# coding=utf-8 # Copyright 2022 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. from pathlib import Path from typing import Dict, List, Tuple import datasets import pandas as pd from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA, Licenses, Tasks) _CITATION = """\ @misc{zhang2022mdia, title={MDIA: A Benchmark for Multilingual Dialogue Generation in 46 Languages}, author={Qingyu Zhang and Xiaoyu Shen and Ernie Chang and Jidong Ge and Pengke Chen}, year={2022}, eprint={2208.13078}, archivePrefix={arXiv}, primaryClass={cs.CL} } """ _DATASETNAME = "mdia" _DESCRIPTION = """\ This is a multilingual benchmark for dialogue generation containing real-life Reddit conversations (parent and response comment pairs) in 46 languages, including Indonesian, Tagalog and Vietnamese. English translations are also provided for comments. """ _HOMEPAGE = "https://github.com/DoctorDream/mDIA" _LANGUAGES = ["ind", "tgl", "vie"] _LICENSE = Licenses.CC_BY_4_0.value _LOCAL = False _URLS = { "raw": "https://github.com/DoctorDream/mDIA/raw/master/datasets/raw.zip", "translated": "https://github.com/DoctorDream/mDIA/raw/master/datasets/translated.zip", } _SUPPORTED_TASKS = [Tasks.DIALOGUE_SYSTEM, Tasks.MACHINE_TRANSLATION] # DS, MT _SEACROWD_SCHEMA = {task.value: f"seacrowd_{str(TASK_TO_SCHEMA[task]).lower()}" for task in _SUPPORTED_TASKS} # t2t _SUBSETS = [ "ind_dialogue", "ind_eng", "tgl_dialogue", "tgl_eng", "vie_dialogue", "vie_eng", ] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class MdiaDataset(datasets.GeneratorBasedBuilder): """Multilingual benchmark for dialogue generation containing real-life Reddit conversations""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = [] for subset in _SUBSETS: if "dialogue" in subset: BUILDER_CONFIGS += [ SEACrowdConfig( name=f"{_DATASETNAME}_{subset}_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} {subset} source schema", schema="source", subset_id=subset, ), SEACrowdConfig( name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA['DS']}", version=SEACROWD_VERSION, description=f"{_DATASETNAME} {subset} SEACrowd schema", schema=_SEACROWD_SCHEMA["DS"], subset_id=subset, ), ] else: BUILDER_CONFIGS += [ SEACrowdConfig( name=f"{_DATASETNAME}_{subset}_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} {subset} source schema", schema="source", subset_id=subset, ), SEACrowdConfig( name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA['MT']}", version=SEACROWD_VERSION, description=f"{_DATASETNAME} {subset} SEACrowd schema", schema=_SEACROWD_SCHEMA["MT"], subset_id=subset, ), ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_{_SUBSETS[0]}_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "lang": datasets.Value("string"), "title": datasets.Value("string"), "source_body": datasets.Value("string"), "target_body": datasets.Value("string"), "link_id": datasets.Value("string"), "source_id": datasets.Value("string"), "target_id": datasets.Value("string"), "translated_source_body": datasets.Value("string"), "translated_target_body": datasets.Value("string"), } ) elif self.config.schema == _SEACROWD_SCHEMA["DS"]: # same schema with _SEACROWD_SCHEMA["MT"] features = SCHEMA_TO_FEATURES[TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]] # text2text_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" lang_map = {"ind": "id", "tgl": "tl", "vie": "vi"} lang = lang_map[self.config.subset_id.split("_")[0]] data_url = _URLS["translated"] data_dir = Path(dl_manager.download_and_extract(data_url)) / "translated" data_path = "{split}_data/{lang}2en_{split}.csv" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "data_path": data_dir / data_path.format(split="train", lang=lang), }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "data_path": data_dir / data_path.format(split="test", lang=lang), }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "data_path": data_dir / data_path.format(split="eval", lang=lang), }, ), ] def _generate_examples(self, data_path: Path) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" df = pd.read_csv(data_path) # source schema if self.config.schema == "source": for i, row in df.iterrows(): yield i, { "lang": row["lang"], "title": row["title"], "source_body": row["source_body"], "target_body": row["target_body"], "link_id": row["link_id"], "source_id": row["source_id"], "target_id": row["target_id"], "translated_source_body": row["translated_source_body"], "translated_target_body": row["translated_target_body"], } # t2t schema for dialogue elif "dialogue" in self.config.subset_id: for i, row in df.iterrows(): yield i, { "id": str(i), "text_1": row["source_body"], "text_2": row["target_body"], "text_1_name": "source_body", "text_2_name": "target_body", } # t2t schema for machine translation elif "eng" in self.config.subset_id: for i, row in df.iterrows(): for j in range(2): idx = i * 2 + j if j == 0: yield idx, { "id": str(idx), "text_1": row["source_body"], "text_2": row["translated_source_body"], "text_1_name": "source_body", "text_2_name": "translated_source_body", } else: yield idx, { "id": str(idx), "text_1": row["target_body"], "text_2": row["translated_target_body"], "text_1_name": "target_body", "text_2_name": "translated_target_body", }