# Copyright 2023 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Token classification Dataset."""
import os
from mindformers.tools.register import MindFormerRegister, MindFormerModuleType
from mindformers.tools.logger import logger
from mindformers.version_control import get_dataset_map
from .dataloader import build_dataset_loader
from ..models.build_tokenizer import build_tokenizer
from .transforms import build_transforms
from .sampler import build_sampler
from .base_dataset import BaseDataset
[文档]@MindFormerRegister.register(MindFormerModuleType.DATASET)
class TokenClassificationDataset(BaseDataset):
"""
Token classification Dataset.
Examples:
>>> from mindformers.tools.register import MindFormerConfig
>>> from mindformers import MindFormerBook
>>> from mindformers.dataset import TokenClassificationDataset
>>> from mindformers.dataset import build_dataset, check_dataset_config
>>> config_dict_list = MindFormerBook.get_trainer_support_task_list()
>>> config_path = config_dict_list['token_classification']['tokcls_bert_base_chinese']
>>> # Initialize a MindFormerConfig instance with a specific config file of yaml.
>>> config = MindFormerConfig(config_path)
>>> config.train_dataset.data_loader.dataset_dir = "The required task dataset path"
Note:
The detailed data setting could refer to
https://gitee.com/mindspore/mindformers/blob/r0.3/docs/task_cards/token_classification.md
>>> check_dataset_config(config)
>>> # 1) use config dict to build dataset
>>> dataset_from_config = build_dataset(config.train_dataset_task)
>>> # 2) use class name to build dataset
>>> dataset_from_name = build_dataset(class_name='TokenClassificationDataset',
... dataset_config=config.train_dataset_task.dataset_config)
>>> # 3) use class to build dataset
>>> dataset_from_class = TokenClassificationDataset(config.train_dataset_task.dataset_config)
"""
def __new__(cls, dataset_config: dict = None):
"""new method"""
logger.info("Now Create Token classification Dataset.")
cls.init_dataset_config(dataset_config)
rank_id = int(os.getenv("RANK_ID", "0"))
device_num = int(os.getenv("RANK_SIZE", "1"))
dataset = build_dataset_loader(
dataset_config.data_loader, default_args={'num_shards': device_num, 'shard_id': rank_id})
tokenizer = build_tokenizer(dataset_config.tokenizer)
text_transforms = build_transforms(dataset_config.text_transforms,
default_args={"tokenizer": tokenizer})
label_transforms = build_transforms(dataset_config.label_transforms)
sampler = build_sampler(dataset_config.sampler)
if sampler is not None:
dataset = dataset.use_sampler(sampler)
if text_transforms is not None:
dataset = get_dataset_map(dataset,
input_columns=dataset_config.input_columns,
operations=text_transforms,
output_columns=dataset_config.output_columns,
num_parallel_workers=dataset_config.num_parallel_workers,
python_multiprocessing=dataset_config.python_multiprocessing)
if label_transforms is not None:
dataset = get_dataset_map(dataset,
input_columns=dataset_config.input_columns[1],
operations=label_transforms,
num_parallel_workers=dataset_config.num_parallel_workers,
python_multiprocessing=dataset_config.python_multiprocessing)
dataset = dataset.batch(dataset_config.batch_size,
drop_remainder=dataset_config.drop_remainder,
num_parallel_workers=dataset_config.num_parallel_workers)
dataset = dataset.repeat(dataset_config.repeat)
return dataset