Convert all DataProcessors, _truncate_seq_pair and convert_examples_to_features
This commit is contained in:
parent
b1dade34db
commit
f8e347b557
|
@ -18,11 +18,17 @@ from __future__ import absolute_import
|
|||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
# import csv
|
||||
# import os
|
||||
import csv
|
||||
import os
|
||||
# import modeling_pytorch
|
||||
# import optimization
|
||||
# import tokenization
|
||||
import tokenization_pytorch
|
||||
|
||||
import logging
|
||||
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
|
||||
datefmt = '%m/%d/%Y %H:%M:%S',
|
||||
level = logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
import argparse
|
||||
|
||||
|
@ -142,4 +148,237 @@ parser.add_argument("--num_tpu_cores",
|
|||
help = "Only used if `use_tpu` is True. Total number of TPU cores to use.")
|
||||
### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
|
||||
|
||||
args = parser.parse_args()
|
||||
args = parser.parse_args()
|
||||
|
||||
class InputExample(object):
|
||||
"""A single training/test example for simple sequence classification."""
|
||||
|
||||
def __init__(self, guid, text_a, text_b=None, label=None):
|
||||
"""Constructs a InputExample.
|
||||
|
||||
Args:
|
||||
guid: Unique id for the example.
|
||||
text_a: string. The untokenized text of the first sequence. For single
|
||||
sequence tasks, only this sequence must be specified.
|
||||
text_b: (Optional) string. The untokenized text of the second sequence.
|
||||
Only must be specified for sequence pair tasks.
|
||||
label: (Optional) string. The label of the example. This should be
|
||||
specified for train and dev examples, but not for test examples.
|
||||
"""
|
||||
self.guid = guid
|
||||
self.text_a = text_a
|
||||
self.text_b = text_b
|
||||
self.label = label
|
||||
|
||||
|
||||
class InputFeatures(object):
|
||||
"""A single set of features of data."""
|
||||
|
||||
def __init__(self, input_ids, input_mask, segment_ids, label_id):
|
||||
self.input_ids = input_ids
|
||||
self.input_mask = input_mask
|
||||
self.segment_ids = segment_ids
|
||||
self.label_id = label_id
|
||||
|
||||
|
||||
class DataProcessor(object):
|
||||
"""Base class for data converters for sequence classification data sets."""
|
||||
|
||||
def get_train_examples(self, data_dir):
|
||||
"""Gets a collection of `InputExample`s for the train set."""
|
||||
raise NotImplementedError()
|
||||
|
||||
def get_dev_examples(self, data_dir):
|
||||
"""Gets a collection of `InputExample`s for the dev set."""
|
||||
raise NotImplementedError()
|
||||
|
||||
def get_labels(self):
|
||||
"""Gets the list of labels for this data set."""
|
||||
raise NotImplementedError()
|
||||
|
||||
@classmethod
|
||||
def _read_tsv(cls, input_file, quotechar=None):
|
||||
"""Reads a tab separated value file."""
|
||||
with open(input_file, "r") as f:
|
||||
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
|
||||
lines = []
|
||||
for line in reader:
|
||||
lines.append(line)
|
||||
return lines
|
||||
|
||||
|
||||
class MnliProcessor(DataProcessor):
|
||||
"""Processor for the MultiNLI data set (GLUE version)."""
|
||||
|
||||
def get_train_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
return self._create_examples(
|
||||
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
|
||||
|
||||
def get_dev_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
return self._create_examples(
|
||||
self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
|
||||
"dev_matched")
|
||||
|
||||
def get_labels(self):
|
||||
"""See base class."""
|
||||
return ["contradiction", "entailment", "neutral"]
|
||||
|
||||
def _create_examples(self, lines, set_type):
|
||||
"""Creates examples for the training and dev sets."""
|
||||
examples = []
|
||||
for (i, line) in enumerate(lines):
|
||||
if i == 0:
|
||||
continue
|
||||
guid = "%s-%s" % (set_type, tokenization_pytorch.convert_to_unicode(line[0]))
|
||||
text_a = tokenization_pytorch.convert_to_unicode(line[8])
|
||||
text_b = tokenization_pytorch.convert_to_unicode(line[9])
|
||||
label = tokenization_pytorch.convert_to_unicode(line[-1])
|
||||
examples.append(
|
||||
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
|
||||
return examples
|
||||
|
||||
|
||||
class ColaProcessor(DataProcessor):
|
||||
"""Processor for the CoLA data set (GLUE version)."""
|
||||
|
||||
def get_train_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
return self._create_examples(
|
||||
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
|
||||
|
||||
def get_dev_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
return self._create_examples(
|
||||
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
|
||||
|
||||
def get_labels(self):
|
||||
"""See base class."""
|
||||
return ["0", "1"]
|
||||
|
||||
def _create_examples(self, lines, set_type):
|
||||
"""Creates examples for the training and dev sets."""
|
||||
examples = []
|
||||
for (i, line) in enumerate(lines):
|
||||
guid = "%s-%s" % (set_type, i)
|
||||
text_a = tokenization_pytorch.convert_to_unicode(line[3])
|
||||
label = tokenization_pytorch.convert_to_unicode(line[1])
|
||||
examples.append(
|
||||
InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
|
||||
return examples
|
||||
|
||||
|
||||
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
|
||||
|
||||
features = []
|
||||
for (ex_index, example) in enumerate(examples):
|
||||
tokens_a = tokenizer.tokenize(example.text_a)
|
||||
|
||||
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"
|
||||
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
|
||||
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)]
|
||||
|
||||
# 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
|
||||
# (b) For single sequences:
|
||||
# tokens: [CLS] the dog is hairy . [SEP]
|
||||
# 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
|
||||
# `type=1` were learned during pre-training and are added to the wordpiece
|
||||
# embedding vector (and position vector). This is not *strictly* necessary
|
||||
# since the [SEP] token unambigiously separates the sequences, but it makes
|
||||
# it easier for the model to learn the concept of sequences.
|
||||
#
|
||||
# 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)
|
||||
|
||||
if tokens_b:
|
||||
for token in tokens_b:
|
||||
tokens.append(token)
|
||||
segment_ids.append(1)
|
||||
tokens.append("[SEP]")
|
||||
segment_ids.append(1)
|
||||
|
||||
input_ids = tokenizer.convert_tokens_to_ids(tokens)
|
||||
|
||||
# The mask has 1 for real tokens and 0 for padding tokens. Only real
|
||||
# tokens are attended to.
|
||||
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)
|
||||
|
||||
assert len(input_ids) == max_seq_length
|
||||
assert len(input_mask) == max_seq_length
|
||||
assert len(segment_ids) == max_seq_length
|
||||
|
||||
label_id = label_map[example.label]
|
||||
if ex_index < 5:
|
||||
logger.info("*** Example ***")
|
||||
logger.info("guid: %s" % (example.guid))
|
||||
logger.info("tokens: %s" % " ".join(
|
||||
[tokenization_pytorch.printable_text(x) for x in tokens]))
|
||||
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
|
||||
logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
|
||||
logger.info(
|
||||
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
|
||||
logger.info("label: %s (id = %d)" % (example.label, label_id))
|
||||
|
||||
features.append(
|
||||
InputFeatures(
|
||||
input_ids=input_ids,
|
||||
input_mask=input_mask,
|
||||
segment_ids=segment_ids,
|
||||
label_id=label_id))
|
||||
return features
|
||||
|
||||
|
||||
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
|
||||
"""Truncates a sequence pair in place to the maximum length."""
|
||||
|
||||
# This is a simple heuristic which will always truncate the longer sequence
|
||||
# one token at a time. This makes more sense than truncating an equal percent
|
||||
# of tokens from each, since if one sequence is very short then each token
|
||||
# that's truncated likely contains more information than a longer sequence.
|
||||
while True:
|
||||
total_length = len(tokens_a) + len(tokens_b)
|
||||
if total_length <= max_length:
|
||||
break
|
||||
if len(tokens_a) > len(tokens_b):
|
||||
tokens_a.pop()
|
||||
else:
|
||||
tokens_b.pop()
|
Loading…
Reference in New Issue