Delete tagging_dataloader.py

tagging_dataloader.py

@@ -1,90 +0,0 @@
-# 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)