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fa84ae26d6
This is the result of:
$ black --line-length 119 examples templates transformers utils hubconf.py setup.py
There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.
This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.
209 lines
8.3 KiB
Python
209 lines
8.3 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Named entity recognition fine-tuning: utilities to work with CoNLL-2003 task. """
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from __future__ import absolute_import, division, print_function
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import logging
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import os
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from io import open
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logger = logging.getLogger(__name__)
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class InputExample(object):
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"""A single training/test example for token classification."""
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def __init__(self, guid, words, labels):
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"""Constructs a InputExample.
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Args:
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guid: Unique id for the example.
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words: list. The words of the sequence.
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labels: (Optional) list. The labels for each word of the sequence. This should be
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specified for train and dev examples, but not for test examples.
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"""
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self.guid = guid
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self.words = words
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self.labels = labels
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class InputFeatures(object):
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"""A single set of features of data."""
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def __init__(self, input_ids, input_mask, segment_ids, label_ids):
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.segment_ids = segment_ids
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self.label_ids = label_ids
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def read_examples_from_file(data_dir, mode):
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file_path = os.path.join(data_dir, "{}.txt".format(mode))
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guid_index = 1
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examples = []
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with open(file_path, encoding="utf-8") as f:
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words = []
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labels = []
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for line in f:
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if line.startswith("-DOCSTART-") or line == "" or line == "\n":
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if words:
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examples.append(InputExample(guid="{}-{}".format(mode, guid_index), words=words, labels=labels))
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guid_index += 1
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words = []
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labels = []
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else:
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splits = line.split(" ")
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words.append(splits[0])
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if len(splits) > 1:
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labels.append(splits[-1].replace("\n", ""))
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else:
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# Examples could have no label for mode = "test"
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labels.append("O")
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if words:
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examples.append(InputExample(guid="%s-%d".format(mode, guid_index), words=words, labels=labels))
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return examples
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def convert_examples_to_features(
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examples,
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label_list,
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max_seq_length,
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tokenizer,
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cls_token_at_end=False,
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cls_token="[CLS]",
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cls_token_segment_id=1,
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sep_token="[SEP]",
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sep_token_extra=False,
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pad_on_left=False,
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pad_token=0,
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pad_token_segment_id=0,
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pad_token_label_id=-100,
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sequence_a_segment_id=0,
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mask_padding_with_zero=True,
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):
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""" Loads a data file into a list of `InputBatch`s
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`cls_token_at_end` define the location of the CLS token:
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- False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
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- True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
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`cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
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"""
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label_map = {label: i for i, label in enumerate(label_list)}
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features = []
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for (ex_index, example) in enumerate(examples):
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if ex_index % 10000 == 0:
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logger.info("Writing example %d of %d", ex_index, len(examples))
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tokens = []
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label_ids = []
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for word, label in zip(example.words, example.labels):
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word_tokens = tokenizer.tokenize(word)
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tokens.extend(word_tokens)
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# Use the real label id for the first token of the word, and padding ids for the remaining tokens
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label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))
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# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
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special_tokens_count = 3 if sep_token_extra else 2
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if len(tokens) > max_seq_length - special_tokens_count:
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tokens = tokens[: (max_seq_length - special_tokens_count)]
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label_ids = label_ids[: (max_seq_length - special_tokens_count)]
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# The convention in BERT is:
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# (a) For sequence pairs:
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# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
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# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
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# (b) For single sequences:
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# tokens: [CLS] the dog is hairy . [SEP]
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# type_ids: 0 0 0 0 0 0 0
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#
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# Where "type_ids" are used to indicate whether this is the first
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# sequence or the second sequence. The embedding vectors for `type=0` and
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# `type=1` were learned during pre-training and are added to the wordpiece
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# embedding vector (and position vector). This is not *strictly* necessary
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# since the [SEP] token unambiguously separates the sequences, but it makes
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# it easier for the model to learn the concept of sequences.
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#
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# For classification tasks, the first vector (corresponding to [CLS]) is
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# used as as the "sentence vector". Note that this only makes sense because
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# the entire model is fine-tuned.
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tokens += [sep_token]
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label_ids += [pad_token_label_id]
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if sep_token_extra:
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# roberta uses an extra separator b/w pairs of sentences
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tokens += [sep_token]
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label_ids += [pad_token_label_id]
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segment_ids = [sequence_a_segment_id] * len(tokens)
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if cls_token_at_end:
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tokens += [cls_token]
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label_ids += [pad_token_label_id]
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segment_ids += [cls_token_segment_id]
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else:
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tokens = [cls_token] + tokens
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label_ids = [pad_token_label_id] + label_ids
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segment_ids = [cls_token_segment_id] + segment_ids
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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# tokens are attended to.
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input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
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# Zero-pad up to the sequence length.
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padding_length = max_seq_length - len(input_ids)
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if pad_on_left:
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input_ids = ([pad_token] * padding_length) + input_ids
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input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
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segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
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label_ids = ([pad_token_label_id] * padding_length) + label_ids
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else:
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input_ids += [pad_token] * padding_length
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input_mask += [0 if mask_padding_with_zero else 1] * padding_length
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segment_ids += [pad_token_segment_id] * padding_length
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label_ids += [pad_token_label_id] * padding_length
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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assert len(label_ids) == max_seq_length
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if ex_index < 5:
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logger.info("*** Example ***")
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logger.info("guid: %s", example.guid)
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logger.info("tokens: %s", " ".join([str(x) for x in tokens]))
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logger.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
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logger.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
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logger.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
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logger.info("label_ids: %s", " ".join([str(x) for x in label_ids]))
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features.append(
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InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_ids=label_ids)
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)
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return features
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def get_labels(path):
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if path:
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with open(path, "r") as f:
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labels = f.read().splitlines()
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if "O" not in labels:
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labels = ["O"] + labels
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return labels
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else:
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return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
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