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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8
examples/run_squad.py
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@@ -32,7 +32,7 @@ from tqdm import tqdm, trange
from tensorboardX import SummaryWriter
from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
from transformers import (WEIGHTS_NAME, BertConfig,
BertForQuestionAnswering, BertTokenizer,
XLMConfig, XLMForQuestionAnswering,
XLMTokenizer, XLNetConfig,
@@ -40,7 +40,7 @@ from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
XLNetTokenizer,
DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
from pytorch_transformers import AdamW, WarmupLinearSchedule
from transformers import AdamW, WarmupLinearSchedule
from utils_squad import (read_squad_examples, convert_examples_to_features,
RawResult, write_predictions,
@@ -142,7 +142,7 @@ def train(args, train_dataset, model, tokenizer):
inputs.update({'cls_index': batch[5],
'p_mask': batch[6]})
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 (not distributed) training
@@ -510,7 +510,7 @@ 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 model loading logs
logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce model loading logs
logger.info("Evaluate the following checkpoints: %s", checkpoints)