# 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 from filelock import FileLock from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available 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 TokenClassificationTask: @staticmethod def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]: raise NotImplementedError @staticmethod def get_labels(path: str) -> List[str]: raise NotImplementedError @staticmethod 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 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 if is_torch_available(): import torch from torch import nn from torch.utils.data import Dataset class TokenClassificationDataset(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, token_classification_task: TokenClassificationTask, data_dir: str, tokenizer: PreTrainedTokenizer, labels: List[str], model_type: str, max_seq_length: Optional[int] = None, overwrite_cache=False, mode: Split = Split.train, ): # 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)), ) # Make sure only the first process in distributed training processes the dataset, # and the others will use the cache. lock_path = cached_features_file + ".lock" with FileLock(lock_path): 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 = token_classification_task.read_examples_from_file(data_dir, mode) # TODO clean up all this to leverage built-in features of tokenizers self.features = token_classification_task.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=False, # 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, ) 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] if is_tf_available(): import tensorflow as tf class TFTokenClassificationDataset: """ This will be superseded by a framework-agnostic approach soon. """ features: List[InputFeatures] pad_token_label_id: int = -100 # Use cross entropy ignore_index as padding label id so that only # real label ids contribute to the loss later. def __init__( self, token_classification_task: TokenClassificationTask, data_dir: str, tokenizer: PreTrainedTokenizer, labels: List[str], model_type: str, max_seq_length: Optional[int] = None, overwrite_cache=False, mode: Split = Split.train, ): examples = token_classification_task.read_examples_from_file(data_dir, mode) # TODO clean up all this to leverage built-in features of tokenizers self.features = token_classification_task.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=False, # 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, ) def gen(): for ex in self.features: if ex.token_type_ids is None: yield ( {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label_ids, ) else: yield ( { "input_ids": ex.input_ids, "attention_mask": ex.attention_mask, "token_type_ids": ex.token_type_ids, }, ex.label_ids, ) if "token_type_ids" not in tokenizer.model_input_names: self.dataset = tf.data.Dataset.from_generator( gen, ({"input_ids": tf.int32, "attention_mask": tf.int32}, tf.int64), ( {"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([None]), ), ) else: self.dataset = tf.data.Dataset.from_generator( gen, ({"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32}, tf.int64), ( { "input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None]), "token_type_ids": tf.TensorShape([None]), }, tf.TensorShape([None]), ), ) def get_dataset(self): self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features))) return self.dataset def __len__(self): return len(self.features) def __getitem__(self, i) -> InputFeatures: return self.features[i]