Datasets:
Tasks:
Text Classification
Sub-tasks:
natural-language-inference
Languages:
Bengali
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
extended
ArXiv:
Tags:
License:
| """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"]} | |