Repository: heartexlabs/label-studio-transformers
Branch: master
Commit: de267cbd903e
Files: 8
Total size: 51.2 KB
Directory structure:
gitextract_m_a1r8n7/
├── .gitignore
├── LICENSE
├── README.md
├── models/
│ ├── __init__.py
│ ├── bert_classifier.py
│ ├── ner.py
│ └── utils.py
└── requirements.txt
================================================
FILE CONTENTS
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================================================
FILE: .gitignore
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FILE: LICENSE
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================================================
FILE: README.md
================================================
# Label Studio for Hugging Face's Transformers
[Website](https://labelstud.io/) • [Docs](https://labelstud.io/guide) • [Twitter](https://twitter.com/heartexlabs) • [Join Slack Community 
](https://slack.labelstud.io/?source=github-1)
**Transfer learning for NLP models by annotating your textual data without any additional coding.**
This package provides a ready-to-use container that links together:
- [Label Studio](https://github.com/heartexlabs/label-studio) as annotation frontend
- [Hugging Face's transformers](https://github.com/huggingface/transformers) as machine learning backend for NLP
[
](https://github.com/heartexlabs/label-studio-transformers)
### Quick Usage
#### Install Label Studio and other dependencies
```bash
pip install -r requirements.txt
```
##### Create ML backend with BERT classifier
```bash
label-studio-ml init my-ml-backend --script models/bert_classifier.py
cp models/utils.py my-ml-backend/utils.py
# Start ML backend at http://localhost:9090
label-studio-ml start my-ml-backend
# Start Label Studio in the new terminal with the same python environment
label-studio start
```
1. Create a project with `Choices` and `Text` tags in the labeling config.
2. Connect the ML backend in the Project settings with `http://localhost:9090`
##### Create ML backend with BERT named entity recognizer
```bash
label-studio-ml init my-ml-backend --script models/ner.py
cp models/utils.py my-ml-backend/utils.py
# Start ML backend at http://localhost:9090
label-studio-ml start my-ml-backend
# Start Label Studio in the new terminal with the same python environment
label-studio start
```
1. Create a project with `Labels` and `Text` tags in the labeling config.
2. Connect the ML backend in the Project settings with `http://localhost:9090`
#### Training and inference
The browser opens at `http://localhost:8080`. Upload your data on **Import** page then annotate by selecting **Labeling** page.
Once you've annotate sufficient amount of data, go to **Model** page and press **Start Training** button. Once training is finished, model automatically starts serving for inference from Label Studio, and you'll find all model checkpoints inside `my-ml-backend//` directory.
[Click here](https://labelstud.io/guide/ml.html) to read more about how to use Machine Learning backend and build Human-in-the-Loop pipelines with Label Studio
## License
This software is licensed under the [Apache 2.0 LICENSE](/LICENSE) © [Heartex](https://www.heartex.com/). 2020
================================================
FILE: models/__init__.py
================================================
================================================
FILE: models/bert_classifier.py
================================================
import torch
import numpy as np
import os
from torch.utils.data import SequentialSampler
from tqdm import tqdm, trange
from collections import deque
from tensorboardX import SummaryWriter
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import AdamW, get_linear_schedule_with_warmup
from torch.utils.data import TensorDataset, DataLoader, RandomSampler
from label_studio_ml.model import LabelStudioMLBase
from utils import prepare_texts, calc_slope
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
class BertClassifier(LabelStudioMLBase):
def __init__(
self, pretrained_model='bert-base-multilingual-cased', maxlen=64,
batch_size=32, num_epochs=100, logging_steps=1, train_logs=None, **kwargs
):
super(BertClassifier, self).__init__(**kwargs)
self.pretrained_model = pretrained_model
self.maxlen = maxlen
self.batch_size = batch_size
self.num_epochs = num_epochs
self.logging_steps = logging_steps
self.train_logs = train_logs
# then collect all keys from config which will be used to extract data from task and to form prediction
# Parsed label config contains only one output of type
assert len(self.parsed_label_config) == 1
self.from_name, self.info = list(self.parsed_label_config.items())[0]
assert self.info['type'] == 'Choices'
# the model has only one textual input
assert len(self.info['to_name']) == 1
assert len(self.info['inputs']) == 1
assert self.info['inputs'][0]['type'] == 'Text'
self.to_name = self.info['to_name'][0]
self.value = self.info['inputs'][0]['value']
if not self.train_output:
self.labels = self.info['labels']
self.reset_model('bert-base-multilingual-cased', cache_dir=None, device='cpu')
print('Initialized with from_name={from_name}, to_name={to_name}, labels={labels}'.format(
from_name=self.from_name, to_name=self.to_name, labels=str(self.labels)
))
else:
self.load(self.train_output)
print('Loaded from train output with from_name={from_name}, to_name={to_name}, labels={labels}'.format(
from_name=self.from_name, to_name=self.to_name, labels=str(self.labels)
))
def reset_model(self, pretrained_model, cache_dir, device):
model = BertForSequenceClassification.from_pretrained(
pretrained_model,
num_labels=len(self.labels),
output_attentions=False,
output_hidden_states=False,
cache_dir=cache_dir
)
model.to(device)
return model
def load(self, train_output):
pretrained_model = train_output['model_path']
self.tokenizer = BertTokenizer.from_pretrained(pretrained_model)
self.model = BertForSequenceClassification.from_pretrained(pretrained_model)
self.model.to(device)
self.model.eval()
self.batch_size = train_output['batch_size']
self.labels = train_output['labels']
self.maxlen = train_output['maxlen']
@property
def not_trained(self):
return not hasattr(self, 'tokenizer')
def predict(self, tasks, **kwargs):
if self.not_trained:
print('Can\'t get prediction because model is not trained yet.')
return []
texts = [task['data'][self.value] for task in tasks]
predict_dataloader = prepare_texts(texts, self.tokenizer, self.maxlen, SequentialSampler, self.batch_size)
pred_labels, pred_scores = [], []
for batch in predict_dataloader:
batch = tuple(t.to(device) for t in batch)
inputs = {
'input_ids': batch[0],
'attention_mask': batch[1]
}
with torch.no_grad():
outputs = self.model(**inputs)
logits = outputs[0]
batch_preds = logits.detach().cpu().numpy()
argmax_batch_preds = np.argmax(batch_preds, axis=-1)
pred_labels.extend(str(self.labels[i]) for i in argmax_batch_preds)
max_batch_preds = np.max(batch_preds, axis=-1)
pred_scores.extend(float(s) for s in max_batch_preds)
predictions = []
for predicted_label, score in zip(pred_labels, pred_scores):
result = [{
'from_name': self.from_name,
'to_name': self.to_name,
'type': 'choices',
'value': {'choices': [predicted_label]}
}]
predictions.append({'result': result, 'score': score})
return predictions
def fit(self, completions, workdir=None, cache_dir=None, **kwargs):
input_texts = []
output_labels, output_labels_idx = [], []
label2idx = {l: i for i, l in enumerate(self.labels)}
for completion in completions:
# get input text from task data
if completion['annotations'][0].get('skipped'):
continue
input_text = completion['data'][self.value]
input_texts.append(input_text)
# get an annotation
output_label = completion['annotations'][0]['result'][0]['value']['choices'][0]
output_labels.append(output_label)
output_label_idx = label2idx[output_label]
output_labels_idx.append(output_label_idx)
new_labels = set(output_labels)
added_labels = new_labels - set(self.labels)
if len(added_labels) > 0:
print('Label set has been changed. Added ones: ' + str(list(added_labels)))
self.labels = list(sorted(new_labels))
label2idx = {l: i for i, l in enumerate(self.labels)}
output_labels_idx = [label2idx[label] for label in output_labels]
tokenizer = BertTokenizer.from_pretrained(self.pretrained_model, cache_dir=cache_dir)
train_dataloader = prepare_texts(input_texts, tokenizer, self.maxlen, RandomSampler, self.batch_size, output_labels_idx)
model = self.reset_model(self.pretrained_model, cache_dir, device)
total_steps = len(train_dataloader) * self.num_epochs
optimizer = AdamW(model.parameters(), lr=2e-5, eps=1e-8)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps)
global_step = 0
total_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(self.num_epochs, desc='Epoch')
if self.train_logs:
tb_writer = SummaryWriter(logdir=os.path.join(self.train_logs, os.path.basename(self.output_dir)))
else:
tb_writer = None
loss_queue = deque(maxlen=10)
for epoch in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc='Iteration')
for step, batch in enumerate(epoch_iterator):
model.train()
batch = tuple(t.to(device) for t in batch)
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'labels': batch[2]}
outputs = model(**inputs)
loss = outputs[0]
loss.backward()
total_loss += loss.item()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
scheduler.step()
model.zero_grad()
global_step += 1
if global_step % self.logging_steps == 0:
last_loss = (total_loss - logging_loss) / self.logging_steps
loss_queue.append(last_loss)
if tb_writer:
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', last_loss, global_step)
logging_loss = total_loss
# slope-based early stopping
if len(loss_queue) == loss_queue.maxlen:
slope = calc_slope(loss_queue)
if tb_writer:
tb_writer.add_scalar('slope', slope, global_step)
if abs(slope) < 1e-2:
break
if tb_writer:
tb_writer.close()
model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training # noqa
model_to_save.save_pretrained(workdir)
tokenizer.save_pretrained(workdir)
return {
'model_path': workdir,
'batch_size': self.batch_size,
'maxlen': self.maxlen,
'pretrained_model': self.pretrained_model,
'labels': self.labels
}
================================================
FILE: models/ner.py
================================================
import torch
import numpy as np
import re
import os
import io
import logging
from functools import partial
from itertools import groupby
from operator import itemgetter
from torch.nn import CrossEntropyLoss
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm, trange
from tensorboardX import SummaryWriter
from collections import deque
from transformers import (
BertTokenizer, BertForTokenClassification, BertConfig,
RobertaConfig, RobertaForTokenClassification, RobertaTokenizer,
DistilBertConfig, DistilBertForTokenClassification, DistilBertTokenizer,
CamembertConfig, CamembertForTokenClassification, CamembertTokenizer,
AutoConfig, AutoModelForTokenClassification, AutoTokenizer
)
from transformers import AdamW, get_linear_schedule_with_warmup
from label_studio_ml.model import LabelStudioMLBase
from utils import calc_slope
logger = logging.getLogger(__name__)
ALL_MODELS = sum(
[list(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, RobertaConfig, DistilBertConfig)],
[])
MODEL_CLASSES = {
'bert': (BertConfig, BertForTokenClassification, BertTokenizer),
'roberta': (RobertaConfig, RobertaForTokenClassification, RobertaTokenizer),
'distilbert': (DistilBertConfig, DistilBertForTokenClassification, DistilBertTokenizer),
'camembert': (CamembertConfig, CamembertForTokenClassification, CamembertTokenizer),
}
class SpanLabeledTextDataset(Dataset):
def __init__(
self, list_of_strings, list_of_spans=None, tokenizer=None, tag_idx_map=None,
cls_token='[CLS]', sep_token='[SEP]', pad_token_label_id=-1, max_seq_length=128, sep_token_extra=False,
cls_token_at_end=False, sequence_a_segment_id=0, cls_token_segment_id=1, mask_padding_with_zero=True
):
self.list_of_strings = list_of_strings
self.list_of_spans = list_of_spans or [[] * len(list_of_strings)]
self.tokenizer = tokenizer
self.cls_token = cls_token
self.sep_token = sep_token
self.pad_token_label_id = pad_token_label_id
self.max_seq_length = max_seq_length
self.sep_token_extra = sep_token_extra
self.cls_token_at_end = cls_token_at_end
self.sequence_a_segment_id = sequence_a_segment_id
self.cls_token_segment_id = cls_token_segment_id
self.mask_padding_with_zero = mask_padding_with_zero
(self.original_list_of_tokens, self.original_list_of_tags, tag_idx_map_,
original_list_of_tokens_start_map) = self._prepare_data()
if tag_idx_map is None:
self.tag_idx_map = tag_idx_map_
else:
self.tag_idx_map = tag_idx_map
(self.list_of_tokens, self.list_of_token_ids, self.list_of_labels, self.list_of_label_ids,
self.list_of_segment_ids, self.list_of_token_start_map) = [], [], [], [], [], []
for original_tokens, original_tags, original_token_start_map in zip(
self.original_list_of_tokens,
self.original_list_of_tags,
original_list_of_tokens_start_map
):
tokens, token_ids, labels, label_ids, segment_ids, token_start_map = self._convert_to_features(
original_tokens, original_tags, self.tag_idx_map, original_token_start_map)
self.list_of_token_ids.append(token_ids)
self.list_of_tokens.append(tokens)
self.list_of_labels.append(labels)
self.list_of_segment_ids.append(segment_ids)
self.list_of_label_ids.append(label_ids)
self.list_of_token_start_map.append(token_start_map)
def get_params_dict(self):
return {
'cls_token': self.cls_token,
'sep_token': self.sep_token,
'pad_token_label_id': self.pad_token_label_id,
'max_seq_length': self.max_seq_length,
'sep_token_extra': self.sep_token_extra,
'cls_token_at_end': self.cls_token_at_end,
'sequence_a_segment_id': self.sequence_a_segment_id,
'cls_token_segment_id': self.cls_token_segment_id,
'mask_padding_with_zero': self.mask_padding_with_zero
}
def dump(self, output_file):
with io.open(output_file, mode='w') as f:
for tokens, labels in zip(self.list_of_tokens, self.list_of_labels):
for token, label in zip(tokens, labels):
f.write(f'{token} {label}\n')
f.write('\n')
def _convert_to_features(self, words, labels, label_map, list_token_start_map):
tokens, out_labels, label_ids, tokens_idx_map = [], [], [], []
for i, (word, label, token_start) in enumerate(zip(words, labels, list_token_start_map)):
word_tokens = self.tokenizer.tokenize(word)
tokens.extend(word_tokens)
tokens_idx_map.extend([token_start] * len(word_tokens))
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
label_ids.extend([label_map[label]] + [self.pad_token_label_id] * (len(word_tokens) - 1))
out_labels.extend([label] + ['X'] * (len(word_tokens) - 1))
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
special_tokens_count = 3 if self.sep_token_extra else 2
if len(tokens) > self.max_seq_length - special_tokens_count:
tokens = tokens[:(self.max_seq_length - special_tokens_count)]
label_ids = label_ids[:(self.max_seq_length - special_tokens_count)]
out_labels = out_labels[:(self.max_seq_length - special_tokens_count)]
tokens_idx_map = tokens_idx_map[:(self.max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens += [self.sep_token]
label_ids += [self.pad_token_label_id]
out_labels += ['X']
tokens_idx_map += [-1]
if self.sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [self.sep_token]
label_ids += [self.pad_token_label_id]
out_labels += ['X']
tokens_idx_map += [-1]
segment_ids = [self.sequence_a_segment_id] * len(tokens)
if self.cls_token_at_end:
tokens += [self.cls_token]
label_ids += [self.pad_token_label_id]
out_labels += ['X']
segment_ids += [self.cls_token_segment_id]
tokens_idx_map += [-1]
else:
tokens = [self.cls_token] + tokens
label_ids = [self.pad_token_label_id] + label_ids
out_labels = ['X'] + out_labels
segment_ids = [self.cls_token_segment_id] + segment_ids
tokens_idx_map = [-1] + tokens_idx_map
token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
return tokens, token_ids, out_labels, label_ids, segment_ids, tokens_idx_map
def _apply_tokenizer(self, original_tokens, original_tags):
out_tokens, out_tags, out_maps = [], [], []
for i, (original_token, original_tag) in enumerate(zip(original_tokens, original_tags)):
tokens = self.tokenizer.tokenize(original_token)
out_tokens.extend(tokens)
out_maps.extend([i] * len(tokens))
start_tag = original_tag.startswith('B-')
for j in range(len(tokens)):
if (j == 0 and start_tag) or original_tag == 'O':
out_tags.append(original_tag)
else:
out_tags.append(f'I-{original_tag[2:]}')
return out_tokens, out_tags, out_maps
def _prepare_data(self):
list_of_tokens, list_of_tags, list_of_token_idx_maps = [], [], []
tag_idx_map = {'O': 0}
for text, spans in zip(self.list_of_strings, self.list_of_spans):
if not text:
continue
tokens = []
start = 0
for t in text.split():
tokens.append((t, start))
start += len(t) + 1
if spans:
spans = list(sorted(spans, key=itemgetter('start')))
span = spans.pop(0)
prefix = 'B-'
tags = []
for token, token_start in tokens:
token_end = token_start + len(token) - 1
# token precedes current span
if not span or token_end < span['start']:
tags.append('O')
continue
# token jumps over the span (it could happens
# when prev label ends with whitespaces, e.g. "cat " "too" or span created for whitespace)
if token_start > span['end']:
prefix = 'B-'
no_more_spans = False
while token_start > span['end']:
if not len(spans):
no_more_spans = True
break
span = spans.pop(0)
if no_more_spans:
tags.append('O')
span = None
continue
if token_end < span['start']:
tags.append('O')
continue
label = span['label']
if label.startswith(prefix):
tag = label
else:
tag = f'{prefix}{label}'
tags.append(tag)
if tag not in tag_idx_map:
tag_idx_map[tag] = len(tag_idx_map)
if span['end'] > token_end:
prefix = 'I-'
elif len(spans):
span = spans.pop(0)
prefix = 'B-'
else:
span = None
else:
tags = ['O'] * len(tokens)
list_of_tokens.append([t[0] for t in tokens])
list_of_token_idx_maps.append([t[1] for t in tokens])
list_of_tags.append(tags)
return list_of_tokens, list_of_tags, tag_idx_map, list_of_token_idx_maps
def __len__(self):
return len(self.list_of_token_ids)
def __getitem__(self, idx):
return {
'tokens': self.list_of_token_ids[idx],
'labels': self.list_of_label_ids[idx],
'segments': self.list_of_segment_ids[idx],
'token_start_map': self.list_of_token_start_map[idx],
'string': self.list_of_strings[idx]
}
@property
def num_labels(self):
return len(self.tag_idx_map)
@classmethod
def pad_sequences(cls, batch, mask_padding_with_zero, pad_on_left, pad_token, pad_token_segment_id, pad_token_label_id):
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
max_seq_length = max(map(len, (sample['tokens'] for sample in batch)))
batch_input_ids, batch_label_ids, batch_segment_ids, batch_input_mask, batch_token_start_map = [], [], [], [], []
batch_strings = []
for sample in batch:
input_ids = sample['tokens']
label_ids = sample['labels']
segment_ids = sample['segments']
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
padding_length = max_seq_length - len(input_ids)
if pad_on_left:
input_ids = ([pad_token] * padding_length) + input_ids
input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
label_ids = ([pad_token_label_id] * padding_length) + label_ids
else:
input_ids += ([pad_token] * padding_length)
input_mask += ([0 if mask_padding_with_zero else 1] * padding_length)
segment_ids += ([pad_token_segment_id] * padding_length)
label_ids += ([pad_token_label_id] * padding_length)
batch_input_ids.append(input_ids)
batch_label_ids.append(label_ids)
batch_segment_ids.append(segment_ids)
batch_input_mask.append(input_mask)
batch_token_start_map.append(sample['token_start_map'])
batch_strings.append(sample['string'])
return {
'input_ids': torch.tensor(batch_input_ids, dtype=torch.long),
'label_ids': torch.tensor(batch_label_ids, dtype=torch.long),
'segment_ids': torch.tensor(batch_segment_ids, dtype=torch.long),
'input_mask': torch.tensor(batch_input_mask, dtype=torch.long),
'token_start_map': batch_token_start_map,
'strings': batch_strings
}
@classmethod
def get_padding_function(cls, model_type, tokenizer, pad_token_label_id):
return partial(
cls.pad_sequences,
mask_padding_with_zero=True,
pad_on_left=model_type in ['xlnet'],
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
pad_token_segment_id=4 if model_type in ['xlnet'] else 0,
pad_token_label_id=pad_token_label_id
)
class TransformersBasedTagger(LabelStudioMLBase):
def __init__(self, **kwargs):
super(TransformersBasedTagger, self).__init__(**kwargs)
assert len(self.parsed_label_config) == 1
self.from_name, self.info = list(self.parsed_label_config.items())[0]
assert self.info['type'] == 'Labels'
# the model has only one textual input
assert len(self.info['to_name']) == 1
assert len(self.info['inputs']) == 1
assert self.info['inputs'][0]['type'] == 'Text'
self.to_name = self.info['to_name'][0]
self.value = self.info['inputs'][0]['value']
if not self.train_output:
self.labels = self.info['labels']
else:
self.load(self.train_output)
def load(self, train_output):
pretrained_model = train_output['model_path']
self._model_type = train_output['model_type']
_, model_class, tokenizer_class = MODEL_CLASSES[train_output['model_type']]
self._tokenizer = AutoTokenizer.from_pretrained(pretrained_model)
self._model = AutoModelForTokenClassification.from_pretrained(pretrained_model)
self._batch_size = train_output['batch_size']
self._pad_token = self._tokenizer.convert_tokens_to_ids([self._tokenizer.pad_token])[0]
self._pad_token_label_id = train_output['pad_token_label_id']
self._label_map = train_output['label_map']
self._mask_padding_with_zero = True
self._dataset_params_dict = train_output['dataset_params_dict']
self._batch_padding = SpanLabeledTextDataset.get_padding_function(
self._model_type, self._tokenizer, self._pad_token_label_id)
def predict(self, tasks, **kwargs):
texts = [task['data'][self.value] for task in tasks]
predict_set = SpanLabeledTextDataset(texts, tokenizer=self._tokenizer, **self._dataset_params_dict)
from_name = self.from_name
to_name = self.to_name
predict_loader = DataLoader(
dataset=predict_set,
batch_size=self._batch_size,
collate_fn=self._batch_padding
)
results = []
for batch in tqdm(predict_loader, desc='Prediction'):
inputs = {
'input_ids': batch['input_ids'],
'attention_mask': batch['input_mask'],
'token_type_ids': batch['segment_ids']
}
if self._model_type == 'distilbert':
inputs.pop('token_type_ids')
with torch.no_grad():
model_output = self._model(**inputs)
logits = model_output[0]
batch_preds = logits.detach().cpu().numpy()
argmax_batch_preds = np.argmax(batch_preds, axis=-1)
max_batch_preds = np.max(batch_preds, axis=-1)
input_mask = batch['input_mask'].detach().cpu().numpy()
batch_token_start_map = batch['token_start_map']
batch_strings = batch['strings']
for max_preds, argmax_preds, mask_tokens, token_start_map, string in zip(
max_batch_preds, argmax_batch_preds, input_mask, batch_token_start_map, batch_strings
):
preds, scores, starts = [], [], []
for max_pred, argmax_pred, mask_token, token_start in zip(max_preds, argmax_preds, mask_tokens, token_start_map):
if token_start != -1:
preds.append(self._label_map[str(argmax_pred)])
scores.append(max_pred)
starts.append(token_start)
mean_score = np.mean(scores) if len(scores) > 0 else 0
result = []
for label, group in groupby(zip(preds, starts, scores), key=lambda i: re.sub('^(B-|I-)', '', i[0])):
_, group_start, _ = list(group)[0]
if len(result) > 0:
if group_start == 0:
result.pop(-1)
else:
result[-1]['value']['end'] = group_start - 1
if label != 'O':
result.append({
'from_name': from_name,
'to_name': to_name,
'type': 'labels',
'value': {
'labels': [label],
'start': group_start,
'end': None,
'text': '...'
}
})
if result and result[-1]['value']['end'] is None:
result[-1]['value']['end'] = len(string)
results.append({
'result': result,
'score': float(mean_score),
'cluster': None
})
return results
def get_spans(self, completion):
spans = []
for r in completion['result']:
if r['from_name'] == self.from_name and r['to_name'] == self.to_name:
labels = r['value'].get('labels')
if not isinstance(labels, list) or len(labels) == 0:
logger.warning(f'Error while parsing {r}: list type expected for "labels"')
continue
label = labels[0]
start, end = r['value'].get('start'), r['value'].get('end')
if start is None or end is None:
logger.warning(f'Error while parsing {r}: "labels" should contain "start" and "end" fields')
spans.append({
'label': label,
'start': start,
'end': end
})
return spans
def fit(
self, completions, workdir=None, model_type='bert', pretrained_model='bert-base-uncased',
batch_size=32, learning_rate=5e-5, adam_epsilon=1e-8, num_train_epochs=100, weight_decay=0.0, logging_steps=1,
warmup_steps=0, save_steps=50, dump_dataset=True, cache_dir='~/.heartex/cache', train_logs=None,
**kwargs
):
train_logs = train_logs or os.path.join(workdir, 'train_logs')
os.makedirs(train_logs, exist_ok=True)
logger.debug('Prepare models')
cache_dir = os.path.expanduser(cache_dir)
os.makedirs(cache_dir, exist_ok=True)
model_type = model_type.lower()
# assert model_type in MODEL_CLASSES.keys(), f'Input model type {model_type} not in {MODEL_CLASSES.keys()}'
# assert pretrained_model in ALL_MODELS, f'Pretrained model {pretrained_model} not in {ALL_MODELS}'
tokenizer = AutoTokenizer.from_pretrained(pretrained_model, cache_dir=cache_dir)
logger.debug('Read data')
# read input data stream
texts, list_of_spans = [], []
for item in completions:
texts.append(item['data'][self.value])
list_of_spans.append(self.get_spans(item['annotations'][0]))
logger.debug('Prepare dataset')
pad_token_label_id = CrossEntropyLoss().ignore_index
train_set = SpanLabeledTextDataset(
texts, list_of_spans, tokenizer,
cls_token_at_end=model_type in ['xlnet'],
cls_token_segment_id=2 if model_type in ['xlnet'] else 0,
sep_token_extra=model_type in ['roberta'],
pad_token_label_id=pad_token_label_id
)
if dump_dataset:
dataset_file = os.path.join(workdir, 'train_set.txt')
train_set.dump(dataset_file)
# config = config_class.from_pretrained(pretrained_model, num_labels=train_set.num_labels, cache_dir=cache_dir)
config = AutoConfig.from_pretrained(pretrained_model, num_labels=train_set.num_labels, cache_dir=cache_dir)
# model = model_class.from_pretrained(pretrained_model, config=config, cache_dir=cache_dir)
model = AutoModelForTokenClassification.from_pretrained(pretrained_model, config=config, cache_dir=cache_dir)
batch_padding = SpanLabeledTextDataset.get_padding_function(model_type, tokenizer, pad_token_label_id)
train_loader = DataLoader(
dataset=train_set,
batch_size=batch_size,
shuffle=True,
collate_fn=batch_padding
)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': weight_decay},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
num_training_steps = len(train_loader) * num_train_epochs
optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate, eps=adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=warmup_steps, num_training_steps=num_training_steps)
tr_loss, logging_loss = 0, 0
global_step = 0
if train_logs:
tb_writer = SummaryWriter(logdir=os.path.join(train_logs, os.path.basename(workdir)))
epoch_iterator = trange(num_train_epochs, desc='Epoch')
loss_queue = deque(maxlen=10)
for _ in epoch_iterator:
batch_iterator = tqdm(train_loader, desc='Batch')
for step, batch in enumerate(batch_iterator):
model.train()
inputs = {
'input_ids': batch['input_ids'],
'attention_mask': batch['input_mask'],
'labels': batch['label_ids'],
'token_type_ids': batch['segment_ids']
}
if model_type == 'distilbert':
inputs.pop('token_type_ids')
model_output = model(**inputs)
loss = model_output[0]
loss.backward()
tr_loss += loss.item()
optimizer.step()
scheduler.step()
model.zero_grad()
global_step += 1
if global_step % logging_steps == 0:
last_loss = (tr_loss - logging_loss) / logging_steps
loss_queue.append(last_loss)
if train_logs:
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', last_loss, global_step)
logging_loss = tr_loss
# slope-based early stopping
if len(loss_queue) == loss_queue.maxlen:
slope = calc_slope(loss_queue)
if train_logs:
tb_writer.add_scalar('slope', slope, global_step)
if abs(slope) < 1e-2:
break
if train_logs:
tb_writer.close()
model_to_save = model.module if hasattr(model, "module") else model # Take care of distributed/parallel training
model_to_save.save_pretrained(workdir)
tokenizer.save_pretrained(workdir)
label_map = {i: t for t, i in train_set.tag_idx_map.items()}
return {
'model_path': workdir,
'batch_size': batch_size,
'pad_token_label_id': pad_token_label_id,
'dataset_params_dict': train_set.get_params_dict(),
'model_type': model_type,
'pretrained_model': pretrained_model,
'label_map': label_map
}
================================================
FILE: models/utils.py
================================================
import torch
import numpy as np
from torch.utils.data import TensorDataset, DataLoader
def pad_sequences(input_ids, maxlen):
padded_ids = []
for ids in input_ids:
nonpad = min(len(ids), maxlen)
pids = [ids[i] for i in range(nonpad)]
for i in range(nonpad, maxlen):
pids.append(0)
padded_ids.append(pids)
return padded_ids
def prepare_texts(texts, tokenizer, maxlen, sampler_class, batch_size, choices_ids=None):
# create input token indices
input_ids = []
for text in texts:
input_ids.append(tokenizer.encode(text, add_special_tokens=True))
# input_ids = pad_sequences(input_ids, maxlen=maxlen, dtype='long', value=0, truncating='post', padding='post')
input_ids = pad_sequences(input_ids, maxlen)
# Create attention masks
attention_masks = []
for sent in input_ids:
attention_masks.append([int(token_id > 0) for token_id in sent])
if choices_ids is not None:
dataset = TensorDataset(torch.tensor(input_ids, dtype=torch.long), torch.tensor(attention_masks, dtype=torch.long), torch.tensor(choices_ids, dtype=torch.long))
else:
dataset = TensorDataset(torch.tensor(input_ids, dtype=torch.long), torch.tensor(attention_masks, dtype=torch.long))
sampler = sampler_class(dataset)
dataloader = DataLoader(dataset, sampler=sampler, batch_size=batch_size)
return dataloader
def calc_slope(y):
n = len(y)
if n == 1:
raise ValueError('Can\'t compute slope for array of length=1')
x_mean = (n + 1) / 2
x2_mean = (n + 1) * (2 * n + 1) / 6
xy_mean = np.average(y, weights=np.arange(1, n + 1))
y_mean = np.mean(y)
slope = (xy_mean - x_mean * y_mean) / (x2_mean - x_mean * x_mean)
return slope
================================================
FILE: requirements.txt
================================================
torch==1.13.1
transformers==4.4.2
tensorboardX==1.9
label-studio>=1.0.0
git+git://github.com/heartexlabs/label-studio-ml-backend@master#egg=label-studio-ml