from pathlib import Path from typing import Dict, List, Tuple import json import datasets from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Tasks _CITATION = """\ @article{DBLP:journals/corr/abs-2011-00677, author = {Fajri Koto and Afshin Rahimi and Jey Han Lau and Timothy Baldwin}, title = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language Model for Indonesian {NLP}}, journal = {CoRR}, volume = {abs/2011.00677}, year = {2020}, url = {https://arxiv.org/abs/2011.00677}, eprinttype = {arXiv}, eprint = {2011.00677}, timestamp = {Fri, 06 Nov 2020 15:32:47 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _LOCAL = False _LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data) _DATASETNAME = "indolem_ntp" _DESCRIPTION = """\ NTP (Next Tweet prediction) is one of the comprehensive Indonesian benchmarks that given a list of tweets and an option, we predict if the option is the next tweet or not. This task is similar to the next sentence prediction (NSP) task used to train BERT (Devlin et al., 2019). In NTP, each instance consists of a Twitter thread (containing 2 to 4 tweets) that we call the premise, and four possible options for the next tweet, one of which is the actual response from the original thread. Train: 5681 threads Development: 811 threads Test: 1890 threads """ _HOMEPAGE = "https://indolem.github.io/" _LICENSE = "Creative Commons Attribution 4.0" _URLS = { _DATASETNAME: { "train": "https://raw.githubusercontent.com/indolem/indolem/main/next_tweet_prediction/data/train.json", "validation": "https://raw.githubusercontent.com/indolem/indolem/main/next_tweet_prediction/data/dev.json", "test": "https://raw.githubusercontent.com/indolem/indolem/main/next_tweet_prediction/data/test.json", } } _SUPPORTED_TASKS = [Tasks.NEXT_SENTENCE_PREDICTION] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class IndolemNTPDataset(datasets.GeneratorBasedBuilder): """NTP (Next Tweet prediction) is based on next sentence prediction (NSP), consists of a Twitter thread (containing 2 to 4 tweets) and four possible options for the next tweet, one of which is the actual response from the original thread.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = [ SEACrowdConfig( name="indolem_ntp_source", version=SOURCE_VERSION, description="Indolem NTP source schema", schema="source", subset_id="indolem_ntp", ), SEACrowdConfig( name="indolem_ntp_seacrowd_pairs", version=SEACROWD_VERSION, description="Indolem NTP Nusantara schema", schema="seacrowd_pairs", subset_id="indolem_ntp", ), ] DEFAULT_CONFIG_NAME = "indolem_ntp_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "tweets": datasets.Value("string"), "next_tweet": datasets.Value("string"), "label": datasets.Value("int8"), } ) elif self.config.schema == "seacrowd_pairs": features = schemas.pairs_features([0, 1]) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: urls = _URLS[_DATASETNAME] data_dir = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_dir["train"], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_dir["test"], "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": data_dir["validation"], "split": "dev", }, ), ] def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: data = self._read_data(filepath) if self.config.schema == "source": for i, row in enumerate(data): ex = { "id": str(i), "tweets": row[0], "next_tweet": row[1], "label": row[2], } yield i, ex elif self.config.schema == "seacrowd_pairs": for i, row in enumerate(data): ex = { "id": str(i), "text_1": row[0], "text_2": row[1], "label": row[2], } yield i, ex def _read_data(self, fname): data = json.load(open(fname, "r")) results = [] for datum in data: tweets = " ".join(datum["tweets"]) for key, option in datum["next_tweet"]: results.append((tweets, option, key)) return results