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- # 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
- import pdb
- 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 = "devel"
- test = "test"
- if is_torch_available():
- import torch
- from torch import nn
- from torch.utils.data.dataset import Dataset
- 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- This will be superseded by a framework-agnostic approach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,
- ):
- # 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 = 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=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 TFNerDataset:
- """
- This will be superseded by a framework-agnostic approach
- soon.
- """
- features: List[InputFeatures]
- pad_token_label_id: int = -1
- # 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,
- ):
- 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=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):
- return self.dataset
- 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:
- splits_replace = splits[-1].replace("\n", "")
- if splits_replace == 'O':
- labels.append(splits_replace)
- else:
- labels.append(splits_replace + "-bio")
- 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()
- labels = [i+'-bio' if i != 'O' else 'O' for i in labels]
- 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"]
- return ["O", "B-bio", "I-bio"]
|