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Merge pull request #91 from rodgzilla/convert-examples-code-improvement

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run_classifier.py improvements
This commit is contained in:
Thomas Wolf
2018-12-11 05:12:04 -05:00
committed by GitHub
parent a2b6918a11 a994bf4076
commit e622790a93
1 changed files with 24 additions and 30 deletions
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44
examples/run_classifier.py
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@@ -196,9 +196,7 @@ class ColaProcessor(DataProcessor):
def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):
"""Loads a data file into a list of `InputBatch`s."""
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
label_map = {label : i for i, label in enumerate(label_list)}
features = []
for (ex_index, example) in enumerate(examples):
@@ -207,8 +205,6 @@ def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
@@ -216,7 +212,7 @@ def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
tokens_a = tokens_a[:(max_seq_length - 2)]
# The convention in BERT is:
# (a) For sequence pairs:
@@ -236,22 +232,12 @@ def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in tokens_a:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
segment_ids = [0] * len(tokens)
if tokens_b:
for token in tokens_b:
tokens.append(token)
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)
tokens += tokens_b + ["[SEP]"]
segment_ids += [1] * (len(tokens_b) + 1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
@@ -260,10 +246,10 @@ def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
padding = [0] * (max_seq_length - len(input_ids))
input_ids += padding
input_mask += padding
segment_ids += padding
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
@@ -437,6 +423,12 @@ def main():
"mrpc": MrpcProcessor,
}
num_labels_task = {
"cola": 2,
"mnli": 3,
"mrpc": 2,
}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
@@ -475,6 +467,7 @@ def main():
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
num_labels = num_labels_task[task_name]
label_list = processor.get_labels()
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
@@ -488,7 +481,8 @@ def main():
# Prepare model
model = BertForSequenceClassification.from_pretrained(args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank))
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank),
num_labels = num_labels)
if args.fp16:
model.half()
model.to(device)