metadata

license: cc-by-sa-4.0
task_categories:
  - text-classification
  - text2text-generation
language:
  - hi
  - en
  - mr
  - pa
  - ne
  - sd
  - as
  - gu
  - ta
  - te
  - ur
  - or
tags:
  - multilingual
  - language-identification
  - text-classification
  - indian
pretty_name: Language Identification Dataset
size_categories:
  - 100K<n<1M

Dataset Card for Language Identification Dataset

Dataset Description

Repository: processvenue/language_identification
Total Samples: 135784
Number of Languages: 18
Splits:
- Train: 95048 samples
- Validation: 20367 samples
- Test: 20369 samples

Sample Notebook:

https://www.kaggle.com/code/rishabhbhartiya/indian-language-classification-smote-resampled

Kaggle Dataset link:

https://www.kaggle.com/datasets/processvenue/indian-language-identification

Dataset Summary

A comprehensive dataset for Indian language identification and text classification. The dataset contains text samples across 18 major Indian languages, making it suitable for developing language identification systems and multilingual NLP applications.

Languages and Distribution

Language Distribution:
1. Punjabi      15075
2. Odia         14258
3. Konkani      14098
4. Hindi        13469
5. Sanskrit     11788
6. Bengali      10036
7. English       9819
8. Sindhi        8838
9. Nepali        8694
10. Marathi       6625
11. Gujarati      3788
12. Telugu        3563
13. Malayalam     3423
14. Tamil         3195
15. Kannada       2651
16. Kashmiri      2282
17. Urdu          2272
18. Assamese      1910

Data Fields

Headline: The input text sample
Language: The language label (one of the 18 languages listed above)

Usage Example

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Process-Venue/Language-Identification-v2")

# Access splits
train_data = dataset['train']
validation_data = dataset['validation']
test_data = dataset['test']

# Example usage
print(f"Sample text: {train_data[0]['Headline']}")
print(f"Language: {train_data[0]['Language']}")

Applications

Language Identification Systems
- Automatic language detection
- Text routing in multilingual systems
- Content filtering by language
Machine Translation
- Language-pair identification
- Translation system selection
Content Analysis
- Multilingual content categorization
- Language-specific content analysis

Citation

If you use this dataset in your research, please cite:

@dataset{language_identification_2025,
  author = {ProcessVenue Team},
  website = {https://processvenue.com},
  title = {Multilingual Headlines Language Identification Dataset},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/processvenue/language-identification},
  version = {1.1}
}

###reference

  1. @misc{disisbig_news_datasets,
author = {Gaurav},
title = {Indian Language News Datasets},
year = {2019},
publisher = {Kaggle},
url = {https://www.kaggle.com/datasets/disisbig/}
}

    2. @misc{bhattarai_nepali_financial_news,
  author = {Anuj Bhattarai},
  title = {The Nepali Financial News Dataset},
  year = {2024},
  publisher = {Kaggle},
  url = {https://www.kaggle.com/datasets/anujbhatrai/the-nepali-financial-news-dataset}
    }

    3. @misc{sourav_inshorts_hindi,
  author = {Shivam Sourav},
  title = {Inshorts-Hindi},
  year = {2023},
  publisher = {Kaggle},
  url = {https://www.kaggle.com/datasets/shivamsourav2002/inshorts-hindi}
    }