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Directory structure:
gitextract_ybq2rcc9/
├── .gitignore
├── README.md
├── experiments/
│ ├── case_hold.py
│ ├── casehold_helpers.py
│ ├── ecthr.py
│ ├── eurlex.py
│ ├── ledgar.py
│ ├── scotus.py
│ ├── trainer.py
│ └── unfair_tos.py
├── models/
│ ├── deberta.py
│ ├── hierbert.py
│ └── tfidf_svm.py
├── requirements.txt
├── scripts/
│ ├── run_case_hold.sh
│ ├── run_ecthr.sh
│ ├── run_eurlex.sh
│ ├── run_ledgar.sh
│ ├── run_scotus.sh
│ ├── run_tfidf_svm.sh
│ └── run_unfair_tos.sh
├── statistics/
│ ├── compute_avg_lexglue_scores.py
│ ├── compute_avg_scores.py
│ ├── compute_lexglue_scores.py
│ ├── report_model_results.py
│ └── report_train_time.py
└── utils/
├── fix_casehold.py
├── load_hierbert.py
├── preprocess_unfair_tos.py
└── subsample_ledgar.py
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FILE: README.md
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# LexGLUE: A Benchmark Dataset for Legal Language Understanding in English :balance_scale: :trophy: :student: :woman_judge:
## Dataset Summary
Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset ([Wang et al., 2018](https://aclanthology.org/W18-5446/)), the subsequent more difficult SuperGLUE ([Wang et al., 2109](https://openreview.net/forum?id=rJ4km2R5t7)), other previous multi-task NLP benchmarks ([Conneau and Kiela,2018](https://aclanthology.org/L18-1269/); [McCann et al., 2018](https://arxiv.org/abs/1806.08730)), and similar initiatives in other domains ([Peng et al., 2019](https://arxiv.org/abs/1906.05474)), we introduce LexGLUE, a benchmark dataset to evaluate the performance of NLP methods in legal tasks. LexGLUE is based on seven existing legal NLP datasets, selected using criteria largely from SuperGLUE.
We anticipate that more datasets, tasks, and languages will be added in later versions of LexGLUE. As more legal NLP datasets become available, we also plan to favor datasets checked thoroughly for validity (scores reflecting real-life performance), annotation quality, statistical power,and social bias ([Bowman and Dahl, 2021](https://aclanthology.org/2021.naacl-main.385/)).
As in GLUE and SuperGLUE ([Wang et al., 2109](https://openreview.net/forum?id=rJ4km2R5t7)) one of our goals is to push towards generic (or *foundation*) models that can cope with multiple NLP tasks, in our case legal NLP tasks,possibly with limited task-specific fine-tuning. An-other goal is to provide a convenient and informative entry point for NLP researchers and practitioners wishing to explore or develop methods for legalNLP. Having these goals in mind, the datasets we include in LexGLUE and the tasks they address have been simplified in several ways, discussed below, to make it easier for newcomers and generic models to address all tasks. We provide PythonAPIs integrated with Hugging Face (Wolf et al.,2020; Lhoest et al., 2021) to easily import all the datasets, experiment with and evaluate their performance.
By unifying and facilitating the access to a set of law-related datasets and tasks, we hope to attract not only more NLP experts, but also more interdisciplinary researchers (e.g., law doctoral students willing to take NLP courses). More broadly, we hope LexGLUE will speed up the adoption and transparent evaluation of new legal NLP methods and approaches in the commercial sector too. Indeed, there have been many commercial press releases in legal-tech industry, but almost no independent evaluation of the veracity of the performance of various machine learning and NLP-based offerings. A standard publicly available benchmark would also allay concerns of undue influence in predictive models, including the use of metadata which the relevant law expressly disregards.
If you participate, use the LexGLUE benchmark, or our experimentation library, please cite:
[*Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras.*
*LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.*
*2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.*](https://aclanthology.org/2022.acl-long.297/)
```
@inproceedings{chalkidis-etal-2022-lexglue,
title = "{L}ex{GLUE}: A Benchmark Dataset for Legal Language Understanding in {E}nglish",
author = "Chalkidis, Ilias and
Jana, Abhik and
Hartung, Dirk and
Bommarito, Michael and
Androutsopoulos, Ion and
Katz, Daniel and
Aletras, Nikolaos",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.297",
pages = "4310--4330",
}
```
## Supported Tasks
<table>
<tr><td><b>Dataset</b></td><td><b>Source</b></td><td><b>Sub-domain</b></td><td><b>Task Type</b></td><td><b>Train/Dev/Test Instances</b></td><td><b>Classes</b></td><tr>
<tr><td>ECtHR (Task A)</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>Multi-label classification</td><td>9,000/1,000/1,000</td><td>10+1</td></tr>
<tr><td>ECtHR (Task B)</td><td> <a href="https://aclanthology.org/2021.naacl-main.22/">Chalkidis et al. (2021a)</a> </td><td>ECHR</td><td>Multi-label classification </td><td>9,000/1,000/1,000</td><td>10+1</td></tr>
<tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>Multi-class classification</td><td>5,000/1,400/1,400</td><td>14</td></tr>
<tr><td>EUR-LEX</td><td> <a href="https://arxiv.org/abs/2109.00904">Chalkidis et al. (2021b)</a></td><td>EU Law</td><td>Multi-label classification</td><td>55,000/5,000/5,000</td><td>100</td></tr>
<tr><td>LEDGAR</td><td> <a href="https://aclanthology.org/2020.lrec-1.155/">Tuggener et al. (2020)</a></td><td>Contracts</td><td>Multi-class classification</td><td>60,000/10,000/10,000</td><td>100</td></tr>
<tr><td>UNFAIR-ToS</td><td><a href="https://arxiv.org/abs/1805.01217"> Lippi et al. (2019)</a></td><td>Contracts</td><td>Multi-label classification</td><td>5,532/2,275/1,607</td><td>8+1</td></tr>
<tr><td>CaseHOLD</td><td><a href="https://arxiv.org/abs/2104.08671">Zheng et al. (2021)</a></td><td>US Law</td><td>Multiple choice QA</td><td>45,000/3,900/3,900</td><td>n/a</td></tr>
</table>
### ECtHR (Task A)
The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of the ECHR that were violated (if any).
### ECtHR (Task B)
The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of ECHR that were allegedly violated (considered by the court).
### SCOTUS
The US Supreme Court (SCOTUS) is the highest federal court in the United States of America and generally hears only the most controversial or otherwise complex cases which have not been sufficiently well solved by lower courts. This is a single-label multi-class classification task, where given a document (court opinion), the task is to predict the relevant issue areas. The 14 issue areas cluster 278 issues whose focus is on the subject matter of the controversy (dispute).
### EUR-LEX
European Union (EU) legislation is published in EUR-Lex portal. All EU laws are annotated by EU's Publications Office with multiple concepts from the EuroVoc thesaurus, a multilingual thesaurus maintained by the Publications Office. The current version of EuroVoc contains more than 7k concepts referring to various activities of the EU and its Member States (e.g., economics, health-care, trade). Given a document, the task is to predict its EuroVoc labels (concepts).
### LEDGAR
LEDGAR dataset aims contract provision (paragraph) classification. The contract provisions come from contracts obtained from the US Securities and Exchange Commission (SEC) filings, which are publicly available from EDGAR. Each label represents the single main topic (theme) of the corresponding contract provision.
### UNFAIR-ToS
The UNFAIR-ToS dataset contains 50 Terms of Service (ToS) from on-line platforms (e.g., YouTube, Ebay, Facebook, etc.). The dataset has been annotated on the sentence-level with 8 types of unfair contractual terms (sentences), meaning terms that potentially violate user rights according to the European consumer law.
### CaseHOLD
The CaseHOLD (Case Holdings on Legal Decisions) dataset includes multiple choice questions about holdings of US court cases from the Harvard Law Library case law corpus. Holdings are short summaries of legal rulings accompany referenced decisions relevant for the present case. The input consists of an excerpt (or prompt) from a court decision, containing a reference to a particular case, while the holding statement is masked out. The model must identify the correct (masked) holding statement from a selection of five choices.
## Leaderboard
### Averaged LexGLUE Scores
We report the arithmetic, harmonic, and geometric mean across tasks following [Shavrina and Malykh (2021)](https://openreview.net/pdf?id=PPGfoNJnLKd). We acknowledge that the use of scores aggregated over tasks has been criticized in general NLU benchmarks (e.g., GLUE), as models are trained with different numbers of samples, task complexity, and evaluation metrics per task. We believe that the use of a standard common metric (F1) across tasks and averaging with harmonic mean alleviate this issue.
<table>
<tr><td><b>Averaging</b></td><td><b>Arithmetic</b></td><td><b>Harmonic</b></td><td><b>Geometric</b></td></tr>
<tr><td><b>Model</b></td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td></tr>
<tr><td>BERT</td><td> 77.8 / 69.5 </td><td> 76.7 / 68.2 </td><td> 77.2 / 68.8 </td></tr>
<tr><td>RoBERTa</td><td> 77.8 / 68.7 </td><td> 76.8 / 67.5 </td><td> 77.3 / 68.1 </td></tr>
<tr><td>RoBERTa (Large)</td><td> 79.4 / 70.8 </td><td> 78.4 / 69.1 </td><td> 78.9 / 70.0 </td></tr>
<tr><td>DeBERTa</td><td> 78.3 / 69.7 </td><td> 77.4 / 68.5 </td><td> 77.8 / 69.1 </td></tr>
<tr><td>Longformer</td><td> 78.5 / 70.5 </td><td> 77.5 / 69.5 </td><td> 78.0 / 70.0 </td></tr>
<tr><td>BigBird</td><td> 78.2 / 69.6 </td><td> 77.2 / 68.5 </td><td> 77.7 / 69.0 </td></tr>
<tr><td>Legal-BERT</td><td> <b>79.8</b> / <b>72.0</b> </td><td> <b>78.9</b> / <b>70.8</b> </td><td> <b>79.3</b> / <b>71.4</b> </td></tr>
<tr><td>CaseLaw-BERT</td><td> 79.4 / 70.9 </td><td> 78.5 / 69.7 </td><td> 78.9 / 70.3 </td></tr>
</table>
### Task-wise LexGLUE scores
#### Large-sized (:older_man:) Models [1]
<table>
<tr><td><b>Dataset</b></td><td><b>ECtHR A</b></td><td><b>ECtHR B</b></td><td><b>SCOTUS</b></td><td><b>EUR-LEX</b></td><td><b>LEDGAR</b></td><td><b>UNFAIR-ToS</b></td><td><b>CaseHOLD</b></td></tr>
<tr><td><b>Model</b></td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1</td><td>μ-F1 / m-F1 </td></tr>
<tr><td>RoBERTa</td> <td> 73.8 / 67.6 </td> <td> 79.8 / 71.6 </td> <td> 75.5 / 66.3 </td> <td> 67.9 / 50.3 </td> <td> 88.6 / 83.6 </td> <td> 95.8 / 81.6 </td> <td> 74.4 </td> </tr>
</table>
[1] Results reported by [Chalkidis et al. (2021)](https://arxiv.org/abs/2110.00976). All large-sized transformer-based models follow the same specifications (L=24, H=1024, A=18).
#### Medium-sized (:man:) Models [2]
<table>
<tr><td><b>Dataset</b></td><td><b>ECtHR A</b></td><td><b>ECtHR B</b></td><td><b>SCOTUS</b></td><td><b>EUR-LEX</b></td><td><b>LEDGAR</b></td><td><b>UNFAIR-ToS</b></td><td><b>CaseHOLD</b></td></tr>
<tr><td><b>Model</b></td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1</td><td>μ-F1 / m-F1 </td></tr>
<tr><td>TFIDF+SVM</td><td> 62.6 / 48.9 </td><td>73.0 / 63.8 </td><td> 74.0 / 64.4 </td><td>63.4 / 47.9 </td><td>87.0 / 81.4 </td><td>94.7 / 75.0</td><td>22.4 </td></tr>
<td>BERT</td> <td> <b>71.2</b> / 63.6 </td> <td> 79.7 / 73.4 </td> <td> 68.3 / 58.3 </td> <td> 71.4 / 57.2 </td> <td> 87.6 / 81.8 </td> <td> 95.6 / 81.3 </td> <td> 70.8 </td> </tr>
<td>RoBERTa</td> <td> 69.2 / 59.0 </td> <td> 77.3 / 68.9 </td> <td> 71.6 / 62.0 </td> <td> 71.9 / <b>57.9</b> </td> <td> 87.9 / 82.3 </td> <td> 95.2 / 79.2 </td> <td> 71.4 </td> </tr>
<td>DeBERTa</td> <td> 70.0 / 60.8 </td> <td> 78.8 / 71.0 </td> <td> 71.1 / 62.7 </td> <td> <b>72.1</b> / 57.4 </td> <td> 88.2 / <b>83.1</b> </td> <td> 95.5 / 80.3 </td> <td> 72.6 </td> </tr>
<td>Longformer</td> <td> 69.9 / <b>64.7</b> </td> <td> 79.4 / 71.7 </td> <td> 72.9 / 64.0 </td> <td> 71.6 / 57.7 </td> <td> 88.2 / 83.0 </td> <td> 95.5 / 80.9 </td> <td> 71.9 </td> </tr>
<td>BigBird</td> <td> 70.0 / 62.9 </td> <td> 78.8 / 70.9 </td> <td> 72.8 / 62.0 </td> <td> 71.5 / 56.8 </td> <td> 87.8 / 82.6 </td> <td> 95.7 / 81.3 </td> <td> 70.8 </td> </tr>
<td>Legal-BERT</td> <td> 70.0 / 64.0 </td> <td> <b>80.4</b> / <b>74.7</b> </td> <td> 76.4 / <b>66.5</b> </td> <td> <b>72.1</b> / 57.4 </td> <td> 88.2 / 83.0 </td> <td> <b>96.0</b> / <b>83.0</b> </td> <td> 75.3 </td> </tr>
<td>CaseLaw-BERT</td> <td> 69.8 / 62.9 </td> <td> 78.8 / 70.3 </td> <td> <b>76.6</b> / 65.9 </td> <td> 70.7 / 56.6 </td> <td> <b>88.3</b> / 83.0 </td> <td> <b>96.0</b> / 82.3 </td> <td> <b>75.4</b> </td> </tr>
</table>
[2] Results reported by [Chalkidis et al. (2021)](https://arxiv.org/abs/2110.00976). All medium-sized transformer-based models follow the same specifications (L=12, H=768, A=12).
#### Small-sized (:baby:) Models [3]
<table>
<tr><td><b>Dataset</b></td><td><b>ECtHR A</b></td><td><b>ECtHR B</b></td><td><b>SCOTUS</b></td><td><b>EUR-LEX</b></td><td><b>LEDGAR</b></td><td><b>UNFAIR-ToS</b></td><td><b>CaseHOLD</b></td></tr>
<tr><td><b>Model</b></td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1</td><td>μ-F1 / m-F1 </td></tr>
<tr><td>BERT-Tiny</td><td>n/a</td><td>n/a</td><td> 62.8 / 40.9</td><td> 65.5 / 27.5</td><td> 83.9 / 74.7</td><td> 94.3 / 11.1</td><td> 68.3</td></tr>
<tr><td>Mini-LM (v2)</td><td>n/a</td><td>n/a</td><td> 60.8 / 45.5</td><td> 62.2 / 35.6</td><td> 86.7 / 79.6</td><td> 93.9 / 13.2</td><td> 71.3</td></tr>
<tr><td>Distil-BERT</td><td>n/a</td><td>n/a</td><td> 67.0 / 55.9</td><td> 66.0 / 51.5</td><td> 87.5 / <b>81.5</b></td><td> <b>97.1</b> / <b>79.4</b></td><td> 68.6</td>
<tr><td>Legal-BERT </td><td>n/a</b></td><td>n/a</td><td><b>75.6</b> / <b>68.5</b></td><td> <b>73.4</b> / <b>54.4</b><td><b>87.8</b> /81.4</td><td><b>97.1</b> / 76.3</td><td><b>74.7</b></td></tr>
</table>
[3] Results reported by Atreya Shankar ([@atreyasha](https://github.com/atreyasha)) :hugs: :partying_face:. More details (e.g., validation scores, log files) are provided [here](https://github.com/coastalcph/lex-glue/discussions/categories/new-results). The small-sized models' specifications are:
* BERT-Tiny (L=2, H=128, A=2) by [Turc et al. (2020)](https://arxiv.org/abs/1908.08962)
* Mini-LM (v2) (L=12, H=386, A=12) by [Wang et al. (2020)](https://arxiv.org/abs/2002.10957)
* Distil-BERT (L=6, H=768, A=12) by [Sanh et al. (2019)](https://arxiv.org/abs/1910.01108)
* Legal-BERT (L=6, H=512, A=8) by [Chalkidis et al. (2020)](https://arxiv.org/abs/2010.02559)
## Frequently Asked Questions (FAQ)
### Where are the datasets?
We provide access to LexGLUE on [Hugging Face Datasets](https://huggingface.co/datasets) (Lhoest et al., 2021) at https://huggingface.co/datasets/coastalcph/lex_glue.
For example to load the SCOTUS [Spaeth et al. (2020)](http://scdb.wustl.edu) dataset, you first simply install the datasets python library and then make the following call:
```python
from datasets import load_dataset
dataset = load_dataset("coastalcph/lex_glue", "scotus")
```
### How to run experiments?
Furthermore, to make reproducing the results for the already examined models or future models even easier, we release our code in this repository. In folder `/experiments`, there are Python scripts, relying on the [Hugging Face Transformers](https://huggingface.co/transformers/) library, to run and evaluate any Transformer-based model (e.g., BERT, RoBERTa, LegalBERT, and their hierarchical variants, as well as, Longforrmer, and BigBird). We also provide bash scripts in folder `/scripts` to replicate the experiments for each dataset with 5 randoms seeds, as we did for the reported results for the original leaderboard.
Make sure that all required packages are installed:
```
torch>=1.9.0
transformers>=4.9.0
scikit-learn>=0.24.1
tqdm>=4.61.1
numpy>=1.20.1
datasets>=1.12.1
nltk>=3.5
scipy>=1.6.3
```
For example to replicate the results for RoBERTa ([Liu et al., 2019](https://arxiv.org/abs/1907.11692)) on UNFAIR-ToS [Lippi et al. (2019)](https://arxiv.org/abs/1805.01217), you have to configure the relevant bash script (`run_unfair_tos.sh`):
```
> nano run_unfair_tos.sh
GPU_NUMBER=1
MODEL_NAME='roberta-base'
LOWER_CASE='False'
BATCH_SIZE=8
ACCUMULATION_STEPS=1
TASK='unfair_tos'
```
and then run it:
```
> sh run_unfair_tos.sh
```
**Note:** The bash scripts make use of two HF arguments/parameters (`--fp16`, `--fp16_full_eval`), which are only applicable (working) when there are available (and correctly configured) NVIDIA GPUs in a machine station (server or cluster), while also `torch` is correctly configured to use these compute resources.
So, in case you don't have such resources, just delete these two arguments from the scripts to train models with standard `fp32` precision. In case you have such resources, make sure to correctly install the NVIDIA CUDA drivers, and also correctly install `torch` to identify these resources (Consider this page to figure out the appropriate steps: https://pytorch.org/get-started/locally/)
### I don't have the resources to run all these Muppets. What can I do?
You can use Google Colab with GPU acceleration for free online (https://colab.research.google.com).
- Set Up a new notebook (https://colab.research.google.com) and git clone the project.
- Navigate to Edit → Notebook Settings and select GPU from the Hardware Accelerator drop-down. You will probably get assigned with an NVIDIA Tesla K80 12GB.
- You will also have to decrease the batch size and increase the accumulation steps for hierarchical models.
But, this is an interesting open problem (Efficient NLP), please consider using lighter pre-trained (smaller/faster) models, like:
- The smaller [Legal-BERT](https://huggingface.co/nlpaueb/legal-bert-small-uncased) of [Chalkidis et al. (2020)](https://arxiv.org/abs/2010.02559),
- Smaller [BERT](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) models of [Turc et al. (2020)](https://arxiv.org/abs/1908.08962),
- [Mini-LM](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) of [Wang et al. (2020)](https://arxiv.org/abs/2002.10957),
, or non transformer-based neural models, like:
- LSTM-based [(Hochreiter and Schmidhuber, 1997)](https://dl.acm.org/doi/10.1162/neco.1997.9.8.1735) models, like the Hierarchical Attention Network (HAN) of [Yang et al. (2016)](https://aclanthology.org/N16-1174/),
- Graph-based models, like the Graph Attention Network (GAT) of [Veličković et al. (2017)](https://arxiv.org/abs/1710.10903)
, or even non neural models, like:
- Bag of Word (BoW) models using TF-IDF representations (e.g., SVM, Random Forest),
- The eXtreme Gradient Boosting (XGBoost) of [Chen and Guestrin (2016)](http://arxiv.org/abs/1603.02754),
and report back the results. We are curious!
### How to participate?
We are currently still lacking some technical infrastructure, e.g., an integrated submission environment comprised of an automated evaluation and an automatically updated leaderboard. We plan to develop the necessary publicly available web infrastructure extend the public infrastructure of LexGLUE in the near future.
In the mean-time, we ask participants to re-use and expand our code to submit new results, if possible, and open a new discussion (submission) in our repository (https://github.com/coastalcph/lex-glue/discussions/new?category=new-results) presenting their results, providing the auto-generated result logs and the relevant publication (or pre-print), if available, accompanied with a pull request including the code amendments that are needed to reproduce their experiments. Upon reviewing your results, we'll update the public leaderboard accordingly.
### I want to re-load fine-tuned HierBERT models. How can I do this?
You can re-load fine-tuned HierBERT models following our example python script ["Re-load HierBERT models"](https://github.com/coastalcph/lex-glue/blob/main/utils/load_hierbert.py).
### I still have open questions...
Please post your question on [Discussions](https://github.com/coastalcph/lex-glue/discussions) section or communicate with the corresponding author via e-mail.
## Credits
Thanks to [@JamesLYC88](https://github.com/JamesLYC88) and [@danigoju](https://github.com/danigoju) for digging up for :bug:s!
================================================
FILE: experiments/case_hold.py
================================================
#!/usr/bin/env python
# coding=utf-8
""" Finetuning models on CaseHOLD (e.g. Bert, RoBERTa, LEGAL-BERT)."""
import logging
import os
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import random
import shutil
import glob
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
from transformers import EarlyStoppingCallback
from casehold_helpers import MultipleChoiceDataset, Split
from sklearn.metrics import f1_score
from models.deberta import DebertaForMultipleChoice
logger = logging.getLogger(__name__)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
task_name: str = field(default="case_hold", metadata={"help": "The name of the task to train on"})
max_seq_length: int = field(
default=256,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
pad_to_max_length: bool = field(
default=True,
metadata={
"help": "Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
# Add custom arguments for computing pre-train loss
parser.add_argument("--ptl", type=bool, default=False)
model_args, data_args, training_args, custom_args = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s", training_args)
# Set seed
set_seed(training_args.seed)
# Load pretrained model and tokenizer
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=5,
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
)
if config.model_type == 'big_bird':
config.attention_type = 'original_full'
elif config.model_type == 'longformer':
config.attention_window = [data_args.max_seq_length] * config.num_hidden_layers
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
# Default fast tokenizer is buggy on CaseHOLD task, switch to legacy tokenizer
use_fast=True,
)
if config.model_type != 'deberta':
model = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
else:
model = DebertaForMultipleChoice.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
train_dataset = None
eval_dataset = None
# If do_train passed, train_dataset by default loads train split from file named train.csv in data directory
if training_args.do_train:
train_dataset = \
MultipleChoiceDataset(
tokenizer=tokenizer,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
mode=Split.train,
)
# If do_eval or do_predict passed, eval_dataset by default loads dev split from file named dev.csv in data directory
if training_args.do_eval:
eval_dataset = \
MultipleChoiceDataset(
tokenizer=tokenizer,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
mode=Split.dev,
)
if training_args.do_predict:
predict_dataset = \
MultipleChoiceDataset(
tokenizer=tokenizer,
task=data_args.task_name,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
mode=Split.test,
)
if training_args.do_train:
if data_args.max_train_samples is not None:
train_dataset = train_dataset[:data_args.max_train_samples]
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
if training_args.do_eval:
if data_args.max_eval_samples is not None:
eval_dataset = eval_dataset[:data_args.max_eval_samples]
if training_args.do_predict:
if data_args.max_predict_samples is not None:
predict_dataset = predict_dataset[:data_args.max_predict_samples]
# Define custom compute_metrics function, returns macro F1 metric for CaseHOLD task
def compute_metrics(p: EvalPrediction):
preds = np.argmax(p.predictions, axis=1)
# Compute macro and micro F1 for 5-class CaseHOLD task
macro_f1 = f1_score(y_true=p.label_ids, y_pred=preds, average='macro', zero_division=0)
micro_f1 = f1_score(y_true=p.label_ids, y_pred=preds, average='micro', zero_division=0)
return {'macro-f1': macro_f1, 'micro-f1': micro_f1}
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]
)
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
)
trainer.save_model()
# Re-save the tokenizer for model sharing
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation on eval_dataset
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate(eval_dataset=eval_dataset)
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Predict on eval_dataset
if training_args.do_predict:
logger.info("*** Predict ***")
predictions, labels, metrics = trainer.predict(predict_dataset, metric_key_prefix="predict")
max_predict_samples = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
)
metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
trainer.log_metrics("predict", metrics)
trainer.save_metrics("predict", metrics)
output_predict_file = os.path.join(training_args.output_dir, "test_predictions.csv")
if trainer.is_world_process_zero():
with open(output_predict_file, "w") as writer:
for index, pred_list in enumerate(predictions):
pred_line = '\t'.join([f'{pred:.5f}' for pred in pred_list])
writer.write(f"{index}\t{pred_line}\n")
# Clean up checkpoints
checkpoints = [filepath for filepath in glob.glob(f'{training_args.output_dir}/*/') if '/checkpoint' in filepath]
for checkpoint in checkpoints:
shutil.rmtree(checkpoint)
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
================================================
FILE: experiments/casehold_helpers.py
================================================
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional
import tqdm
import re
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
import datasets
logger = logging.getLogger(__name__)
@dataclass(frozen=True)
class InputFeatures:
"""
A single set of features of data.
Property names are the same names as the corresponding inputs to a model.
"""
input_ids: List[List[int]]
attention_mask: Optional[List[List[int]]]
token_type_ids: Optional[List[List[int]]]
label: Optional[int]
class Split(Enum):
train = "train"
dev = "dev"
test = "test"
if is_torch_available():
import torch
from torch.utils.data.dataset import Dataset
class MultipleChoiceDataset(Dataset):
"""
PyTorch multiple choice dataset class
"""
features: List[InputFeatures]
def __init__(
self,
tokenizer: PreTrainedTokenizer,
task: str,
max_seq_length: Optional[int] = None,
overwrite_cache=False,
mode: Split = Split.train,
):
dataset = datasets.load_dataset('lex_glue', task)
tokenizer_name = re.sub('[^a-z]+', ' ', tokenizer.name_or_path).title().replace(' ', '')
cached_features_file = os.path.join(
'.cache',
task,
"cached_{}_{}_{}_{}".format(
mode.value,
tokenizer_name,
str(max_seq_length),
task,
),
)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lock_path = cached_features_file + ".lock"
if not os.path.exists(os.path.join('.cache', task)):
if not os.path.exists('.cache'):
os.mkdir('.cache')
os.mkdir(os.path.join('.cache', task))
with FileLock(lock_path):
if os.path.exists(cached_features_file) and not overwrite_cache:
logger.info(f"Loading features from cached file {cached_features_file}")
self.features = torch.load(cached_features_file)
else:
logger.info(f"Creating features from dataset file at {task}")
if mode == Split.dev:
examples = dataset['validation']
elif mode == Split.test:
examples = dataset['test']
elif mode == Split.train:
examples = dataset['train']
logger.info("Training examples: %s", len(examples))
self.features = convert_examples_to_features(
examples,
max_seq_length,
tokenizer,
)
logger.info("Saving features into cached file %s", cached_features_file)
torch.save(self.features, cached_features_file)
def __len__(self):
return len(self.features)
def __getitem__(self, i) -> InputFeatures:
return self.features[i]
if is_tf_available():
import tensorflow as tf
class TFMultipleChoiceDataset:
"""
TensorFlow multiple choice dataset class
"""
features: List[InputFeatures]
def __init__(
self,
tokenizer: PreTrainedTokenizer,
task: str,
max_seq_length: Optional[int] = 256,
overwrite_cache=False,
mode: Split = Split.train,
):
dataset = datasets.load_dataset('lex_glue')
logger.info(f"Creating features from dataset file at {task}")
if mode == Split.dev:
examples = dataset['validation']
elif mode == Split.test:
examples = dataset['test']
else:
examples = dataset['train']
logger.info(f"{mode.name.title()} examples: %s", len(examples))
self.features = convert_examples_to_features(
examples,
max_seq_length,
tokenizer,
)
def gen():
for (ex_index, ex) in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
if ex_index % 10000 == 0:
logger.info("Writing example %d of %d" % (ex_index, len(examples)))
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
self.dataset = tf.data.Dataset.from_generator(
gen,
(
{
"input_ids": tf.int32,
"attention_mask": tf.int32,
"token_type_ids": tf.int32,
},
tf.int64,
),
(
{
"input_ids": tf.TensorShape([None, None]),
"attention_mask": tf.TensorShape([None, None]),
"token_type_ids": tf.TensorShape([None, None]),
},
tf.TensorShape([]),
),
)
def get_dataset(self):
self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))
return self.dataset
def __len__(self):
return len(self.features)
def __getitem__(self, i) -> InputFeatures:
return self.features[i]
def convert_examples_to_features(
examples: datasets.Dataset,
max_length: int,
tokenizer: PreTrainedTokenizer,
) -> List[InputFeatures]:
"""
Loads a data file into a list of `InputFeatures`
"""
features = []
for (ex_index, example) in tqdm.tqdm(enumerate(examples), desc="convert examples to features"):
if ex_index % 10000 == 0:
logger.info("Writing example %d of %d" % (ex_index, len(examples)))
choices_inputs = []
for ending_idx, ending in enumerate(example['endings']):
context = example['context']
inputs = tokenizer(
context,
ending,
add_special_tokens=True,
max_length=max_length,
padding="max_length",
truncation=True,
)
choices_inputs.append(inputs)
label = example['label']
input_ids = [x["input_ids"] for x in choices_inputs]
attention_mask = (
[x["attention_mask"] for x in choices_inputs] if "attention_mask" in choices_inputs[0] else None
)
token_type_ids = (
[x["token_type_ids"] for x in choices_inputs] if "token_type_ids" in choices_inputs[0] else None
)
features.append(
InputFeatures(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
label=label,
)
)
for f in features[:2]:
logger.info("*** Example ***")
logger.info("feature: %s" % f)
return features
================================================
FILE: experiments/ecthr.py
================================================
#!/usr/bin/env python
# coding=utf-8
""" Finetuning models on the ECtHR dataset (e.g. Bert, RoBERTa, LEGAL-BERT)."""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
from datasets import load_dataset
from sklearn.metrics import f1_score
from trainer import MultilabelTrainer
from scipy.special import expit
from torch import nn
import glob
import shutil
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TrainingArguments,
default_data_collator,
set_seed,
EarlyStoppingCallback,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
from models.hierbert import HierarchicalBert
from models.deberta import DebertaForSequenceClassification
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.9.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
logger = logging.getLogger(__name__)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
max_seq_length: Optional[int] = field(
default=4096,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
max_segments: Optional[int] = field(
default=64,
metadata={
"help": "The maximum number of segments (paragraphs) to be considered. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
max_seg_length: Optional[int] = field(
default=128,
metadata={
"help": "The maximum segment (paragraph) length to be considered. Segments longer "
"than this will be truncated, sequences shorter will be padded."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
pad_to_max_length: bool = field(
default=True,
metadata={
"help": "Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
},
)
task: Optional[str] = field(
default='ecthr_a',
metadata={
"help": "Define downstream task"
},
)
server_ip: Optional[str] = field(default=None, metadata={"help": "For distant debugging."})
server_port: Optional[str] = field(default=None, metadata={"help": "For distant debugging."})
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
default=None, metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
hierarchical: bool = field(
default=True, metadata={"help": "Whether to use a hierarchical variant or not"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
do_lower_case: Optional[bool] = field(
default=True,
metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
"with private models)."
},
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Fix boolean parameter
if model_args.do_lower_case == 'False' or not model_args.do_lower_case:
model_args.do_lower_case = False
else:
model_args.do_lower_case = True
if model_args.hierarchical == 'False' or not model_args.hierarchical:
model_args.hierarchical = False
else:
model_args.hierarchical = True
# Setup distant debugging if needed
if data_args.server_ip and data_args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(data_args.server_ip, data_args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading eurlex dataset from the hub.
if training_args.do_train:
train_dataset = load_dataset("lex_glue", name=data_args.task, split="train", data_dir='data', cache_dir=model_args.cache_dir)
if training_args.do_eval:
eval_dataset = load_dataset("lex_glue", name=data_args.task, split="validation", data_dir='data', cache_dir=model_args.cache_dir)
if training_args.do_predict:
predict_dataset = load_dataset("lex_glue", name=data_args.task, split="test", data_dir='data', cache_dir=model_args.cache_dir)
# Labels
label_list = list(range(10))
num_labels = len(label_list)
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task=f"{data_args.task}",
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
do_lower_case=model_args.do_lower_case,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
if config.model_type == 'deberta' and model_args.hierarchical:
model = DebertaForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
else:
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
if model_args.hierarchical:
# Hack the classifier encoder to use hierarchical BERT
if config.model_type in ['bert', 'deberta']:
if config.model_type == 'bert':
segment_encoder = model.bert
else:
segment_encoder = model.deberta
model_encoder = HierarchicalBert(encoder=segment_encoder,
max_segments=data_args.max_segments,
max_segment_length=data_args.max_seg_length)
if config.model_type == 'bert':
model.bert = model_encoder
elif config.model_type == 'deberta':
model.deberta = model_encoder
else:
raise NotImplementedError(f"{config.model_type} is no supported yet!")
elif config.model_type == 'roberta':
model_encoder = HierarchicalBert(encoder=model.roberta, max_segments=data_args.max_segments,
max_segment_length=data_args.max_seg_length)
model.roberta = model_encoder
# Build a new classification layer, as well
dense = nn.Linear(config.hidden_size, config.hidden_size)
dense.load_state_dict(model.classifier.dense.state_dict()) # load weights
dropout = nn.Dropout(config.hidden_dropout_prob).to(model.device)
out_proj = nn.Linear(config.hidden_size, config.num_labels).to(model.device)
out_proj.load_state_dict(model.classifier.out_proj.state_dict()) # load weights
model.classifier = nn.Sequential(dense, dropout, out_proj).to(model.device)
elif config.model_type in ['longformer', 'big_bird']:
pass
else:
raise NotImplementedError(f"{config.model_type} is no supported yet!")
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
padding = "max_length"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
padding = False
def preprocess_function(examples):
# Tokenize the texts
if model_args.hierarchical:
case_template = [[0] * data_args.max_seg_length]
if config.model_type == 'roberta':
batch = {'input_ids': [], 'attention_mask': []}
for case in examples['text']:
case_encodings = tokenizer(case[:data_args.max_segments], padding=padding,
max_length=data_args.max_seg_length, truncation=True)
batch['input_ids'].append(case_encodings['input_ids'] + case_template * (
data_args.max_segments - len(case_encodings['input_ids'])))
batch['attention_mask'].append(case_encodings['attention_mask'] + case_template * (
data_args.max_segments - len(case_encodings['attention_mask'])))
else:
batch = {'input_ids': [], 'attention_mask': [], 'token_type_ids': []}
for case in examples['text']:
case_encodings = tokenizer(case[:data_args.max_segments], padding=padding,
max_length=data_args.max_seg_length, truncation=True)
batch['input_ids'].append(case_encodings['input_ids'] + case_template * (
data_args.max_segments - len(case_encodings['input_ids'])))
batch['attention_mask'].append(case_encodings['attention_mask'] + case_template * (
data_args.max_segments - len(case_encodings['attention_mask'])))
batch['token_type_ids'].append(case_encodings['token_type_ids'] + case_template * (
data_args.max_segments - len(case_encodings['token_type_ids'])))
elif config.model_type in ['longformer', 'big_bird']:
cases = []
max_position_embeddings = config.max_position_embeddings - 2 if config.model_type == 'longformer' \
else config.max_position_embeddings
for case in examples['text']:
cases.append(f' {tokenizer.sep_token} '.join(
[' '.join(fact.split()[:data_args.max_seg_length]) for fact in case[:data_args.max_segments]]))
batch = tokenizer(cases, padding=padding, max_length=max_position_embeddings, truncation=True)
if config.model_type == 'longformer':
global_attention_mask = np.zeros((len(cases), max_position_embeddings), dtype=np.int32)
# global attention on cls token
global_attention_mask[:, 0] = 1
batch['global_attention_mask'] = list(global_attention_mask)
else:
cases = []
for case in examples['text']:
cases.append(f'\n'.join(case))
batch = tokenizer(cases, padding=padding, max_length=512, truncation=True)
batch["labels"] = [[1 if label in labels else 0 for label in label_list] for labels in examples["labels"]]
return batch
if training_args.do_train:
if data_args.max_train_samples is not None:
train_dataset = train_dataset.select(range(data_args.max_train_samples))
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
if training_args.do_eval:
if data_args.max_eval_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
with training_args.main_process_first(desc="validation dataset map pre-processing"):
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
if training_args.do_predict:
if data_args.max_predict_samples is not None:
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
with training_args.main_process_first(desc="prediction dataset map pre-processing"):
predict_dataset = predict_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on prediction dataset",
)
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(p: EvalPrediction):
# Fix gold labels
y_true = np.zeros((p.label_ids.shape[0], p.label_ids.shape[1] + 1), dtype=np.int32)
y_true[:, :-1] = p.label_ids
y_true[:, -1] = (np.sum(p.label_ids, axis=1) == 0).astype('int32')
# Fix predictions
logits = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
preds = (expit(logits) > 0.5).astype('int32')
y_pred = np.zeros((p.label_ids.shape[0], p.label_ids.shape[1] + 1), dtype=np.int32)
y_pred[:, :-1] = preds
y_pred[:, -1] = (np.sum(preds, axis=1) == 0).astype('int32')
# Compute scores
macro_f1 = f1_score(y_true=y_true, y_pred=y_pred, average='macro', zero_division=0)
micro_f1 = f1_score(y_true=y_true, y_pred=y_pred, average='micro', zero_division=0)
return {'macro-f1': macro_f1, 'micro-f1': micro_f1}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
data_collator = default_data_collator
elif training_args.fp16:
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
else:
data_collator = None
# Initialize our Trainer
trainer = MultilabelTrainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
compute_metrics=compute_metrics,
tokenizer=tokenizer,
data_collator=data_collator,
callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate(eval_dataset=eval_dataset)
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Prediction
if training_args.do_predict:
logger.info("*** Predict ***")
predictions, labels, metrics = trainer.predict(predict_dataset, metric_key_prefix="predict")
max_predict_samples = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
)
metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
trainer.log_metrics("predict", metrics)
trainer.save_metrics("predict", metrics)
output_predict_file = os.path.join(training_args.output_dir, "test_predictions.csv")
if trainer.is_world_process_zero():
with open(output_predict_file, "w") as writer:
for index, pred_list in enumerate(predictions[0]):
pred_line = '\t'.join([f'{pred:.5f}' for pred in pred_list])
writer.write(f"{index}\t{pred_line}\n")
# Clean up checkpoints
checkpoints = [filepath for filepath in glob.glob(f'{training_args.output_dir}/*/') if '/checkpoint' in filepath]
for checkpoint in checkpoints:
shutil.rmtree(checkpoint)
if __name__ == "__main__":
main()
================================================
FILE: experiments/eurlex.py
================================================
#!/usr/bin/env python
# coding=utf-8
""" Finetuning models on EUR-LEX (e.g. Bert, RoBERTa, LEGAL-BERT)."""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
from datasets import load_dataset
from sklearn.metrics import f1_score
from trainer import MultilabelTrainer
from scipy.special import expit
import glob
import shutil
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TrainingArguments,
default_data_collator,
set_seed,
EarlyStoppingCallback,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.9.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
logger = logging.getLogger(__name__)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
max_seq_length: Optional[int] = field(
default=512,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
pad_to_max_length: bool = field(
default=True,
metadata={
"help": "Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
},
)
server_ip: Optional[str] = field(default=None, metadata={"help": "For distant debugging."})
server_port: Optional[str] = field(default=None, metadata={"help": "For distant debugging."})
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
default=None, metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
do_lower_case: Optional[bool] = field(
default=True,
metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
"with private models)."
},
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Setup distant debugging if needed
if data_args.server_ip and data_args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(data_args.server_ip, data_args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
# Fix boolean parameter
if model_args.do_lower_case == 'False' or not model_args.do_lower_case:
model_args.do_lower_case = False
'Tokenizer do_lower_case False'
else:
model_args.do_lower_case = True
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading eurlex dataset from the hub.
if training_args.do_train:
train_dataset = load_dataset("lex_glue", "eurlex", split="train", cache_dir=model_args.cache_dir)
if training_args.do_eval:
eval_dataset = load_dataset("lex_glue", "eurlex", split="validation", cache_dir=model_args.cache_dir)
if training_args.do_predict:
predict_dataset = load_dataset("lex_glue", "eurlex", split="test", cache_dir=model_args.cache_dir)
# Labels
label_list = list(range(100))
num_labels = len(label_list)
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task="eurlex",
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
if config.model_type == 'big_bird':
config.attention_type = 'original_full'
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
do_lower_case=model_args.do_lower_case,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
padding = "max_length"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
padding = False
def preprocess_function(examples):
# Tokenize the texts
batch = tokenizer(
examples["text"],
padding=padding,
max_length=data_args.max_seq_length,
truncation=True,
)
batch["labels"] = [[1 if label in labels else 0 for label in label_list] for labels in examples["labels"]]
return batch
if training_args.do_train:
if data_args.max_train_samples is not None:
train_dataset = train_dataset.select(range(data_args.max_train_samples))
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
if training_args.do_eval:
if data_args.max_eval_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
with training_args.main_process_first(desc="validation dataset map pre-processing"):
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
if training_args.do_predict:
if data_args.max_predict_samples is not None:
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
with training_args.main_process_first(desc="prediction dataset map pre-processing"):
predict_dataset = predict_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on prediction dataset",
)
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(p: EvalPrediction):
logits = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
preds = (expit(logits) > 0.5).astype('int32')
macro_f1 = f1_score(y_true=p.label_ids, y_pred=preds, average='macro', zero_division=0)
micro_f1 = f1_score(y_true=p.label_ids, y_pred=preds, average='micro', zero_division=0)
return {'macro-f1': macro_f1, 'micro-f1': micro_f1}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
data_collator = default_data_collator
elif training_args.fp16:
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
else:
data_collator = None
# Initialize our Trainer
trainer = MultilabelTrainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
compute_metrics=compute_metrics,
tokenizer=tokenizer,
data_collator=data_collator,
callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate(eval_dataset=eval_dataset)
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Prediction
if training_args.do_predict:
logger.info("*** Predict ***")
predictions, labels, metrics = trainer.predict(predict_dataset, metric_key_prefix="predict")
max_predict_samples = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
)
metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
trainer.log_metrics("predict", metrics)
trainer.save_metrics("predict", metrics)
output_predict_file = os.path.join(training_args.output_dir, "test_predictions.csv")
if trainer.is_world_process_zero():
with open(output_predict_file, "w") as writer:
for index, pred_list in enumerate(predictions):
pred_line = '\t'.join([f'{pred:.5f}' for pred in pred_list])
writer.write(f"{index}\t{pred_line}\n")
# Clean up checkpoints
checkpoints = [filepath for filepath in glob.glob(f'{training_args.output_dir}/*/') if '/checkpoint' in filepath]
for checkpoint in checkpoints:
shutil.rmtree(checkpoint)
if __name__ == "__main__":
main()
================================================
FILE: experiments/ledgar.py
================================================
#!/usr/bin/env python
# coding=utf-8
""" Finetuning models on LEDGAR (e.g. Bert, RoBERTa, LEGAL-BERT)."""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
from datasets import load_dataset
from sklearn.metrics import f1_score
import numpy as np
import glob
import shutil
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TrainingArguments,
default_data_collator,
set_seed,
EarlyStoppingCallback,
Trainer
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.9.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
logger = logging.getLogger(__name__)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
max_seq_length: Optional[int] = field(
default=512,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
pad_to_max_length: bool = field(
default=True,
metadata={
"help": "Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
},
)
server_ip: Optional[str] = field(default=None, metadata={"help": "For distant debugging."})
server_port: Optional[str] = field(default=None, metadata={"help": "For distant debugging."})
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
default=None, metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
do_lower_case: Optional[bool] = field(
default=True,
metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
"with private models)."
},
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Setup distant debugging if needed
if data_args.server_ip and data_args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(data_args.server_ip, data_args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
# Fix boolean parameter
if model_args.do_lower_case == 'False' or not model_args.do_lower_case:
model_args.do_lower_case = False
'Tokenizer do_lower_case False'
else:
model_args.do_lower_case = True
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading eurlex dataset from the hub.
if training_args.do_train:
train_dataset = load_dataset("lex_glue", "ledgar", split="train", cache_dir=model_args.cache_dir)
if training_args.do_eval:
eval_dataset = load_dataset("lex_glue", "ledgar", split="validation", cache_dir=model_args.cache_dir)
if training_args.do_predict:
predict_dataset = load_dataset("lex_glue", "ledgar", split="test", cache_dir=model_args.cache_dir)
# Labels
label_list = list(range(100))
num_labels = len(label_list)
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task="eurlex",
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
if config.model_type == 'big_bird':
config.attention_type = 'original_full'
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
do_lower_case=model_args.do_lower_case,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
padding = "max_length"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
padding = False
def preprocess_function(examples):
# Tokenize the texts
batch = tokenizer(
examples["text"],
padding=padding,
max_length=data_args.max_seq_length,
truncation=True,
)
batch["label"] = [label_list.index(label) for label in examples["label"]]
return batch
if training_args.do_train:
if data_args.max_train_samples is not None:
train_dataset = train_dataset.select(range(data_args.max_train_samples))
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
if training_args.do_eval:
if data_args.max_eval_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
with training_args.main_process_first(desc="validation dataset map pre-processing"):
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
if training_args.do_predict:
if data_args.max_predict_samples is not None:
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
with training_args.main_process_first(desc="prediction dataset map pre-processing"):
predict_dataset = predict_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on prediction dataset",
)
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(p: EvalPrediction):
logits = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
preds = np.argmax(logits, axis=1)
macro_f1 = f1_score(y_true=p.label_ids, y_pred=preds, average='macro', zero_division=0)
micro_f1 = f1_score(y_true=p.label_ids, y_pred=preds, average='micro', zero_division=0)
return {'macro-f1': macro_f1, 'micro-f1': micro_f1}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
data_collator = default_data_collator
elif training_args.fp16:
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
else:
data_collator = None
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
compute_metrics=compute_metrics,
tokenizer=tokenizer,
data_collator=data_collator,
callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate(eval_dataset=eval_dataset)
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Prediction
if training_args.do_predict:
logger.info("*** Predict ***")
predictions, labels, metrics = trainer.predict(predict_dataset, metric_key_prefix="predict")
max_predict_samples = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
)
metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
trainer.log_metrics("predict", metrics)
trainer.save_metrics("predict", metrics)
output_predict_file = os.path.join(training_args.output_dir, "test_predictions.csv")
if trainer.is_world_process_zero():
with open(output_predict_file, "w") as writer:
for index, pred_list in enumerate(predictions):
pred_line = '\t'.join([f'{pred:.5f}' for pred in pred_list])
writer.write(f"{index}\t{pred_line}\n")
# Clean up checkpoints
checkpoints = [filepath for filepath in glob.glob(f'{training_args.output_dir}/*/') if '/checkpoint' in filepath]
for checkpoint in checkpoints:
shutil.rmtree(checkpoint)
if __name__ == "__main__":
main()
================================================
FILE: experiments/scotus.py
================================================
#!/usr/bin/env python
# coding=utf-8
""" Finetuning models on SCOTUS (e.g. Bert, RoBERTa, LEGAL-BERT)."""
import logging
import os
import random
import re
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
from datasets import load_dataset
from sklearn.metrics import f1_score
from models.hierbert import HierarchicalBert
import numpy as np
from torch import nn
import glob
import shutil
import transformers
from transformers import (
Trainer,
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TrainingArguments,
default_data_collator,
set_seed,
EarlyStoppingCallback,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
from models.deberta import DebertaForSequenceClassification
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.9.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
logger = logging.getLogger(__name__)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
max_seq_length: Optional[int] = field(
default=128,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
max_segments: Optional[int] = field(
default=64,
metadata={
"help": "The maximum number of segments (paragraphs) to be considered. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
max_seg_length: Optional[int] = field(
default=128,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
pad_to_max_length: bool = field(
default=True,
metadata={
"help": "Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
},
)
server_ip: Optional[str] = field(default=None, metadata={"help": "For distant debugging."})
server_port: Optional[str] = field(default=None, metadata={"help": "For distant debugging."})
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
default=None, metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
hierarchical: bool = field(
default=True, metadata={"help": "Whether to use a hierarchical variant or not"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
do_lower_case: Optional[bool] = field(
default=True,
metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
"with private models)."
},
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Setup distant debugging if needed
if data_args.server_ip and data_args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(data_args.server_ip, data_args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
# Fix boolean parameter
if model_args.do_lower_case == 'False' or not model_args.do_lower_case:
model_args.do_lower_case = False
else:
model_args.do_lower_case = True
if model_args.hierarchical == 'False' or not model_args.hierarchical:
model_args.hierarchical = False
else:
model_args.hierarchical = True
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading eurlex dataset from the hub.
if training_args.do_train:
train_dataset = load_dataset("lex_glue", "scotus", split="train", cache_dir=model_args.cache_dir)
if training_args.do_eval:
eval_dataset = load_dataset("lex_glue", "scotus", split="validation", cache_dir=model_args.cache_dir)
if training_args.do_predict:
predict_dataset = load_dataset("lex_glue", "scotus", split="test", cache_dir=model_args.cache_dir)
# Labels
label_list = list(range(14))
num_labels = len(label_list)
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task="scotus",
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
do_lower_case=model_args.do_lower_case,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
if config.model_type == 'deberta' and model_args.hierarchical:
model = DebertaForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
else:
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
if model_args.hierarchical:
# Hack the classifier encoder to use hierarchical BERT
if config.model_type in ['bert', 'deberta']:
if config.model_type == 'bert':
segment_encoder = model.bert
else:
segment_encoder = model.deberta
model_encoder = HierarchicalBert(encoder=segment_encoder,
max_segments=data_args.max_segments,
max_segment_length=data_args.max_seg_length)
if config.model_type == 'bert':
model.bert = model_encoder
elif config.model_type == 'deberta':
model.deberta = model_encoder
else:
raise NotImplementedError(f"{config.model_type} is no supported yet!")
elif config.model_type == 'roberta':
model_encoder = HierarchicalBert(encoder=model.roberta, max_segments=data_args.max_segments,
max_segment_length=data_args.max_seg_length)
model.roberta = model_encoder
# Build a new classification layer, as well
dense = nn.Linear(config.hidden_size, config.hidden_size)
dense.load_state_dict(model.classifier.dense.state_dict()) # load weights
dropout = nn.Dropout(config.hidden_dropout_prob).to(model.device)
out_proj = nn.Linear(config.hidden_size, config.num_labels).to(model.device)
out_proj.load_state_dict(model.classifier.out_proj.state_dict()) # load weights
model.classifier = nn.Sequential(dense, dropout, out_proj).to(model.device)
elif config.model_type in ['longformer', 'big_bird']:
pass
else:
raise NotImplementedError(f"{config.model_type} is no supported yet!")
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
padding = "max_length"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
padding = False
def preprocess_function(examples):
# Tokenize the texts
if model_args.hierarchical:
case_template = [[0] * data_args.max_seq_length]
if config.model_type == 'roberta':
batch = {'input_ids': [], 'attention_mask': []}
for doc in examples['text']:
doc = re.split('\n{2,}', doc)
doc_encodings = tokenizer(doc[:data_args.max_segments], padding=padding,
max_length=data_args.max_seg_length, truncation=True)
batch['input_ids'].append(doc_encodings['input_ids'] + case_template * (
data_args.max_segments - len(doc_encodings['input_ids'])))
batch['attention_mask'].append(doc_encodings['attention_mask'] + case_template * (
data_args.max_segments - len(doc_encodings['attention_mask'])))
else:
batch = {'input_ids': [], 'attention_mask': [], 'token_type_ids': []}
for doc in examples['text']:
doc = re.split('\n{2,}', doc)
doc_encodings = tokenizer(doc[:data_args.max_segments], padding=padding,
max_length=data_args.max_seg_length, truncation=True)
batch['input_ids'].append(doc_encodings['input_ids'] + case_template * (
data_args.max_segments - len(doc_encodings['input_ids'])))
batch['attention_mask'].append(doc_encodings['attention_mask'] + case_template * (
data_args.max_segments - len(doc_encodings['attention_mask'])))
batch['token_type_ids'].append(doc_encodings['token_type_ids'] + case_template * (
data_args.max_segments - len(doc_encodings['token_type_ids'])))
elif config.model_type in ['longformer', 'big_bird']:
cases = []
max_position_embeddings = config.max_position_embeddings - 2 if config.model_type == 'longformer' \
else config.max_position_embeddings
for doc in examples['text']:
doc = re.split('\n{2,}', doc)
cases.append(f' {tokenizer.sep_token} '.join([' '.join(paragraph.split()[:data_args.max_seg_length])
for paragraph in doc[:data_args.max_segments]]))
batch = tokenizer(cases, padding=padding, max_length=max_position_embeddings, truncation=True)
if config.model_type == 'longformer':
global_attention_mask = np.zeros((len(cases), max_position_embeddings), dtype=np.int32)
# global attention on cls token
global_attention_mask[:, 0] = 1
batch['global_attention_mask'] = list(global_attention_mask)
else:
batch = tokenizer(examples['text'], padding=padding, max_length=512, truncation=True)
batch["label"] = [label_list.index(labels) for labels in examples["label"]]
return batch
if training_args.do_train:
if data_args.max_train_samples is not None:
train_dataset = train_dataset.select(range(data_args.max_train_samples))
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
if training_args.do_eval:
if data_args.max_eval_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
with training_args.main_process_first(desc="validation dataset map pre-processing"):
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
if training_args.do_predict:
if data_args.max_predict_samples is not None:
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
with training_args.main_process_first(desc="prediction dataset map pre-processing"):
predict_dataset = predict_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on prediction dataset",
)
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(p: EvalPrediction):
logits = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
preds = np.argmax(logits, axis=1)
macro_f1 = f1_score(y_true=p.label_ids, y_pred=preds, average='macro', zero_division=0)
micro_f1 = f1_score(y_true=p.label_ids, y_pred=preds, average='micro', zero_division=0)
return {'macro-f1': macro_f1, 'micro-f1': micro_f1}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
data_collator = default_data_collator
elif training_args.fp16:
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
else:
data_collator = None
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
compute_metrics=compute_metrics,
tokenizer=tokenizer,
data_collator=data_collator,
callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate(eval_dataset=eval_dataset)
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Prediction
if training_args.do_predict:
logger.info("*** Predict ***")
predictions, labels, metrics = trainer.predict(predict_dataset, metric_key_prefix="predict")
max_predict_samples = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
)
metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
trainer.log_metrics("predict", metrics)
trainer.save_metrics("predict", metrics)
output_predict_file = os.path.join(training_args.output_dir, "test_predictions.csv")
if trainer.is_world_process_zero():
with open(output_predict_file, "w") as writer:
for index, pred_list in enumerate(predictions[0]):
pred_line = '\t'.join([f'{pred:.5f}' for pred in pred_list])
writer.write(f"{index}\t{pred_line}\n")
# Clean up checkpoints
checkpoints = [filepath for filepath in glob.glob(f'{training_args.output_dir}/*/') if '/checkpoint' in filepath]
for checkpoint in checkpoints:
shutil.rmtree(checkpoint)
if __name__ == "__main__":
main()
================================================
FILE: experiments/trainer.py
================================================
from torch import nn
from transformers import Trainer
class MultilabelTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
labels = inputs.pop("labels")
outputs = model(**inputs)
logits = outputs.logits
loss_fct = nn.BCEWithLogitsLoss()
loss = loss_fct(logits.view(-1, self.model.config.num_labels),
labels.float().view(-1, self.model.config.num_labels))
return (loss, outputs) if return_outputs else loss
================================================
FILE: experiments/unfair_tos.py
================================================
#!/usr/bin/env python
# coding=utf-8
""" Finetuning models on UNFAIR-ToC (e.g. Bert, RoBERTa, LEGAL-BERT)."""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
from datasets import load_dataset
from sklearn.metrics import f1_score
from trainer import MultilabelTrainer
from scipy.special import expit
import glob
import shutil
import numpy as np
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TrainingArguments,
default_data_collator,
set_seed,
EarlyStoppingCallback,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.9.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
logger = logging.getLogger(__name__)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
max_seq_length: Optional[int] = field(
default=128,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
pad_to_max_length: bool = field(
default=True,
metadata={
"help": "Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
},
)
server_ip: Optional[str] = field(default=None, metadata={"help": "For distant debugging."})
server_port: Optional[str] = field(default=None, metadata={"help": "For distant debugging."})
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
default=None, metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
do_lower_case: Optional[bool] = field(
default=True,
metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
"with private models)."
},
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Setup distant debugging if needed
if data_args.server_ip and data_args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(data_args.server_ip, data_args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
# Fix boolean parameter
if model_args.do_lower_case == 'False' or not model_args.do_lower_case:
model_args.do_lower_case = False
'Tokenizer do_lower_case False'
else:
model_args.do_lower_case = True
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading eurlex dataset from the hub.
if training_args.do_train:
train_dataset = load_dataset("lex_glue", "unfair_tos", split="train", data_dir='data', cache_dir=model_args.cache_dir)
if training_args.do_eval:
eval_dataset = load_dataset("lex_glue", "unfair_tos", split="validation", data_dir='data', cache_dir=model_args.cache_dir)
if training_args.do_predict:
predict_dataset = load_dataset("lex_glue", "unfair_tos", split="test", data_dir='data', cache_dir=model_args.cache_dir)
# Labels
label_list = list(range(8))
num_labels = len(label_list)
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task="unfair_toc",
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
if config.model_type == 'big_bird':
config.attention_type = 'original_full'
if config.model_type == 'longformer':
config.attention_window = [128] * config.num_hidden_layers
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
do_lower_case=model_args.do_lower_case,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
padding = "max_length"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
padding = False
def preprocess_function(examples):
# Tokenize the texts
batch = tokenizer(
examples["text"],
padding=padding,
max_length=data_args.max_seq_length,
truncation=True,
)
batch["labels"] = [[1 if label in labels else 0 for label in label_list] for labels in
examples["labels"]]
return batch
if training_args.do_train:
if data_args.max_train_samples is not None:
train_dataset = train_dataset.select(range(data_args.max_train_samples))
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
if training_args.do_eval:
if data_args.max_eval_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
with training_args.main_process_first(desc="validation dataset map pre-processing"):
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
if training_args.do_predict:
if data_args.max_predict_samples is not None:
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
with training_args.main_process_first(desc="prediction dataset map pre-processing"):
predict_dataset = predict_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on prediction dataset",
)
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(p: EvalPrediction):
# Fix gold labels
y_true = np.zeros((p.label_ids.shape[0], p.label_ids.shape[1] + 1), dtype=np.int32)
y_true[:, :-1] = p.label_ids
y_true[:, -1] = (np.sum(p.label_ids, axis=1) == 0).astype('int32')
# Fix predictions
logits = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
preds = (expit(logits) > 0.5).astype('int32')
y_pred = np.zeros((p.label_ids.shape[0], p.label_ids.shape[1] + 1), dtype=np.int32)
y_pred[:, :-1] = preds
y_pred[:, -1] = (np.sum(preds, axis=1) == 0).astype('int32')
# Compute scores
macro_f1 = f1_score(y_true=y_true, y_pred=y_pred, average='macro', zero_division=0)
micro_f1 = f1_score(y_true=y_true, y_pred=y_pred, average='micro', zero_division=0)
return {'macro-f1': macro_f1, 'micro-f1': micro_f1}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
data_collator = default_data_collator
elif training_args.fp16:
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
else:
data_collator = None
# Initialize our Trainer
trainer = MultilabelTrainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
compute_metrics=compute_metrics,
tokenizer=tokenizer,
data_collator=data_collator,
callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate(eval_dataset=eval_dataset)
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Prediction
if training_args.do_predict:
logger.info("*** Predict ***")
predictions, labels, metrics = trainer.predict(predict_dataset, metric_key_prefix="predict")
max_predict_samples = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
)
metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
trainer.log_metrics("predict", metrics)
trainer.save_metrics("predict", metrics)
output_predict_file = os.path.join(training_args.output_dir, "test_predictions.csv")
if trainer.is_world_process_zero():
with open(output_predict_file, "w") as writer:
for index, pred_list in enumerate(predictions[0]):
pred_line = '\t'.join([f'{pred:.5f}' for pred in pred_list])
writer.write(f"{index}\t{pred_line}\n")
# Clean up checkpoints
checkpoints = [filepath for filepath in glob.glob(f'{training_args.output_dir}/*/') if '/checkpoint' in filepath]
for checkpoint in checkpoints:
shutil.rmtree(checkpoint)
if __name__ == "__main__":
main()
================================================
FILE: models/deberta.py
================================================
import torch
from torch import nn
from transformers import DebertaPreTrainedModel, DebertaModel
from transformers.modeling_outputs import SequenceClassifierOutput, MultipleChoiceModelOutput
from transformers.activations import ACT2FN
class ContextPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)
self.dropout = StableDropout(config.pooler_dropout)
self.config = config
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
context_token = hidden_states[:, 0]
context_token = self.dropout(context_token)
pooled_output = self.dense(context_token)
pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)
return pooled_output
@property
def output_dim(self):
return self.config.hidden_size
class DropoutContext(object):
def __init__(self):
self.dropout = 0
self.mask = None
self.scale = 1
self.reuse_mask = True
def get_mask(input, local_context):
if not isinstance(local_context, DropoutContext):
dropout = local_context
mask = None
else:
dropout = local_context.dropout
dropout *= local_context.scale
mask = local_context.mask if local_context.reuse_mask else None
if dropout > 0 and mask is None:
mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).bool()
if isinstance(local_context, DropoutContext):
if local_context.mask is None:
local_context.mask = mask
return mask, dropout
class XDropout(torch.autograd.Function):
"""Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""
@staticmethod
def forward(ctx, input, local_ctx):
mask, dropout = get_mask(input, local_ctx)
ctx.scale = 1.0 / (1 - dropout)
if dropout > 0:
ctx.save_for_backward(mask)
return input.masked_fill(mask, 0) * ctx.scale
else:
return input
@staticmethod
def backward(ctx, grad_output):
if ctx.scale > 1:
(mask,) = ctx.saved_tensors
return grad_output.masked_fill(mask, 0) * ctx.scale, None
else:
return grad_output, None
class StableDropout(nn.Module):
"""
Optimized dropout module for stabilizing the training
Args:
drop_prob (float): the dropout probabilities
"""
def __init__(self, drop_prob):
super().__init__()
self.drop_prob = drop_prob
self.count = 0
self.context_stack = None
def forward(self, x):
"""
Call the module
Args:
x (:obj:`torch.tensor`): The input tensor to apply dropout
"""
if self.training and self.drop_prob > 0:
return XDropout.apply(x, self.get_context())
return x
def clear_context(self):
self.count = 0
self.context_stack = None
def init_context(self, reuse_mask=True, scale=1):
if self.context_stack is None:
self.context_stack = []
self.count = 0
for c in self.context_stack:
c.reuse_mask = reuse_mask
c.scale = scale
def get_context(self):
if self.context_stack is not None:
if self.count >= len(self.context_stack):
self.context_stack.append(DropoutContext())
ctx = self.context_stack[self.count]
ctx.dropout = self.drop_prob
self.count += 1
return ctx
else:
return self.drop_prob
class DebertaForSequenceClassification(DebertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
num_labels = getattr(config, "num_labels", 2)
self.num_labels = num_labels
self.deberta = DebertaModel(config)
self.classifier = nn.Linear(config.hidden_size, num_labels)
drop_out = getattr(config, "cls_dropout", None)
drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
self.dropout = nn.Dropout(drop_out)
self.init_weights()
def get_input_embeddings(self):
return self.deberta.get_input_embeddings()
def set_input_embeddings(self, new_embeddings):
self.deberta.set_input_embeddings(new_embeddings)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.deberta(
input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = self.dropout(outputs[1])
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.num_labels == 1:
# regression task
loss_fn = nn.MSELoss()
logits = logits.view(-1).to(labels.dtype)
loss = loss_fn(logits, labels.view(-1))
elif labels.dim() == 1 or labels.size(-1) == 1:
label_index = (labels >= 0).nonzero()
labels = labels.long()
if label_index.size(0) > 0:
labeled_logits = torch.gather(logits, 0, label_index.expand(label_index.size(0), logits.size(1)))
labels = torch.gather(labels, 0, label_index.view(-1))
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1))
else:
loss = torch.tensor(0).to(logits)
else:
log_softmax = nn.LogSoftmax(-1)
loss = -((log_softmax(logits) * labels).sum(-1)).mean()
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
else:
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class DebertaForMultipleChoice(DebertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.deberta = DebertaModel(config)
self.pooler = ContextPooler(config)
output_dim = self.pooler.output_dim
drop_out = getattr(config, "cls_dropout", None)
drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
self.dropout = StableDropout(drop_out)
self.classifier = nn.Linear(output_dim, 1)
self.init_weights()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
num_choices-1]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
:obj:`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.deberta(
input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
encoder_layer = outputs[0]
pooled_output = self.pooler(encoder_layer)
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
================================================
FILE: models/hierbert.py
================================================
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
import numpy as np
from torch import nn
from transformers.file_utils import ModelOutput
@dataclass
class SimpleOutput(ModelOutput):
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
def sinusoidal_init(num_embeddings: int, embedding_dim: int):
# keep dim 0 for padding token position encoding zero vector
position_enc = np.array([
[pos / np.power(10000, 2 * i / embedding_dim) for i in range(embedding_dim)]
if pos != 0 else np.zeros(embedding_dim) for pos in range(num_embeddings)])
position_enc[1:, 0::2] = np.sin(position_enc[1:, 0::2]) # dim 2i
position_enc[1:, 1::2] = np.cos(position_enc[1:, 1::2]) # dim 2i+1
return torch.from_numpy(position_enc).type(torch.FloatTensor)
class HierarchicalBert(nn.Module):
def __init__(self, encoder, max_segments=64, max_segment_length=128):
super(HierarchicalBert, self).__init__()
supported_models = ['bert', 'roberta', 'deberta']
assert encoder.config.model_type in supported_models # other model types are not supported so far
# Pre-trained segment (token-wise) encoder, e.g., BERT
self.encoder = encoder
# Specs for the segment-wise encoder
self.hidden_size = encoder.config.hidden_size
self.max_segments = max_segments
self.max_segment_length = max_segment_length
# Init sinusoidal positional embeddings
self.seg_pos_embeddings = nn.Embedding(max_segments + 1, encoder.config.hidden_size,
padding_idx=0,
_weight=sinusoidal_init(max_segments + 1, encoder.config.hidden_size))
# Init segment-wise transformer-based encoder
self.seg_encoder = nn.Transformer(d_model=encoder.config.hidden_size,
nhead=encoder.config.num_attention_heads,
batch_first=True, dim_feedforward=encoder.config.intermediate_size,
activation=encoder.config.hidden_act,
dropout=encoder.config.hidden_dropout_prob,
layer_norm_eps=encoder.config.layer_norm_eps,
num_encoder_layers=2, num_decoder_layers=0).encoder
def forward(self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
# Hypothetical Example
# Batch of 4 documents: (batch_size, n_segments, max_segment_length) --> (4, 64, 128)
# BERT-BASE encoder: 768 hidden units
# Squash samples and segments into a single axis (batch_size * n_segments, max_segment_length) --> (256, 128)
input_ids_reshape = input_ids.contiguous().view(-1, input_ids.size(-1))
attention_mask_reshape = attention_mask.contiguous().view(-1, attention_mask.size(-1))
if token_type_ids is not None:
token_type_ids_reshape = token_type_ids.contiguous().view(-1, token_type_ids.size(-1))
else:
token_type_ids_reshape = None
# Encode segments with BERT --> (256, 128, 768)
encoder_outputs = self.encoder(input_ids=input_ids_reshape,
attention_mask=attention_mask_reshape,
token_type_ids=token_type_ids_reshape)[0]
# Reshape back to (batch_size, n_segments, max_segment_length, output_size) --> (4, 64, 128, 768)
encoder_outputs = encoder_outputs.contiguous().view(input_ids.size(0), self.max_segments,
self.max_segment_length,
self.hidden_size)
# Gather CLS outputs per segment --> (4, 64, 768)
encoder_outputs = encoder_outputs[:, :, 0]
# Infer real segments, i.e., mask paddings
seg_mask = (torch.sum(input_ids, 2) != 0).to(input_ids.dtype)
# Infer and collect segment positional embeddings
seg_positions = torch.arange(1, self.max_segments + 1).to(input_ids.device) * seg_mask
# Add segment positional embeddings to segment inputs
encoder_outputs += self.seg_pos_embeddings(seg_positions)
# Encode segments with segment-wise transformer
seg_encoder_outputs = self.seg_encoder(encoder_outputs)
# Collect document representation
outputs, _ = torch.max(seg_encoder_outputs, 1)
return SimpleOutput(last_hidden_state=outputs, hidden_states=outputs)
if __name__ == "__main__":
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
# Use as a stand-alone encoder
bert = AutoModel.from_pretrained('bert-base-uncased')
model = HierarchicalBert(encoder=bert, max_segments=64, max_segment_length=128)
fake_inputs = {'input_ids': [], 'attention_mask': [], 'token_type_ids': []}
for i in range(4):
# Tokenize segment
temp_inputs = tokenizer(['dog ' * 126] * 64)
fake_inputs['input_ids'].append(temp_inputs['input_ids'])
fake_inputs['attention_mask'].append(temp_inputs['attention_mask'])
fake_inputs['token_type_ids'].append(temp_inputs['token_type_ids'])
fake_inputs['input_ids'] = torch.as_tensor(fake_inputs['input_ids'])
fake_inputs['attention_mask'] = torch.as_tensor(fake_inputs['attention_mask'])
fake_inputs['token_type_ids'] = torch.as_tensor(fake_inputs['token_type_ids'])
output = model(fake_inputs['input_ids'], fake_inputs['attention_mask'], fake_inputs['token_type_ids'])
# 4 document representations of 768 features are expected
assert output[0].shape == torch.Size([4, 768])
# Use with HuggingFace AutoModelForSequenceClassification and Trainer API
# Init Classifier
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=10)
# Replace flat BERT encoder with hierarchical BERT encoder
model.bert = HierarchicalBert(encoder=model.bert, max_segments=64, max_segment_length=128)
output = model(fake_inputs['input_ids'], fake_inputs['attention_mask'], fake_inputs['token_type_ids'])
# 4 document outputs with 10 (num_labels) logits are expected
assert output.logits.shape == torch.Size([4, 10])
================================================
FILE: models/tfidf_svm.py
================================================
import pandas
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer
from sklearn.model_selection import GridSearchCV
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
from sklearn import metrics
from sklearn.model_selection import PredefinedSplit
from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
import pandas as pd
from sklearn.preprocessing import FunctionTransformer
from sklearn.pipeline import FeatureUnion, Pipeline
from datasets import load_dataset
import logging
import os
import argparse
dataset_n_classes = {'ecthr_a': 10, 'ecthr_b': 10, 'scotus': 14, 'eurlex': 100, 'ledgar': 100, 'unfair_tos': 8, 'case_hold': 5}
def main():
parser = argparse.ArgumentParser()
# Required arguments
parser.add_argument('--dataset', default='case_hold', type=str)
parser.add_argument('--task_type', default='multi_class', type=str)
parser.add_argument('--text_limit', default=-1, type=int)
config = parser.parse_args()
n_classes = dataset_n_classes[config.dataset]
if not os.path.exists(f'logs/{config.dataset}'):
if not os.path.exists(f'logs'):
os.mkdir(f'logs')
os.mkdir(f'logs/{config.dataset}')
handlers = [logging.FileHandler(f'logs/{config.dataset}_svm.txt'), logging.StreamHandler()]
logging.basicConfig(handlers=handlers, level=logging.INFO)
def get_text(dataset):
if 'ecthr' in config.dataset:
texts = [' '.join(text) for text in dataset['text']]
return [' '.join(text.split()[:config.text_limit]) for text in texts]
elif config.dataset == 'case_hold':
data = [[context] + endings for context, endings in zip(dataset['context'], dataset['endings'])]
return pd.DataFrame(data=data,
columns=['context', 'option_1', 'option_2', 'option_3', 'options_4', 'option_5']
)
else:
return [' '.join(text.split()[:config.text_limit]) for text in dataset['text']]
def get_labels(dataset, mlb=None):
if config.task_type == 'multi_class':
return dataset['label']
else:
return mlb.transform(dataset['labels']).tolist()
def add_zero_class(labels):
augmented_labels = np.zeros((len(labels), len(labels[0]) + 1), dtype=np.int32)
augmented_labels[:, :-1] = labels
augmented_labels[:, -1] = (np.sum(labels, axis=1) == 0).astype('int32')
return augmented_labels
scores = {'micro-f1': [], 'macro-f1': []}
dataset = load_dataset('lex_glue', config.dataset)
for seed in range(1, 6):
if config.task_type == 'multi_label':
classifier = OneVsRestClassifier(LinearSVC(random_state=seed, max_iter=50000))
parameters = {
'vect__max_features': [10000, 20000, 40000],
'clf__estimator__C': [0.1, 1, 10],
'clf__estimator__loss': ('hinge', 'squared_hinge')
}
elif config.dataset == 'case_hold':
classifier = LinearSVC(random_state=seed, max_iter=50000)
parameters = {
'clf__C': [0.1, 1, 10],
'clf__loss': ('hinge', 'squared_hinge')
}
else:
classifier = LinearSVC(random_state=seed, max_iter=50000)
parameters = {
'vect__max_features': [10000, 20000, 40000],
'clf__C': [0.1, 1, 10],
'clf__loss': ('hinge', 'squared_hinge')
}
# Init Pipeline (TF-IDF, SVM)
if config.dataset == 'case_hold':
text_clf = Pipeline([
('union', FeatureUnion([('context_tfidf',
Pipeline([('extract_field', FunctionTransformer(lambda x: x['context'], validate=False)),
('vect', CountVectorizer(stop_words=stopwords.words('english'),
ngram_range=(1, 3), min_df=5, max_features=40000)),
('tfidf', TfidfTransformer())]))] +
[(f'option_{idx}_tfidf',
Pipeline([('extract_field', FunctionTransformer(lambda x: x[f'option_{idx}'], validate=False)),
('vect', CountVectorizer(stop_words=stopwords.words('english'),
ngram_range=(1, 3), min_df=5, max_features=40000)),
('tfidf', TfidfTransformer())]))
for idx in range(1, 6)]
)),
('clf', classifier)
])
else:
text_clf = Pipeline([('vect', CountVectorizer(stop_words=stopwords.words('english'),
ngram_range=(1, 3), min_df=5)),
('tfidf', TfidfTransformer()),
('clf', classifier),
])
# Fixate Validation Split
split_index = [-1] * len(dataset['train']) + [0] * len(dataset['validation'])
val_split = PredefinedSplit(test_fold=split_index)
gs_clf = GridSearchCV(text_clf, parameters, cv=val_split, n_jobs=32, verbose=4, refit = False)
# Pre-process inputs, outputs
x_train = get_text(dataset['train'])
x_val = get_text(dataset['validation'])
x_train_val = pd.concat([x_train, x_val])
if config.task_type == 'multi_label':
mlb = MultiLabelBinarizer(classes=range(n_classes))
mlb.fit(dataset['train']['labels'])
else:
mlb = None
y_train = get_labels(dataset['train'], mlb)
y_val = get_labels(dataset['validation'], mlb)
y_train_val = y_train + y_val
# Train classifier
gs_clf = gs_clf.fit(x_train_val, y_train_val)
# Print best hyper-parameters
logging.info('Best Parameters:')
for param_name in sorted(parameters.keys()):
logging.info("%s: %r" % (param_name, gs_clf.best_params_[param_name]))
# Retrain model with best CV parameters only with train data
text_clf.set_params(**gs_clf.best_params_)
gs_clf = text_clf.fit(x_train, y_train)
# Report results
logging.info('VALIDATION RESULTS:')
y_pred = gs_clf.predict(get_text(dataset['validation']))
y_true = get_labels(dataset["validation"], mlb)
if config.task_type == 'multi_label' and config.dataset != 'eurlex':
y_true = add_zero_class(y_true)
y_pred = add_zero_class(y_pred)
logging.info(f'Micro-F1: {metrics.f1_score(y_true, y_pred, average="micro")*100:.1f}')
logging.info(f'Macro-F1: {metrics.f1_score(y_true, y_pred, average="macro")*100:.1f}')
logging.info('TEST RESULTS:')
y_pred = gs_clf.predict(get_text(dataset['test']))
y_true = get_labels(dataset["test"], mlb)
if config.task_type == 'multi_label' and config.dataset != 'eurlex':
y_true = add_zero_class(y_true)
y_pred = add_zero_class(y_pred)
logging.info(f'Micro-F1: {metrics.f1_score(y_true, y_pred, average="micro")*100:.1f}')
logging.info(f'Macro-F1: {metrics.f1_score(y_true, y_pred, average="macro")*100:.1f}')
scores['micro-f1'].append(metrics.f1_score(y_true, y_pred, average="micro"))
scores['macro-f1'].append(metrics.f1_score(y_true, y_pred, average="macro"))
# Report averaged results across runs
logging.info('-' * 100)
logging.info(f'Micro-F1: {np.mean(scores["micro-f1"])*100:.1f} +/- {np.std(scores["micro-f1"])*100:.1f}\t'
f'Macro-F1: {np.mean(scores["macro-f1"])*100:.1f} +/- {np.std(scores["macro-f1"])*100:.1f}')
if __name__ == '__main__':
main()
================================================
FILE: requirements.txt
================================================
torch>=1.9.0
transformers>=4.9.0
scikit-learn>=0.24.1
tqdm>=4.61.1
numpy>=1.20.1
datasets>=1.18.1
nltk>=3.5
scipy>=1.6.3
================================================
FILE: scripts/run_case_hold.sh
================================================
GPU_NUMBER=0
MODEL_NAME='bert-base-uncased'
BATCH_SIZE=8
ACCUMULATION_STEPS=1
TASK='case_hold'
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/case_hold.py --task_name ${TASK} --model_name_or_path ${MODEL_NAME} --output_dir logs/${TASK}/${MODEL_NAME}/seed_1 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 1 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/case_hold.py --task_name ${TASK} --model_name_or_path ${MODEL_NAME} --output_dir logs/${TASK}/${MODEL_NAME}/seed_2 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 2 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/case_hold.py --task_name ${TASK} --model_name_or_path ${MODEL_NAME} --output_dir logs/${TASK}/${MODEL_NAME}/seed_3 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 3 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/case_hold.py --task_name ${TASK} --model_name_or_path ${MODEL_NAME} --output_dir logs/${TASK}/${MODEL_NAME}/seed_4 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 4 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/case_hold.py --task_name ${TASK} --model_name_or_path ${MODEL_NAME} --output_dir logs/${TASK}/${MODEL_NAME}/seed_5 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 5 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
python statistics/compute_avg_scores.py --dataset ${TASK}
================================================
FILE: scripts/run_ecthr.sh
================================================
GPU_NUMBER=0
MODEL_NAME='bert-base-uncased'
LOWER_CASE='True'
BATCH_SIZE=2
ACCUMULATION_STEPS=4
TASK='ecthr_a'
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/ecthr.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --task ${TASK} --output_dir logs/${TASK}/${MODEL_NAME}/seed_1 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 1 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/ecthr.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --task ${TASK} --output_dir logs/${TASK}/${MODEL_NAME}/seed_2 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 2 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/ecthr.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --task ${TASK} --output_dir logs/${TASK}/${MODEL_NAME}/seed_3 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 3 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/ecthr.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --task ${TASK} --output_dir logs/${TASK}/${MODEL_NAME}/seed_4 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 4 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/ecthr.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --task ${TASK} --output_dir logs/${TASK}/${MODEL_NAME}/seed_5 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 5 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
================================================
FILE: scripts/run_eurlex.sh
================================================
GPU_NUMBER=6
MODEL_NAME='bert-base-uncased'
LOWER_CASE='True'
BATCH_SIZE=8
ACCUMULATION_STEPS=1
TASK='eurlex'
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/eurlex.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_1 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 2 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 1 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/eurlex.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_2 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 2 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 2 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/eurlex.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_3 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 2 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 3 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/eurlex.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_4 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 2 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 4 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/eurlex.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_5 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 2 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 5 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
python statistics/compute_avg_scores.py --dataset ${TASK}
================================================
FILE: scripts/run_ledgar.sh
================================================
GPU_NUMBER=0
MODEL_NAME='bert-base-uncased'
LOWER_CASE='True'
BATCH_SIZE=8
ACCUMULATION_STEPS=1
TASK='ledgar'
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/ledgar.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_1 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 1 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/ledgar.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_2 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 2 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/ledgar.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_3 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 3 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/ledgar.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_4 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 4 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/ledgar.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_5 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 5 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
python statistics/compute_avg_scores.py --dataset ${TASK}
================================================
FILE: scripts/run_scotus.sh
================================================
GPU_NUMBER=0
MODEL_NAME='bert-base-uncased'
LOWER_CASE='True'
BATCH_SIZE=2
ACCUMULATION_STEPS=4
TASK='scotus'
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/scotus.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_1 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 1 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/scotus.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_2 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 2 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/scotus.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_3 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 3 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/scotus.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_4 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 4 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/scotus.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_5 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 5 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
python statistics/compute_avg_scores.py --dataset ${TASK}
================================================
FILE: scripts/run_tfidf_svm.sh
================================================
DATASET='eurlex'
TASK_TYPE='multi_label'
N_CLASSES=100
python models/tfidf_svm.py --dataset ${DATASET} --task_type ${TASK_TYPE} --n_classes ${N_CLASSES}
================================================
FILE: scripts/run_unfair_tos.sh
================================================
GPU_NUMBER=0
MODEL_NAME='bert-base-uncased'
LOWER_CASE='True'
BATCH_SIZE=8
ACCUMULATION_STEPS=1
TASK='unfair_tos'
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/unfair_tos.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_1 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 1 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/unfair_tos.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_2 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 2 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/unfair_tos.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_3 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 3 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/unfair_tos.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir logs/${TASK}/${MODEL_NAME}/seed_4 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 4 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
CUDA_VISIBLE_DEVICES=${GPU_NUMBER} python experiments/unfair_tos.py --model_name_or_path ${MODEL_NAME} --do_lower_case ${LOWER_CASE} --output_dir .logs/${TASK}/${MODEL_NAME}/seed_5 --do_train --do_eval --do_pred --overwrite_output_dir --load_best_model_at_end --metric_for_best_model micro-f1 --greater_is_better True --evaluation_strategy epoch --save_strategy epoch --save_total_limit 5 --num_train_epochs 20 --learning_rate 3e-5 --per_device_train_batch_size ${BATCH_SIZE} --per_device_eval_batch_size ${BATCH_SIZE} --seed 5 --fp16 --fp16_full_eval --gradient_accumulation_steps ${ACCUMULATION_STEPS} --eval_accumulation_steps ${ACCUMULATION_STEPS}
python statistics/compute_avg_scores.py --dataset ${TASK}
================================================
FILE: statistics/compute_avg_lexglue_scores.py
================================================
import copy
import json
import os
import argparse
import numpy as np
from scipy.stats import hmean, gmean
def main():
''' set default hyperparams in default_hyperparams.py '''
parser = argparse.ArgumentParser()
# Required arguments
parser.add_argument('--filter_outliers', default=True)
parser.add_argument('--top_k', default=1)
config = parser.parse_args()
MODELS = ['bert-base-uncased', 'roberta-base', 'microsoft/deberta-base', 'allenai/longformer-base-4096',
'google/bigbird-roberta-base', 'nlpaueb/legal-bert-base-uncased', 'zlucia/custom-legalbert', 'roberta-large']
DATASETS = ['ecthr_a', 'ecthr_b', 'eurlex', 'scotus', 'ledgar', 'unfair_tos', 'casehold']
MODEL_NAMES = ['BERT', 'RoBERTa', 'DeBERTa', 'Longformer', 'BigBird', 'Legal-BERT', 'CaseLaw-BERT', 'RoBERTa']
score_dicts = {model: {'dev': {'micro': [], 'macro': []}, 'test': {'micro': [], 'macro': []}}
for model in MODELS}
for model in MODELS:
for dataset in DATASETS:
BASE_DIR = f'/Users/rwg642/Desktop/LEXGLUE/RESULTS/{dataset}'
score_dict = {'dev': {'micro': [], 'macro': []},
'test': {'micro': [], 'macro': []}}
for seed in range(1, 6):
try:
seed = f'seed_{seed}'
with open(os.path.join(BASE_DIR, model, seed, 'all_results.json')) as json_file:
json_data = json.load(json_file)
score_dict['dev']['micro'].append(float(json_data['eval_micro-f1']))
score_dict['dev']['macro'].append(float(json_data['eval_macro-f1']))
score_dict['test']['micro'].append(float(json_data['predict_micro-f1']))
score_dict['test']['macro'].append(float(json_data['predict_macro-f1']))
except:
continue
temp_stats = copy.deepcopy(score_dict)
if config.filter_outliers:
seed_scores = [(idx, score) for (idx, score) in enumerate(score_dict['dev']['macro'])]
sorted_scores = sorted(seed_scores, key=lambda tup: tup[1], reverse=True)
top_k_ids = [idx for idx, score in sorted_scores[:config.top_k]]
for subset in ['dev', 'test']:
temp_stats[subset]['micro'] = [score for idx, score in enumerate(score_dict[subset]['micro']) if
idx in top_k_ids]
temp_stats[subset]['macro'] = [score for idx, score in enumerate(score_dict[subset]['macro']) if
idx in top_k_ids]
for subset in ['dev', 'test']:
for avg in ['micro', 'macro']:
score_dicts[model][subset][avg].append(np.mean(temp_stats[subset][avg]))
print('-' * 253)
print(f'{"MODEL NAME":>35} | {"A-MEAN":<33} | {"H-MEAN":<33} | {"G-MEAN":<33} |')
print('-' * 253)
for idx, (method, stats) in enumerate(score_dicts.items()):
algo_means = {'dev': {'micro': [0.0, 0.0, 0.0], 'macro': [0.0, 0.0, 0.0]},
'test': {'micro': [0.0, 0.0, 0.0], 'macro': [0.0, 0.0, 0.0]}}
for subset in ['dev', 'test']:
for avg in ['micro', 'macro']:
algo_means[subset][avg][0] = np.mean(stats[subset][avg])
algo_means[subset][avg][1] = hmean(stats[subset][avg])
algo_means[subset][avg][2] = gmean(stats[subset][avg])
report_line = f'<tr><td>{MODEL_NAMES[idx]}</td>'
for task_idx in range(3):
report_line += f'<td> {algo_means["test"]["micro"][task_idx] * 100:.1f} / '
report_line += f' {algo_means["test"]["macro"][task_idx] * 100:.1f} </td>'
report_line += '</tr>'
print(report_line)
if __name__ == '__main__':
main()
================================================
FILE: statistics/compute_avg_scores.py
================================================
import copy
import json
import os
import argparse
import numpy as np
import warnings
warnings.filterwarnings('ignore')
def main():
''' set default hyperparams in default_hyperparams.py '''
parser = argparse.ArgumentParser()
# Required arguments
parser.add_argument('--dataset', default='scotus')
parser.add_argument('--filter_outliers', default=True)
parser.add_argument('--top_k', default=3)
config = parser.parse_args()
BASE_DIR = f'logs/{config.dataset}'
if os.path.exists(BASE_DIR):
print(f'{BASE_DIR} exists!')
score_dicts = {}
MODELS = ['bert-base-uncased', 'roberta-base', 'microsoft/deberta-base', 'allenai/longformer-base-4096',
'google/bigbird-roberta-base', 'nlpaueb/legal-bert-base-uncased', 'zlucia/custom-legalbert', 'roberta-large']
for model in MODELS:
score_dict = {'dev': {'micro': [], 'macro': []},
'test': {'micro': [], 'macro': []}}
for seed in range(1, 6):
seed = f'seed_{seed}'
try:
with open(os.path.join(BASE_DIR, model, seed, 'all_results.json')) as json_file:
json_data = json.load(json_file)
score_dict['dev']['micro'].append(float(json_data['eval_micro-f1']))
score_dict['dev']['macro'].append(float(json_data['eval_macro-f1']))
score_dict['test']['micro'].append(float(json_data['predict_micro-f1']))
score_dict['test']['macro'].append(float(json_data['predict_macro-f1']))
except:
continue
score_dicts[model] = score_dict
print(f'{" " * 36} {"VALIDATION":<47} | {"TEST"}')
print('-' * 200)
for algo, stats in score_dicts.items():
temp_stats = copy.deepcopy(stats)
if config.filter_outliers:
seed_scores = [(idx, score) for (idx, score) in enumerate(stats['dev']['macro'])]
sorted_scores = sorted(seed_scores, key=lambda tup: tup[1], reverse=True)
top_k_ids = [idx for idx, score in sorted_scores[:config.top_k]]
temp_stats['dev']['micro'] = [score for idx, score in enumerate(stats['dev']['micro']) if
idx in top_k_ids]
temp_stats['dev']['macro'] = [score for idx, score in enumerate(stats['dev']['macro']) if
idx in top_k_ids]
temp_stats['test']['micro'] = [score for idx, score in enumerate(stats['test']['micro']) if
idx in top_k_ids[:1]]
temp_stats['test']['macro'] = [score for idx, score in enumerate(stats['test']['macro']) if
idx in top_k_ids[:1]]
report_line = f'{algo:>35}: MICRO-F1: {np.mean(temp_stats["dev"]["micro"])*100:.1f}\t ± {np.std(temp_stats["dev"]["micro"])*100:.1f}\t'
report_line += f'MACRO-F1: {np.mean(temp_stats["dev"]["macro"])*100:.1f}\t ± {np.std(temp_stats["dev"]["macro"])*100:.1f}\t'
report_line += ' | '
report_line += f'MICRO-F1: {np.mean(temp_stats["test"]["micro"])*100:.1f}\t'
report_line += f'MACRO-F1: {np.mean(temp_stats["test"]["macro"])*100:.1f}\t'
print(report_line)
if __name__ == '__main__':
main()
================================================
FILE: statistics/compute_lexglue_scores.py
================================================
import copy
import json
import os
import argparse
import numpy as np
def main():
''' set default hyperparams in default_hyperparams.py '''
parser = argparse.ArgumentParser()
# Required arguments
parser.add_argument('--filter_outliers', default=True)
parser.add_argument('--top_k', default=1)
config = parser.parse_args()
MODELS = ['bert-base-uncased', 'roberta-base', 'microsoft/deberta-base', 'allenai/longformer-base-4096',
'google/bigbird-roberta-base', 'nlpaueb/legal-bert-base-uncased', 'zlucia/custom-legalbert']
DATASETS = ['ecthr_a', 'ecthr_b', 'eurlex', 'scotus', 'ledgar', 'unfair_tos', 'casehold']
MODEL_NAMES = ['BERT', 'RoBERTa', 'DeBERTa', 'Longformer', 'BigBird', 'Legal-BERT', 'CaseLaw-BERT']
score_dicts = {model: {'dev': {'micro': [], 'macro': []}, 'test': {'micro': [], 'macro': []}}
for model in MODELS}
for model in MODELS:
for dataset in DATASETS:
BASE_DIR = f'logs/{dataset}'
score_dict = {'dev': {'micro': [], 'macro': []},
'test': {'micro': [], 'macro': []}}
for seed in range(1, 6):
try:
seed = f'seed_{seed}'
with open(os.path.join(BASE_DIR, model, seed, 'all_results.json')) as json_file:
json_data = json.load(json_file)
score_dict['dev']['micro'].append(float(json_data['eval_micro-f1']))
score_dict['dev']['macro'].append(float(json_data['eval_macro-f1']))
score_dict['test']['micro'].append(float(json_data['predict_micro-f1']))
score_dict['test']['macro'].append(float(json_data['predict_macro-f1']))
except:
continue
temp_stats = copy.deepcopy(score_dict)
if config.filter_outliers:
seed_scores = [(idx, score) for (idx, score) in enumerate(score_dict['dev']['macro'])]
sorted_scores = sorted(seed_scores, key=lambda tup: tup[1], reverse=True)
top_k_ids = [idx for idx, score in sorted_scores[:config.top_k]]
for subset in ['dev', 'test']:
temp_stats[subset]['micro'] = [score for idx, score in enumerate(score_dict[subset]['micro']) if
idx in top_k_ids]
temp_stats[subset]['macro'] = [score for idx, score in enumerate(score_dict[subset]['macro']) if
idx in top_k_ids]
for subset in ['dev', 'test']:
for avg in ['micro', 'macro']:
score_dicts[model][subset][avg].append(np.mean(temp_stats[subset][avg]))
print('-' * 253)
print(f'{"DATASET":>35} & ', ' & '.join([f"{dataset}" for dataset in DATASETS]).upper(), ' \\\\')
print('-' * 253)
for idx, (method, stats) in enumerate(score_dicts.items()):
report_line = f'<tr><td>{MODEL_NAMES[idx]}</td> '
for task_idx in range(len(DATASETS)):
report_line += f'<td> {stats["test"]["micro"][task_idx] * 100:.1f} / '
report_line += f' {stats["test"]["macro"][task_idx] * 100:.1f} </td> '
# report_line += '</tr>' if task_idx == len(DATASETS) - 1 else '&'
report_line += '</tr>'
print(report_line)
# print('-' * 253)
if __name__ == '__main__':
main()
================================================
FILE: statistics/report_model_results.py
================================================
import json
import os
import argparse
def main():
''' set default hyperparams in default_hyperparams.py '''
parser = argparse.ArgumentParser()
# Required arguments
parser.add_argument('--model', default='roberta-base')
config = parser.parse_args()
MODEL = config.model
TASKS = ['ecthr_a', 'ecthr_b', 'scotus', 'eurlex', 'ledgar', 'unfair_tos', 'case_hold']
for task in TASKS:
print('-' * 100)
print(task.upper())
print('-' * 100)
BASE_DIR = f'logs/{task}'
print(f'{" " * 10} | {"VALIDATION":<40} | {"TEST":<40}')
print('-' * 100)
for seed in range(1, 6):
seed = f'seed_{seed}'
try:
with open(os.path.join(BASE_DIR, MODEL, seed, 'all_results.json')) as json_file:
json_data = json.load(json_file)
dev_micro_f1 = float(json_data['eval_micro-f1'])
dev_macro_f1 = float(json_data['eval_macro-f1'])
test_micro_f1 = float(json_data['predict_micro-f1'])
test_macro_f1 = float(json_data['predict_macro-f1'])
epoch = float(json_data['epoch'])
report_line = f'EPOCH: {epoch: 2.1f} | '
report_line += f'MICRO-F1: {dev_micro_f1 * 100:.1f}\t'
report_line += f'MACRO-F1: {dev_macro_f1 * 100:.1f}\t'
report_line += ' | '
report_line += f'MICRO-F1: {test_micro_f1 * 100:.1f}\t'
report_line += f'MACRO-F1: {test_macro_f1 * 100:.1f}\t'
print(report_line)
except:
continue
if __name__ == '__main__':
main()
================================================
FILE: statistics/report_train_time.py
================================================
import json
import os
import numpy as np
import datetime
def main():
for dataset in ['ecthr_a', 'ecthr_b', 'scotus', 'eurlex', 'ledgar', 'unfair_tos']:
print(f'{dataset.upper()}')
print('-'*100)
BASE_DIR = f'logs/{dataset}'
score_dicts = {}
MODELS = ['bert-base-uncased', 'roberta-base', 'microsoft/deberta-base', 'nlpaueb/legal-bert-base-uncased',
'zlucia/custom-legalbert', 'allenai/longformer-base-4096', 'google/bigbird-roberta-base']
for model in MODELS:
score_dict = {'time': [], 'epochs': [], 'time/epoch': []}
for seed in range(1, 6):
seed = f'seed_{seed}'
try:
with open(os.path.join(BASE_DIR, model, seed, 'trainer_state.json')) as json_file:
json_data = json.load(json_file)
score_dict['time'].append(json_data['log_history'][-1]['train_runtime'])
score_dict['epochs'].append(json_data['log_history'][-1]['epoch'])
score_dict['time/epoch'].append(json_data['log_history'][-1]['train_runtime']/json_data['log_history'][-1]['epoch'])
except:
continue
score_dicts[model] = score_dict
for algo, stats in score_dicts.items():
total_time = np.mean(stats["time"])
time_epoch = np.mean(stats["time/epoch"])
print(f'{algo:>35}: TRAIN TIME: {str(datetime.timedelta(seconds=total_time)).split(".")[0]}\t '
f'TIME/EPOCH: {str(datetime.timedelta(seconds=time_epoch)).split(".")[0]}\t'
f' EPOCHS: {np.mean(stats["epochs"]):.1f} ± {np.std(stats["epochs"]):.1f}')
if __name__ == '__main__':
main()
================================================
FILE: utils/fix_casehold.py
================================================
import re
import csv
import numpy as np
prompts = []
texts = []
with open('casehold_fixed.csv', "w", encoding="utf-8") as out_f:
with open('casehold.csv', "r", encoding="utf-8") as f:
for line in f.readlines():
# Eliminate broken records
if not re.match('\d', line) or not re.match('.+\d\n$', line):
continue
else:
# Discard samples that are extremely long
if len(line) < 5000:
out_f.write(line)
# Reload cleansed data and count text
with open('casehold_fixed.csv', "r", encoding="utf-8") as f:
data = list(csv.reader(f))[1:]
for idx, sample in enumerate(data):
for choice in sample[2:7]:
texts.append(sample[1] + ' ' + choice)
# Compute approximate length per sample
t_lengths = [len(text.split()) for text in texts]
print(np.mean(t_lengths))
print(np.median(t_lengths))
================================================
FILE: utils/load_hierbert.py
================================================
from models.hierbert import HierarchicalBert
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
MODEL_PATH = '...'
# Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
# Load BERT base model
model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
# Transform BERT base model to Hierarchical BERT
segment_encoder = model.bert
model_encoder = HierarchicalBert(encoder=segment_encoder, max_segments=64, max_segment_length=128)
model.bert = model_encoder
# Load Hierarchical BERT model
model_state_dict = torch.load(f'{MODEL_PATH}/pytorch_model.bin', map_location=torch.device('cpu'))
model.load_state_dict(model_state_dict)
# Pre-process text following the hierarchical 3D pre-processing
# as described either in experiments/ecthr.py, or experiments/scotus.py
inputs = ...
# Inference
soft_predictions = model.predict(inputs)
# Post-process predictions, e.g., sigmoid or argmax
hard_predictions = torch.argmax(soft_predictions)
================================================
FILE: utils/preprocess_unfair_tos.py
================================================
import glob
import json
import re
filenames = glob.glob('/Users/rwg642/Downloads/ToS/sentences/*.txt')
data = {}
total_sentence_count = 0
companies = []
for filename in filenames:
with open(filename) as file:
company = filename.split('/')[-1].split('.')[0]
data[f'{company}'] = []
text = ''
for line in file.readlines():
total_sentence_count += 1
data[f'{company}'].append(
{'company': company, 'release_year': '-', 'labels': [], 'text': line.replace('-lrb-', '(').replace('-rrb-', ')')})
text += line + ' '
matches = re.findall('20[0-2][0-9]', text)
if matches:
date = matches[0]
else:
date = '-'
companies.append((company, date))
print('All sentences: ', total_sentence_count)
annotated_sentences = 0
for label_type, label_name in zip(
['Labels_A', 'Labels_CH', 'Labels_CR', 'Labels_J', 'Labels_LAW', 'Labels_LTD', 'Labels_TER', 'Labels_USE'],
['Arbitration', 'Unilateral change', 'Content removal', 'Jurisdiction', 'Choice of law',
'Limitation of liability', 'Unilateral termination', 'Contract by using']):
filenames = glob.glob(f'/Users/rwg642/Downloads/ToS/{label_type}/*.txt')
sentence_count = 0
for filename in filenames:
gitextract_ybq2rcc9/
├── .gitignore
├── README.md
├── experiments/
│ ├── case_hold.py
│ ├── casehold_helpers.py
│ ├── ecthr.py
│ ├── eurlex.py
│ ├── ledgar.py
│ ├── scotus.py
│ ├── trainer.py
│ └── unfair_tos.py
├── models/
│ ├── deberta.py
│ ├── hierbert.py
│ └── tfidf_svm.py
├── requirements.txt
├── scripts/
│ ├── run_case_hold.sh
│ ├── run_ecthr.sh
│ ├── run_eurlex.sh
│ ├── run_ledgar.sh
│ ├── run_scotus.sh
│ ├── run_tfidf_svm.sh
│ └── run_unfair_tos.sh
├── statistics/
│ ├── compute_avg_lexglue_scores.py
│ ├── compute_avg_scores.py
│ ├── compute_lexglue_scores.py
│ ├── report_model_results.py
│ └── report_train_time.py
└── utils/
├── fix_casehold.py
├── load_hierbert.py
├── preprocess_unfair_tos.py
└── subsample_ledgar.py
SYMBOL INDEX (68 symbols across 16 files)
FILE: experiments/case_hold.py
class ModelArguments (line 37) | class ModelArguments:
class DataTrainingArguments (line 58) | class DataTrainingArguments:
function main (line 104) | def main():
function _mp_fn (line 299) | def _mp_fn(index):
FILE: experiments/casehold_helpers.py
class InputFeatures (line 18) | class InputFeatures:
class Split (line 30) | class Split(Enum):
class MultipleChoiceDataset (line 40) | class MultipleChoiceDataset(Dataset):
method __init__ (line 47) | def __init__(
method __len__ (line 97) | def __len__(self):
method __getitem__ (line 100) | def __getitem__(self, i) -> InputFeatures:
class TFMultipleChoiceDataset (line 107) | class TFMultipleChoiceDataset:
method __init__ (line 114) | def __init__(
method get_dataset (line 173) | def get_dataset(self):
method __len__ (line 178) | def __len__(self):
method __getitem__ (line 181) | def __getitem__(self, i) -> InputFeatures:
function convert_examples_to_features (line 185) | def convert_examples_to_features(
FILE: experiments/ecthr.py
class DataTrainingArguments (line 51) | class DataTrainingArguments:
class ModelArguments (line 123) | class ModelArguments:
function main (line 165) | def main():
FILE: experiments/eurlex.py
class DataTrainingArguments (line 47) | class DataTrainingArguments:
class ModelArguments (line 99) | class ModelArguments:
function main (line 138) | def main():
FILE: experiments/ledgar.py
class DataTrainingArguments (line 47) | class DataTrainingArguments:
class ModelArguments (line 99) | class ModelArguments:
function main (line 138) | def main():
FILE: experiments/scotus.py
class DataTrainingArguments (line 51) | class DataTrainingArguments:
class ModelArguments (line 117) | class ModelArguments:
function main (line 159) | def main():
FILE: experiments/trainer.py
class MultilabelTrainer (line 5) | class MultilabelTrainer(Trainer):
method compute_loss (line 6) | def compute_loss(self, model, inputs, return_outputs=False):
FILE: experiments/unfair_tos.py
class DataTrainingArguments (line 48) | class DataTrainingArguments:
class ModelArguments (line 100) | class ModelArguments:
function main (line 139) | def main():
FILE: models/deberta.py
class ContextPooler (line 8) | class ContextPooler(nn.Module):
method __init__ (line 9) | def __init__(self, config):
method forward (line 15) | def forward(self, hidden_states):
method output_dim (line 26) | def output_dim(self):
class DropoutContext (line 30) | class DropoutContext(object):
method __init__ (line 31) | def __init__(self):
function get_mask (line 38) | def get_mask(input, local_context):
class XDropout (line 57) | class XDropout(torch.autograd.Function):
method forward (line 61) | def forward(ctx, input, local_ctx):
method backward (line 71) | def backward(ctx, grad_output):
class StableDropout (line 79) | class StableDropout(nn.Module):
method __init__ (line 87) | def __init__(self, drop_prob):
method forward (line 93) | def forward(self, x):
method clear_context (line 104) | def clear_context(self):
method init_context (line 108) | def init_context(self, reuse_mask=True, scale=1):
method get_context (line 116) | def get_context(self):
class DebertaForSequenceClassification (line 128) | class DebertaForSequenceClassification(DebertaPreTrainedModel):
method __init__ (line 129) | def __init__(self, config):
method get_input_embeddings (line 144) | def get_input_embeddings(self):
method set_input_embeddings (line 147) | def set_input_embeddings(self, new_embeddings):
method forward (line 150) | def forward(
class DebertaForMultipleChoice (line 216) | class DebertaForMultipleChoice(DebertaPreTrainedModel):
method __init__ (line 217) | def __init__(self, config):
method forward (line 230) | def forward(
FILE: models/hierbert.py
class SimpleOutput (line 11) | class SimpleOutput(ModelOutput):
function sinusoidal_init (line 19) | def sinusoidal_init(num_embeddings: int, embedding_dim: int):
class HierarchicalBert (line 30) | class HierarchicalBert(nn.Module):
method __init__ (line 32) | def __init__(self, encoder, max_segments=64, max_segment_length=128):
method forward (line 55) | def forward(self,
FILE: models/tfidf_svm.py
function main (line 22) | def main():
FILE: statistics/compute_avg_lexglue_scores.py
function main (line 9) | def main():
FILE: statistics/compute_avg_scores.py
function main (line 10) | def main():
FILE: statistics/compute_lexglue_scores.py
function main (line 8) | def main():
FILE: statistics/report_model_results.py
function main (line 6) | def main():
FILE: statistics/report_train_time.py
function main (line 7) | def main():
Condensed preview — 30 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (210K chars).
[
{
"path": ".gitignore",
"chars": 1841,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "README.md",
"chars": 20770,
"preview": "# LexGLUE: A Benchmark Dataset for Legal Language Understanding in English :balance_scale: :trophy: :student: :woman_jud"
},
{
"path": "experiments/case_hold.py",
"chars": 9928,
"preview": "#!/usr/bin/env python\n# coding=utf-8\n\"\"\" Finetuning models on CaseHOLD (e.g. Bert, RoBERTa, LEGAL-BERT).\"\"\"\n\nimport logg"
},
{
"path": "experiments/casehold_helpers.py",
"chars": 7620,
"preview": "import logging\nimport os\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import List, Optional\n\nimpo"
},
{
"path": "experiments/ecthr.py",
"chars": 22420,
"preview": "#!/usr/bin/env python\n# coding=utf-8\n\"\"\" Finetuning models on the ECtHR dataset (e.g. Bert, RoBERTa, LEGAL-BERT).\"\"\"\n\nim"
},
{
"path": "experiments/eurlex.py",
"chars": 15795,
"preview": "#!/usr/bin/env python\n# coding=utf-8\n\"\"\" Finetuning models on EUR-LEX (e.g. Bert, RoBERTa, LEGAL-BERT).\"\"\"\n\nimport loggi"
},
{
"path": "experiments/ledgar.py",
"chars": 15700,
"preview": "#!/usr/bin/env python\n# coding=utf-8\n\"\"\" Finetuning models on LEDGAR (e.g. Bert, RoBERTa, LEGAL-BERT).\"\"\"\n\nimport loggin"
},
{
"path": "experiments/scotus.py",
"chars": 21698,
"preview": "#!/usr/bin/env python\n# coding=utf-8\n\"\"\" Finetuning models on SCOTUS (e.g. Bert, RoBERTa, LEGAL-BERT).\"\"\"\n\nimport loggin"
},
{
"path": "experiments/trainer.py",
"chars": 510,
"preview": "from torch import nn\nfrom transformers import Trainer\n\n\nclass MultilabelTrainer(Trainer):\n def compute_loss(self, mod"
},
{
"path": "experiments/unfair_tos.py",
"chars": 16490,
"preview": "#!/usr/bin/env python\n# coding=utf-8\n\"\"\" Finetuning models on UNFAIR-ToC (e.g. Bert, RoBERTa, LEGAL-BERT).\"\"\"\n\nimport lo"
},
{
"path": "models/deberta.py",
"chars": 10355,
"preview": "import torch\nfrom torch import nn\nfrom transformers import DebertaPreTrainedModel, DebertaModel\nfrom transformers.modeli"
},
{
"path": "models/hierbert.py",
"chars": 6998,
"preview": "from dataclasses import dataclass\nfrom typing import Optional, Tuple\n\nimport torch\nimport numpy as np\nfrom torch import "
},
{
"path": "models/tfidf_svm.py",
"chars": 8007,
"preview": "import pandas\nfrom nltk.corpus import stopwords\nfrom sklearn.feature_extraction.text import TfidfTransformer, CountVecto"
},
{
"path": "requirements.txt",
"chars": 121,
"preview": "torch>=1.9.0\ntransformers>=4.9.0\nscikit-learn>=0.24.1\ntqdm>=4.61.1\nnumpy>=1.20.1\ndatasets>=1.18.1\nnltk>=3.5\nscipy>=1.6.3"
},
{
"path": "scripts/run_case_hold.sh",
"chars": 3358,
"preview": "GPU_NUMBER=0\nMODEL_NAME='bert-base-uncased'\nBATCH_SIZE=8\nACCUMULATION_STEPS=1\nTASK='case_hold'\n\nCUDA_VISIBLE_DEVICES=${G"
},
{
"path": "scripts/run_ecthr.sh",
"chars": 3421,
"preview": "GPU_NUMBER=0\nMODEL_NAME='bert-base-uncased'\nLOWER_CASE='True'\nBATCH_SIZE=2\nACCUMULATION_STEPS=4\nTASK='ecthr_a'\n\nCUDA_VIS"
},
{
"path": "scripts/run_eurlex.sh",
"chars": 3404,
"preview": "GPU_NUMBER=6\nMODEL_NAME='bert-base-uncased'\nLOWER_CASE='True'\nBATCH_SIZE=8\nACCUMULATION_STEPS=1\nTASK='eurlex'\n\nCUDA_VISI"
},
{
"path": "scripts/run_ledgar.sh",
"chars": 3409,
"preview": "GPU_NUMBER=0\nMODEL_NAME='bert-base-uncased'\nLOWER_CASE='True'\nBATCH_SIZE=8\nACCUMULATION_STEPS=1\nTASK='ledgar'\n\nCUDA_VISI"
},
{
"path": "scripts/run_scotus.sh",
"chars": 3410,
"preview": "GPU_NUMBER=0\nMODEL_NAME='bert-base-uncased'\nLOWER_CASE='True'\nBATCH_SIZE=2\nACCUMULATION_STEPS=4\nTASK='scotus'\n\nCUDA_VISI"
},
{
"path": "scripts/run_tfidf_svm.sh",
"chars": 153,
"preview": "DATASET='eurlex'\nTASK_TYPE='multi_label'\nN_CLASSES=100\n\npython models/tfidf_svm.py --dataset ${DATASET} --task_type ${TA"
},
{
"path": "scripts/run_unfair_tos.sh",
"chars": 3434,
"preview": "GPU_NUMBER=0\nMODEL_NAME='bert-base-uncased'\nLOWER_CASE='True'\nBATCH_SIZE=8\nACCUMULATION_STEPS=1\nTASK='unfair_tos'\n\nCUDA_"
},
{
"path": "statistics/compute_avg_lexglue_scores.py",
"chars": 3904,
"preview": "import copy\nimport json\nimport os\nimport argparse\nimport numpy as np\nfrom scipy.stats import hmean, gmean\n\n\ndef main():\n"
},
{
"path": "statistics/compute_avg_scores.py",
"chars": 3311,
"preview": "import copy\nimport json\nimport os\nimport argparse\nimport numpy as np\nimport warnings\n\nwarnings.filterwarnings('ignore')\n"
},
{
"path": "statistics/compute_lexglue_scores.py",
"chars": 3467,
"preview": "import copy\nimport json\nimport os\nimport argparse\nimport numpy as np\n\n\ndef main():\n ''' set default hyperparams in de"
},
{
"path": "statistics/report_model_results.py",
"chars": 1688,
"preview": "import json\nimport os\nimport argparse\n\n\ndef main():\n ''' set default hyperparams in default_hyperparams.py '''\n pa"
},
{
"path": "statistics/report_train_time.py",
"chars": 1775,
"preview": "import json\nimport os\nimport numpy as np\nimport datetime\n\n\ndef main():\n\n for dataset in ['ecthr_a', 'ecthr_b', 'scotu"
},
{
"path": "utils/fix_casehold.py",
"chars": 910,
"preview": "import re\nimport csv\nimport numpy as np\nprompts = []\ntexts = []\n\nwith open('casehold_fixed.csv', \"w\", encoding=\"utf-8\") "
},
{
"path": "utils/load_hierbert.py",
"chars": 1005,
"preview": "from models.hierbert import HierarchicalBert\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification\n"
},
{
"path": "utils/preprocess_unfair_tos.py",
"chars": 4511,
"preview": "import glob\nimport json\nimport re\n\nfilenames = glob.glob('/Users/rwg642/Downloads/ToS/sentences/*.txt')\n\ndata = {}\ntotal"
},
{
"path": "utils/subsample_ledgar.py",
"chars": 2282,
"preview": "import json\nimport random\nimport tqdm\nfrom collections import Counter\n\n# NOTE: The dataset has been first enriched with "
}
]
About this extraction
This page contains the full source code of the coastalcph/lex-glue GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 30 files (197.0 KB), approximately 48.3k tokens, and a symbol index with 68 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.