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_classifier.py
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@@ -35,9 +35,9 @@ from torch.nn import CrossEntropyLoss, MSELoss
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import matthews_corrcoef, f1_score
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from pytorch_pretrained_bert.modeling import BertForSequenceClassification, BertConfig, WEIGHTS_NAME, CONFIG_NAME
from pytorch_pretrained_bert.tokenization import BertTokenizer, VOCAB_NAME
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
from pytorch_pretrained_bert.modeling import BertForSequenceClassification, BertConfig
from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
@@ -863,15 +863,14 @@ 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 = BertForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
else:
model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels)
model.to(device)