Traditional_Chinese-aya_collection / traditional_chinese_aya_collection.py
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# 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 collection},
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_dataset',
'templated_ntx_llm',
'templated_uner_llm',
'templated_xcsqa',
'templated_xlel_wd',
'templated_xwikis',
'translated_adversarial_qa',
'translated_cnn_dailymail',
'translated_dolly',
'translated_flan_coqa',
'translated_flan_cot',
'translated_flan_gem_wiki',
'translated_flan_lambada',
'translated_flan_qa',
'translated_hotpotqa',
'translated_joke_explaination',
'translated_mintaka',
'translated_mlqa',
'translated_nqopen',
'translated_paws',
'translated_piqa',
'translated_wikiqa'
]
# 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 ChineseAyaCollectionConfig(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 ChineseAyaCollectionDataset(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIG_CLASS = ChineseAyaCollectionConfig
BUILDER_CONFIGS = [
ChineseAyaCollectionConfig(
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"),
"dataset_name": datasets.Value("string"),
"sub_dataset_name": datasets.Value("string"),
"task_type": datasets.Value("string"),
"template_id": datasets.Value("string"),
"language_code": datasets.Value("string"),
"split": 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("test")
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],
"dataset_name": row[3],
"sub_dataset_name": row[4],
"task_type": row[5],
"template_id": row[6],
"language_code": row[7],
"split": row[8],
"script": row[9],
}