traditional_chinese_aya_evaluation_suite.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.
# TODO: Address all TODOs and remove all explanatory comments
"""ted2020_tw_mt"""


import csv
import json
import os
import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {中文 Aya evaluation_suite},
author={Heng-Shiou Sheu
},
year={2024}
}
"""

# You can copy an official description
_DESCRIPTION = """\
是一個精心策劃的資料集，源自 CohereForAI 的綜合 Aya 集合，特別關注繁體中文資料。
此資料集聚合了 CohereForAI/aya_collection、CohereForAI/aya_dataset 和 CohereForAI/aya_evaluation_suite 中的內容，
過濾掉除中文內容之外的所有內容，包括繁體中文與簡體中文。
"""

# TODO 請使用 MAC 讀取資料夾內容來做更新
_Subset_names = [
    'aya_human_annotated',
    'dolly_machine_translated'
 ]

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://huggingface.co/Heng666"

_LICENSE = "apache-2.0"

# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    "aya_collection": "https://huggingface.co/datasets/CohereForAI/aya_collection",
    "aya_dataset": "https://huggingface.co/datasets/CohereForAI/aya_dataset",
    "evaluation_suite": "https://huggingface.co/datasets/CohereForAI/aya_evaluation_suite"
}

class ChineseAyaEvalSuiteConfig(datasets.BuilderConfig):
    """BuilderConfig for Chinese Aya"""

    def __init__(self, subset, **kwargs):
        super().__init__(**kwargs)
        """
        Args:
            subset: subset, you want to load
            **kwargs: keyword arguments forwarded to super.
        """
        self.subset = subset


class ChineseAyaEvalSuiteDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIG_CLASS = ChineseAyaEvalSuiteConfig
    
    BUILDER_CONFIGS = [
        ChineseAyaEvalSuiteConfig(
            name=subset, 
            description=_DESCRIPTION, 
            subset=subset 
        )
        for subset in _Subset_names
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "id": datasets.Value("int64"),
                "inputs": datasets.Value("string"),
                "targets": datasets.Value("string"),
                "language": datasets.Value("string"),
                "script": datasets.Value("string"),
            }),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE
        )

    def _split_generators(self, dl_manager):

        subset = self.config.subset
        
        files = {}
    
        train_path = os.path.join("train/", f"CohereForAI-{subset}-train.csv")
        files["train"] = train_path
        test_path = os.path.join("test", f"CohereForAI-{subset}-test.csv")
        files["test"] = test_path
        validation_path = os.path.join("validation", f"CohereForAI-{subset}-validation.csv")
        files["validation"] = validation_path

        try:
            data_dir = dl_manager.download_and_extract(files)
        except:
            files.pop("train")
            files.pop("validation")
            data_dir = dl_manager.download_and_extract(files)
            
        output = []
        if "train" in files:
            train = datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_dir["train"]
                }
            )
            output.append(train)

        if "test" in files:
            test = datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": data_dir["test"]
                }
            )
            output.append(test)
        
        if "validation" in files:
            validation = datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": data_dir["validation"]
                }
            )
            output.append(validation)

        return output

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as f:
            reader = csv.reader(f, delimiter=",", quotechar='"')
            for id_, row in enumerate(reader):
                if id_ == 0:
                    continue
                yield id_, {
                    "id": row[0],
                    "inputs": row[1],
                    "targets": row[2],
                    "language": row[3],
                    "script": row[4],
                }