a421e43aca3f0507a195623ef", "209efc5b56eb4acda1a59bdc50040da6", "fe928771f52d4e0aaa69cd59a8d32f06", "d0cbd8d8f5c44ce18fe3c56898032531", "66dbc3cb68e549788aca9b885916ac21", "d77d2d03adff4193a1b48194341221b3", "c03c80a730eb4e77926d202999355433", "2ab34edf1481420f9dd350b1d8b8767c", "34f5c9847be647c2bee711dd612d4e48", "8fd31916b9a34145844878916488ac3e" ] }, "id": "_m6uUjjcfbjH", "outputId": "666f9fde-05d6-4687-c1e7-da68bdd1edcc" }, "source": [ "vocab_train = common_voice_train.map(\n", " extract_all_chars, batched=True, \n", " batch_size=-1, keep_in_memory=True, \n", " remove_columns=common_voice_train.column_names\n", ")\n", "vocab_test = common_voice_test.map(\n", " extract_all_chars, batched=True, \n", " batch_size=-1, keep_in_memory=True, \n", " remove_columns=common_voice_test.column_names\n", ")" ], "execution_count": 8, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bb7cca1dbee0473f9ffa601f26de2bff", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00 to the vocabulary\n", "Adding to the vocabulary\n", "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "KvL12DrNV4cx" }, "source": [ "If one wants to re-use the just created tokenizer with the fine-tuned model of this notebook, it is strongly advised to upload the `tokenizer` to the [🤗 Hub](https://huggingface.co/). Let's call the repo to which we will upload the files\n", "`\"wav2vec2-large-xlsr-turkish-demo-colab\"`:" ] }, { "cell_type": "code", "metadata": { "id": "A1XApZBAF2zr" }, "source": [ "model_checkpoint_name = model_checkpoint.split(\"/\")[-1]\n", "repo_name = f\"{model_checkpoint_name}-demo-colab\"" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "B1BiezWZF16d" }, "source": [ "and upload the tokenizer to the [🤗 Hub](https://huggingface.co/)." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 161 }, "id": "zytE1175GAKM", "outputId": "38618d69-21f2-46b2-d4ac-4d54f51a4068" }, "source": [ "tokenizer.push_to_hub(repo_name)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Cloning https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-demo-colab into local empty directory.\n", "tokenizer config file saved in wav2vec2-large-xlsr-53-demo-colab/tokenizer_config.json\n", "Special tokens file saved in wav2vec2-large-xlsr-53-demo-colab/special_tokens_map.json\n", "added tokens file saved in wav2vec2-large-xlsr-53-demo-colab/added_tokens.json\n", "To https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-demo-colab\n", " d42ec75..6c70130 main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-demo-colab/commit/6c70130c37a0bcfe24913105c3e6264e2f5c368d'" ] }, "metadata": {}, "execution_count": 67 } ] }, { "cell_type": "markdown", "metadata": { "id": "SwQM8lH_GGuP" }, "source": [ "Great, you can see the just created repository under `https://huggingface.co//wav2vec2-large-xlsr-turkish-demo-colab` ." ] }, { "cell_type": "markdown", "metadata": { "id": "YFmShnl7RE35" }, "source": [ "### Preprocess Data\n", "\n", "So far, we have not looked at the actual values of the speech signal but just the transcription. In addition to `sentence`, our datasets include two more column names `path` and `audio`. `path` states the absolute path of the audio file. Let's take a look.\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "TTCS7W6XJ9BG", "outputId": "ddafaa49-7f03-421e-e6a5-334b41f3287e" }, "source": [ "common_voice_train[0][\"path\"]" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3'" ] }, "metadata": {}, "execution_count": 68 } ] }, { "cell_type": "markdown", "metadata": { "id": "T6ndIjHGFp0W" }, "source": [ "`XLSR-Wav2Vec2` expects the input in the format of a 1-dimensional array of 16 kHz. This means that the audio file has to be loaded and resampled.\n", "\n", " Thankfully, `datasets` does this automatically by calling the other column `audio`. Let try it out. " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qj_z5Zc3GAs9", "outputId": "a910e54e-b2a6-4101-e01a-2c26eb7fe2bb" }, "source": [ "common_voice_train[0][\"audio\"]" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " -8.8930130e-05, -3.8027763e-05, -2.9146671e-05], dtype=float32),\n", " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3',\n", " 'sampling_rate': 48000}" ] }, "metadata": {}, "execution_count": 69 } ] }, { "cell_type": "markdown", "metadata": { "id": "WUUTgI1bGHW-" }, "source": [ "Great, we can see that the audio file has automatically been loaded. This is thanks to the new [`\"Audio\"` feature](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=audio#datasets.Audio) introduced in `datasets == 1.13.3`, which loads and resamples audio files on-the-fly upon calling.\n", "\n", "In the example above we can see that the audio data is loaded with a sampling rate of 48kHz whereas 16kHz are expected by the model. We can set the audio feature to the correct sampling rate by making use of [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column):" ] }, { "cell_type": "code", "metadata": { "id": "rrv65aj7G95i" }, "source": [ "common_voice_train = common_voice_train.cast_column(\"audio\", Audio(sampling_rate=16_000))\n", "common_voice_test = common_voice_test.cast_column(\"audio\", Audio(sampling_rate=16_000))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "PcnO4x-NGBEi" }, "source": [ "Let's take a look at `\"audio\"` again." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aKtkc1o_HWHC", "outputId": "7b565ed2-d842-4096-ac59-2fb68ae38664" }, "source": [ "common_voice_train[0][\"audio\"]" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " -7.4556941e-05, -1.4621433e-05, -5.7861507e-05], dtype=float32),\n", " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3',\n", " 'sampling_rate': 16000}" ] }, "metadata": {}, "execution_count": 71 } ] }, { "cell_type": "markdown", "metadata": { "id": "SOckzFd4Mbzq" }, "source": [ "This seemed to have worked! Let's listen to a couple of audio files to better understand the dataset and verify that the audio was correctly loaded. \n", "\n", "**Note**: *You can click the following cell a couple of times to listen to different speech samples.*" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 92 }, "id": "dueM6U7Ev0OA", "outputId": "0af491c1-f19e-4419-92c6-7f3fc43ee28b" }, "source": [ "import IPython.display as ipd\n", "import numpy as np\n", "import random\n", "\n", "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(common_voice_train[rand_int][\"sentence\"])\n", "ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "seçimlerde önemli bir olay bildirilmedi \n" ] }, { "output_type": "execute_result", "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "execution_count": 72 } ] }, { "cell_type": "markdown", "metadata": { "id": "gY8m3vARHYTa" }, "source": [ "It seems like the data is now correctly loaded and resampled. " ] }, { "cell_type": "markdown", "metadata": { "id": "1MaL9J2dNVtG" }, "source": [ "It can be heard, that the speakers change along with their speaking rate, accent, and background environment, etc. Overall, the recordings sound acceptably clear though, which is to be expected from a crowd-sourced read speech corpus.\n", "\n", "Let's do a final check that the data is correctly prepared, by printing the shape of the speech input, its transcription, and the corresponding sampling rate.\n", "\n", "**Note**: *You can click the following cell a couple of times to verify multiple samples.*" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Po2g7YPuRTx", "outputId": "df268899-0558-4f55-d9ce-68a2e6148186" }, "source": [ "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n", "print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n", "print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Target text: çok güzel \n", "Input array shape: (34560,)\n", "Sampling rate: 16000\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "M9teZcSwOBJ4" }, "source": [ "Good! Everything looks fine - the data is a 1-dimensional array, the sampling rate always corresponds to 16kHz, and the target text is normalized.\n", "\n", "Next, we should process the data with the model's feature extractor. Let's load the feature extractor" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "f5ed78d4c93d4ad8a3c36e9c6be42954", "5923ee1f3d31466c949f9abb7f98ee48", "4201952adf02450bb106bc90b65b21a8", "136e3698e7ae4f37b2b6eaabd74fff37", "bb2ce51969174648805e61c6fe5bbc26", "1eb0e9573a374f36a9c23ff9be46eec5", "92b6d40d90b74f1ab3085fd39ae7fb8a", "20da06bc68034ad889ee2d243c1871a6", "b37445039fca441aac2eba6153b52e41", "b74262bbe4ba4798b737eb687c77493f", "10d392f730e24029a1d0cf27de2f757b" ] }, "id": "UuA-9bgBYT4x", "outputId": "3c13fd12-3982-42ce-db39-b42d75e77b83" }, "source": [ "from transformers import AutoFeatureExtractor\n", "\n", "feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "loading configuration file https://huggingface.co/facebook/wav2vec2-large-xlsr-53/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/8508c73cd595eb416a1d517b90762416c0bc6cfbef529578079aeae4d8c14336.7581ed2ee0c677f1e933180df51bd1a668c4a2b6d5fd1297d32069373dac097c\n", "Model config Wav2Vec2Config {\n", " \"activation_dropout\": 0.0,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 768,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": true,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"sum\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": true,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_dropout\": 0.0,\n", " \"feat_extract_norm\": \"layer\",\n", " \"feat_proj_dropout\": 0.1,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 4096,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.1,\n", " \"mask_channel_length\": 10,\n", " \"mask_channel_min_space\": 1,\n", " \"mask_channel_other\": 0.0,\n", " \"mask_channel_prob\": 0.0,\n", " \"mask_channel_selection\": \"static\",\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_space\": 1,\n", " \"mask_time_other\": 0.0,\n", " \"mask_time_prob\": 0.075,\n", " \"mask_time_selection\": \"static\",\n", " \"model_type\": \"wav2vec2\",\n", " \"num_attention_heads\": 16,\n", " \"num_codevector_groups\": 2,\n", " \"num_codevectors_per_group\": 320,\n", " \"num_conv_pos_embedding_groups\": 16,\n", " \"num_conv_pos_embeddings\": 128,\n", " \"num_feat_extract_layers\": 7,\n", " \"num_hidden_layers\": 24,\n", " \"num_negatives\": 100,\n", " \"pad_token_id\": 0,\n", " \"proj_codevector_dim\": 768,\n", " \"transformers_version\": \"4.12.0.dev0\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 32\n", "}\n", "\n", "https://huggingface.co/facebook/wav2vec2-large-xlsr-53/resolve/main/preprocessor_config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpupzebk0d\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f5ed78d4c93d4ad8a3c36e9c6be42954", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/212 [00:00=\n", " 7.5 (Volta).\n", " \"\"\"\n", "\n", " processor: Wav2Vec2Processor\n", " padding: Union[bool, str] = True\n", " max_length: Optional[int] = None\n", " max_length_labels: Optional[int] = None\n", " pad_to_multiple_of: Optional[int] = None\n", " pad_to_multiple_of_labels: Optional[int] = None\n", "\n", " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n", " # split inputs and labels since they have to be of different lenghts and need\n", " # different padding methods\n", " input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", "\n", " batch = self.processor.pad(\n", " input_features,\n", " padding=self.padding,\n", " max_length=self.max_length,\n", " pad_to_multiple_of=self.pad_to_multiple_of,\n", " return_tensors=\"pt\",\n", " )\n", " with self.processor.as_target_processor():\n", " labels_batch = self.processor.pad(\n", " label_features,\n", " padding=self.padding,\n", " max_length=self.max_length_labels,\n", " pad_to_multiple_of=self.pad_to_multiple_of_labels,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " # replace padding with -100 to ignore loss correctly\n", " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", "\n", " batch[\"labels\"] = labels\n", "\n", " return batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "lbQf5GuZyQ4_" }, "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xO-Zdj-5cxXp" }, "source": [ "Next, the evaluation metric is defined. As mentioned earlier, the \n", "predominant metric in ASR is the word error rate (WER), hence we will use it in this notebook as well." ] }, { "cell_type": "code", "metadata": { "id": "9Xsux2gmyXso" }, "source": [ "wer_metric = load_metric(\"wer\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "E1qZU5p-deqB" }, "source": [ "The model will return a sequence of logit vectors:\n", "$\\mathbf{y}_1, \\ldots, \\mathbf{y}_m$ with $\\mathbf{y}_1 = f_{\\theta}(x_1, \\ldots, x_n)[0]$ and $n >> m$.\n", "\n", "A logit vector $\\mathbf{y}_1$ contains the log-odds for each word in the vocabulary we defined earlier, thus $\\text{len}(\\mathbf{y}_i) =$ `config.vocab_size`. We are interested in the most likely prediction of the model and thus take the `argmax(...)` of the logits. Also, we transform the encoded labels back to the original string by replacing `-100` with the `pad_token_id` and decoding the ids while making sure that consecutive tokens are **not** grouped to the same token in CTC style ${}^1$." ] }, { "cell_type": "code", "metadata": { "id": "1XZ-kjweyTy_" }, "source": [ "def compute_metrics(pred):\n", " pred_logits = pred.predictions\n", " pred_ids = np.argmax(pred_logits, axis=-1)\n", "\n", " pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n", "\n", " pred_str = processor.batch_decode(pred_ids)\n", " # we do not want to group tokens when computing the metrics\n", " label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n", "\n", " wer = wer_metric.compute(predictions=pred_str, references=label_str)\n", "\n", " return {\"wer\": wer}" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Xmgrx4bRwLIH" }, "source": [ "Now, we can load the pretrained speech checkpoint. The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of a CTC speech model also CTC's *blank token* ${}^2$.\n", "\n", "Because the dataset is quite small (~6h of training data) and because Common Voice is quite noisy, fine-tuning might require some hyper-parameter tuning, which is why a couple of hyperparameters are set in the following.\n", "\n", "**Note**: When using this notebook to train speech models on another language of Common Voice those hyper-parameter settings might not work very well. Feel free to adapt those depending on your use case. " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "4fe9e026e1e74cf4ba5554e7a0460c2f", "521e2c26f96a4de79782855121ea0766", "db21a864f02e438dafefbf2ecf4192ae", "fbd3b32a954c45c788eb8967103dec9d", "f6c41bd10465450287aa50dd569a96a9", "5799f1ce85ec4722a48d45a666aee28b", "762da8b71d714f3c9898d54c26ded701", "34cf654ccffa445c92307c28e74496db", "218f3651b2f84f15b4b3c7bb2c05e260", "36bd4de2f3974c39ba1c8becb10001aa", "db84a3fcf62e41318e13d4f36efee8c9" ] }, "id": "e7cqAWIayn6w", "outputId": "a8bb67d5-2b38-4170-82b5-c76ceb53483e" }, "source": [ "from transformers import AutoModelForCTC\n", "\n", "model = AutoModelForCTC.from_pretrained(\n", " model_checkpoint,\n", " attention_dropout=0.1,\n", " hidden_dropout=0.1,\n", " feat_proj_dropout=0.0,\n", " mask_time_prob=0.05,\n", " layerdrop=0.1,\n", " ctc_loss_reduction=\"mean\", \n", " pad_token_id=processor.tokenizer.pad_token_id,\n", " vocab_size=len(processor.tokenizer)\n", ")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "loading configuration file https://huggingface.co/facebook/wav2vec2-large-xlsr-53/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/8508c73cd595eb416a1d517b90762416c0bc6cfbef529578079aeae4d8c14336.7581ed2ee0c677f1e933180df51bd1a668c4a2b6d5fd1297d32069373dac097c\n", "Model config Wav2Vec2Config {\n", " \"activation_dropout\": 0.0,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 768,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": true,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"mean\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": true,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_dropout\": 0.0,\n", " \"feat_extract_norm\": \"layer\",\n", " \"feat_proj_dropout\": 0.0,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 4096,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.1,\n", " \"mask_channel_length\": 10,\n", " \"mask_channel_min_space\": 1,\n", " \"mask_channel_other\": 0.0,\n", " \"mask_channel_prob\": 0.0,\n", " \"mask_channel_selection\": \"static\",\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_space\": 1,\n", " \"mask_time_other\": 0.0,\n", " \"mask_time_prob\": 0.05,\n", " \"mask_time_selection\": \"static\",\n", " \"model_type\": \"wav2vec2\",\n", " \"num_attention_heads\": 16,\n", " \"num_codevector_groups\": 2,\n", " \"num_codevectors_per_group\": 320,\n", " \"num_conv_pos_embedding_groups\": 16,\n", " \"num_conv_pos_embeddings\": 128,\n", " \"num_feat_extract_layers\": 7,\n", " \"num_hidden_layers\": 24,\n", " \"num_negatives\": 100,\n", " \"pad_token_id\": 39,\n", " \"proj_codevector_dim\": 768,\n", " \"transformers_version\": \"4.12.0.dev0\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 42\n", "}\n", "\n", "https://huggingface.co/facebook/wav2vec2-large-xlsr-53/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmppd8nibgp\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4fe9e026e1e74cf4ba5554e7a0460c2f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.18G [00:00\n", " \n", " \n", " [2850/2850 1:44:25, Epoch 30/30]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining LossValidation LossWer
4005.0179001.4935411.024921
8000.7075000.4546090.607088
12000.3072000.3946860.540088
16000.2145000.4049220.519355
20000.1647000.4199370.500255
24000.1338000.4144010.485854
28000.1160000.4055310.480033

" ], "text/plain": [ "" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/preprocessor_config.json\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800/preprocessor_config.json\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2400\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2400/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2800\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2800/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000] due to args.save_total_limit\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=2850, training_loss=0.9370169477295457, metrics={'train_runtime': 6267.5878, 'train_samples_per_second': 14.489, 'train_steps_per_second': 0.455, 'total_flos': 1.0303791368179046e+19, 'train_loss': 0.9370169477295457, 'epoch': 30.0})" ] }, "metadata": {}, "execution_count": 80 } ] }, { "cell_type": "markdown", "metadata": { "id": "kPNxoqifM7R3" }, "source": [ "You can now upload the final result of the training to the Hub. Just execute this instruction:" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 351, "referenced_widgets": [ "4014d89119054c25ac0d1938ac4e2f8a", "c8db6791b1e64c62b6df2f098738eaa4", "fa08a1c6fc6f49038a878076ae2809c5", "f8e74bf4449f4d55bfc52d765ba44882", "115ae9ce4fcd4ff5b5c18648e034c446", "953293c28fa14e71a8e354ea1b844b75", "e84826abaf754217b98672dfd41d35fc", "d506cfa68fef41b6933a71d6d00b7b1e", "946ad0cbe0eb4ee3af73d2b1de3ea95a", "7a16d09a448d47938a4c016a54522ae3", "5738d171b6744ae2a991751a6bc2d850", "5ac1a0aaf868461ca207a09079ca6c53", "5e9e02a9b0bd473081cafbd5bf128507", "2ea0e8ecacb74670b9f9a3455e496eda", "a89587d630fa44bab17fed5a58ebb68b", "7ca6e27713b54a03b5bd52e626f33fc7", "222eef3cbb084b3d8a5ffe8068060340", "cb4652f2b0974680b9f224ef1ef97ddb", "678ac926ffa64e4482f0461aecbf4484", "b46ceb3a5c0d4f4eb7d222f1639c88f5", "2896391751074c528768de57d29d807e", "de70b5deb8894bf8ab9f75a79a228f8d" ] }, "id": "FKgqc1FhAxAP", "outputId": "cac62ad7-d950-43b6-e6cb-d5d76a721f38" }, "source": [ "trainer.push_to_hub()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/preprocessor_config.json\n", "Several commits (2) will be pushed upstream.\n", "The progress bars may be unreliable.\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4014d89119054c25ac0d1938ac4e2f8a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.36k/1.18G [00:00 main\n", "\n", "Dropping the following result as it does not have all the necessary field:\n", "{'dataset': {'name': 'common_voice', 'type': 'common_voice', 'args': 'tr'}}\n", "To https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab\n", " f642252..38e6fca main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab/commit/f64225259534e44179de0f715b96ada1a621551f'" ] }, "metadata": {}, "execution_count": 81 } ] }, { "cell_type": "markdown", "metadata": { "id": "gKSOruDqNJxO" }, "source": [ "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier \"your-username/the-name-you-picked\" so for instance:" ] }, { "cell_type": "markdown", "metadata": { "id": "2x-GybJDNQoH" }, "source": [ "```python\n", "from transformers import AutoModelForCTC, Wav2Vec2Processor\n", "\n", "model = AutoModelForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab\")\n", "processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab\")\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "RHIVc44_fY2N" }, "source": [ "To fine-tune larger models on larger datasets using CTC loss, one should take a look at the official speech-recognition examples [here](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition#connectionist-temporal-classification-without-language-model-ctc-wo-lm) 🤗." ] } ] } ================================================ FILE: getting_the_most_out_of_LLMs.md ================================================ # Getting the most out of LLMs Large Language Models (LLMs) such as GPT3/4, [Falcon](https://huggingface.co/tiiuae/falcon-40b), and [LLama](https://huggingface.co/meta-llama/Llama-2-70b-hf) are rapidly advancing in their ability to tackle human-centric tasks, establishing themselves as essential tools in modern knowledge-based industries. Deploying these models in real-world tasks remains challenging, however: - To exhibit near-human text understanding and generation capabilities, LLMs currently require to be composed of billions of parameters (see [Kaplan et al](https://arxiv.org/abs/2001.08361), [Wei et. al](https://arxiv.org/abs/2206.07682)). This consequently amplifies the memory demands for inference. - In many real-world tasks, LLMs need to be given extensive contextual information. This necessitates the model's capability to manage very long input sequences during inference. The crux of these challenges lies in augmenting the computational and memory capabilities of LLMs, especially when handling expansive input sequences. In this blog post, we will go over the most effective techniques to tackle these challenges for efficient LLM deployment: 1. **Lower Precision**: Research has shown that operating at reduced numerical precision, namely 8-bit and 4-bit, can achieve computational advantages without a considerable decline in model performance. 2. **Flash Attention:** Flash Attention is a variation of the attention algorithm that not only provides a more memory-efficient approach but also realizes increased efficiency due to optimized GPU memory utilization. 3. **Architectural Innovations:** Considering that LLMs are always deployed in the same way during inference, namely autoregressive text generation with a long input context, specialized model architectures have been proposed that allow for more efficient inference. The most important advancement in model architectures hereby are [Alibi](https://arxiv.org/abs/2108.12409), [Rotary embeddings](https://arxiv.org/abs/2104.09864), [Multi-Query Attention (MQA)](https://arxiv.org/abs/1911.02150) and [Grouped-Query-Attention (GQA)]((https://arxiv.org/abs/2305.13245)). Throughout this notebook, we will offer an analysis of auto-regressive generation from a tensor's perspective. We delve into the pros and cons of adopting lower precision, provide a comprehensive exploration of the latest attention algorithms, and discuss improved LLM architectures. While doing so, we run practical examples showcasing each of the feature improvements. ## 1. Harnessing the Power of Lower Precision Memory requirements of LLMs can be best understood by seeing the LLM as a set of weight matrices and vectors and the text inputs as a sequence of vectors. In the following, the definition *weights* will be used to signify all model weight matrices and vectors. At the time of writing this paper, LLMs consist of at least a couple billion parameters. Each parameter thereby is made of a decimal number, e.g. `4.5689` which is usually stored in either [float32](https://en.wikipedia.org/wiki/Single-precision_floating-point_format), [bfloat16](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format), or [float16](https://en.wikipedia.org/wiki/Half-precision_floating-point_format) format. This allows us to easily compute the memory requirement to load the LLM into memory: > *Loading the weights of a model having X billion parameters requires roughly 4 * X GB of VRAM in float32 precision\* Nowadays, models are however rarely trained in full float32 precision, but usually in bfloat16 precision or less frequently in float16 precision. Therefore the role of thumb becomes: > *Loading the weights of a model having X billion parameters requires roughly 2 * X GB of VRAM in bfloat16/float16 precision\* For shorter text inputs (less than 1024 tokens), the memory requirement for inference is very much dominated by the memory requirement to load the weights. Therefore, for now, let's assume that the memory requirement for inference is equal to the memory requirement to load the model into the GPU VRAM. To give some examples of how much VRAM it roughly takes to load a model in bfloat16: - **GPT3** requires 2 \* 175 GB = **350 GB** VRAM - [**Bloom**](https://huggingface.co/bigscience/bloom) requires 2 \* 176 GB = **352 GB** VRAM - [**Llama-2-70b**](https://huggingface.co/meta-llama/Llama-2-70b-hf) requires 2 \* 70 GB = **140 GB** VRAM - [**Falcon-40b**](https://huggingface.co/tiiuae/falcon-40b) requires 2 \* 40 GB = **80 GB** VRAM - [**MPT-30b**](https://huggingface.co/mosaicml/mpt-30b) requires 2 \* 30 GB = **60 GB** VRAM - [**bigcode/starcoder**](https://huggingface.co/bigcode/starcoder) requires 2 \* 15.5 = **31 GB** VRAM As of writing this document, the largest GPU chip on the market is the A100 offering 80GB of VRAM. Most of the models listed before require more than 80GB just to be loaded and therefore necessarily require [tensor parallelism](https://huggingface.co/docs/transformers/v4.15.0/parallelism#tensor-parallelism) and/or [pipeline parallelism](https://huggingface.co/docs/transformers/v4.15.0/parallelism#naive-model-parallel-vertical-and-pipeline-parallel) 🤗 Transformers does not support tensor parallelism out of the box as it requires the model architecture to be written in a specific way. If you're interested in writing models in a tensor-parallelism-friendly way, feel free to have a look at [the text-generation-inference library](https://github.com/huggingface/text-generation-inference/tree/main/server/text_generation_server/models/custom_modeling). Pipeline parallelism is supported out of the box. For this, simply load the model with `device="auto"` which will automatically place the different layers on the available GPUs as explained [here](https://huggingface.co/docs/accelerate/v0.22.0/en/concept_guides/big_model_inference). If you have access to an 8 x 80GB A100 node, you could load BLOOM as follows ```bash !pip install transformers accelerate bitsandbytes ``` ```python # from transformers import AutoModelForCausalLM # model = AutoModelForCausalLM.from_pretrained("bigscience/bloom", device_map="auto") ``` By using `device_map="auto"` the attention layers would be equally distributed over all available GPUs. In this notebook, we will use [bigcode/octocoder](https://huggingface.co/bigcode/octocoder) as it can be run on a single 40 GB A100 GPU device chip. Note that all memory and speed optimizations that we will apply going forward, are equally applicable to models that require model or tensor parallelism. Since the model is loaded in bfloat16 precision, using our rule of thumb above, we would expect the memory requirement to run inference with `bigcode/octocoder` to be around 31 GB VRAM. Let's give it a try. We first load the model and tokenizer and then pass both to Transformers' [pipeline](https://huggingface.co/docs/transformers/main_classes/pipelines) object. ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import torch model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", torch_dtype=torch.bfloat16, device_map="auto") tokenizer = AutoTokenizer.from_pretrained("bigcode/octocoder") pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) ``` ```python prompt = "Question: Please write a function in Python that transforms bytes to Giga bytes.\n\nAnswer:" result = pipe(prompt)[0]["generated_text"][len(prompt):] result ``` **Output**: ``` Here is a Python function that transforms bytes to Giga bytes:\n\n```python\ndef bytes_to_giga_bytes(bytes):\n return bytes / 1024 / 1024 / 1024\n```\n\nThis function takes a single ``` Nice, we can now directly use the result to convert bytes into Gigabytes. ```python def bytes_to_giga_bytes(bytes): return bytes / 1024 / 1024 / 1024 ``` Let's call [`torch.cuda.max_memory_allocated`](https://pytorch.org/docs/stable/generated/torch.cuda.max_memory_allocated.html) to measure the peak GPU memory allocation. ```python bytes_to_giga_bytes(torch.cuda.max_memory_allocated()) ``` **Output**: ```bash 29.0260648727417 ``` Close enough to our back-of-the-envelope computation! We can see the number is not exactly correct as going from bytes to kilobytes requires a multiplication of 1024 instead of 1000. Therefore the back-of-the-envelope formula can also be understood as an "at most X GB" computation. Note that if we had tried to run the model in full float32 precision, a whopping 64 GB of VRAM would have been required. > Almost all models are trained in bfloat16 nowadays, there is no reason to run the model in full float32 precision if [your GPU supports bfloat16](https://discuss.pytorch.org/t/bfloat16-native-support/117155/5). Float32 won't give better inference results than the precision that was used to train the model. Let's define a `flush(...)` function to free all allocated memory so that we can accurately measure the peak allocated GPU memory. ```python del pipe del model import gc import torch def flush(): gc.collect() torch.cuda.empty_cache() torch.cuda.reset_peak_memory_stats() ``` Let's call it now for the next experiment. ```python flush() ``` Now what if your GPU does not have 32 GB of VRAM? It has been found that model weights can be quantized to 8-bit or 4-bits without a significant loss in performance (see [Dettmers et al.](https://arxiv.org/abs/2208.07339)). Model can be quantized to even 3 or 2 bits with an acceptable loss in performance as shown in the recent [GPTQ paper](https://huggingface.co/docs/transformers/main_classes/quantization#general-usage) 🤯. Without going into too many details, quantization schemes aim at reducing the precision of weights while trying to keep the model's inference results as accurate as possible (*a.k.a* as close as possible to bfloat16). Note that quantization works especially well for text generation since all we care about is choosing the *most likely next token* and don't really care about the exact values of the next token *logit* distribution. All that matters is that the next token *logit* distribution stays roughly the same so that an `argmax` or `topk` operation gives the same results. There are various quantization techniques, which we won't discuss in detail here, but in general, all quantization techniques work as follows: - 1. Quantize all weights to the target precision - 2. Load the quantized weights, and pass the input sequence of vectors in bfloat16 precision - 3. Dynamically dequantize weights to bfloat16 to perform the computation with their input vectors in bfloat16 precision - 4. Quantize the weights again to the target precision after computation with their inputs. In a nutshell, this means that *inputs-weight matrix* multiplications, with $X$ being the *inputs*, $W$ being a weight matrix and $Y$ being the output: $Y = X * W$ are changed to $ Y = X * \text{dequantize}(W); \text{quantize}(W) $ for every matrix multiplication. Dequantization and re-quantization is performed sequentially for all weight matrices as the inputs run through the network graph. Therefore, inference time is often **not** reduced when using quantized weights, but rather increases. Enough theory, let's give it a try! To quantize the weights with Transformers, you need to make sure that the [`bitsandbytes`](https://github.com/TimDettmers/bitsandbytes) library is installed. ```bash # !pip install bitsandbytes ``` We can then load models in 8-bit quantization by simply adding a `load_in_8bit=True` flag to `from_pretrained`. ```python model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", load_in_8bit=True, low_cpu_mem_usage=True) ``` Now, let's run our example again and measure the memory usage. ```python pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) result = pipe(prompt, max_new_tokens=60)[0]["generated_text"][len(prompt):] result ``` **Output**: ``` Here is a Python function that transforms bytes to Giga bytes:\n\n```python\ndef bytes_to_giga_bytes(bytes):\n return bytes / 1024 / 1024 / 1024\n```\n\nThis function takes a single ``` Nice, we're getting the same result as before, so no loss in accuracy! Let's look at how much memory was used this time. ```python bytes_to_giga_bytes(torch.cuda.max_memory_allocated()) ``` **Output**: ``` 17.503968238830566 ``` Significantly less! We're down to just 17.5 GBs and could therefore run this model on consumer GPUs like the 4090. We're seeing a very nice gain in memory efficiency and more or less no degradation to the model's output. However, we can also notice a slight slow-down during inference. We delete the models and flush the memory again. ```python del model del pipe ``` ```python flush() ``` Let's see what peak GPU memory consumption 4-bit quantization gives. Quantizing the model to 4-bit can be done with the same API as before - this time by passing `load_in_4bit=True` instead of `load_in_8bit=True`. ```python model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", load_in_4bit=True, low_cpu_mem_usage=True) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) result = pipe(prompt, max_new_tokens=60)[0]["generated_text"][len(prompt):] result ``` **Output**: ``` 'Question: Please write a function in Python that transforms bytes to Giga bytes.\n\nAnswer: Here '}] ``` We see that this time the generate function prematurely stopped, showing a loss in accuracy. Let's see how much memory was required. ```python bytes_to_giga_bytes(torch.cuda.max_memory_allocated()) ``` **Output**: ``` 9.543574333190918 ``` Just 9.5GB! That's really not much for a >15 billion parameter model, but we see that 4-bit quantization often comes with a degradation in performance. ```python del model del pipe ``` ```python flush() ``` Running OctoCoder in 4-bit precision reduces the required GPU VRAM from 32GB to just a bit over 9GB which is very low for a 15.5 billion parameter model. 4-bit quantization allows the model to be run on GPUs such as RTX3090, V100, and T4 which are quite accessible for most people. To quantize the model even further, we recommend looking into the [`AutoGPTQ`](https://huggingface.co/docs/transformers/main/en/main_classes/quantization#autogptq-integration%60) implementation. > Overall, it is important to remember that model quantization trades improved memory efficiency against accuracy and in most cases inference time. If GPU memory is not a constraint for your use case, there is often no need to look into quantization. However many GPUs simply can't run LLMs without quantization methods and in this case, 4-bit and 8-bit quantization schemes are extremely useful tools. For more in-detail usage information, we strongly recommend taking a look at the [Transformers Quantization Docs](https://huggingface.co/docs/transformers/main_classes/quantization#general-usage). Next, let's look into how we can improve computational and memory efficiency by using better algorithms and an improved model architecture. # 2. Flash Attention: A Leap Forward {#2-flash-attention-a-leap-forward} Today's top-performing LLMs share more or less the same fundamental architecture that consists of feed-forward layers, activation layers, layer normalization layers, and most crucially, self-attention layers. Self-attention layers are central to Large Language Models (LLMs) in that they enable the model to understand the contextual relationships between input tokens. However, the peak GPU memory consumption for self-attention layers grows *quadratically* both in time and memory complexity with number of input tokens (also called *sequence length*) that we denote in the following by $N$. While this is not really noticeable for shorter input sequences (of up to 1000 input tokens), it becomes a serious problem for longer input sequences (at around 16000 input tokens). Let's take a closer look. The formula to compute the output $\mathbf{O}$ of a self-attention layer for an input $\mathbf{X}$ of length $N$ is: $$ \textbf{O} = \text{Attn}(\mathbf{X}) = \mathbf{V} \times \text{Softmax}(\mathbf{QK}^T) \text{ with } \mathbf{Q} = \mathbf{W}_q \mathbf{X}, \mathbf{V} = \mathbf{W}_v \mathbf{X}, \mathbf{K} = \mathbf{W}_k \mathbf{X} $$ $\mathbf{X} = (\mathbf{x}_1, ... \mathbf{x}_{N})$ is thereby the input sequence to the attention layer. The projections $\mathbf{Q}$ and $\mathbf{K}$ will each consist of $N$ vectors resulting in the $\mathbf{QK}^T$ being of size $N^2$. LLMs usually have multiple attention heads, thus doing multiple self-attention computations in parallel. Assuming, the LLM has 40 attention heads and runs in bfloat16 precision, we can calculate the memory requirement to store the $\mathbf{QK^T}$ matrices to be $40 * 2 * N^2$ bytes. For $N=1000$ only around 50 MB of VRAM are needed, however, for $N=16000$ we would need 19 GB of VRAM, and for $N=100,000$ we would need almost 1TB just to store the $\mathbf{QK}^T$ matrices. Long story short, the default self-attention algorithm quickly becomes prohibitively memory-expensive for large input contexts. As LLMs improve in text comprehension and generation, they are applied to increasingly complex tasks. While models once handled the translation or summarization of a few sentences, they now manage entire pages, demanding the capability to process extensive input lengths. How can we get rid of the exorbitant memory requirements for large input lengths? We need a new way to compute the self-attention mechanism that gets rid of the $QK^T$ matrix. [Tri Dao et al.](FlashAttention:%20Fast%20and%20Memory-Efficient%20Exact%20Attention%20with%20IO-Awareness) developed exactly such a new algorithm and called it **Flash Attention**. In a nutshell, Flash Attention breaks the $ \mathbf{V} \times \text{Softmax}(\mathbf{QK}^T) $ computation apart and instead computes smaller chunks of the output by iterating oven multiple softmax computation steps: $$ \textbf{O}_i \leftarrow s^a_{ij} * \textbf{O}_i + s^b_{ij} * \mathbf{V}_{j} \times \text{Softmax}(\mathbf{QK}^T_{i,j}) \text{ for multiple } i, j \text{ iterations} $$ with $s^a_{ij}$ and $s^b_{ij}$ being some softmax normalization statistics that need to be recomputed for every $i$ and $j$. Please note that the whole Flash Attention is a bit more complex and is greatly simplified here as going in too much depth is out of scope for this notebook. The reader is invited to take a look at the well-written [Flash Attention paper](https://arxiv.org/pdf/2205.14135.pdf) for more details. The main takeaway here is: > By keeping track of softmax normalization statistics and by using some smart mathematics, Flash Attention gives **numerical identical** outputs compared to the default self-attention layer at a memory cost that only increases linearly with $N$. Looking at the formula, one would intuitively say that Flash Attention must be much slower compared to the default self-attention formula as more computation needs to be done. Indeed Flash Attention requires more FLOPs compared to normal attenion as the softmax normalization statistics have to constantly be recomputed (see [paper](https://arxiv.org/pdf/2205.14135.pdf) for more details if interested) > However Flash Attenion is much faster in inference compared to default attention which comes from its ability to significantly reduce the demands on the slower, high-bandwidth memory of the GPU (VRAM), focusing instead on the faster on-chip memory (SRAM). Essentially, Flash Attention makes sure that all intermediate write and read operations can be done using the fast *on-chip* SRAM memory instead of having to access the slower VRAM memory to compute the output vector $\mathbf{O}$. In practice, there is currently absolutely no reason to **not** use Flash Attention if available. The algorithm gives mathematically the same outputs, and is both faster and more memory-efficient. Let's look at a practical example. Our OctoCoder model now gets a significantly longer input prompt which includes a so-called *system prompt*. System prompts are used to steer the LLM into a better assistant that is tailored to the users' task. In the following, we use a system prompt that will make OctoCoder a better coding assistant. ```python system_prompt = """Below are a series of dialogues between various people and an AI technical assistant. The assistant tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble but knowledgeable. The assistant is happy to help with code questions and will do their best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn't entirely sure about the right answer. That said, the assistant is practical really does its best, and doesn't let caution get too much in the way of being useful. The Starcoder models are a series of 15.5B parameter models trained on 80+ programming languages from The Stack (v1.2) (excluding opt-out requests). The model uses Multi Query Attention, was trained using the Fill-in-the-Middle objective, and with 8,192 tokens context window for a trillion tokens of heavily deduplicated data. ----- Question: Write a function that takes two lists and returns a list that has alternating elements from each input list. Answer: Sure. Here is a function that does that. def alternating(list1, list2): results = [] for i in range(len(list1)): results.append(list1[i]) results.append(list2[i]) return results Question: Can you write some test cases for this function? Answer: Sure, here are some tests. assert alternating([10, 20, 30], [1, 2, 3]) == [10, 1, 20, 2, 30, 3] assert alternating([True, False], [4, 5]) == [True, 4, False, 5] assert alternating([], []) == [] Question: Modify the function so that it returns all input elements when the lists have uneven length. The elements from the longer list should be at the end. Answer: Here is the modified function. def alternating(list1, list2): results = [] for i in range(min(len(list1), len(list2))): results.append(list1[i]) results.append(list2[i]) if len(list1) > len(list2): results.extend(list1[i+1:]) else: results.extend(list2[i+1:]) return results ----- """ ``` For demonstration purposes, we duplicate the system by five so that the input length is long enough to observe Flash Attention's memory savings. We append the original text prompt `"Question: Please write a function in Python that transforms bytes to Giga bytes.\n\nAnswer: Here"` ```python long_prompt = 5 * system_prompt + prompt ``` We instantiate our model again in bfloat16 precision. ```python model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", torch_dtype=torch.bfloat16, device_map="auto") tokenizer = AutoTokenizer.from_pretrained("bigcode/octocoder") pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) ``` Let's now run the model just like before *without Flash Attention* and measure the peak GPU memory requirement and inference time. ```python import time start_time = time.time() result = pipe(long_prompt, max_new_tokens=60)[0]["generated_text"][len(long_prompt):] print(f"Generated in {time.time() - start_time} seconds.") result ``` TODO(Patrick) ```python bytes_to_giga_bytes(torch.cuda.max_memory_allocated()) ``` **Output**: ```bash ``` Let's free the peak memory. ```python flush() ``` For comparison, let's run the same function, but enable Flash Attention instead. To do so, we convert the model to [BetterTransformers](https://huggingface.co/docs/optimum/bettertransformer/overview) and by doing so enabling PyTorch's [SDPA self-attention](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention) which in turn is based on Flash Attention. ```python model.to_bettertransformer() ``` Now we run the exact same code snippet as before and under the hood Transformers will make use of Flash Attention. ```py start_time = time.time() result = pipe(long_prompt, max_new_tokens=60)[0]["generated_text"][len(long_prompt):] print(f"Generated in {time.time() - start_time} seconds.") result ``` As we can see TODO(Patrick) ## 3. The Science Behind LLM Architectures: Strategic Selection for Long Text Inputs and Chat {#3-the-science-behind-llm-architectures-strategic-selection-for-long-text-inputs-and-chat} So far we have looked into improving computational and memory efficiency by: - Casting the weights to a lower precision format - Replacing the self-attention algorithm with a more memory- and compute efficient version Let's now look into how we can change the architecture of an LLM so that it is most effective and efficient for task that require long text inputs, *e.g.*: - Retrieval augmented Questions Answering, - Summarization, - Chat Note that *chat* not only requires the LLM to handle long text inputs, but it also necessitates that the LLM is able to efficiently handle the back-and-forth dialogue between user and assistant (such as ChatGPT). Once trained, the fundamental LLM architecture is difficult to change, so it is important to make considerations about the LLM's tasks beforehand and accordingly optimize the model's architecture. There are two important components of the model architecture that quickly become memory and/or performance bottlenecks for large input sequences. - The positional embeddings - The key-value cache Let's go over each component in more detail ### 3.1 Improving positional embeddings of LLMs {#31-improving-positional-embeddings-of-llms} Self-attention puts each token in relation to each other's tokens. As an example, the $\text{Softmax}(\mathbf{QK}^T)$ matrix of the text input sequence *"Hello", "I", "love", "you"* could look as follows: ![](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/self_attn_tokens.png) Each word token is given a probability mass at which it attends all other word tokens and, therefore is put into relation with all other word tokens. E.g. the word *"love"* attends to the word *"Hello"* with 0.05%, to *"I"* with 0.3%, and to itself with 0.65%. A LLM based on self-attention, but without position embeddings would have great difficulties in understanding the positions of the text inputs to each other. This is because the probability score computed by $\mathbf{QK}^T$ relates each word token to each other word token in $O(1)$ computations regardless of their relative positional distance to each other. Therefore, for the LLM without position embeddings each token appears to be have the same distance to all other tokens, *e.g.* differentiating between *"Hello I love you"* and *"You love I hello"* would be very challenging. For the LLM to understand sentence order, an additional *cue* is needed and is usually applied in the form of *positional encodings* (or also called *positional embeddings*). Positional encodings, encode the position of each token into a numerical presentation that the LLM can leverage to better understand sentence order. The authors of the [*Attention Is All You Need*](https://arxiv.org/abs/1706.03762) paper introduced sinusoidal positional embeddings $\mathbf{P} = \mathbf{p}_1, \ldots, \mathbf{p}_N$, where each vector $\mathbf{p}_i$ is computed as a sinusoidal function of its position $i$. The positional encodings are then simply added to the input sequence vectors $\mathbf{\hat{X}} = \mathbf{\hat{x}}_1, \ldots, \mathbf{\hat{x}}_N$ = \$\\mathbf{x}\_1 + \\mathbf{p}\_1, \\ldots, \\mathbf{x}\_N + \\mathbf{x}\_N \$, thereby cueing the model to better learn sentence order. Instead of using fixed position embeddings, others (such as [Devlin et al.](https://arxiv.org/abs/1810.04805)) used learned positional encodings for which the positional embeddings $\mathbf{P} are learned during training. Sinusoidal and learned position embeddings used to be the predominant methods to encode sentence order into LLMs, but a couple of problems related to these positional encodings were found: - 1.) Sinusoidal and learned position embeddings are both absolute positional embeddings, *i.e.* encoding a unique embedding for each position id: $0, \ldots, N$. As shown by [Huang et al.](https://arxiv.org/abs/2009.13658) and [Su et al.](https://arxiv.org/abs/2104.09864)\], absolute positional embeddings lead to poor LLM performance for long text inputs. For long text inputs, it is advantageous if the model learns the relative positional distance input input tokens have to each other instead of their absolute position. - 2.) When using learned position embeddings, the LLM has to be trained on a fixed input length $N$, which makes it difficult to extrapolate to an input length longer than what it was trained on. Recently, relative positional embeddings that can tackle the above mentioned problems have become more popular, most notably: - [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) - [ALiBi](https://arxiv.org/abs/2108.12409) Both *RoPE* and *ALiBi* argue that it's best to cue the LLM about sentence order directly in the self-attention algorithm as it's there that word tokens are put into relation with each other. More specifically, sentence order should be cued by modifying the $\mathbf{QK}^T$ computation. Without going into too many details, *RoPE* notes that positional information can be encoded into query-key pairs, *e.g.* $\mathbf{q}_i$ and $\mathbf{x}_j$ by rotating each vector by an angle $\theta * i$ and $\theta * j$ respectively with $i, j$ describing each vectors sentence position: $$ \mathbf{\hat{q}}_i^T \mathbf{\hat{x}}_j = \mathbf{{q}}_i^T \mathbf{R}_{\theta, i -j} \mathbf{{x}}_j. $$ $R_{\theta, i - j}$ thereby represents a rotational matrix. $\theta$ is *not* learned during training, but instead set to a pre-defined value that depends on the maximum input sequence length during training. > By doing so, the propability score between $\mathbf{q}_i$ and $\mathbf{q}_j$ is only affected if $i \ne j$ and solely depends on the relative distance $i - j$ regardless of each vector's specific positions $i$ and $j$. *RoPE* is used in multiple of today's most important LLMs, such as: - [**Falcon**](https://huggingface.co/tiiuae/falcon-40b) - [**Llama**](https://arxiv.org/abs/2302.13971) - [**PaLM**](https://arxiv.org/pdf/2204.02311.pdf) As an alternative, *ALiBi* proposes a much simpler relative position encoding scheme. The relative distance that input tokens have to each other is added as a negative integer scaled by a pre-defined value `m` to each query-key entry of the $\mathbf{QK}^T$ matrix right before the softmax computation. ![](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/alibi.png) As shown in the [ALiBi](https://arxiv.org/abs/2108.12409) paper, this simple relative positional encoding allows the model to retain a high performance even at very long text input sequences. *ALiBi* is used in multiple of today's most important LLMs, such as: - [**MPT**](https://huggingface.co/mosaicml/mpt-30b) - [**BLOOM**](https://huggingface.co/bigscience/bloom) Both *RoPE* and *ALiBi* position encodings can extrapolate to input lengths not seen during training whereas it has been shown that extrapolation work much better out-of-the-box for ALiBi as compared to *RoPE*. For ALiBi, one simply increases the values of the lower triangular position matrix to match the length of the input sequence. For *RoPE*, keeping the same $\theta$ that was used during training leads to poor results when passing text inputs much longer than those seen during training, *c.f* [Press et al.](https://arxiv.org/abs/2108.12409). However, the community has found a couple of effective tricks that adapt $\theta$, thereby allowing *RoPE* position embeddings to work well for extrapolated text input sequences (see [here](https://github.com/huggingface/transformers/pull/24653)). > Both RoPE and ALiBi are relative positional embeddings that are *not* learned during training, but instead are based on the following intuitions: - Positional cues about the text inputs should be given directly to the $QK^T$ matrix of the self-attention layer - The LLM should be incentivized to learn a constant *relative* distance positional encodings have to each other - The further text input tokens are from each other, the lower the probability of their query-value probability. Both RoPE and ALiBi lower the query-key probability of tokens far away from each other. RoPE by decreasing their vector product by increasing the angle between the query-key vectors. ALiBi by adding large negative numbers to the vector product In conclusion, LLMs that are intended to be deployed in tasks that require handling large text inputs are better trained with relative positional embeddings, such as RoPE and ALiBi. Also note that even if an LLM with RoPE and ALiBi has been trained only on a fixed length of say $N_1 = 2048$ it can still be used in practice with text inputs much larger than $N_1$, like $N_2 = 8192 > N_1$ by extrapolating the positional embeddings. ### 3.2 The key-value cache Auto-regressive text generation with LLMs works by iteratively putting in an input sequence, sampling the next token, appending the next token to the input sequence, and continuing to do so until the LLM produces a token that signifies that the generation has finished. Please have a look at [Transformer's Generate Text Tutorial](https://huggingface.co/docs/transformers/llm_tutorial#generate-text) to get a more visual explanation of how auto-regressive generation works. Let's run a quick code snippet to show how auto-regressive works in practice. We will simply take the most likely next token via `torch.argmax`. ```python input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"] for _ in range(5): next_logits = model(input_ids)["logits"][:, -1] next_token_id = torch.argmax(next_logits) input_ids = torch.cat([input_ids, next_token_id]) print("shape of input_ids", input_ids.shape) generated_text = tokenizer.batch_decode(input_ids[:, -5:]) generated_text ``` As we can see every time we increase the text input tokens by the just sampled token. With very few exceptions, LLMs are trained using the [causal language modeling objective](https://huggingface.co/docs/transformers/tasks/language_modeling#causal-language-modeling) and therefore mask the upper triangle matrix of the attention score - this is why in the two diagrams above the attention scores are left blank (*a.k.a* have 0 probability). For a quick recap on causal language modeling you can refer to the [*Illustrated Self Attention blog*](https://jalammar.github.io/illustrated-gpt2/#part-2-illustrated-self-attention). As a consequence, tokens *never* depend on previous tokens, more specifically the $\mathbf{q}_i$ vector is never put in relation with any key, values vectors $\mathbf{k}_j, \mathbf{v}_j$ if $j > i$. Instead $\mathbf{q}_i$ only attends to previous key-value vectors $\mathbf{k}_{m < i}, \mathbf{v}_{m < i} \text{ , for } m \in \{0, \ldots i - 1\}$. In order to reduce unnecessary computation, one can therefore cache each layer's key-value vectors for all previous timesteps. In the following, we will tell the LLM to make user of the key-value cache by retrieving and forwarding it for each forward pass. In Transformers, we can retrieve the key-value cache by passing the `use_cache` flag to the `forward` call and can then pass it with the current token. ```python past_key_values = None # past_key_values is the key-value cache generated_tokens = [] for _ in range(5): next_logits, past_key_values = model(input_ids, past_key_values=past_key_values, use_cache=True).to_tuple() input_ids = torch.argmax(next_logits) print("shape of input_ids", input_ids.shape) print("length of key-value cache", len(past_key_values[0][0][1])) # past_key_values are of shape [num_layers, 0 for k, 1 for v, batch_size, length, hidden_dim] generated_tokens.append(input_ids) generated_text = tokenizer.batch_decode(generated_tokens) generated_text ``` As one can see, when using the key-value cache the text input tokens are *not* increased in length, but remain a single input vector. The length of the key-value cache on the other hand is increased by one at every decoding step. > Making use of the key-value cache means that the $\mathbf{QK}^T$ is essentially reduced to $\mathbf{q}_c\mathbf{K}^T$ with $\mathbf{q}_c$ being the query projection of the currently passed input token which is *always* just a single vector. Using the key-value cache has two advantages: - Significant increase in computational efficiency as less computations are performed compared to computing the full $\mathbf{QK}^T$ matrix. This leads to an increase in inference speed - The maximum required memory is not increased quadratically with the number of generated tokens, but only increases linearly. > One should *always* make use of the key-value cache as it leads to identical results and a significant speed-up for longer input sequences. Transformers has the key-value cache enabled by default when making use of the text pipeline or the [`generate` method](https://huggingface.co/docs/transformers/main_classes/text_generation). Note that the key-value cache is especially useful for applications such as chat where multiple passes of auto-regressive decoding are required. Let's look at an example. ``` User: How many people live in France? Assistant: Roughly 75 million people live in France User: And how many are in Germany? Assistant: Germany has ca. 81 million inhabitants ``` In this chat, the LLM runs auto-regressive decoding twice: - 1. The first time, the key-value cache is empty and the input prompt is `"User: How many people live in France?"` and the model auto-regressively generates the text `"Roughly 75 million people live in France"` while increasing the key-value cache at every decoding step. - 2. The second time the input prompt is `"User: How many people live in France? \n Assistant: Roughly 75 million people live in France \n User: And how many in Germany?"`. Thanks to the cache, all key-value vectors for the first two sentences are already computed. Therefore the input prompt only consists of `"User: And how many in Germany?"`. While processing the shortened input prompt, it's computed key-value vectors are concatenated to the key-value cache of the first decoding. The second Assistant's answer `"Germany has ca. 81 million inhabitants"` is then auto-regressively generated with the key-value cache consisting of encoded key-value vectors of `"User: How many people live in France? \n Assistant: Roughly 75 million people live in France \n User: And how many are in Germany?"`. Two things should be noted here: - 1. Keeping all the context is crucial for LLMs deployed in chat so that the LLM understands all the previous context of the conversation. E.g. for the example above the LLM needs to understand that the user refers to the population when asking `"And how many are in Germany"`. - 2. The key-value cache is extremely useful for chat as it allows us to continuously grow the encoded chat history instead of having to re-encode the chat history again from scratch (as e.g. would be the case when using an encoder-decoder architecture). There is however one catch. While the required peak memory for the $\mathbf{QK}^T$ matrix is significantly reduced, holding the key-value cache in memory can become very memory expensive for long input sequence or multi-turn chat. Remember that the key-value cache needs to store the key-value vectors for all previous input vectors $\mathbf{x}_i \text{, for } i \in \{1, \ldots, c - 1\}$ for all self-attention layers and for all attention heads. Let's compute the number of float values that need to be stored in the key-value cache for the LLM `bigcode/octocoder` that we used before. The number of float values amounts to: $$ 2 \times (\text{seq_len} - 1) \times \text{num_attn_heads} \times \text{attn_head_dim} \times \text{num_layers} $$ Computing this for our LLM at a hypothetical input sequence length of 16000 gives: ```python config = model.config 2 * 16_000 * config.n_layer * config.n_head * config.n_embd // config.n_head ``` Roughly 8 billion float values! Storing 8 billion float values in `float16` precision requires around 15 GB of RAM which is circa half as much as the model weights themselves! Researchers have proposed two methods that allow to significantly reduce the memory cost of storing the key-value cache: - 1. [Multi-Query-Attention (MQA)](https://arxiv.org/abs/1911.02150) Multi-Query-Attention was proposed in Noam Shazeer's *Fast Transformer Decoding: One Write-Head is All You Need* paper. As the title says, Noam found out that instead of using `n_head` key-value projections weights, one can use a single head-value projection weight pair that is shared across all attention heads without that the model's performance significantly degrades. > By using a single head-value projection weight pair, the key value vectors $\mathbf{k}_i, \mathbf{v}_i$ have to be identical across all attention heads which in turn means that we only need to store 1 key-value projection pair in the cache instead of `n_head` ones. As most LLMs use between 20 and 100 attention heads, MQA significantly reduces the memory consumption of the key-value cache. For the LLM used in this notebook we could therefore reduce the required memory consumption from 15 GB to less than 400 MB at an input sequence length of 16000. In addition to memory savings, MQA also leads to improved computational efficiency as explained in the following. In auto-regressive decoding, large key-value vectors need to be reloaded, concatenated with the current key-value vector pair to be then fed into the $\mathbf{q}_c\mathbf{K}^T$ computation at every step. For auto-regressive decoding, the required memory bandwidth for the constant reloading can become a serious time bottleneck. By reducing the size of the key-value vectors less memory needs to be accessed, thus reducing the memory bandwidth bottleneck. For more detail, please have a look into [Noam's paper](https://arxiv.org/abs/1911.02150). The important part to understand here is that reducing the number of key-value attention heads to 1 only makes sense if a key-value cache is used. The peak memory consumption of the model for a single forward pass without key-value cache stays unchanged as every attention head still has a unique query vector so that each attention head still has a different $\mathbf{QK}^T$ matrix. MQA has seen wide adoption by the community and is now used by many of the most popular LLMs: - [**Falcon**](https://huggingface.co/tiiuae/falcon-40b) - [**PaLM**](https://arxiv.org/pdf/2204.02311.pdf) - [**MPT**](https://huggingface.co/mosaicml/mpt-30b) - [**BLOOM**](https://huggingface.co/bigscience/bloom) Also, the checkpoint used in this notebook - `bigcode/octocoder` - makes use of MQA. - 2. [Grouped-Query-Attention (GQA)](https://arxiv.org/abs/2305.13245) Grouped-Query-Attention, as proposed by Ainslie et al. from Google, found that using MQA can often lead to quality degradation compared to using vanilla multi-key-value head projections. The paper argues that more model performance can be kept by less drastically reducing the number of query head projection weights. Instead of using just a single key-value projection weight, `n < n_head` key-value projection weights should be used. By choosing `n` to a significantly smaller value than `n_head`, such as 2,4 or 8 almost all of the memory and speed gains from MQA can be kept while sacrificing less model capacity and thus arguably less performance. Moreover, the authors of GQA found out that existing model checkpoints can be *uptrained* to have a GQA architecture with as little as 5% of the original pre-training compute. While 5% of the original pre-training compute can still be a massive amount, GQA *uptraining* allows existing checkpoints to be useful for longer input sequences. GQA was only recently proposed which is why there is less adoption at the time of writing this notebook. The most notable application of GQA is [Llama-v2](https://huggingface.co/meta-llama/Llama-2-70b-hf). > As a conclusion, it is strongly recommended to make use of either GQA or MQA if the LLM is deployed with auto-regressive decoding and is required to handle large input sequences as is the case for example for chat. ## Conclusion The research community is constantly coming up with new, nifty ways to speed up inference time for ever-larger LLMs. As an example, one such promising research direction is [speculative decoding](https://arxiv.org/abs/2211.17192) where "easy tokens" are generated by smaller, faster language models and only "hard tokens" are generated by the LLM itself. Going into more detail is out of the scope of this notebook, but can be read upon in this [nice blog post](https://huggingface.co/blog/assisted-generation). The reason massive LLMs such as GPT3/4, Llama-2-70b, Claude, PaLM can run so quickly in chat-interfaces such as [Hugging Face Chat](https://huggingface.co/chat/) or ChatGPT is to a big part thanks to the above-mentioned improvements in precision, algorithms, and architecture. Going forward, accelerators such as GPUs, TPUs, etc... will only get faster and allow for more memory, but one should nevertheless always make sure to use the best available algorithms and architectures to get the most bang for your buck 🤗 ================================================ FILE: osdc.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "rtieJhpxMVBe" }, "source": [ "# A full training" ] }, { "cell_type": "markdown", "metadata": { "id": "API8SGR0MVBj" }, "source": [ "Install the Transformers and Datasets libraries to run this notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZMtls8pYMVBk" }, "outputs": [], "source": [ "!pip install datasets transformers[sentencepiece]\n", "!pip install accelerate\n", "# To run the training on TPU, you will need to uncomment the followin line:\n", "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EjVdH406MVBn" }, "outputs": [], "source": [ "from datasets import load_dataset\n", "from transformers import AutoTokenizer, DataCollatorWithPadding\n", "\n", "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n", "checkpoint = \"bert-base-uncased\"\n", "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n", "\n", "\n", "def tokenize_function(example):\n", " return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n", "\n", "\n", "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n", "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NaSHvjDEMVBo" }, "outputs": [], "source": [ "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n", "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n", "tokenized_datasets.set_format(\"torch\")\n", "tokenized_datasets[\"train\"].column_names" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YvevUTqpMVBp" }, "outputs": [], "source": [ "[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cf5uHxi3MVBp" }, "outputs": [], "source": [ "from torch.utils.data import DataLoader\n", "\n", "train_dataloader = DataLoader(\n", " tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n", ")\n", "eval_dataloader = DataLoader(\n", " tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "RhPcGyj7MVBq", "outputId": "8bf6dca7-ead7-4eb9-b68c-c9f4a59fcf5a" }, "outputs": [ { "data": { "text/plain": [ "{'attention_mask': torch.Size([8, 65]),\n", " 'input_ids': torch.Size([8, 65]),\n", " 'labels': torch.Size([8]),\n", " 'token_type_ids': torch.Size([8, 65])}" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for batch in train_dataloader:\n", " break\n", "{k: v.shape for k, v in batch.items()}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "s3pgiEFsMVBt" }, "outputs": [], "source": [ "from transformers import AutoModelForSequenceClassification\n", "\n", "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JfDeEpdZMVBu", "outputId": "1b07ebd8-93f3-40c0-b0fe-6a6f66952d17" }, "outputs": [ { "data": { "text/plain": [ "tensor(0.5441, grad_fn=) torch.Size([8, 2])" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "outputs = model(**batch)\n", "print(outputs.loss, outputs.logits.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gszDNAKDMVBu" }, "outputs": [], "source": [ "from transformers import AdamW\n", "\n", "optimizer = AdamW(model.parameters(), lr=5e-5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "O6Dl83OsMVBv", "outputId": "1025c964-6b7d-4f23-982c-9f12e5b0a605" }, "outputs": [ { "data": { "text/plain": [ "1377" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from transformers import get_scheduler\n", "\n", "num_epochs = 3\n", "num_training_steps = num_epochs * len(train_dataloader)\n", "lr_scheduler = get_scheduler(\n", " \"linear\",\n", " optimizer=optimizer,\n", " num_warmup_steps=0,\n", " num_training_steps=num_training_steps,\n", ")\n", "print(num_training_steps)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7jF3b28JMVBw", "outputId": "61f5821d-9626-4943-df4a-fc76afa44d34" }, "outputs": [ { "data": { "text/plain": [ "device(type='cuda')" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "\n", "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", "model.to(device)\n", "device" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZqmZG3bhMVBw" }, "outputs": [], "source": [ "from tqdm.auto import tqdm\n", "\n", "progress_bar = tqdm(range(num_training_steps))\n", "\n", "model.train()\n", "for epoch in range(num_epochs):\n", " for batch in train_dataloader:\n", " batch = {k: v.to(device) for k, v in batch.items()}\n", " outputs = model(**batch)\n", " loss = outputs.loss\n", " loss.backward()\n", "\n", " optimizer.step()\n", " lr_scheduler.step()\n", " optimizer.zero_grad()\n", " progress_bar.update(1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KA3sNcHvMVBx", "outputId": "8a5efcaa-ece6-41e8-c5bb-1b3a2213835f" }, "outputs": [ { "data": { "text/plain": [ "{'accuracy': 0.8431372549019608, 'f1': 0.8907849829351535}" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from datasets import load_metric\n", "\n", "metric = load_metric(\"glue\", \"mrpc\")\n", "model.eval()\n", "for batch in eval_dataloader:\n", " batch = {k: v.to(device) for k, v in batch.items()}\n", " with torch.no_grad():\n", " outputs = model(**batch)\n", "\n", " logits = outputs.logits\n", " predictions = torch.argmax(logits, dim=-1)\n", " metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n", "\n", "metric.compute()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CAGJ3NqDMVBy" }, "outputs": [], "source": [ "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n", "\n", "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n", "optimizer = AdamW(model.parameters(), lr=3e-5)\n", "\n", "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", "model.to(device)\n", "\n", "num_epochs = 3\n", "num_training_steps = num_epochs * len(train_dataloader)\n", "lr_scheduler = get_scheduler(\n", " \"linear\",\n", " optimizer=optimizer,\n", " num_warmup_steps=0,\n", " num_training_steps=num_training_steps,\n", ")\n", "\n", "progress_bar = tqdm(range(num_training_steps))\n", "\n", "model.train()\n", "for epoch in range(num_epochs):\n", " for batch in train_dataloader:\n", " batch = {k: v.to(device) for k, v in batch.items()}\n", " outputs = model(**batch)\n", " loss = outputs.loss\n", " loss.backward()\n", "\n", " optimizer.step()\n", " lr_scheduler.step()\n", " optimizer.zero_grad()\n", " progress_bar.update(1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9_vWdEkJMVBz" }, "outputs": [], "source": [ "from accelerate import Accelerator\n", "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n", "\n", "accelerator = Accelerator()\n", "\n", "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n", "optimizer = AdamW(model.parameters(), lr=3e-5)\n", "\n", "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n", " train_dataloader, eval_dataloader, model, optimizer\n", ")\n", "\n", "num_epochs = 3\n", "num_training_steps = num_epochs * len(train_dl)\n", "lr_scheduler = get_scheduler(\n", " \"linear\",\n", " optimizer=optimizer,\n", " num_warmup_steps=0,\n", " num_training_steps=num_training_steps,\n", ")\n", "\n", "progress_bar = tqdm(range(num_training_steps))\n", "\n", "model.train()\n", "for epoch in range(num_epochs):\n", " for batch in train_dl:\n", " outputs = model(**batch)\n", " loss = outputs.loss\n", " accelerator.backward(loss)\n", "\n", " optimizer.step()\n", " lr_scheduler.step()\n", " optimizer.zero_grad()\n", " progress_bar.update(1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Kd5QmiLLMVB0" }, "outputs": [], "source": [ "from accelerate import notebook_launcher\n", "\n", "notebook_launcher(training_function)" ] } ], "metadata": { "colab": { "name": "A full training", "provenance": [] } }, "nbformat": 4, "nbformat_minor": 0 }