SUMIN/HuggingFace_transformer
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added best practices for serialization in README and examples

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thomwolf
2019-04-15 15:00:33 +02:00
parent 179a2c2ff6
commit 60ea6c59d2
11 changed files with 106 additions and 34 deletions
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examples/run_squad.py
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@@ -34,12 +34,12 @@ from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from pytorch_pretrained_bert.modeling import BertForQuestionAnswering, BertConfig, WEIGHTS_NAME, CONFIG_NAME
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
from pytorch_pretrained_bert.modeling import BertForQuestionAnswering, BertConfig
from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
from pytorch_pretrained_bert.tokenization import (BasicTokenizer,
BertTokenizer,
whitespace_tokenize, VOCAB_NAME)
whitespace_tokenize)
if sys.version_info[0] == 2:
import cPickle as pickle
@@ -1015,15 +1015,14 @@ def main():
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
output_vocab_file = os.path.join(args.output_dir, VOCAB_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(output_vocab_file)
tokenizer.save_vocabulary(args.output_dir)
# Load a trained model and vocabulary that you have fine-tuned
model = BertForQuestionAnswering.from_pretrained(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
else:
model = BertForQuestionAnswering.from_pretrained(args.bert_model)