mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-28 16:52:24 +06:00
dd9d483d03
* doc
* [tests] Add sample files for a regression task
* [HUGE] Trainer
* Feedback from @sshleifer
* Feedback from @thomwolf + logging tweak
* [file_utils] when downloading concurrently, get_from_cache will use the cached file for subsequent processes
* [glue] Use default max_seq_length of 128 like before
* [glue] move DataTrainingArguments around
* [ner] Change interface of InputExample, and align run_{tf,pl}
* Re-align the pl scripts a little bit
* ner
* [ner] Add integration test
* Fix language_modeling with API tweak
* [ci] Tweak loss target
* Don't break console output
* amp.initialize: model must be on right device before
* [multiple-choice] update for Trainer
* Re-align to 827d6d6ef0
303 lines
12 KiB
Python
303 lines
12 KiB
Python
# 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"]
|