SUMIN/HuggingFace_transformer
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[BIG] pytorch-transformers => transformers

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thomwolf
2019-09-26 10:15:53 +02:00
parent 2f071fcb02
commit 31c23bd5ee
148 changed files with 540 additions and 539 deletions
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examples/run_glue.py
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@@ -31,7 +31,7 @@ from torch.utils.data.distributed import DistributedSampler
from tensorboardX import SummaryWriter
from tqdm import tqdm, trange
from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
from transformers import (WEIGHTS_NAME, BertConfig,
BertForSequenceClassification, BertTokenizer,
RobertaConfig,
RobertaForSequenceClassification,
@@ -44,12 +44,12 @@ from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
DistilBertForSequenceClassification,
DistilBertTokenizer)
from pytorch_transformers import AdamW, WarmupLinearSchedule
from transformers import AdamW, WarmupLinearSchedule
from pytorch_transformers import glue_compute_metrics as compute_metrics
from pytorch_transformers import glue_output_modes as output_modes
from pytorch_transformers import glue_processors as processors
from pytorch_transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_output_modes as output_modes
from transformers import glue_processors as processors
from transformers import glue_convert_examples_to_features as convert_examples_to_features
logger = logging.getLogger(__name__)
@@ -137,7 +137,7 @@ def train(args, train_dataset, model, tokenizer):
'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None, # XLM, DistilBERT and RoBERTa don't use segment_ids
'labels': batch[3]}
outputs = model(**inputs)
loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc)
loss = outputs[0] # model outputs are always tuple in transformers (see doc)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
@@ -483,7 +483,7 @@ def main():
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""