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# Copyright 2024 The TensorFlow Authors. 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.
"""Loads dataset for the tagging (e.g., NER/POS) task."""
import dataclasses
from typing import Mapping, Optional
import tensorflow as tf, tf_keras
from official.common import dataset_fn
from official.core import config_definitions as cfg
from official.core import input_reader
from official.nlp.data import data_loader
from official.nlp.data import data_loader_factory
@dataclasses.dataclass
class TaggingDataConfig(cfg.DataConfig):
"""Data config for tagging (tasks/tagging)."""
is_training: bool = True
seq_length: int = 128
include_sentence_id: bool = False
file_type: str = 'tfrecord'
@data_loader_factory.register_data_loader_cls(TaggingDataConfig)
class TaggingDataLoader(data_loader.DataLoader):
"""A class to load dataset for tagging (e.g., NER and POS) task."""
def __init__(self, params: TaggingDataConfig):
self._params = params
self._seq_length = params.seq_length
self._include_sentence_id = params.include_sentence_id
def _decode(self, record: tf.Tensor):
"""Decodes a serialized tf.Example."""
name_to_features = {
'input_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
'input_mask': tf.io.FixedLenFeature([self._seq_length], tf.int64),
'segment_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
'label_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
}
if self._include_sentence_id:
name_to_features['sentence_id'] = tf.io.FixedLenFeature([], tf.int64)
name_to_features['sub_sentence_id'] = tf.io.FixedLenFeature([], tf.int64)
example = tf.io.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in example:
t = example[name]
if t.dtype == tf.int64:
t = tf.cast(t, tf.int32)
example[name] = t
return example
def _parse(self, record: Mapping[str, tf.Tensor]):
"""Parses raw tensors into a dict of tensors to be consumed by the model."""
x = {
'input_word_ids': record['input_ids'],
'input_mask': record['input_mask'],
'input_type_ids': record['segment_ids']
}
if self._include_sentence_id:
x['sentence_id'] = record['sentence_id']
x['sub_sentence_id'] = record['sub_sentence_id']
y = record['label_ids']
return (x, y)
def load(self, input_context: Optional[tf.distribute.InputContext] = None):
"""Returns a tf.dataset.Dataset."""
reader = input_reader.InputReader(
params=self._params,
dataset_fn=dataset_fn.pick_dataset_fn(self._params.file_type),
decoder_fn=self._decode,
parser_fn=self._parse)
return reader.read(input_context)
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