├── .gitignore
├── LICENSE
├── README.md
├── download_data.bash
├── download_model.bash
├── img
└── talkdown.png
├── requirements.txt
└── src
├── bert.py
└── utils.py
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | *.egg-info/
24 | .installed.cfg
25 | *.egg
26 | MANIFEST
27 |
28 | # PyInstaller
29 | # Usually these files are written by a python script from a template
30 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
31 | *.manifest
32 | *.spec
33 |
34 | # Installer logs
35 | pip-log.txt
36 | pip-delete-this-directory.txt
37 |
38 | # Unit test / coverage reports
39 | htmlcov/
40 | .tox/
41 | .coverage
42 | .coverage.*
43 | .cache
44 | nosetests.xml
45 | coverage.xml
46 | *.cover
47 | .hypothesis/
48 | .pytest_cache/
49 |
50 | # Translations
51 | *.mo
52 | *.pot
53 |
54 | # Django stuff:
55 | *.log
56 | local_settings.py
57 | db.sqlite3
58 |
59 | # Flask stuff:
60 | instance/
61 | .webassets-cache
62 |
63 | # Scrapy stuff:
64 | .scrapy
65 |
66 | # Sphinx documentation
67 | docs/_build/
68 |
69 | # PyBuilder
70 | target/
71 |
72 | # Jupyter Notebook
73 | .ipynb_checkpoints
74 |
75 | # pyenv
76 | .python-version
77 |
78 | # celery beat schedule file
79 | celerybeat-schedule
80 |
81 | # SageMath parsed files
82 | *.sage.py
83 |
84 | # Environments
85 | .env
86 | .venv
87 | env/
88 | venv/
89 | ENV/
90 | env.bak/
91 | venv.bak/
92 |
93 | # Spyder project settings
94 | .spyderproject
95 | .spyproject
96 |
97 | # Rope project settings
98 | .ropeproject
99 |
100 | # mkdocs documentation
101 | /site
102 |
103 | # mypy
104 | .mypy_cache/
105 |
106 | .DS_Store
107 | data
108 | runs
109 | .idea
110 | models
111 |
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | #
TalkDown: A Corpus for Condescension Detection in Context
2 |
3 |
4 | ## Introduction
5 |
6 | This is the code release for the paper [TalkDown: A Corpus for Condescension Detection in Context](https://www.aclweb.org/anthology/D19-1385) by Zijian Wang and Christopher Potts in proceedings of EMNLP-IJCNLP 2019.
7 |
8 | ## Dependencies
9 | ### Python dependencies
10 | Run `pip install -r requirements.txt`. This codebase requires Python version >= 3.6.
11 | ### Data
12 | Run `bash download_data.bash` to download and uncompress the TalkDown dataset. Or you could use this [link](https://nlp.stanford.edu/~zijwang/talkdown/talkdown.tar.gz).
13 | ### Pretrained model (optional)
14 | Run `bash download_model.bash` to download our best pretrained model to reproduce the result. It is not required if you want to train your model from scratch.
15 |
16 |
17 | ## Sample commands for training and evaluation
18 |
19 | ### Train
20 | You could train a BERT model using the following command.
21 | ```
22 | python -m src.bert --do_train --use_quoted --use_context --output_dir test
23 | ```
24 | ### Evaluate
25 | You could evaluate your model using the following command. This command also reproduces our best result in the paper (make sure you have downloaded the pretrained model).
26 | ```
27 | python -m src.bert --do_eval --use_quoted --use_context --eval_on_test --output_dir pretrained_full
28 | ```
29 | which should return `Model's F1 is 0.6835111677776263`
30 | ## Citation
31 |
32 | @inproceedings{wang2019talkdown,
33 | author = {Wang, Zijian and Potts, Christopher}
34 | title = {{TalkDown}: A Corpus for Condescension Detection in Context},
35 | booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing},
36 | url = {https://www.aclweb.org/anthology/D19-1385},
37 | year = {2019}
38 | }
39 |
40 | ## Contact
41 |
42 | You may reach out us at zijwang@stanford.edu and cgpotts@stanford.edu.
43 |
--------------------------------------------------------------------------------
/download_data.bash:
--------------------------------------------------------------------------------
1 | curl https://nlp.stanford.edu/~zijwang/talkdown/talkdown.tar.gz -o talkdown.tar.gz
2 | mkdir -p data
3 | tar xzf talkdown.tar.gz -C data/
4 | rm talkdown.tar.gz
5 | echo 'Done!'
6 |
--------------------------------------------------------------------------------
/download_model.bash:
--------------------------------------------------------------------------------
1 | curl https://nlp.stanford.edu/~zijwang/talkdown/pretrained_full.tar.gz -o pretrained_full.tar.gz
2 | tar xzf pretrained_full.tar.gz
3 | rm pretrained_full.tar.gz
4 | echo 'Done!'
5 |
--------------------------------------------------------------------------------
/img/talkdown.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zijwang/talkdown/85c44e779273a410f0b8700ac17b0f80272966f1/img/talkdown.png
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | torch>=1.1.0
2 | pytorch_transformers>=1.1.0
3 | imbalanced-learn==0.4.3
4 | pandas
5 | numpy
6 | gputil
7 | tensorboardX
8 | tqdm
9 | scikit-learn
--------------------------------------------------------------------------------
/src/bert.py:
--------------------------------------------------------------------------------
1 | # author: Zijian Wang
2 | # many pieces of code were adapted from `pytorch_transformer` repo
3 |
4 | import argparse
5 | import glob
6 | import json
7 | import sys
8 |
9 | from pytorch_transformers import AdamW, WarmupLinearSchedule
10 | from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
11 | BertForSequenceClassification, BertTokenizer,
12 | RobertaConfig,
13 | RobertaForSequenceClassification,
14 | RobertaTokenizer,
15 | XLMConfig, XLMForSequenceClassification,
16 | XLMTokenizer, XLNetConfig,
17 | XLNetForSequenceClassification,
18 | XLNetTokenizer)
19 | from sklearn.metrics import f1_score
20 | from tensorboardX import SummaryWriter
21 | from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
22 | from tqdm import trange, tqdm
23 |
24 | from .utils import *
25 |
26 | import warnings
27 |
28 | # skip numpy future warning
29 | warnings.simplefilter(action='ignore', category=FutureWarning)
30 |
31 | logging.basicConfig(format='%(asctime)s - %(name)s - %(message)s',
32 | datefmt='%m/%d/%Y %H:%M:%S',
33 | level=logging.INFO)
34 | logger = logging.getLogger(__name__)
35 |
36 | # List all model types here from `pytorch_transformer`. Only bert was tested.
37 | ALL_MODELS = sum(
38 | (tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig, XLMConfig, RobertaConfig)),
39 | ())
40 | MODEL_CLASSES = {
41 | 'bert': (BertConfig, BertForSequenceClassification, BertTokenizer),
42 | 'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
43 | 'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
44 | 'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
45 | }
46 |
47 |
48 |
49 | def get_dataloader(args, processor, label_list, tokenizer, is_train=False):
50 | if is_train:
51 | logger.info('getting train dataloader')
52 | examples = processor.get_train_examples(args.data_dir, args.train_file,
53 | sampling_strategy=args.sampling_strategy)
54 | else:
55 | logger.info(f'getting {"test" if args.eval_on_test else "dev"} dataloader')
56 | if args.eval_on_test:
57 | examples = processor.get_test_examples(args.data_dir, args.test_file)
58 | else:
59 | examples = processor.get_dev_examples(args.data_dir, args.dev_file)
60 |
61 | features = convert_examples_to_features(
62 | examples, label_list, args.max_seq_length, tokenizer, output_mode='classification',
63 | cls_token_at_end=bool(args.model_type in ['xlnet']), # xlnet has a cls token at the end
64 | cls_token=tokenizer.cls_token,
65 | cls_token_segment_id=2 if args.model_type in ['xlnet'] else 0,
66 | sep_token=tokenizer.sep_token,
67 | sep_token_extra=bool(args.model_type in ['roberta']),
68 | # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
69 | pad_on_left=bool(args.model_type in ['xlnet']), # pad on the left for xlnet
70 | pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
71 | pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0,
72 | )
73 | all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
74 | all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
75 | all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
76 | all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
77 | data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
78 | # Run prediction for full data
79 | if is_train:
80 | sampler = SortedBatchSampler([sum(i) for i in all_input_mask.numpy()], args.train_batch_size)
81 | else:
82 | sampler = SequentialSampler(data)
83 |
84 | dataloader = DataLoader(data, sampler=sampler,
85 | batch_size=max(args.n_gpu, 1) * args.per_gpu_train_batch_size if is_train
86 | else max(args.n_gpu, 1) * args.per_gpu_eval_batch_size,
87 | num_workers=args.n_gpu * 2, pin_memory=True, drop_last=True if is_train else False)
88 | return dataloader
89 |
90 |
91 | def evaluate(eval_dataloader, model, args):
92 | labels = []
93 | preds = []
94 |
95 | for batch in tqdm(eval_dataloader):
96 | batch = tuple(t.to(args.device) for t in batch)
97 | inputs = {'input_ids': batch[0],
98 | 'attention_mask': batch[1],
99 | 'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,
100 | # XLM and RoBERTa don't use segment_ids
101 | 'labels': batch[3]}
102 |
103 | with torch.no_grad():
104 | outputs = model(**inputs)
105 | _, logits = outputs[:2]
106 |
107 | logits = logits.detach().cpu().numpy()
108 | label_ids = batch[3].to('cpu').numpy()
109 | pred = np.argmax(logits, axis=1)
110 | labels.append(label_ids)
111 | preds.append(pred)
112 |
113 | f1 = f1_score(np.concatenate(labels), np.concatenate(preds), average="macro")
114 | return f1
115 |
116 | def main():
117 | parser = argparse.ArgumentParser()
118 |
119 | ## Required parameters
120 | parser.add_argument("--data_dir", default='./data/', type=str, required=False,
121 | help="The input data dir. Should contain the jsonl files for the task.")
122 | parser.add_argument("--model_type", default='bert', type=str, required=False,
123 | help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
124 | parser.add_argument("--model_name_or_path", default='bert-base-cased', type=str, required=False,
125 | help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(
126 | ALL_MODELS))
127 | parser.add_argument("--task_name", default='test', type=str, required=False)
128 | parser.add_argument("--output_dir", default=None, type=str, required=False,
129 | help="The output directory where the model predictions and checkpoints will be written.")
130 |
131 | ## Other parameters
132 | parser.add_argument("--config_name", default="", type=str,
133 | help="Pretrained config name or path if not the same as model_name")
134 | parser.add_argument("--tokenizer_name", default="", type=str,
135 | help="Pretrained tokenizer name or path if not the same as model_name")
136 | parser.add_argument("--cache_dir", default="", type=str,
137 | help="Where do you want to store the pre-trained models downloaded from s3")
138 | parser.add_argument("--max_seq_length", default=512, type=int,
139 | help="The maximum total input sequence length after tokenization. Sequences longer "
140 | "than this will be truncated, sequences shorter will be padded.")
141 | parser.add_argument("--do_train", action='store_true',
142 | help="Whether to run training.")
143 | parser.add_argument("--do_eval", action='store_true',
144 | help="Whether to run eval on the dev set.")
145 | parser.add_argument("--do_lower_case", action='store_true',
146 | help="Set this flag if you are using an uncased model.")
147 | parser.add_argument("--per_gpu_train_batch_size", default=2, type=int,
148 | help="Batch size per GPU/CPU for training.")
149 | parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
150 | help="Batch size per GPU/CPU for evaluation.")
151 | parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
152 | help="Number of updates steps to accumulate before performing a backward/update pass.")
153 | parser.add_argument("--learning_rate", default=1e-5, type=float,
154 | help="The initial learning rate for Adam.")
155 | parser.add_argument("--weight_decay", default=0.0, type=float,
156 | help="Weight deay if we apply some.")
157 | parser.add_argument("--adam_epsilon", default=1e-8, type=float,
158 | help="Epsilon for Adam optimizer.")
159 | parser.add_argument("--max_grad_norm", default=1.0, type=float,
160 | help="Max gradient norm.")
161 | parser.add_argument("--num_train_epochs", default=3.0, type=float,
162 | help="Total number of training epochs to perform.")
163 | parser.add_argument("--max_steps", default=-1, type=int,
164 | help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
165 | parser.add_argument("--warmup_steps", default=400, type=int,
166 | help="Linear warmup over warmup_steps.")
167 |
168 | parser.add_argument('--logging_steps', type=int, default=50,
169 | help="Log every X updates steps.")
170 | parser.add_argument('--save_steps', type=int, default=1000,
171 | help="Save checkpoint every X updates steps.")
172 | parser.add_argument("--eval_all_checkpoints", action='store_true',
173 | help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
174 | parser.add_argument("--no_cuda", action='store_true',
175 | help="Avoid using CUDA when available")
176 | parser.add_argument('--overwrite_output_dir', action='store_true',
177 | help="Overwrite the content of the output directory")
178 | parser.add_argument('--overwrite_cache', action='store_true',
179 | help="Overwrite the cached training and evaluation sets")
180 | parser.add_argument('--seed', type=int, default=42,
181 | help="Random seed for initialization")
182 |
183 | parser.add_argument('--fp16', action='store_true',
184 | help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
185 | parser.add_argument('--fp16_opt_level', type=str, default='O1',
186 | help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
187 | "See details at https://nvidia.github.io/apex/amp.html")
188 |
189 | parser.add_argument("--eval_on_test", action='store_true', help="Whether to evaluate on test (final) or dev set")
190 |
191 | parser.add_argument("--use_quoted",
192 | action='store_true',
193 | help="Whether to use quoted part as dataset")
194 |
195 | parser.add_argument("--use_context",
196 | action='store_true',
197 | help="Whether to use context part as dataset")
198 |
199 | parser.add_argument("--tag",
200 | default='exp_0',
201 | type=str,
202 | help="The name of this experiment")
203 |
204 | parser.add_argument("--sampling_strategy", type=float, default=-1,
205 | help='The oversampling ratio for training dataset. 1 means oversampling to balance and -1 means '
206 | 'no oversampling')
207 |
208 | parser.add_argument("--train_file", type=str, default="imbalanced_train.jsonl")
209 | parser.add_argument("--dev_file", type=str, default="imbalanced_dev.jsonl")
210 | parser.add_argument("--test_file", type=str, default="imbalanced_test.jsonl")
211 |
212 | args = parser.parse_args()
213 |
214 | if args.do_train and args.output_dir is None:
215 | setattr(args, 'output_dir', os.path.join('models', args.task_name, args.tag))
216 |
217 | if args.do_train:
218 | logger.info(f"Model will be saved to {args.output_dir}")
219 | else:
220 | logger.info(f"Model will be loaded to {args.output_dir}")
221 |
222 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
223 | args.device = device
224 | args.n_gpu = torch.cuda.device_count()
225 |
226 | logger.warning(f"Device: {device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fp16}")
227 |
228 | set_seed(args)
229 |
230 | if not os.path.exists(args.output_dir) or os.listdir(args.output_dir):
231 | os.makedirs(args.output_dir, exist_ok=True)
232 |
233 | processor = CondProcessor(use_quoted=args.use_quoted, use_context=args.use_context)
234 | label_list = processor.get_labels()
235 | num_labels = len(label_list)
236 | args.model_type = args.model_type.lower()
237 |
238 | tb_writer = SummaryWriter(logdir=f'runs/{args.task_name}_{args.tag}')
239 |
240 | args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
241 | args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
242 | args.output_mode = 'classification'
243 |
244 | config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
245 | config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
246 | num_labels=num_labels, finetuning_task=args.task_name)
247 | tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
248 | do_lower_case=args.do_lower_case)
249 | model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path),
250 | config=config)
251 | model.to(device)
252 |
253 | eval_f1s = []
254 |
255 | if args.do_train:
256 |
257 | # get data
258 | train_dataloader = get_dataloader(args, processor, label_list, tokenizer, is_train=True)
259 |
260 | # get training setting
261 |
262 | t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
263 |
264 | param_optimizer = list(model.named_parameters())
265 | no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
266 | optimizer_grouped_parameters = [
267 | {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
268 | {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
269 | ]
270 |
271 | optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
272 | scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
273 |
274 | if args.fp16:
275 | try:
276 | from apex import amp
277 | except ImportError:
278 | raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
279 | model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
280 |
281 | if args.n_gpu > 1:
282 | model = torch.nn.DataParallel(model)
283 |
284 | logger.info("***** Running training *****")
285 | logger.info(" Num examples = %d", len(train_dataloader.dataset))
286 | logger.info(" Num Epochs = %d", args.num_train_epochs)
287 | logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
288 | logger.info(" Total train batch size = %d",
289 | args.train_batch_size * args.gradient_accumulation_steps)
290 | logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
291 | logger.info(" Total optimization steps = %d", t_total)
292 |
293 | global_step = 0
294 | tr_loss, logging_loss = 0.0, 0.0
295 |
296 | model.zero_grad()
297 |
298 | # reset seed before training
299 | set_seed(args)
300 |
301 | # train
302 | for _ in trange(int(args.num_train_epochs), desc="Epoch"):
303 | model.train()
304 |
305 | for step, batch in tqdm(enumerate(train_dataloader), total=len(train_dataloader), mininterval=5):
306 |
307 | min_len = torch.min(torch.tensor(args.max_seq_length), torch.max(torch.sum(batch[1], dim=1)) + 2)
308 | batch = [
309 | t.to(device, non_blocking=True) if len(t.shape) == 1 else t[:, :min_len].contiguous().to(device,
310 | non_blocking=True)
311 | for t in batch]
312 |
313 | inputs = {'input_ids': batch[0],
314 | 'attention_mask': batch[1],
315 | 'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,
316 | # XLM don't use segment_ids
317 | 'labels': batch[3]}
318 | outputs = model(**inputs)
319 |
320 | loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc)
321 |
322 | if args.n_gpu > 1:
323 | loss = loss.mean() # mean() to average on multi-gpu parallel training
324 | if args.gradient_accumulation_steps > 1:
325 | loss = loss / args.gradient_accumulation_steps
326 |
327 | if args.fp16:
328 | with amp.scale_loss(loss, optimizer) as scaled_loss:
329 | scaled_loss.backward()
330 | torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
331 | else:
332 | loss.backward()
333 | torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
334 |
335 | tr_loss += loss.item()
336 |
337 | if (step + 1) % args.gradient_accumulation_steps == 0:
338 | optimizer.step()
339 | scheduler.step() # Update learning rate schedule
340 |
341 | model.zero_grad()
342 | global_step += 1
343 |
344 | if args.logging_steps > 0 and global_step % args.logging_steps == 0:
345 | # Log metrics
346 | tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
347 | tb_writer.add_scalar('loss', (tr_loss - logging_loss) / args.logging_steps, global_step)
348 | logging_loss = tr_loss
349 |
350 | if args.save_steps > 0 and global_step % args.save_steps == 0:
351 | # Save model checkpoint
352 | output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
353 | if not os.path.exists(output_dir):
354 | os.makedirs(output_dir)
355 | model_to_save = model.module if hasattr(model,
356 | 'module') else model # Take care of parallel training
357 | model_to_save.save_pretrained(output_dir)
358 | torch.save(args, os.path.join(output_dir, 'training_args.bin'))
359 | logger.info("Saving model checkpoint to %s", output_dir)
360 |
361 | tb_writer.close()
362 |
363 | output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
364 | if not os.path.exists(output_dir):
365 | os.makedirs(output_dir)
366 | model_to_save = model.module if hasattr(model,
367 | 'module') else model # Take care of distributed/parallel training
368 | model_to_save.save_pretrained(output_dir)
369 | torch.save(args, os.path.join(output_dir, 'training_args.bin'))
370 | logger.info("Saving model checkpoint to %s", output_dir)
371 |
372 | if args.do_eval:
373 | eval_dataloader = get_dataloader(args, processor, label_list, tokenizer, is_train=False)
374 |
375 | if args.eval_all_checkpoints:
376 | best_f1 = -1
377 | checkpoints = list(os.path.dirname(c) for c in
378 | sorted(glob.glob(args.output_dir + '/checkpoint*/' + WEIGHTS_NAME, recursive=True)))
379 | logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
380 | logger.info("Evaluate the following checkpoints: %s", checkpoints)
381 |
382 | for checkpoint in checkpoints:
383 | model = model_class.from_pretrained(checkpoint)
384 | model.to(device)
385 | model.eval()
386 | if args.n_gpu > 1:
387 | model = torch.nn.DataParallel(model)
388 |
389 | f1 = evaluate(eval_dataloader, model, args)
390 |
391 | logger.info(f"{checkpoint}'s F1 is {f1}")
392 | eval_f1s.append(f1)
393 | if f1 > best_f1:
394 |
395 | best_f1 = f1
396 | logger.info("Best F1 %s" % best_f1)
397 | result = {'eval_f1': f1,
398 | 'ckpt': checkpoint}
399 |
400 | output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
401 | with open(output_eval_file, "w") as writer:
402 | logger.info("***** Eval results *****")
403 | for key in sorted(result.keys()):
404 | logger.info(" %s = %s", key, str(result[key]))
405 | writer.write("%s = %s\n" % (key, str(result[key])))
406 |
407 | model_to_save = model.module if hasattr(model, 'module') else model #
408 | output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
409 | torch.save(model_to_save.state_dict(), output_model_file)
410 | output_param_file = os.path.join(args.output_dir, "param")
411 | with open(output_param_file, 'w') as f:
412 | json.dump(args.__dict__, f, indent=2, sort_keys=True)
413 |
414 | else:
415 | model = model_class.from_pretrained(args.output_dir)
416 | model.to(device)
417 | model.eval()
418 | if args.n_gpu > 1:
419 | model = torch.nn.DataParallel(model)
420 | f1 = evaluate(eval_dataloader, model, args)
421 | logger.info(f"Model's F1 is {f1}")
422 |
423 | if __name__ == "__main__":
424 | main()
425 |
--------------------------------------------------------------------------------
/src/utils.py:
--------------------------------------------------------------------------------
1 | import logging
2 | import os
3 | import random
4 | from collections import Counter
5 |
6 | import GPUtil
7 | import numpy as np
8 | import pandas as pd
9 | import torch
10 | from imblearn.over_sampling import RandomOverSampler
11 | from torch.utils.data import Sampler
12 |
13 | logging.basicConfig(format='%(asctime)s - %(name)s - %(message)s',
14 | datefmt='%m/%d/%Y %H:%M:%S',
15 | level=logging.INFO)
16 | logger = logging.getLogger(__name__)
17 |
18 |
19 | class InputExample(object):
20 | """A single training/test example for simple sequence classification."""
21 |
22 | def __init__(self, guid, text_a, text_b=None, label=None, extra=None):
23 | """Constructs a InputExample.
24 | Args:
25 | guid: Unique id for the example.
26 | text_a: string. The untokenized text of the first sequence. For single
27 | sequence tasks, only this sequence must be specified.
28 | text_b: (Optional) string. The untokenized text of the second sequence.
29 | Only must be specified for sequence pair tasks.
30 | label: (Optional) string. The label of the example. This should be
31 | specified for train and dev examples, but not for test examples.
32 | """
33 | self.guid = guid
34 | self.text_a = text_a
35 | self.text_b = text_b
36 | self.label = label
37 | self.extra = extra
38 |
39 |
40 | class InputFeatures(object):
41 | """A single set of features of data."""
42 |
43 | def __init__(self, input_ids, input_mask, segment_ids, label_id):
44 | self.input_ids = input_ids
45 | self.input_mask = input_mask
46 | self.segment_ids = segment_ids
47 | self.label_id = label_id
48 |
49 |
50 | def _truncate_seq_pair(tokens_a, tokens_b, max_length):
51 | """Truncates a sequence pair in place to the maximum length."""
52 |
53 | # This is a simple heuristic which will always truncate the longer sequence
54 | # one token at a time. This makes more sense than truncating an equal percent
55 | # of tokens from each, since if one sequence is very short then each token
56 | # that's truncated likely contains more information than a longer sequence.
57 | if len(tokens_a) > max_length:
58 | tokens_a = tokens_a[:max_length]
59 | tokens_b = []
60 | return tokens_a, tokens_b
61 | total_length = len(tokens_a) + len(tokens_b)
62 | if total_length > max_length:
63 | tokens_b = tokens_b[total_length - max_length:]
64 | while True:
65 | cnt = 0
66 | if len(tokens_b) and tokens_b[0] not in ["?", "!", ".", ",", ";", ""]:
67 | tokens_b.pop(0)
68 | cnt += 1
69 | else:
70 | break
71 | if cnt > 10:
72 | print(tokens_b)
73 | return tokens_a, tokens_b
74 |
75 |
76 | class CondProcessor:
77 | """Processor for the condescension dataset"""
78 |
79 | def __init__(self, use_quoted=True, use_context=False):
80 | assert use_quoted or use_context
81 | self.use_quoted = use_quoted
82 | self.use_context = use_context
83 |
84 | def get_train_examples(self, data_dir, filename, sampling_strategy=-1):
85 | """See base class."""
86 | logger.info("Get train data")
87 | return self._create_examples(
88 | self._read_jsonl(os.path.join(data_dir, filename)), "train", sampling_strategy=sampling_strategy)
89 |
90 | def get_dev_examples(self, data_dir, filename):
91 | """See base class."""
92 | logger.info("Get dev data")
93 | return self._create_examples(
94 | self._read_jsonl(os.path.join(data_dir, filename)), "dev")
95 |
96 | def get_test_examples(self, data_dir, filename):
97 | """See base class."""
98 | logger.info("Get test data")
99 | return self._create_examples(
100 | self._read_jsonl(os.path.join(data_dir, filename)), "test")
101 |
102 | @classmethod
103 | def get_labels(cls):
104 | """See base class."""
105 | return ["0", "1"]
106 |
107 | @classmethod
108 | def _read_jsonl(cls, input_file):
109 | """Reads a tab separated value file."""
110 | logger.debug("trying to load pickle file %s" % input_file)
111 | df = pd.read_json(input_file, orient='records', lines=True)
112 | return df
113 |
114 | def _create_examples(self, df, set_type, sampling_strategy=-1):
115 | """Creates examples for the training and dev sets."""
116 | examples = []
117 | if set_type == "train":
118 | cnt = Counter(df.label)
119 | if cnt[True] != cnt[False]:
120 | logger.info(f'training dataset: {cnt}')
121 | if sampling_strategy == -1:
122 | logger.info('no oversampling')
123 | pass
124 | else:
125 | logger.info(f"setting sampling strategy to {sampling_strategy}")
126 | ros = RandomOverSampler(random_state=42, sampling_strategy=sampling_strategy)
127 | ids, _ = ros.fit_resample(np.arange(len(df)).reshape(-1, 1), df.label)
128 | df = df.iloc[ids.reshape(-1)]
129 | logger.info(f'Now training dataset: {Counter(df.label)}')
130 | for idx, row in enumerate(df.itertuples()):
131 | if self.use_quoted:
132 | text_a = row.quotedpost
133 | text_b = row.post[:row.start_offset] if self.use_context else None
134 | else:
135 | text_a = row.post[:row.start_offset]
136 | text_b = None
137 |
138 | label = 1 if row.label is True else 0
139 | guid = "%s-%s" % (set_type, idx)
140 |
141 | examples.append(
142 | InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
143 |
144 | return examples
145 |
146 |
147 | def convert_examples_to_features(examples, label_list, max_seq_length,
148 | tokenizer, output_mode='classification',
149 | cls_token_at_end=False,
150 | cls_token='[CLS]',
151 | cls_token_segment_id=1,
152 | sep_token='[SEP]',
153 | sep_token_extra=False,
154 | pad_on_left=False,
155 | pad_token=0,
156 | pad_token_segment_id=0,
157 | sequence_a_segment_id=0,
158 | sequence_b_segment_id=1,
159 | mask_padding_with_zero=True):
160 | """ Loads a data file into a list of `InputBatch`s
161 | `cls_token_at_end` define the location of the CLS token:
162 | - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
163 | - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
164 | `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
165 | """
166 |
167 | label_map = {label: i for i, label in enumerate(label_list)}
168 |
169 | features = []
170 | for (ex_index, example) in enumerate(examples):
171 | if ex_index % 10000 == 0:
172 | logger.info("Writing example %d of %d" % (ex_index, len(examples)))
173 |
174 | tokens_a = tokenizer.tokenize(example.text_a)
175 |
176 | tokens_b = None
177 | if example.text_b:
178 | tokens_b = tokenizer.tokenize(example.text_b)
179 | # Modifies `tokens_a` and `tokens_b` in place so that the total
180 | # length is less than the specified length.
181 | # Account for [CLS], [SEP], [SEP] with "- 3". " -4" for RoBERTa.
182 | special_tokens_count = 4 if sep_token_extra else 3
183 | tokens_a, tokens_b = _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - special_tokens_count)
184 | else:
185 | # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
186 | special_tokens_count = 3 if sep_token_extra else 2
187 | if len(tokens_a) > max_seq_length - special_tokens_count:
188 | tokens_a = tokens_a[:(max_seq_length - special_tokens_count)]
189 |
190 | # The convention in BERT is:
191 | # (a) For sequence pairs:
192 | # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
193 | # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
194 | # (b) For single sequences:
195 | # tokens: [CLS] the dog is hairy . [SEP]
196 | # type_ids: 0 0 0 0 0 0 0
197 | #
198 | # Where "type_ids" are used to indicate whether this is the first
199 | # sequence or the second sequence. The embedding vectors for `type=0` and
200 | # `type=1` were learned during pre-training and are added to the wordpiece
201 | # embedding vector (and position vector). This is not *strictly* necessary
202 | # since the [SEP] token unambiguously separates the sequences, but it makes
203 | # it easier for the model to learn the concept of sequences.
204 | #
205 | # For classification tasks, the first vector (corresponding to [CLS]) is
206 | # used as as the "sentence vector". Note that this only makes sense because
207 | # the entire model is fine-tuned.
208 | tokens = tokens_a + [sep_token]
209 | if sep_token_extra:
210 | # roberta uses an extra separator b/w pairs of sentences
211 | tokens += [sep_token]
212 | segment_ids = [sequence_a_segment_id] * len(tokens)
213 |
214 | if tokens_b:
215 | tokens += tokens_b + [sep_token]
216 | segment_ids += [sequence_b_segment_id] * (len(tokens_b) + 1)
217 |
218 | if cls_token_at_end:
219 | tokens = tokens + [cls_token]
220 | segment_ids = segment_ids + [cls_token_segment_id]
221 | else:
222 | tokens = [cls_token] + tokens
223 | segment_ids = [cls_token_segment_id] + segment_ids
224 |
225 | input_ids = tokenizer.convert_tokens_to_ids(tokens)
226 |
227 | # The mask has 1 for real tokens and 0 for padding tokens. Only real
228 | # tokens are attended to.
229 | input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
230 |
231 | # Zero-pad up to the sequence length.
232 | padding_length = max_seq_length - len(input_ids)
233 | if pad_on_left:
234 | input_ids = ([pad_token] * padding_length) + input_ids
235 | input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
236 | segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
237 | else:
238 | input_ids = input_ids + ([pad_token] * padding_length)
239 | input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
240 | segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)
241 |
242 | # print(len(input_ids), max_seq_length)
243 | assert len(input_ids) == max_seq_length
244 | assert len(input_mask) == max_seq_length
245 | assert len(segment_ids) == max_seq_length
246 |
247 | if output_mode == "classification":
248 | label_id = label_map[str(example.label)]
249 | elif output_mode == "regression":
250 | label_id = float(example.label)
251 | else:
252 | raise KeyError(output_mode)
253 |
254 | features.append(
255 | InputFeatures(input_ids=input_ids,
256 | input_mask=input_mask,
257 | segment_ids=segment_ids,
258 | label_id=label_id))
259 | return features
260 |
261 |
262 | def set_seed(args):
263 | logger.info(f'setting seed to {args.seed}')
264 | random.seed(args.seed)
265 | np.random.seed(args.seed)
266 | torch.manual_seed(args.seed)
267 | if args.n_gpu > 0:
268 | torch.cuda.manual_seed_all(args.seed)
269 |
270 |
271 | class SortedBatchSampler(Sampler):
272 |
273 | def __init__(self, lengths, batch_size, shuffle=True):
274 | self.lengths = lengths
275 | self.batch_size = batch_size
276 | self.shuffle = shuffle
277 |
278 | def __iter__(self):
279 | lengths = np.array(
280 | [(-l, np.random.random()) for l in self.lengths],
281 | dtype=[('l1', np.int_), ('rand', np.float_)]
282 | )
283 | indices = np.argsort(lengths, order=('l1', 'rand'))
284 | batches = [indices[i:i + self.batch_size]
285 | for i in range(0, len(indices), self.batch_size)]
286 | if self.shuffle:
287 | np.random.shuffle(batches)
288 | return iter([i for batch in batches for i in batch])
289 |
290 | def __len__(self):
291 | return len(self.lengths)
292 |
293 |
294 | def gpu_util():
295 | """
296 | As of Aug. 24th, 2019, the official GPUtil package (if installed using `pip install gputil`) does not come with
297 | the correct functionality to show GPU util with customized `attrList`. You may want to download from github and
298 | install from the source code.
299 | """
300 |
301 | GPUtil.showUtilization(attrList=[[{'attr': 'id', 'name': 'ID'},
302 | {'attr': 'name', 'name': 'Name', 'transform': lambda x: x.replace("GeForce", "")},
303 | {'attr': 'load', 'name': 'GPU util.', 'suffix': '%',
304 | 'transform': lambda x: x * 100, 'precision': 0},
305 | {'attr': 'memoryUtil', 'name': 'Mem. util.', 'suffix': '%',
306 | 'transform': lambda x: x * 100, 'precision': 0}],
307 | [{'attr': 'memoryTotal', 'name': 'Mem. total', 'suffix': 'MB', 'precision': 0},
308 | {'attr': 'memoryUsed', 'name': 'Mem. used', 'suffix': 'MB', 'precision': 0},
309 | {'attr': 'memoryFree', 'name': 'Mem. free', 'suffix': 'MB', 'precision': 0}]]
310 | )
311 |
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