# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, 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. """ Named entity recognition fine-tuning: utilities to work with CoNLL-2003 task. """ import logging import os logger = logging.getLogger(__name__) class InputExample(object): """A single training/test example for token classification.""" def __init__(self, guid, words, labels): """Constructs a InputExample. Args: guid: Unique id for the example. words: list. The words of the sequence. labels: (Optional) list. The labels for each word of the sequence. This should be specified for train and dev examples, but not for test examples. """ self.guid = guid self.words = words self.labels = labels class InputFeatures(object): """A single set of features of data.""" def __init__(self, input_ids, input_mask, segment_ids, label_ids): self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.label_ids = label_ids def read_examples_from_file(data_dir, mode): file_path = os.path.join(data_dir, "{}.txt".format(mode)) guid_index = 1 examples = [] with open(file_path, encoding="utf-8") as f: words = [] labels = [] for line in f: if line.startswith("-DOCSTART-") or line == "" or line == "\n": if words: examples.append(InputExample(guid="{}-{}".format(mode, guid_index), words=words, labels=labels)) guid_index += 1 words = [] labels = [] else: splits = line.split(" ") words.append(splits[0]) if len(splits) > 1: labels.append(splits[-1].replace("\n", "")) else: # Examples could have no label for mode = "test" labels.append("O") if words: examples.append(InputExample(guid="{}-{}".format(mode, guid_index), words=words, labels=labels)) return examples def convert_examples_to_features( examples, label_list, max_seq_length, tokenizer, cls_token_at_end=False, cls_token="[CLS]", cls_token_segment_id=1, sep_token="[SEP]", sep_token_extra=False, pad_on_left=False, pad_token=0, pad_token_segment_id=0, pad_token_label_id=-100, sequence_a_segment_id=0, mask_padding_with_zero=True, ): """ Loads a data file into a list of `InputBatch`s `cls_token_at_end` define the location of the CLS token: - False (Default, BERT/XLM 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)} features = [] for (ex_index, example) in enumerate(examples): if ex_index % 10000 == 0: logger.info("Writing example %d of %d", ex_index, len(examples)) tokens = [] label_ids = [] for word, label in zip(example.words, example.labels): word_tokens = tokenizer.tokenize(word) tokens.extend(word_tokens) # Use the real label id for the first token of the word, and padding ids for the remaining tokens label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1)) # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa. special_tokens_count = 3 if sep_token_extra else 2 if len(tokens) > max_seq_length - special_tokens_count: tokens = tokens[: (max_seq_length - special_tokens_count)] label_ids = label_ids[: (max_seq_length - special_tokens_count)] # 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 unambiguously 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 += [sep_token] label_ids += [pad_token_label_id] if sep_token_extra: # roberta uses an extra separator b/w pairs of sentences tokens += [sep_token] label_ids += [pad_token_label_id] segment_ids = [sequence_a_segment_id] * len(tokens) if cls_token_at_end: tokens += [cls_token] label_ids += [pad_token_label_id] segment_ids += [cls_token_segment_id] else: tokens = [cls_token] + tokens label_ids = [pad_token_label_id] + label_ids 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 # tokens are attended to. input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids) # Zero-pad up to the sequence length. padding_length = max_seq_length - len(input_ids) if pad_on_left: input_ids = ([pad_token] * padding_length) + input_ids input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids label_ids = ([pad_token_label_id] * padding_length) + label_ids else: input_ids += [pad_token] * padding_length input_mask += [0 if mask_padding_with_zero else 1] * padding_length segment_ids += [pad_token_segment_id] * padding_length label_ids += [pad_token_label_id] * padding_length assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length assert len(label_ids) == max_seq_length if ex_index < 5: logger.info("*** Example ***") logger.info("guid: %s", example.guid) logger.info("tokens: %s", " ".join([str(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_ids: %s", " ".join([str(x) for x in label_ids])) features.append( InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_ids=label_ids) ) return features def get_labels(path): if path: with open(path, "r") as f: labels = f.read().splitlines() if "O" not in labels: labels = ["O"] + labels return labels else: return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]