SUMIN/HuggingFace_transformer
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updating examples

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This commit is contained in:
thomwolf
2018-11-17 10:30:54 +01:00
parent d0673c7dbd
commit 4e46affc34
4 changed files with 19 additions and 70 deletions
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22
examples/extract_features.py
View File

@@ -193,23 +193,16 @@ def main():
## Required parameters
parser.add_argument("--input_file", default=None, type=str, required=True)
parser.add_argument("--vocab_file", default=None, type=str, required=True,
help="The vocabulary file that the BERT model was trained on.")
parser.add_argument("--output_file", default=None, type=str, required=True)
parser.add_argument("--bert_config_file", default=None, type=str, required=True,
help="The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
parser.add_argument("--init_checkpoint", default=None, type=str, required=True,
help="Initial checkpoint (usually from a pre-trained BERT model).")
parser.add_argument("--bert_model", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
## Other parameters
parser.add_argument("--layers", default="-1,-2,-3,-4", type=str)
parser.add_argument("--max_seq_length", default=128, type=int,
help="The maximum total input sequence length after WordPiece tokenization. Sequences longer "
"than this will be truncated, and sequences shorter than this will be padded.")
parser.add_argument("--do_lower_case", default=True, action='store_true',
help="Whether to lower case the input text. Should be True for uncased "
"models and False for cased models.")
parser.add_argument("--batch_size", default=32, type=int, help="Batch size for predictions.")
parser.add_argument("--local_rank",
type=int,
@@ -230,10 +223,7 @@ def main():
layer_indexes = [int(x) for x in args.layers.split(",")]
bert_config = BertConfig.from_json_file(args.bert_config_file)
tokenizer = BertTokenizer(
vocab_file=args.vocab_file, do_lower_case=args.do_lower_case)
tokenizer = BertTokenizer.from_pretrained(args.bert_model)
examples = read_examples(args.input_file)
@@ -244,9 +234,7 @@ def main():
for feature in features:
unique_id_to_feature[feature.unique_id] = feature
model = BertModel(bert_config)
if args.init_checkpoint is not None:
model.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'))
model = BertModel.from_pretrained(args.bert_model)
model.to(device)
if args.local_rank != -1:

31
examples/run_classifier.py
View File

@@ -343,12 +343,9 @@ def main():
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--bert_config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture.")
parser.add_argument("--bert_model", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
parser.add_argument("--task_name",
default=None,
type=str,
@@ -366,14 +363,6 @@ def main():
help="The output directory where the model checkpoints will be written.")
## Other parameters
parser.add_argument("--init_checkpoint",
default=None,
type=str,
help="Initial checkpoint (usually from a pre-trained BERT model).")
parser.add_argument("--do_lower_case",
default=False,
action='store_true',
help="Whether to lower case the input text. True for uncased models, False for cased models.")
parser.add_argument("--max_seq_length",
default=128,
type=int,
@@ -477,13 +466,6 @@ def main():
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
bert_config = BertConfig.from_json_file(args.bert_config_file)
if args.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length {} because the BERT model was only trained up to sequence length {}".format(
args.max_seq_length, bert_config.max_position_embeddings))
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
@@ -496,8 +478,7 @@ def main():
processor = processors[task_name]()
label_list = processor.get_labels()
tokenizer = BertTokenizer(
vocab_file=args.vocab_file, do_lower_case=args.do_lower_case)
tokenizer = BertTokenizer.from_pretrained(args.bert_model)
train_examples = None
num_train_steps = None
@@ -507,9 +488,7 @@ def main():
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare model
model = BertForSequenceClassification(bert_config, len(label_list))
if args.init_checkpoint is not None:
model.bert.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'))
model = BertForSequenceClassification.from_pretrained(args.bert_model, len(label_list))
if args.fp16:
model.half()
model.to(device)

28
examples/run_squad.py
View File

@@ -699,11 +699,9 @@ def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--bert_config_file", default=None, type=str, required=True,
help="The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
parser.add_argument("--vocab_file", default=None, type=str, required=True,
help="The vocabulary file that the BERT model was trained on.")
parser.add_argument("--bert_model", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model checkpoints will be written.")
@@ -711,11 +709,6 @@ def main():
parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json")
parser.add_argument("--predict_file", default=None, type=str,
help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
parser.add_argument("--init_checkpoint", default=None, type=str,
help="Initial checkpoint (usually from a pre-trained BERT model).")
parser.add_argument("--do_lower_case", default=True, action='store_true',
help="Whether to lower case the input text. Should be True for uncased "
"models and False for cased models.")
parser.add_argument("--max_seq_length", default=384, type=int,
help="The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded.")
@@ -815,20 +808,11 @@ def main():
raise ValueError(
"If `do_predict` is True, then `predict_file` must be specified.")
bert_config = BertConfig.from_json_file(args.bert_config_file)
if args.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(args.max_seq_length, bert_config.max_position_embeddings))
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError("Output directory () already exists and is not empty.")
os.makedirs(args.output_dir, exist_ok=True)
tokenizer = BertTokenizer(
vocab_file=args.vocab_file, do_lower_case=args.do_lower_case)
tokenizer = BertTokenizer.from_pretrained(args.bert_model)
train_examples = None
num_train_steps = None
@@ -839,9 +823,7 @@ def main():
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare model
model = BertForQuestionAnswering(bert_config)
if args.init_checkpoint is not None:
model.bert.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'))
model = BertForQuestionAnswering.from_pretrained(args.bert_model)
if args.fp16:
model.half()
model.to(device)