/
punctuation_capitalization_dataset.py
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/
punctuation_capitalization_dataset.py
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# Copyright 2018 The Google AI Language Team Authors and
# The HuggingFace Inc. team.
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Utility functions for Token Classification NLP tasks
Some parts of this code were adapted from the HuggingFace library at
https://github.com/huggingface/pytorch-pretrained-BERT
"""
__all__ = ['BertPunctuationCapitalizationDataset', 'BertPunctuationCapitalizationInferDataset']
import itertools
import os
import pickle
import random
import numpy as np
from torch.utils.data import Dataset
from nemo import logging
from nemo.collections.nlp.data.datasets.datasets_utils.preprocessing import get_label_stats, get_stats
def get_features(
queries,
max_seq_length,
tokenizer,
punct_label_ids=None,
capit_label_ids=None,
pad_label='O',
punct_labels_lines=None,
capit_labels_lines=None,
ignore_extra_tokens=False,
ignore_start_end=False,
):
"""
Args:
queries (list of str): text sequences
max_seq_length (int): max sequence length minus 2 for [CLS] and [SEP]
tokenizer (Tokenizer): such as NemoBertTokenizer
pad_label (str): pad value use for labels.
by default, it's the neutral label.
punct_label_ids (dict): dict to map punctuation labels to label ids.
Starts with pad_label->0 and then increases in alphabetical order.
Required for training and evaluation, not needed for inference.
capit_label_ids (dict): dict to map labels to label ids. Starts
with pad_label->0 and then increases in alphabetical order.
Required for training and evaluation, not needed for inference.
punct_labels (list of str): list of labels for every word in a sequence
capit_labels (list of str): list of labels for every word in a sequence
ignore_extra_tokens (bool): whether to ignore extra tokens in
the loss_mask,
ignore_start_end (bool): whether to ignore bos and eos tokens in
the loss_mask
"""
all_subtokens = []
all_loss_mask = []
all_subtokens_mask = []
all_segment_ids = []
all_input_ids = []
all_input_mask = []
sent_lengths = []
punct_all_labels = []
capit_all_labels = []
with_label = False
if punct_labels_lines and capit_labels_lines:
with_label = True
for i, query in enumerate(queries):
words = query.strip().split()
# add bos token
subtokens = ['[CLS]']
loss_mask = [1 - ignore_start_end]
subtokens_mask = [0]
if with_label:
pad_id = punct_label_ids[pad_label]
punct_labels = [pad_id]
punct_query_labels = [punct_label_ids[lab] for lab in punct_labels_lines[i]]
capit_labels = [pad_id]
capit_query_labels = [capit_label_ids[lab] for lab in capit_labels_lines[i]]
for j, word in enumerate(words):
word_tokens = tokenizer.text_to_tokens(word)
subtokens.extend(word_tokens)
loss_mask.append(1)
loss_mask.extend([int(not ignore_extra_tokens)] * (len(word_tokens) - 1))
subtokens_mask.append(1)
subtokens_mask.extend([0] * (len(word_tokens) - 1))
if with_label:
punct_labels.extend([punct_query_labels[j]] * len(word_tokens))
capit_labels.extend([capit_query_labels[j]] * len(word_tokens))
# add eos token
subtokens.append('[SEP]')
loss_mask.append(1 - ignore_start_end)
subtokens_mask.append(0)
sent_lengths.append(len(subtokens))
all_subtokens.append(subtokens)
all_loss_mask.append(loss_mask)
all_subtokens_mask.append(subtokens_mask)
all_input_mask.append([1] * len(subtokens))
if with_label:
punct_labels.append(pad_id)
punct_all_labels.append(punct_labels)
capit_labels.append(pad_id)
capit_all_labels.append(capit_labels)
max_seq_length = min(max_seq_length, max(sent_lengths))
logging.info(f'Max length: {max_seq_length}')
get_stats(sent_lengths)
too_long_count = 0
for i, subtokens in enumerate(all_subtokens):
if len(subtokens) > max_seq_length:
subtokens = ['[CLS]'] + subtokens[-max_seq_length + 1 :]
all_input_mask[i] = [1] + all_input_mask[i][-max_seq_length + 1 :]
all_loss_mask[i] = [int(not ignore_start_end)] + all_loss_mask[i][-max_seq_length + 1 :]
all_subtokens_mask[i] = [0] + all_subtokens_mask[i][-max_seq_length + 1 :]
if with_label:
punct_all_labels[i] = [pad_id] + punct_all_labels[i][-max_seq_length + 1 :]
capit_all_labels[i] = [pad_id] + capit_all_labels[i][-max_seq_length + 1 :]
too_long_count += 1
all_input_ids.append([tokenizer.tokens_to_ids(t) for t in subtokens])
if len(subtokens) < max_seq_length:
extra = max_seq_length - len(subtokens)
all_input_ids[i] = all_input_ids[i] + [0] * extra
all_loss_mask[i] = all_loss_mask[i] + [0] * extra
all_subtokens_mask[i] = all_subtokens_mask[i] + [0] * extra
all_input_mask[i] = all_input_mask[i] + [0] * extra
if with_label:
punct_all_labels[i] = punct_all_labels[i] + [pad_id] * extra
capit_all_labels[i] = capit_all_labels[i] + [pad_id] * extra
all_segment_ids.append([0] * max_seq_length)
logging.info(f'{too_long_count} are longer than {max_seq_length}')
for i in range(min(len(all_input_ids), 5)):
logging.info("*** Example ***")
logging.info("i: %s" % (i))
logging.info("subtokens: %s" % " ".join(list(map(str, all_subtokens[i]))))
logging.info("loss_mask: %s" % " ".join(list(map(str, all_loss_mask[i]))))
logging.info("input_mask: %s" % " ".join(list(map(str, all_input_mask[i]))))
logging.info("subtokens_mask: %s" % " ".join(list(map(str, all_subtokens_mask[i]))))
if with_label:
logging.info("punct_labels: %s" % " ".join(list(map(str, punct_all_labels[i]))))
logging.info("capit_labels: %s" % " ".join(list(map(str, capit_all_labels[i]))))
return (
all_input_ids,
all_segment_ids,
all_input_mask,
all_loss_mask,
all_subtokens_mask,
punct_all_labels,
capit_all_labels,
punct_label_ids,
capit_label_ids,
)
class BertPunctuationCapitalizationDataset(Dataset):
"""
Creates dataset to use during training for token classification
tasks with a pretrained model.
Converts from raw data to an instance that can be used by
NMDataLayer.
For dataset to use during inference without labels, see
BertPunctuationCapitalizationInferDataset.
Args:
text_file (str): file to sequences, each line should a sentence,
No header.
label_file (str): file to labels, each line corresponds to
word labels for a sentence in the text_file. No header.
max_seq_length (int): max sequence length minus 2 for [CLS] and [SEP]
tokenizer (Tokenizer): such as NemoBertTokenizer
num_samples (int): number of samples you want to use for the dataset.
If -1, use all dataset. Useful for testing.
shuffle (bool): whether to shuffle your data.
pad_label (str): pad value use for labels.
by default, it's the neutral label.
punct_label_ids and capit_label_ids (dict):
dict to map labels to label ids.
Starts with pad_label->0 and then increases in alphabetical order
For dev set use label_ids generated during training to support
cases when not all labels are present in the dev set.
For training set label_ids should be None.
ignore_extra_tokens (bool): whether to ignore extra tokens in
the loss_mask,
ignore_start_end (bool): whether to ignore bos and eos tokens in
the loss_mask
"""
def __init__(
self,
text_file,
label_file,
max_seq_length,
tokenizer,
num_samples=-1,
shuffle=False,
pad_label='O',
punct_label_ids=None,
capit_label_ids=None,
ignore_extra_tokens=False,
ignore_start_end=False,
use_cache=False,
):
if use_cache:
# Cache features
data_dir = os.path.dirname(text_file)
filename = os.path.basename(text_file)
if not filename.endswith('.txt'):
raise ValueError("{text_file} should have extension .txt")
filename = filename[:-4]
features_pkl = os.path.join(data_dir, filename + "_features.pkl")
if use_cache and os.path.exists(features_pkl):
# If text_file was already processed, load from pickle
features = pickle.load(open(features_pkl, 'rb'))
logging.info(f'features restored from {features_pkl}')
else:
if num_samples == 0:
raise ValueError("num_samples has to be positive", num_samples)
with open(text_file, 'r') as f:
text_lines = f.readlines()
# Collect all possible labels
punct_unique_labels = set([])
capit_unique_labels = set([])
punct_labels_lines = []
capit_labels_lines = []
with open(label_file, 'r') as f:
for line in f:
line = line.strip().split()
# extract punctuation and capitalization labels
punct_line, capit_line = zip(*line)
punct_labels_lines.append(punct_line)
capit_labels_lines.append(capit_line)
punct_unique_labels.update(punct_line)
capit_unique_labels.update(capit_line)
if len(punct_labels_lines) != len(text_lines):
raise ValueError("Labels file should contain labels for every word")
if shuffle or num_samples > 0:
dataset = list(zip(text_lines, punct_labels_lines, capit_labels_lines))
random.shuffle(dataset)
if num_samples > 0:
dataset = dataset[:num_samples]
dataset = list(zip(*dataset))
text_lines = dataset[0]
punct_labels_lines = dataset[1]
capit_labels_lines = dataset[2]
# for dev/test sets use label mapping from training set
if punct_label_ids:
if len(punct_label_ids) != len(punct_unique_labels):
logging.info(
'Not all labels from the specified'
+ 'label_ids dictionary are present in the'
+ 'current dataset. Using the provided'
+ 'label_ids dictionary.'
)
else:
logging.info('Using the provided label_ids dictionary.')
else:
logging.info(
'Creating a new label to label_id dictionary.'
+ ' It\'s recommended to use label_ids generated'
+ ' during training for dev/test sets to avoid'
+ ' errors if some labels are not'
+ ' present in the dev/test sets.'
+ ' For training set label_ids should be None.'
)
def create_label_ids(unique_labels, pad_label=pad_label):
label_ids = {pad_label: 0}
if pad_label in unique_labels:
unique_labels.remove(pad_label)
for label in sorted(unique_labels):
label_ids[label] = len(label_ids)
return label_ids
punct_label_ids = create_label_ids(punct_unique_labels)
capit_label_ids = create_label_ids(capit_unique_labels)
features = get_features(
text_lines,
max_seq_length,
tokenizer,
pad_label=pad_label,
punct_labels_lines=punct_labels_lines,
capit_labels_lines=capit_labels_lines,
punct_label_ids=punct_label_ids,
capit_label_ids=capit_label_ids,
ignore_extra_tokens=ignore_extra_tokens,
ignore_start_end=ignore_start_end,
)
if use_cache:
pickle.dump(features, open(features_pkl, "wb"))
logging.info(f'features saved to {features_pkl}')
self.all_input_ids = features[0]
self.all_segment_ids = features[1]
self.all_input_mask = features[2]
self.all_loss_mask = features[3]
self.all_subtokens_mask = features[4]
self.punct_all_labels = features[5]
self.capit_all_labels = features[6]
self.punct_label_ids = features[7]
self.capit_label_ids = features[8]
# save label_ids
def get_stats_and_save(all_labels, label_ids, name):
infold = text_file[: text_file.rfind('/')]
merged_labels = itertools.chain.from_iterable(all_labels)
logging.info('Three most popular labels')
_, label_frequencies = get_label_stats(merged_labels, infold + '/label_count_' + name + '.tsv')
out = open(os.path.join(infold, name + '_label_ids.csv'), 'w')
labels, _ = zip(*sorted(label_ids.items(), key=lambda x: x[1]))
out.write('\n'.join(labels))
logging.info(f'Labels: {label_ids}')
logging.info(f'Labels mapping saved to : {out.name}')
return label_frequencies
self.punct_label_frequencies = get_stats_and_save(self.punct_all_labels, self.punct_label_ids, 'punct')
self.capit_label_frequencies = get_stats_and_save(self.capit_all_labels, self.capit_label_ids, 'capit')
def __len__(self):
return len(self.all_input_ids)
def __getitem__(self, idx):
return (
np.array(self.all_input_ids[idx]),
np.array(self.all_segment_ids[idx]),
np.array(self.all_input_mask[idx], dtype=np.long),
np.array(self.all_loss_mask[idx]),
np.array(self.all_subtokens_mask[idx]),
np.array(self.punct_all_labels[idx]),
np.array(self.capit_all_labels[idx]),
)
class BertPunctuationCapitalizationInferDataset(Dataset):
"""
Creates dataset to use during inference for token classification
tasks with a pretrained model.
Converts from raw data to an instance that can be used by
NMDataLayer.
For dataset to use during training with labels, see
BertPunctuationCapitalizationDataset.
Args:
queries (list): list of queries to run inference on
max_seq_length (int): max sequence length minus 2 for [CLS] and [SEP]
tokenizer (Tokenizer): such as NemoBertTokenizer
"""
def __init__(self, queries, max_seq_length, tokenizer):
features = get_features(queries, max_seq_length, tokenizer)
self.all_input_ids = features[0]
self.all_segment_ids = features[1]
self.all_input_mask = features[2]
self.all_loss_mask = features[3]
self.all_subtokens_mask = features[4]
def __len__(self):
return len(self.all_input_ids)
def __getitem__(self, idx):
return (
np.array(self.all_input_ids[idx]),
np.array(self.all_segment_ids[idx]),
np.array(self.all_input_mask[idx], dtype=np.float32),
np.array(self.all_loss_mask[idx]),
np.array(self.all_subtokens_mask[idx]),
)