350 lines
14 KiB
Python
350 lines
14 KiB
Python
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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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import csv
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import dataclasses
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import json
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from dataclasses import dataclass
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from typing import List, Optional, Union
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from ...utils import is_tf_available, is_torch_available, logging
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logger = logging.get_logger(__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 simple sequence classification.
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Args:
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guid: Unique id for the example.
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text_a: string. The untokenized text of the first sequence. For single
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sequence tasks, only this sequence must be specified.
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text_b: (Optional) string. The untokenized text of the second sequence.
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Only must be specified for sequence pair tasks.
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label: (Optional) string. The label of the example. 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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text_a: str
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text_b: Optional[str] = None
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label: Optional[str] = None
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def to_json_string(self):
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"""Serializes this instance to a JSON string."""
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return json.dumps(dataclasses.asdict(self), indent=2) + "\n"
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@dataclass(frozen=True)
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class InputFeatures:
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"""
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A single set of features of data. Property names are the same names as the corresponding inputs to a model.
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Args:
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input_ids: Indices of input sequence tokens in the vocabulary.
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attention_mask: Mask to avoid performing attention on padding token indices.
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Mask values selected in `[0, 1]`: Usually `1` for tokens that are NOT MASKED, `0` for MASKED (padded)
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tokens.
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token_type_ids: (Optional) Segment token indices to indicate first and second
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portions of the inputs. Only some models use them.
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label: (Optional) Label corresponding to the input. Int for classification problems,
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float for regression problems.
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"""
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input_ids: List[int]
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attention_mask: Optional[List[int]] = None
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token_type_ids: Optional[List[int]] = None
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label: Optional[Union[int, float]] = None
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def to_json_string(self):
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"""Serializes this instance to a JSON string."""
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return json.dumps(dataclasses.asdict(self)) + "\n"
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class DataProcessor:
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"""Base class for data converters for sequence classification data sets."""
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def get_example_from_tensor_dict(self, tensor_dict):
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"""
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Gets an example from a dict with tensorflow tensors.
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Args:
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tensor_dict: Keys and values should match the corresponding Glue
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tensorflow_dataset examples.
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"""
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raise NotImplementedError()
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def get_train_examples(self, data_dir):
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"""Gets a collection of [`InputExample`] for the train set."""
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raise NotImplementedError()
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def get_dev_examples(self, data_dir):
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"""Gets a collection of [`InputExample`] for the dev set."""
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raise NotImplementedError()
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def get_test_examples(self, data_dir):
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"""Gets a collection of [`InputExample`] for the test set."""
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raise NotImplementedError()
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def get_labels(self):
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"""Gets the list of labels for this data set."""
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raise NotImplementedError()
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def tfds_map(self, example):
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"""
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Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
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examples to the correct format.
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"""
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if len(self.get_labels()) > 1:
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example.label = self.get_labels()[int(example.label)]
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return example
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@classmethod
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def _read_tsv(cls, input_file, quotechar=None):
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"""Reads a tab separated value file."""
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with open(input_file, "r", encoding="utf-8-sig") as f:
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return list(csv.reader(f, delimiter="\t", quotechar=quotechar))
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class SingleSentenceClassificationProcessor(DataProcessor):
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"""Generic processor for a single sentence classification data set."""
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def __init__(self, labels=None, examples=None, mode="classification", verbose=False):
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self.labels = [] if labels is None else labels
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self.examples = [] if examples is None else examples
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self.mode = mode
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self.verbose = verbose
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def __len__(self):
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return len(self.examples)
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def __getitem__(self, idx):
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if isinstance(idx, slice):
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return SingleSentenceClassificationProcessor(labels=self.labels, examples=self.examples[idx])
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return self.examples[idx]
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@classmethod
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def create_from_csv(
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cls, file_name, split_name="", column_label=0, column_text=1, column_id=None, skip_first_row=False, **kwargs
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):
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processor = cls(**kwargs)
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processor.add_examples_from_csv(
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file_name,
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split_name=split_name,
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column_label=column_label,
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column_text=column_text,
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column_id=column_id,
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skip_first_row=skip_first_row,
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overwrite_labels=True,
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overwrite_examples=True,
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)
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return processor
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@classmethod
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def create_from_examples(cls, texts_or_text_and_labels, labels=None, **kwargs):
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processor = cls(**kwargs)
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processor.add_examples(texts_or_text_and_labels, labels=labels)
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return processor
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def add_examples_from_csv(
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self,
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file_name,
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split_name="",
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column_label=0,
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column_text=1,
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column_id=None,
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skip_first_row=False,
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overwrite_labels=False,
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overwrite_examples=False,
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):
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lines = self._read_tsv(file_name)
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if skip_first_row:
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lines = lines[1:]
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texts = []
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labels = []
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ids = []
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for i, line in enumerate(lines):
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texts.append(line[column_text])
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labels.append(line[column_label])
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if column_id is not None:
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ids.append(line[column_id])
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else:
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guid = f"{split_name}-{i}" if split_name else str(i)
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ids.append(guid)
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return self.add_examples(
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texts, labels, ids, overwrite_labels=overwrite_labels, overwrite_examples=overwrite_examples
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)
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def add_examples(
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self, texts_or_text_and_labels, labels=None, ids=None, overwrite_labels=False, overwrite_examples=False
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):
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if labels is not None and len(texts_or_text_and_labels) != len(labels):
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raise ValueError(
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f"Text and labels have mismatched lengths {len(texts_or_text_and_labels)} and {len(labels)}"
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)
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if ids is not None and len(texts_or_text_and_labels) != len(ids):
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raise ValueError(f"Text and ids have mismatched lengths {len(texts_or_text_and_labels)} and {len(ids)}")
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if ids is None:
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ids = [None] * len(texts_or_text_and_labels)
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if labels is None:
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labels = [None] * len(texts_or_text_and_labels)
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examples = []
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added_labels = set()
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for text_or_text_and_label, label, guid in zip(texts_or_text_and_labels, labels, ids):
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if isinstance(text_or_text_and_label, (tuple, list)) and label is None:
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text, label = text_or_text_and_label
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else:
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text = text_or_text_and_label
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added_labels.add(label)
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examples.append(InputExample(guid=guid, text_a=text, text_b=None, label=label))
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# Update examples
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if overwrite_examples:
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self.examples = examples
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else:
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self.examples.extend(examples)
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# Update labels
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if overwrite_labels:
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self.labels = list(added_labels)
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else:
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self.labels = list(set(self.labels).union(added_labels))
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return self.examples
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def get_features(
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self,
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tokenizer,
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max_length=None,
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pad_on_left=False,
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pad_token=0,
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mask_padding_with_zero=True,
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return_tensors=None,
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):
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"""
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Convert examples in a list of `InputFeatures`
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Args:
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tokenizer: Instance of a tokenizer that will tokenize the examples
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max_length: Maximum example length
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pad_on_left: If set to `True`, the examples will be padded on the left rather than on the right (default)
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pad_token: Padding token
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mask_padding_with_zero: If set to `True`, the attention mask will be filled by `1` for actual values
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and by `0` for padded values. If set to `False`, inverts it (`1` for padded values, `0` for actual
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values)
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Returns:
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If the `examples` input is a `tf.data.Dataset`, will return a `tf.data.Dataset` containing the
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task-specific features. If the input is a list of `InputExamples`, will return a list of task-specific
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`InputFeatures` which can be fed to the model.
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"""
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if max_length is None:
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max_length = tokenizer.max_len
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label_map = {label: i for i, label in enumerate(self.labels)}
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all_input_ids = []
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for ex_index, example in enumerate(self.examples):
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if ex_index % 10000 == 0:
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logger.info(f"Tokenizing example {ex_index}")
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input_ids = tokenizer.encode(
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example.text_a,
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add_special_tokens=True,
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max_length=min(max_length, tokenizer.max_len),
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)
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all_input_ids.append(input_ids)
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batch_length = max(len(input_ids) for input_ids in all_input_ids)
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features = []
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for ex_index, (input_ids, example) in enumerate(zip(all_input_ids, self.examples)):
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if ex_index % 10000 == 0:
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logger.info(f"Writing example {ex_index}/{len(self.examples)}")
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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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attention_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 = batch_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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attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
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else:
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input_ids = input_ids + ([pad_token] * padding_length)
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attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
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if len(input_ids) != batch_length:
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raise ValueError(f"Error with input length {len(input_ids)} vs {batch_length}")
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if len(attention_mask) != batch_length:
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raise ValueError(f"Error with input length {len(attention_mask)} vs {batch_length}")
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if self.mode == "classification":
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label = label_map[example.label]
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elif self.mode == "regression":
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label = float(example.label)
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else:
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raise ValueError(self.mode)
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if ex_index < 5 and self.verbose:
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logger.info("*** Example ***")
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logger.info(f"guid: {example.guid}")
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logger.info(f"input_ids: {' '.join([str(x) for x in input_ids])}")
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logger.info(f"attention_mask: {' '.join([str(x) for x in attention_mask])}")
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logger.info(f"label: {example.label} (id = {label})")
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features.append(InputFeatures(input_ids=input_ids, attention_mask=attention_mask, label=label))
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if return_tensors is None:
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return features
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elif return_tensors == "tf":
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if not is_tf_available():
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raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported")
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import tensorflow as tf
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def gen():
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for ex in features:
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yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
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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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({"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([])),
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)
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return dataset
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elif return_tensors == "pt":
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if not is_torch_available():
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raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported")
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import torch
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from torch.utils.data import TensorDataset
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all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
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all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
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if self.mode == "classification":
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all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
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elif self.mode == "regression":
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all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
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dataset = TensorDataset(all_input_ids, all_attention_mask, all_labels)
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return dataset
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else:
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raise ValueError("return_tensors should be one of 'tf' or 'pt'")
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