Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Sub-tasks:
natural-language-inference
Languages:
Indonesian
Size:
100K - 1M
License:
File size: 5,514 Bytes
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---
annotations_creators:
- machine-generated
- manual-partial-validation
language_creators:
- expert-generated
language:
- id
license: unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- SQuAD-ID
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: SQuAD-ID-NLI
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
config_name: squadid-nli
splits:
- name: train
num_bytes: 103934750
num_examples: 236890
- name: validation
num_bytes: 10831375
num_examples: 23748
- name: test
num_bytes: 10969750
num_examples: 23746
download_size: 125735875
dataset_size: 284384
---
# Dataset Card for SQuAD-ID-NLI
## 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 Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Hugging Face](https://huggingface.co/datasets/muhammadravi251001/squadid-nli)
- **Point of Contact:** [Hugging Face](https://huggingface.co/datasets/muhammadravi251001/squadid-nli)
- **Experiment:** [Github](https://github.com/muhammadravi251001/multilingual-qas-with-nli)
### Dataset Summary
The SQuAD-ID-NLI dataset is derived from the SQuAD-ID question answering dataset, utilizing named entity recognition (NER), chunking tags, Regex, and embedding similarity techniques to determine its contradiction sets.
Collected through this process, the dataset comprises various columns beyond premise, hypothesis, and label, including properties aligned with NER and chunking tags.
This dataset is designed to facilitate Natural Language Inference (NLI) tasks and contains information extracted from diverse sources to provide comprehensive coverage.
Each data instance encapsulates premise, hypothesis, label, and additional properties pertinent to NLI evaluation.
### Supported Tasks and Leaderboards
- Natural Language Inference for Indonesian
### Languages
Indonesian
## Dataset Structure
### Data Instances
An example of `test` looks as follows.
```
{
"premise": "Beberapa keluarga Yunani Bizantium berasal dari tentara bayaran Norman selama periode Restorasi Comnenian, ketika kaisar Bizantium mencari prajurit Eropa Barat. Raoulii adalah keturunan dari orang Italia-Norman bernama Raoul, Petraliphae adalah keturunan dari Pierre d'Aulps, dan kelompok klan Albania yang dikenal sebagai Maniakate diturunkan dari Normandia yang bertugas di bawah George Maniaces dalam ekspedisi Sisilia tahun 1038.",
"hypothesis": "Dari mana beberapa famili tentara bayaran Norman berasal? Yunani Bizantium",
"label": 0
}
```
### Data Fields
The data fields are:
- `premise`: a `string` feature
- `hypothesis`: a `string` feature
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
### Data Splits #TODO
The data is split across `train`, `valid`, and `test`.
| split | # examples |
|----------|-------:|
|train| 236890|
|valid| 23748|
|test| 23746|
## Dataset Creation
### Curation Rationale
Indonesian NLP is considered under-resourced. We need NLI dataset to fine-tuning the NLI model to utilizing them for QA models in order to improving the performance of the QA's.
### Source Data
#### Initial Data Collection and Normalization
We collect the data from the prominent QA dataset in Indonesian. The annotation fully by the original dataset's researcher.
#### Who are the source language producers?
This synthetic data was produced by machine, but the original data was produced by human.
### Personal and Sensitive Information
There might be some personal information coming from Wikipedia and news, especially the information of famous/important people.
## Considerations for Using the Data
### Discussion of Biases
The QA dataset (so the NLI-derived from them) is created using premise sentences taken from Wikipedia and news. These data sources may contain some bias.
### Other Known Limitations
No other known limitations
## Additional Information
### Dataset Curators
This dataset is the result of the collaborative work of Indonesian researchers from the University of Indonesia, Mohamed bin Zayed University of Artificial Intelligence, and the Korea Advanced Institute of Science & Technology.
### Licensing Information
The license is Unknown. Please contact authors for any information on the dataset. |