---
annotations_creators:
  - manual
language_creators:
  - found
language:
  - en
  - te
  - kn
license: cc-by-4.0
multilinguality:
  - multilingual
pretty_name: Multilingual English-Telugu-Kannada Translation Dataset
size_categories:
  - 1M<n<10M
source_datasets:
  - ai4bharat/samanantar
task_categories:
  - translation
task_ids:
  - translation
dataset_info:
  features:
  - name: target_lang_code
    dtype: string
  - name: input_ids
    sequence: int32
  - name: attention_mask
    sequence: int8
  - name: labels
    sequence: int64
  splits:
  - name: train
    num_bytes: 2231633137
    num_examples: 7966485
  download_size: 700766001
  dataset_size: 2231633137
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# 📚 Multilingual English-Telugu-Kannada Translation Dataset

This dataset is a curated and preprocessed subset of the [AI4Bharat Samanantar](https://huggingface.co/datasets/ai4bharat/samanantar) dataset focused on multilingual translation tasks between English, Telugu (`te_IN`), and Kannada (`kn_IN`).

## ✨ Dataset Features

- Language pairs:
  - `en ↔ te_IN`
  - `en ↔ kn_IN`
- Preprocessed:
  - Filtered for sentence length (min=3, max=128 words)
  - Cleaned and normalized
- Tokenized using Hugging Face Transformers tokenizers:
  - M2M100Tokenizer (for `en↔kn`)
  - MBart50TokenizerFast (for `en↔te`)

## 📦 Dataset Structure

The dataset contains the following fields:

- `src_texts`: Source language sentence (English)
- `tgt_texts`: Target language sentence (Telugu or Kannada)
- `labels`: Tokenized target sequence for model training
- `input_ids`, `attention_mask`: Tokenized source sentence

The dataset is split into:
- `train`: Training samples
- `validation`: Small subset for evaluation

## 📊 Size

- ~7.9M total sentence pairs
- Supports batch training and multilingual fine-tuning

## 💡 Usage Example

```python
from datasets import load_dataset

dataset = load_dataset("Koushim/en-te-kn-translation-dataset")

print(dataset["train"][0])
````

## 🧠 Intended Uses

* Train multilingual translation models (MBart, Marian, M2M100)
* Fine-tune LLMs on Indic translation
* Evaluate BLEU or other metrics for low-resource translation

## 📜 License

CC-BY-4.0

## ✍️ Author

Koushik Reddy
[GitHub](https://github.com/Koushik7893) | [Hugging Face](https://huggingface.co/datasets/Koushim)

## 🙏 Acknowledgements

Thanks to AI4Bharat for providing the [Samanantar](https://huggingface.co/datasets/ai4bharat/samanantar) dataset which served as the base for this project.