BIG Reorganize examples (#4213)
* Created using Colaboratory * [examples] reorganize files * remove run_tpu_glue.py as superseded by TPU support in Trainer * Bugfix: int, not tuple * move files around
This commit is contained in:
Julien Chaumond
398
examples/token-classification/utils_ner.py
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398
examples/token-classification/utils_ner.py
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# 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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import logging
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import os
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from dataclasses import dataclass
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from enum import Enum
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from typing import List, Optional, Union
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from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
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logger = logging.getLogger(__name__)
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@dataclass
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class InputExample:
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"""
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A single training/test example for token classification.
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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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guid: str
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words: List[str]
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labels: Optional[List[str]]
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@dataclass
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class InputFeatures:
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"""
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A single set of features of data.
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Property names are the same names as the corresponding inputs to a model.
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"""
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input_ids: List[int]
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attention_mask: List[int]
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token_type_ids: Optional[List[int]] = None
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label_ids: Optional[List[int]] = None
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class Split(Enum):
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train = "train"
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dev = "dev"
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test = "test"
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if is_torch_available():
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import torch
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from torch import nn
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from torch.utils.data.dataset import Dataset
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from transformers import torch_distributed_zero_first
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class NerDataset(Dataset):
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"""
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This will be superseded by a framework-agnostic approach
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soon.
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"""
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features: List[InputFeatures]
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pad_token_label_id: int = nn.CrossEntropyLoss().ignore_index
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# Use cross entropy ignore_index as padding label id so that only
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# real label ids contribute to the loss later.
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def __init__(
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self,
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data_dir: str,
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tokenizer: PreTrainedTokenizer,
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labels: List[str],
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model_type: str,
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max_seq_length: Optional[int] = None,
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overwrite_cache=False,
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mode: Split = Split.train,
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local_rank=-1,
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):
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# Load data features from cache or dataset file
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cached_features_file = os.path.join(
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data_dir, "cached_{}_{}_{}".format(mode.value, tokenizer.__class__.__name__, str(max_seq_length)),
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)
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with torch_distributed_zero_first(local_rank):
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# Make sure only the first process in distributed training processes the dataset,
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# and the others will use the cache.
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if os.path.exists(cached_features_file) and not overwrite_cache:
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logger.info(f"Loading features from cached file {cached_features_file}")
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self.features = torch.load(cached_features_file)
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else:
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logger.info(f"Creating features from dataset file at {data_dir}")
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examples = read_examples_from_file(data_dir, mode)
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# TODO clean up all this to leverage built-in features of tokenizers
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self.features = convert_examples_to_features(
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examples,
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labels,
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max_seq_length,
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tokenizer,
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cls_token_at_end=bool(model_type in ["xlnet"]),
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# xlnet has a cls token at the end
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cls_token=tokenizer.cls_token,
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cls_token_segment_id=2 if model_type in ["xlnet"] else 0,
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sep_token=tokenizer.sep_token,
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sep_token_extra=bool(model_type in ["roberta"]),
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# roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
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pad_on_left=bool(tokenizer.padding_side == "left"),
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pad_token=tokenizer.pad_token_id,
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pad_token_segment_id=tokenizer.pad_token_type_id,
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pad_token_label_id=self.pad_token_label_id,
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)
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if local_rank in [-1, 0]:
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logger.info(f"Saving features into cached file {cached_features_file}")
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torch.save(self.features, cached_features_file)
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def __len__(self):
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return len(self.features)
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def __getitem__(self, i) -> InputFeatures:
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return self.features[i]
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if is_tf_available():
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import tensorflow as tf
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class TFNerDataset:
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"""
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This will be superseded by a framework-agnostic approach
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soon.
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"""
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features: List[InputFeatures]
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pad_token_label_id: int = -1
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# Use cross entropy ignore_index as padding label id so that only
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# real label ids contribute to the loss later.
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def __init__(
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self,
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data_dir: str,
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tokenizer: PreTrainedTokenizer,
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labels: List[str],
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model_type: str,
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max_seq_length: Optional[int] = None,
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overwrite_cache=False,
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mode: Split = Split.train,
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):
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examples = read_examples_from_file(data_dir, mode)
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# TODO clean up all this to leverage built-in features of tokenizers
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self.features = convert_examples_to_features(
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examples,
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labels,
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max_seq_length,
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tokenizer,
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cls_token_at_end=bool(model_type in ["xlnet"]),
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# xlnet has a cls token at the end
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cls_token=tokenizer.cls_token,
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cls_token_segment_id=2 if model_type in ["xlnet"] else 0,
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sep_token=tokenizer.sep_token,
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sep_token_extra=bool(model_type in ["roberta"]),
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# roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
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pad_on_left=bool(tokenizer.padding_side == "left"),
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pad_token=tokenizer.pad_token_id,
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pad_token_segment_id=tokenizer.pad_token_type_id,
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pad_token_label_id=self.pad_token_label_id,
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)
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def gen():
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for ex in self.features:
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if ex.token_type_ids is None:
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yield (
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{"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
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ex.label_ids,
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)
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else:
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yield (
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{
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"input_ids": ex.input_ids,
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"attention_mask": ex.attention_mask,
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"token_type_ids": ex.token_type_ids,
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},
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ex.label_ids,
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)
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if "token_type_ids" not in tokenizer.model_input_names:
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self.dataset = tf.data.Dataset.from_generator(
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gen,
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({"input_ids": tf.int32, "attention_mask": tf.int32}, tf.int64),
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(
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{"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])},
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tf.TensorShape([None]),
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),
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)
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else:
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self.dataset = tf.data.Dataset.from_generator(
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gen,
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({"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32}, tf.int64),
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(
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{
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"input_ids": tf.TensorShape([None]),
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"attention_mask": tf.TensorShape([None]),
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"token_type_ids": tf.TensorShape([None]),
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},
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tf.TensorShape([None]),
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),
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)
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def get_dataset(self):
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return self.dataset
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def __len__(self):
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return len(self.features)
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def __getitem__(self, i) -> InputFeatures:
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return self.features[i]
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def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]:
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if isinstance(mode, Split):
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mode = mode.value
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file_path = os.path.join(data_dir, f"{mode}.txt")
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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=f"{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=f"{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: List[InputExample],
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label_list: List[str],
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max_seq_length: int,
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tokenizer: PreTrainedTokenizer,
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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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) -> List[InputFeatures]:
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""" Loads a data file into a list of `InputFeatures`
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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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# TODO clean up all this to leverage built-in features of tokenizers
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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 % 10_000 == 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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# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
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if len(word_tokens) > 0:
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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 = tokenizer.num_special_tokens_to_add()
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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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if "token_type_ids" not in tokenizer.model_input_names:
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segment_ids = None
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features.append(
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InputFeatures(
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input_ids=input_ids, attention_mask=input_mask, token_type_ids=segment_ids, label_ids=label_ids
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)
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)
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return features
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def get_labels(path: str) -> List[str]:
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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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