Datasets:
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| # 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. | |
| import html | |
| import os | |
| from pathlib import Path | |
| from typing import Dict, List, Tuple | |
| import datasets | |
| import pandas as pd | |
| from seacrowd.utils import schemas | |
| from seacrowd.utils.configs import SEACrowdConfig | |
| from seacrowd.utils.constants import Licenses, Tasks | |
| _CITATION = """ | |
| @inproccedings{rabinovich-2019-codeswitchreddit, | |
| author = {Rabinovich, Ella and Sultani, Masih and Stevenson, Suzanne}, | |
| title = {CodeSwitch-Reddit: Exploration of Written Multilingual Discourse in Online Discussion Forums}, | |
| booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing}, | |
| publisher = {Association for Computational Linguistics}, | |
| year = {2019}, | |
| url = {https://aclanthology.org/D19-1484}, | |
| doi = {10.18653/v1/D19-1484}, | |
| pages = {4776--4786}, | |
| } | |
| """ | |
| _LOCAL = False | |
| _LANGUAGES = ["eng", "ind", "tgl"] | |
| _DATASETNAME = "codeswitch_reddit" | |
| _DESCRIPTION = """ | |
| This corpus consists of monolingual English and multilingual (English and one other language) posts | |
| from country-specific subreddits, including r/indonesia, r/philippines and r/singapore for Southeast Asia. | |
| Posts were manually classified whether they contained code-switching or not. | |
| """ | |
| _HOMEPAGE = "https://github.com/ellarabi/CodeSwitch-Reddit" | |
| _LICENSE = Licenses.UNKNOWN.value | |
| _URL = "http://www.cs.toronto.edu/~ella/code-switch.reddit.tar.gz" | |
| _SUPPORTED_TASKS = [Tasks.CODE_SWITCHING_IDENTIFICATION, Tasks.SELF_SUPERVISED_PRETRAINING] | |
| _SOURCE_VERSION = "1.0.0" | |
| _SEACROWD_VERSION = "2024.06.20" | |
| class CodeSwitchRedditDataset(datasets.GeneratorBasedBuilder): | |
| """Dataset of monolingual English and multilingual comments from country-specific subreddits.""" | |
| SUBSETS = ["cs", "eng_monolingual"] | |
| INCLUDED_SUBREDDITS = ["indonesia", "Philippines", "singapore"] | |
| INCLUDED_LANGUAGES = {"English": "eng", "Indonesian": "ind", "Tagalog": "tgl"} | |
| BUILDER_CONFIGS = [ | |
| SEACrowdConfig( | |
| name=f"{_DATASETNAME}_{subset}_source", | |
| version=datasets.Version(_SOURCE_VERSION), | |
| description=f"{_DATASETNAME} source schema for {subset} subset", | |
| schema="source", | |
| subset_id=f"{_DATASETNAME}_{subset}", | |
| ) | |
| for subset in SUBSETS | |
| ] + [ | |
| SEACrowdConfig( | |
| name=f"{_DATASETNAME}_eng_monolingual_seacrowd_ssp", | |
| version=datasets.Version(_SEACROWD_VERSION), | |
| description=f"{_DATASETNAME} SEACrowd ssp schema for eng_monolingual subset", | |
| schema="seacrowd_ssp", | |
| subset_id=f"{_DATASETNAME}_eng_monolingual", | |
| ), | |
| SEACrowdConfig( | |
| name=f"{_DATASETNAME}_cs_seacrowd_text_multi", | |
| version=datasets.Version(_SEACROWD_VERSION), | |
| description=f"{_DATASETNAME} SEACrowd text multilabel schema for cs subset", | |
| schema="seacrowd_text_multi", | |
| subset_id=f"{_DATASETNAME}_cs", | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_cs_source" | |
| def _info(self) -> datasets.DatasetInfo: | |
| if self.config.schema == "source": | |
| if "cs" in self.config.subset_id: | |
| features = datasets.Features( | |
| { | |
| "author": datasets.Value("string"), | |
| "subreddit": datasets.Value("string"), | |
| "country": datasets.Value("string"), | |
| "date": datasets.Value("int32"), | |
| "confidence": datasets.Value("int32"), | |
| "lang1": datasets.Value("string"), | |
| "lang2": datasets.Value("string"), | |
| "text": datasets.Value("string"), | |
| "id": datasets.Value("string"), | |
| "link_id": datasets.Value("string"), | |
| "parent_id": datasets.Value("string"), | |
| } | |
| ) | |
| elif "eng_monolingual" in self.config.subset_id: | |
| features = datasets.Features( | |
| { | |
| "author": datasets.Value("string"), | |
| "subreddit": datasets.Value("string"), | |
| "country": datasets.Value("string"), | |
| "date": datasets.Value("int32"), | |
| "confidence": datasets.Value("int32"), | |
| "lang": datasets.Value("string"), | |
| "text": datasets.Value("string"), | |
| } | |
| ) | |
| elif self.config.schema == "seacrowd_ssp": | |
| features = schemas.ssp_features | |
| elif self.config.schema == "seacrowd_text_multi": | |
| features = schemas.text_multi_features(label_names=list(self.INCLUDED_LANGUAGES.values())) | |
| 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.""" | |
| data_dir = dl_manager.download_and_extract(_URL) | |
| if "cs" in self.config.subset_id: | |
| filepath = os.path.join(data_dir, "cs_main_reddit_corpus.csv") | |
| elif "eng_monolingual" in self.config.subset_id: | |
| filepath = os.path.join(data_dir, "eng_monolingual_reddit_corpus.csv") | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": filepath, | |
| "split": "train", | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: | |
| """Yields examples as (key, example) tuples.""" | |
| df = pd.read_csv(filepath, index_col=None, header="infer", encoding="utf-8") | |
| df = df[df["Subreddit"].isin(self.INCLUDED_SUBREDDITS)] | |
| if self.config.subset_id.split("_")[-1] == "cs": | |
| df = df[(df["Lang1"].isin(self.INCLUDED_LANGUAGES)) & (df["Lang2"].isin(self.INCLUDED_LANGUAGES))] | |
| df.reset_index(drop=True, inplace=True) | |
| for index, row in df.iterrows(): | |
| parsed_text = html.unescape(row["Text"]) | |
| if self.config.schema == "source": | |
| example = { | |
| "author": row["Author"], | |
| "subreddit": row["Subreddit"], | |
| "country": row["Country"], | |
| "date": row["Date"], | |
| "confidence": row["confidence"], | |
| "lang1": row["Lang1"], | |
| "lang2": row["Lang2"], | |
| "text": parsed_text, | |
| "id": row["id"], | |
| "link_id": row["link_id"], | |
| "parent_id": row["parent_id"], | |
| } | |
| elif self.config.schema == "seacrowd_text_multi": | |
| lang_one, lang_two = self.INCLUDED_LANGUAGES[row["Lang1"]], self.INCLUDED_LANGUAGES[row["Lang2"]] | |
| example = { | |
| "id": str(index), | |
| "text": parsed_text, | |
| "labels": list(sorted([lang_one, lang_two])), # Language order doesn't matter in original dataset; just arrange alphabetically for consistency | |
| } | |
| yield index, example | |
| else: | |
| df.reset_index(drop=True, inplace=True) | |
| for index, row in df.iterrows(): | |
| parsed_text = html.unescape(row["Text"]) | |
| if self.config.schema == "source": | |
| example = { | |
| "author": row["Author"], | |
| "subreddit": row["Subreddit"], | |
| "country": row["Country"], | |
| "date": row["Date"], | |
| "confidence": row["confidence"], | |
| "lang": row["Lang"], | |
| "text": parsed_text, | |
| } | |
| elif self.config.schema == "seacrowd_ssp": | |
| example = { | |
| "id": str(index), | |
| "text": parsed_text, | |
| } | |
| yield index, example | |