CoT-Collection.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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 json
import os

import datasets



_CITATION = """\
@article{kim2023cot,
  title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning},
  author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon},
  journal={arXiv preprint arXiv:2305.14045},
  year={2023}
}
"""

_DESCRIPTION = """"""

_LICENSE = "CC BY 4.0"

_HOMEPAGE = "https://github.com/kaistAI/CoT-Collection"

_LANGUAGES = {
    "en": "English",
}
# _ALL_LANGUAGES = "all_languages"



class CoTCollectionMultiConfig(datasets.BuilderConfig):
    """BuilderConfig for CoTCollectionMultiConfig."""

    def __init__(self, languages=None, **kwargs):
        super(CoTCollectionMultiConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs),
        self.languages = languages


class CoTCollection(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        CoTCollectionMultiConfig(
            name=lang,
            languages=[lang],
            description=f"{_LANGUAGES[lang]} CoT-Collection data used in the paper 'The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning'",
        )
        for lang in _LANGUAGES
    ]
    BUILDER_CONFIG_CLASS = CoTCollectionMultiConfig
    DEFAULT_CONFIG_NAME = "en"


    def _info(self):
        features = datasets.Features(
            {
                "source": datasets.Value("string"),
                "target": datasets.Value("string"),
                "rationale": datasets.Value("string"),
                "task": datasets.Value("string"),
                "type": datasets.Value("string"),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
        )


    def _split_generators(self, dl_manager):
        train_PATHS = [f"./data/CoT_collection_{lang}.json" for lang in self.config.languages]

        train_paths = dl_manager.download_and_extract(train_PATHS)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_paths})
        ]

    def _generate_examples(self, filepath):
        for _file in filepath:
            with open(_file, "r", encoding="utf-8") as fi:
                data = json.load(fi)
                buffer = []
                for idx, value in data.items():
                    if 'rationale' in value.keys():
                        buffer.append({
                            'source': value['source'],
                            'target': value['target'],
                            'rationale': value['rationale'],
                            'task': value['task'],
                            'type': 'CoT'
                        })
                    else:
                        value['rationale'] = ''
                        buffer.append({
                            'source': value['source'],
                            'target': value['target'],
                            'rationale': value['rationale'],
                            'task': value['task'],
                            'type': 'Direct',
                        })   
                

            for idx,dat in enumerate(buffer):
                yield idx, dat