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updating GLUE utils for compatibility with XLNet

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This commit is contained in:
thomwolf
2019-06-24 14:36:11 +02:00
parent 24ed0b9346
commit 62d78aa37e
9 changed files with 310 additions and 98 deletions
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8
examples/run_xlnet_classifier.py
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@@ -203,7 +203,9 @@ def main():
train_features = pickle.load(reader)
except:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
train_examples, label_list, args.max_seq_length, tokenizer, output_mode,
cls_token_at_end=True, cls_token=tokenizer.CLS_TOKEN,
sep_token=tokenizer.SEP_TOKEN, cls_token_segment_id=2)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving train features into cached file %s", cached_train_features_file)
with open(cached_train_features_file, "wb") as writer:
@@ -347,7 +349,9 @@ def main():
eval_features = pickle.load(reader)
except:
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode,
cls_token_at_end=True, cls_token=tokenizer.CLS_TOKEN,
sep_token=tokenizer.SEP_TOKEN, cls_token_segment_id=2)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving eval features into cached file %s", cached_eval_features_file)
with open(cached_eval_features_file, "wb") as writer:

26
examples/utils_glue.py
View File

@@ -388,8 +388,15 @@ class WnliProcessor(DataProcessor):
def convert_examples_to_features(examples, label_list, max_seq_length,
tokenizer, output_mode):
"""Loads a data file into a list of `InputBatch`s."""
tokenizer, output_mode,
cls_token_at_end=False, cls_token='[CLS]',
sep_token='[SEP]', cls_token_segment_id=0):
""" Loads a data file into a list of `InputBatch`s
`cls_token_at_end` define the location of the CLS token:
- False (BERT pattern): [CLS] + A + [SEP] + B + [SEP]
- True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
`cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
"""
label_map = {label : i for i, label in enumerate(label_list)}
@@ -415,10 +422,10 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
@@ -430,13 +437,20 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
# 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 = ["[CLS]"] + tokens_a + ["[SEP]"]
tokens = tokens_a + [sep_token]
segment_ids = [0] * len(tokens)
if tokens_b:
tokens += tokens_b + ["[SEP]"]
tokens += tokens_b + [sep_token]
segment_ids += [1] * (len(tokens_b) + 1)
if cls_token_at_end:
tokens = tokens + [cls_token]
segment_ids = segment_ids + [cls_token_segment_id]
else:
tokens = [cls_token] + tokens
segment_ids = [cls_token_segment_id] + segment_ids
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real