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---
license: mit
task_categories:
- text-classification
language:
- asm
- ben
- brx
- doi
- gom
- guj
- hin
- kan
- kas
- mai
- mal
- mar
- mni
- npi
- ory
- pan
- san
- sat
- snd
- tam
- tel
- urd
- eng
tags:
- language-identification
- indian-languages
pretty_name: ILID
size_categories:
- 100K<n<1M
---
# ILID: Native Script Language Identification for Indian Languages
[**Paper**](https://huggingface.co/papers/2507.11832) | [**Code**](https://github.com/yashingle-ai/TextLangDetect) | [**Project Page**](https://yashingle-ai.github.io/ILID/)
🗣 **ILID: Indian Language Identification Dataset (23 Languages)**
**Authors:** [Yash Ingle](mailto:yash.ingle003@gmail.com), [Dr. Pruthwik Mishra](mailto:pruthwikmishra@aid.svnit.ac.in)
**Institute:** Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India
---
## 📄 Dataset Description
The **ILID** (Indian Language Identification Dataset) benchmark contains **250,000** sentences from **English and 22 official Indian languages**, designed for training and evaluating **language identification models**. The dataset supports the task of distinguishing between Indian languages, many of which share scripts, vocabulary, and structure.
---
## 📊 Dataset Statistics
| Language | Code | Train | Dev | Test | Total |
|--------------|-----------|-------|------|------|--------|
| Assamese | asm | 8000 | 1000 | 1000 | 10000 |
| Bengali | ben | 8000 | 1000 | 1000 | 10000 |
| Bodo | brx | 8000 | 1000 | 1000 | 10000 |
| Dogri | doi | 8000 | 1000 | 1000 | 10000 |
| Konkani | gom | 8000 | 1000 | 1000 | 10000 |
| Gujarati | guj | 8000 | 1000 | 1000 | 10000 |
| Hindi | hin | 8000 | 1000 | 1000 | 10000 |
| Kannada | kan | 8000 | 1000 | 1000 | 10000 |
| Kashmiri | kas | 8000 | 1000 | 1000 | 10000 |
| Maithili | mai | 8000 | 1000 | 1000 | 10000 |
| Malayalam | mal | 8000 | 1000 | 1000 | 10000 |
| Marathi | mar | 8000 | 1000 | 1000 | 10000 |
| Manipuri (Bengali) | mni_Beng | 8000 | 1000 | 1000 | 10000 |
| Manipuri (Meitei) | mni_Mtei | 8000 | 1000 | 1000 | 10000 |
| Nepali | npi | 8000 | 1000 | 1000 | 10000 |
| Odia | ory | 8000 | 1000 | 1000 | 10000 |
| Punjabi | pan | 8000 | 1000 | 1000 | 10000 |
| Sanskrit | san | 8000 | 1000 | 1000 | 10000 |
| Santali | sat | 8000 | 1000 | 1000 | 10000 |
| Sindhi (Arabic) | snd_Arab | 8000 | 1000 | 1000 | 10000 |
| Sindhi (Devanagari) | snd_Deva | 8000 | 1000 | 1000 | 10000 |
| Tamil | tam | 8000 | 1000 | 1000 | 10000 |
| Telugu | tel | 8000 | 1000 | 1000 | 10000 |
| Urdu | urd | 8000 | 1000 | 1000 | 10000 |
| English | eng | 8000 | 1000 | 1000 | 10000 |
| **Total** | — | **200000** | **25000** | **25000** | **250000** |
---
## 📁 Files Provided
- `shuffled_train_sentences`: Training sentences (80% split – 200,000 samples)
- `shuffled_train_labels`: Corresponding labels for training sentences
- `shuffled_dev_sentences`: Validation (dev) sentences (10% split – 25,000 samples)
- `shuffled_dev_labels`: Corresponding labels for dev sentences
- `shuffled_test_sentences`: Test sentences (10% split – 25,000 samples)
- `shuffled_test_labels`: Corresponding labels for test sentences
## 📌 Tasks
- **Language Identification (LID)**
- **Multilingual Text Classification**
- **Benchmarking ML & DL Models on Indian Languages**
---
## 🧹 Data Collection & Cleaning
- 13 languages collected using **web scraping** from Wikipedia, news portals, and blogs.
- 10 languages sampled from **large monolingual corpora** (Bhashaverse).
- Each sentence underwent **cleaning, normalization**, and **language filtering** via FastText.
---
## 🧠 Models & Results
Baseline models include:
- **TF-IDF + Machine Learning:** SVM, Logistic Regression, Random Forest, etc.
- **FastText Classifier**
- **Fine-tuned MuRIL (BERT for Indian languages)**
Best ensemble models achieve **F1-scores of up to 0.99** on test/dev sets.
---
## 📚 Citation
If you use this dataset, please cite:
```bibtex
@misc{ingle2025ilidnativescriptlanguage,
title={ILID: Native Script Language Identification for Indian Languages},
author={Yash Ingle and Pruthwik Mishra},
year={2025},
eprint={2507.11832},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2507.11832},
}
``` |