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ALBERT for SQuAD

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Lysandre
2019-11-06 20:47:42 +00:00
committed by Lysandre Debut
parent 70d99980de
commit 4374eaea78
3 changed files with 100 additions and 6 deletions
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12
examples/run_squad.py
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@@ -43,7 +43,8 @@ from transformers import (WEIGHTS_NAME, BertConfig,
XLMTokenizer, XLNetConfig,
XLNetForQuestionAnswering,
XLNetTokenizer,
DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer,
AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer)
from transformers import AdamW, get_linear_schedule_with_warmup
@@ -65,7 +66,8 @@ MODEL_CLASSES = {
'bert': (BertConfig, BertForQuestionAnswering, BertTokenizer),
'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer),
'albert': (AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer)
}
def set_seed(args):
@@ -128,7 +130,7 @@ def train(args, train_dataset, model, tokenizer):
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
global_step = 1
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
@@ -537,7 +539,7 @@ def main():
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
# Load a trained model and vocabulary that you have fine-tuned
model = model_class.from_pretrained(args.output_dir)
model = model_class.from_pretrained(args.output_dir, force_download=True)
tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
model.to(args.device)
@@ -555,7 +557,7 @@ def main():
for checkpoint in checkpoints:
# Reload the model
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
model = model_class.from_pretrained(checkpoint)
model = model_class.from_pretrained(checkpoint, force_download=True)
model.to(args.device)
# Evaluate