metadata
dataset_info:
- config_name: as
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_examples: 392702
- name: validation
num_examples: 2490
- name: test
num_examples: 5010
- config_name: bn
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_examples: 392702
- name: validation
num_examples: 2490
- name: test
num_examples: 5010
- config_name: gu
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_examples: 392702
- name: validation
num_examples: 2490
- name: test
num_examples: 5010
- config_name: hi
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_examples: 392702
- name: validation
num_examples: 2490
- name: test
num_examples: 5010
- config_name: kn
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_examples: 392702
- name: validation
num_examples: 2490
- name: test
num_examples: 5010
- config_name: ml
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_examples: 392702
- name: validation
num_examples: 2490
- name: test
num_examples: 5010
- config_name: mr
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_examples: 392702
- name: validation
num_examples: 2490
- name: test
num_examples: 5010
- config_name: or
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_examples: 392702
- name: validation
num_examples: 2490
- name: test
num_examples: 5010
- config_name: pa
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_examples: 392702
- name: validation
num_examples: 2490
- name: test
num_examples: 5010
- config_name: ta
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_examples: 392702
- name: validation
num_examples: 3238
- name: test
num_examples: 5010
- config_name: te
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_examples: 392702
- name: validation
num_examples: 2490
- name: test
num_examples: 5010
configs:
- config_name: as
data_files:
- split: train
path: data/as/train-*
- split: validation
path: data/as/validation-*
- split: test
path: data/as/test-*
- config_name: bn
data_files:
- split: train
path: data/bn/train-*
- split: validation
path: data/bn/validation-*
- split: test
path: data/bn/test-*
- config_name: gu
data_files:
- split: train
path: data/gu/train-*
- split: validation
path: data/gu/validation-*
- split: test
path: data/gu/test-*
- config_name: hi
data_files:
- split: train
path: data/hi/train-*
- split: validation
path: data/hi/validation-*
- split: test
path: data/hi/test-*
- config_name: kn
data_files:
- split: train
path: data/kn/train-*
- split: validation
path: data/kn/validation-*
- split: test
path: data/kn/test-*
- config_name: ml
data_files:
- split: train
path: data/ml/train-*
- split: validation
path: data/ml/validation-*
- split: test
path: data/ml/test-*
- config_name: mr
data_files:
- split: train
path: data/mr/train-*
- split: validation
path: data/mr/validation-*
- split: test
path: data/mr/test-*
- config_name: or
data_files:
- split: train
path: data/or/train-*
- split: validation
path: data/or/validation-*
- split: test
path: data/or/test-*
- config_name: pa
data_files:
- split: train
path: data/pa/train-*
- split: validation
path: data/pa/validation-*
- split: test
path: data/pa/test-*
- config_name: ta
data_files:
- split: train
path: data/ta/train-*
- split: validation
path: data/ta/validation-*
- split: test
path: data/ta/test-*
- config_name: te
data_files:
- split: train
path: data/te/train-*
- split: validation
path: data/te/validation-*
- split: test
path: data/te/test-*
IndicXNLI (Repaired)
This is a repaired version of the Divyanshu/indicxnli dataset, converted to parquet format for compatibility with HuggingFace datasets 4.x+.
Why this exists
The original dataset uses a Python loading script (indicxnli.py) which is no longer supported in HuggingFace datasets 4.x. This version converts the data to native parquet format.
Original Dataset
- Paper: IndicXNLI: Evaluating Multilingual Inference for Indian Languages
- Original Repo: Divyanshu/indicxnli
Languages
| Code | Language |
|---|---|
| as | Assamese |
| bn | Bengali |
| gu | Gujarati |
| hi | Hindi |
| kn | Kannada |
| ml | Malayalam |
| mr | Marathi |
| or | Oriya |
| pa | Punjabi |
| ta | Tamil |
| te | Telugu |
Usage
from datasets import load_dataset
# Load Hindi validation split
ds = load_dataset("AdaMLLab/indicxnli_repaired", "hi", split="validation")
Schema
Each sample contains:
premise(string): The premise sentencehypothesis(string): The hypothesis sentencelabel(int): 0=entailment, 1=neutral, 2=contradiction
Data Source
This dataset uses the "forward" translation direction from the original dataset, where English XNLI was translated to Indic languages.
Citation
If you use this dataset, please cite the original paper:
@inproceedings{aggarwal-etal-2022-indicxnli,
title = "{I}ndic{XNLI}: Evaluating Multilingual Inference for {I}ndian Languages",
author = "Aggarwal, Divyanshu and Gupta, Vivek and Kunchukuttan, Anoop",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
year = "2022"
}