---
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

```python
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

1. **Language Identification Systems**
   - Automatic language detection
   - Text routing in multilingual systems
   - Content filtering by language

2. **Machine Translation**
   - Language-pair identification
   - Translation system selection

3. **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}
    }
```