Datasets:
annotations_creators:
- machine-generated
language_creators:
- found
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended
task_categories:
- text-classification
task_ids:
- natural-language-inference
language:
- bn
license:
- cc-by-nc-sa-4.0
Dataset Card for xnli_bn
Table of Contents
- Dataset Card for
xnli_bn
Dataset Description
- Repository: https://github.com/csebuetnlp/banglabert
- Paper: "BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding"
- Point of Contact: Tahmid Hasan
Dataset Summary
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 introduced here.
Supported Tasks and Leaderboards
Languages
Bengali
Usage
from datasets import load_dataset
dataset = load_dataset("csebuetnlp/xnli_bn")
Dataset Structure
Data Instances
One example from the dataset is given below in JSON format.
{
"sentence1": "আসলে, আমি এমনকি এই বিষয়ে চিন্তাও করিনি, কিন্তু আমি এত হতাশ হয়ে পড়েছিলাম যে, শেষ পর্যন্ত আমি আবার তার সঙ্গে কথা বলতে শুরু করেছিলাম",
"sentence2": "আমি তার সাথে আবার কথা বলিনি।",
"label": "contradiction"
}
Data Fields
The data fields are as follows:
sentence1: astringfeature indicating the premise.sentence2: astringfeature indicating the hypothesis.label: a classification label, where possible values arecontradiction(0),entailment(1),neutral(2) .
Data Splits
| split | count |
|---|---|
train |
381449 |
validation |
2419 |
test |
4895 |
Dataset Creation
The dataset curation procedure was the same as the XNLI dataset: we translated the MultiNLI training data using the English to Bangla translation model introduced here. Due to the possibility of incursions of error during automatic translation, we used the Language-Agnostic BERT Sentence Embeddings (LaBSE) of the translations and original sentences to compute their similarity. All sentences below a similarity threshold of 0.70 were discarded.
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents belongs to the original copyright holders.
Citation Information
If you use the dataset, please cite the following paper:
@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}
}
Contributions
Thanks to @abhik1505040 and @Tahmid for adding this dataset.