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