Divyanshu/indicxnli

---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: IndicXNLI
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
---

# Dataset Card for "IndicXNLI"

## Table of Contents

- [Dataset Card for "IndicXNLI"](#dataset-card-for-indicxnli)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)

## Dataset Description

- **Homepage:** <https://github.com/divyanshuaggarwal/IndicXNLI>
- **Paper:** [IndicXNLI: Evaluating Multilingual Inference for Indian Languages](https://arxiv.org/abs/2204.08776)
- **Point of Contact:** [Divyanshu Aggarwal](mailto:[e-mail])

### Dataset Summary

INDICXNLI is similar to existing
XNLI dataset in shape/form, but focusses on Indic language family. INDICXNLI include NLI
data for eleven major Indic languages that includes
Assamese (‘as’), Gujarat (‘gu’), Kannada (‘kn’),
Malayalam (‘ml’), Marathi (‘mr’), Odia (‘or’),
Punjabi (‘pa’), Tamil (‘ta’), Telugu (‘te’), Hindi
(‘hi’), and Bengali (‘bn’).

### Supported Tasks and Leaderboards

**Tasks:** Natural Language Inference

**Leaderboards:** Currently there is no Leaderboard for this dataset.

### Languages

- `Assamese (as)`
- `Bengali (bn)`
- `Gujarati (gu)`
- `Kannada (kn)`
- `Hindi (hi)`
- `Malayalam (ml)`
- `Marathi (mr)`
- `Oriya (or)`
- `Punjabi (pa)`
- `Tamil (ta)`
- `Telugu (te)`

## Dataset Structure

### Data Instances

One example from the `hi` dataset is given below in JSON format.

```python
{'premise': 'अवधारणात्मक रूप से क्रीम स्किमिंग के दो बुनियादी आयाम हैं-उत्पाद और भूगोल।',
'hypothesis': 'उत्पाद और भूगोल क्रीम स्किमिंग का काम करते हैं।',
'label': 1 (neutral) }
```

### Data Fields

- `premise (string)`: Premise Sentence
- `hypothesis (string)`: Hypothesis Sentence
- `label (integer)`: Integer label `0` if hypothesis `entails` the premise, `2` if hypothesis `negates` the premise and `1` otherwise.

### Data Splits

<!-- Below is the dataset split given for `hi` dataset.

```python
DatasetDict({
train: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 392702
})
test: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 5010
})
validation: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 2490
})
})

``` -->

Language | ISO 639-1 Code |Train | Test | Dev |
--------------|----------------|-------|-----|------|
Assamese | as | 392,702 | 5,010 | 2,490 |
Bengali | bn | 392,702 | 5,010 | 2,490 |
Gujarati | gu | 392,702 | 5,010 | 2,490 |
Hindi | hi | 392,702 | 5,010 | 2,490 |
Kannada | kn | 392,702 | 5,010 | 2,490 |
Malayalam | ml |392,702 | 5,010 | 2,490 |
Marathi | mr |392,702 | 5,010 | 2,490 |
Oriya | or | 392,702 | 5,010 | 2,490 |
Punjabi | pa | 392,702 | 5,010 | 2,490 |
Tamil | ta | 392,702 | 5,010 | 2,490 |
Telugu | te | 392,702 | 5,010 | 2,490 |

<!-- The dataset split remains same across all languages. -->

## Dataset usage

Code snippet for using the dataset using datasets library.

```python
from datasets import load_dataset

dataset = load_dataset("Divyanshu/indicxnli")
```

## Dataset Creation
Machine translation of XNLI english dataset to 11 listed Indic Languages.

### Curation Rationale

[More information needed]

### Source Data

[XNLI dataset](https://cims.nyu.edu/~sbowman/xnli/)

#### Initial Data Collection and Normalization

[Detailed in the paper](https://arxiv.org/abs/2204.08776)

#### Who are the source language producers?

[Detailed in the paper](https://arxiv.org/abs/2204.08776)

#### Human Verification Process

[Detailed in the paper](https://arxiv.org/abs/2204.08776)

## Considerations for Using the Data

### Social Impact of Dataset

[Detailed in the paper](https://arxiv.org/abs/2204.08776)

### Discussion of Biases

[Detailed in the paper](https://arxiv.org/abs/2204.08776)

### Other Known Limitations

[Detailed in the paper](https://arxiv.org/abs/2204.08776)

### Dataset Curators

Divyanshu Aggarwal, Vivek Gupta, Anoop Kunchukuttan

### Licensing Information

Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). Copyright of the dataset contents belongs to the original copyright holders.

### Citation Information

If you use any of the datasets, models or code modules, please cite the following paper:

```
@misc{https://doi.org/10.48550/arxiv.2204.08776,
doi = {10.48550/ARXIV.2204.08776},

url = {https://arxiv.org/abs/2204.08776},

author = {Aggarwal, Divyanshu and Gupta, Vivek and Kunchukuttan, Anoop},

keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},

title = {IndicXNLI: Evaluating Multilingual Inference for Indian Languages},

publisher = {arXiv},

year = {2022},

copyright = {Creative Commons Attribution 4.0 International}
}
```

<!-- ### Contributions -->

原始信息汇总

数据集概述

数据集名称

• IndicXNLI

数据集摘要

• IndicXNLI 是一个专注于印度语言家族的自然语言推理数据集，包含11种主要印度语言的数据。

支持的任务和排行榜

• 任务: 自然语言推理
• 排行榜: 目前没有

语言支持

• Assamese (as)
• Bengali (bn)
• Gujarati (gu)
• Kannada (kn)
• Hindi (hi)
• Malayalam (ml)
• Marathi (mr)
• Oriya (or)
• Punjabi (pa)
• Tamil (ta)
• Telugu (te)

数据集结构

• 数据实例: 包含前提（premise）、假设（hypothesis）和标签（label）。
• 数据字段:
  • premise (string): 前提句子
  • hypothesis (string): 假设句子
  • label (integer): 标签，0表示假设蕴含前提，2表示假设否定前提，1表示其他情况。
• 数据分割: 每个语言的数据集都分为训练集、测试集和验证集，每部分的大小分别为392,702、5,010和2,490条记录。

许可证

• cc0-1.0

数据集大小

• 1M<n<10M

数据来源

• 原始数据

任务类别

• 文本分类

任务ID

• 自然语言推理