# 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 from dataclasses import dataclass from enum import Enum from typing import List, Optional, Union import torch from torch import nn from torch.utils.data.dataset import Dataset from transformers import PreTrainedTokenizer, torch_distributed_zero_first logger = logging.getLogger(__name__) @dataclass class InputExample: """ A single training/test example for token classification. 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. """ guid: str words: List[str] labels: Optional[List[str]] @dataclass class InputFeatures: """ A single set of features of data. Property names are the same names as the corresponding inputs to a model. """ input_ids: List[int] attention_mask: List[int] token_type_ids: Optional[List[int]] = None label_ids: Optional[List[int]] = None class Split(Enum): train = "train" dev = "dev" test = "test" class NerDataset(Dataset): """ This will be superseded by a framework-agnostic approach soon. """ features: List[InputFeatures] pad_token_label_id: int = nn.CrossEntropyLoss().ignore_index # Use cross entropy ignore_index as padding label id so that only # real label ids contribute to the loss later. def __init__( self, data_dir: str, tokenizer: PreTrainedTokenizer, labels: List[str], model_type: str, max_seq_length: Optional[int] = None, overwrite_cache=False, mode: Split = Split.train, local_rank=-1, ): # Load data features from cache or dataset file cached_features_file = os.path.join( data_dir, "cached_{}_{}_{}".format(mode.value, tokenizer.__class__.__name__, str(max_seq_length)), ) with torch_distributed_zero_first(local_rank): # Make sure only the first process in distributed training processes the dataset, # and the others will use the cache. if os.path.exists(cached_features_file) and not overwrite_cache: logger.info(f"Loading features from cached file {cached_features_file}") self.features = torch.load(cached_features_file) else: logger.info(f"Creating features from dataset file at {data_dir}") examples = read_examples_from_file(data_dir, mode) # TODO clean up all this to leverage built-in features of tokenizers self.features = convert_examples_to_features( examples, labels, max_seq_length, tokenizer, cls_token_at_end=bool(model_type in ["xlnet"]), # xlnet has a cls token at the end cls_token=tokenizer.cls_token, cls_token_segment_id=2 if model_type in ["xlnet"] else 0, sep_token=tokenizer.sep_token, sep_token_extra=bool(model_type in ["roberta"]), # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805 pad_on_left=bool(tokenizer.padding_side == "left"), pad_token=tokenizer.pad_token_id, pad_token_segment_id=tokenizer.pad_token_type_id, pad_token_label_id=self.pad_token_label_id, ) if local_rank in [-1, 0]: logger.info(f"Saving features into cached file {cached_features_file}") torch.save(self.features, cached_features_file) def __len__(self): return len(self.features) def __getitem__(self, i) -> InputFeatures: return self.features[i] def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]: if isinstance(mode, Split): mode = mode.value file_path = os.path.join(data_dir, f"{mode}.txt") 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=f"{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=f"{mode}-{guid_index}", words=words, labels=labels)) return examples def convert_examples_to_features( examples: List[InputExample], label_list: List[str], max_seq_length: int, tokenizer: PreTrainedTokenizer, 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, ) -> List[InputFeatures]: """ Loads a data file into a list of `InputFeatures` `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) """ # TODO clean up all this to leverage built-in features of tokenizers label_map = {label: i for i, label in enumerate(label_list)} features = [] for (ex_index, example) in enumerate(examples): if ex_index % 10_000 == 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) # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space. if len(word_tokens) > 0: 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 = tokenizer.num_special_tokens_to_add() 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])) if "token_type_ids" not in tokenizer.model_input_names: segment_ids = None features.append( InputFeatures( input_ids=input_ids, attention_mask=input_mask, token_type_ids=segment_ids, label_ids=label_ids ) ) return features def get_labels(path: str) -> List[str]: 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"]