"""XNLI Bengali dataset""" import json import os import datasets _CITATION = """\ @misc{bhattacharjee2021banglabert, title={BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding}, author={Abhik Bhattacharjee and Tahmid Hasan and Kazi Samin and Md Saiful Islam and M. Sohel Rahman and Anindya Iqbal and Rifat Shahriyar}, year={2021}, eprint={2101.00204}, archivePrefix={arXiv}, primaryClass={cs.CL} } """ _DESCRIPTION = """\ This is a Natural Language Inference (NLI) dataset for Bengali, curated using the subset of MNLI data used in XNLI and state-of-the-art English to Bengali translation model. """ _HOMEPAGE = "https://github.com/csebuetnlp/banglabert" _LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)" _URL = "https://huggingface.co/datasets/csebuetnlp/xnli_bn/resolve/main/data/xnli_bn.tar.bz2" _VERSION = datasets.Version("0.0.1") class XnliBn(datasets.GeneratorBasedBuilder): """XNLI Bengali dataset""" BUILDER_CONFIGS = [ datasets.BuilderConfig( name="xnli_bn", version=_VERSION, description=_DESCRIPTION, ) ] def _info(self): features = datasets.Features( { "sentence1": datasets.Value("string"), "sentence2": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["contradiction", "entailment", "neutral"]), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, version=_VERSION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" data_dir = os.path.join(dl_manager.download_and_extract(_URL), "xnli_bn") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(data_dir, "train.jsonl"), }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": os.path.join(data_dir, "test.jsonl"), }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": os.path.join(data_dir, "validation.jsonl"), }, ), ] def _generate_examples(self, filepath): """Yields examples as (key, example) tuples.""" with open(filepath, encoding="utf-8") as f: for idx_, row in enumerate(f): data = json.loads(row) yield idx_, {"sentence1": data["sentence1"], "sentence2": data["sentence2"], "label": data["label"]}