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import logging
import torch
from torch.utils.data import TensorDataset
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, label_id, valid_ids=None, label_mask=None):
self.input_ids = input_ids
self.input_mask = input_mask
self.label_id = label_id
self.valid_ids = valid_ids
self.label_mask = label_mask
class NerProcessor:
"""Processor for the CoNLL-2003 data set."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_file(os.path.join(data_dir, "train.txt")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_file(os.path.join(data_dir, "valid.txt")), "valid")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_file(os.path.join(data_dir, "test.txt")), "test")
def get_labels(self):
return ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC"]
def _read_file(self, filename):
'''
read file
'''
f = open(filename)
data = []
sentence = []
label = []
for i, line in enumerate(f, 1):
if not line.strip() or len(line) == 0 or line.startswith('-DOCSTART') or line[0] == "\n" or line[0] == '.':
if len(sentence) > 0:
data.append((sentence, label))
sentence = []
label = []
continue
splits = line.split()
assert len(splits) >= 2, "error on line {}. Found {} splits".format(i, len(splits))
word, tag = splits[0], splits[-1]
assert tag in self.get_labels(), "unknown tag {} in line {}".format(tag, i)
sentence.append(word.strip())
label.append(tag.strip())
if len(sentence) > 0:
data.append((sentence, label))
sentence = []
label = []
return data
def _create_examples(self, lines, set_type):
examples = []
for i, (sentence, label) in enumerate(lines):
guid = "%s-%s" % (set_type, i)
text_a = ' '.join(sentence)
text_b = None
label = label
examples.append(InputExample(
guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def convert_examples_to_features(examples, label_list, max_seq_length, encode_method):
"""Converts a set of examples into XLMR compatible format
* Labels are only assigned to the positions correspoinding to the first BPE token of each word.
* Other positions are labeled with 0 ("IGNORE")
"""
ignored_label = "IGNORE"
label_map = {label: i for i, label in enumerate(label_list, 1)}
label_map[ignored_label] = 0 # 0 label is to be ignored
features = []
for (ex_index, example) in enumerate(examples):
textlist = example.text_a.split(' ')
labellist = example.label
labels = []
valid = []
label_mask = []
token_ids = []
for i, word in enumerate(textlist):
tokens = encode_method(word.strip()) # word token ids
token_ids.extend(tokens) # all sentence token ids
label_1 = labellist[i]
for m in range(len(tokens)):
if m == 0: # only label the first BPE token of each work
labels.append(label_1)
valid.append(1)
label_mask.append(1)
else:
labels.append(ignored_label) # unlabeled BPE token
label_mask.append(0)
valid.append(0)
logging.debug("token ids = ")
logging.debug(token_ids)
logging.debug("labels = ")
logging.debug(labels)
logging.debug("valid = ")
logging.debug(valid)
if len(token_ids) >= max_seq_length - 1: # trim extra tokens
token_ids = token_ids[0:(max_seq_length-2)]
labels = labels[0:(max_seq_length-2)]
valid = valid[0:(max_seq_length-2)]
label_mask = label_mask[0:(max_seq_length-2)]
# adding <s>
token_ids.insert(0, 0)
labels.insert(0, ignored_label)
label_mask.insert(0, 0)
valid.insert(0, 0)
# adding </s>
token_ids.append(2)
labels.append(ignored_label)
label_mask.append(0)
valid.append(0)
assert len(token_ids) == len(labels)
assert len(valid) == len(labels)
label_ids = []
for i, _ in enumerate(token_ids):
label_ids.append(label_map[labels[i]])
assert len(token_ids) == len(label_ids)
assert len(valid) == len(label_ids)
input_mask = [1] * len(token_ids)
while len(token_ids) < max_seq_length:
token_ids.append(1) # token padding idx
input_mask.append(0)
label_ids.append(label_map[ignored_label]) # label ignore idx
valid.append(0)
label_mask.append(0)
while len(label_ids) < max_seq_length:
label_ids.append(label_map[ignored_label])
label_mask.append(0)
assert len(token_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(label_ids) == max_seq_length
assert len(valid) == max_seq_length
assert len(label_mask) == max_seq_length
if ex_index < 2:
logging.info("*** Example ***")
logging.info("guid: %s" % (example.guid))
logging.info("tokens: %s" % " ".join(
[str(x) for x in token_ids]))
logging.info("input_ids: %s" %
" ".join([str(x) for x in token_ids]))
logging.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
logging.info("label: %s (id = %s)" % (example.label, " ".join(map(str, label_ids))))
logging.info("label_mask: %s" %
" ".join([str(x) for x in label_mask]))
logging.info("valid mask: %s" %
" ".join([str(x) for x in valid]))
features.append(
InputFeatures(input_ids=token_ids,
input_mask=input_mask,
label_id=label_ids,
valid_ids=valid,
label_mask=label_mask))
return features
def create_dataset(features):
all_input_ids = torch.tensor(
[f.input_ids for f in features], dtype=torch.long)
all_label_ids = torch.tensor(
[f.label_id for f in features], dtype=torch.long)
all_valid_ids = torch.tensor(
[f.valid_ids for f in features], dtype=torch.long)
all_lmask_ids = torch.tensor(
[f.label_mask for f in features], dtype=torch.long)
return TensorDataset(
all_input_ids, all_label_ids, all_lmask_ids, all_valid_ids) |