README.md

---
library_name: transformers
license: cc-by-nc-4.0
---
---
license: cc-by-nc-4.0
tags:
- translation
- nllb
---

# My NLLB-200 Translator

This repository contains a copy of Meta's (Facebook) **NLLB-200-distilled-600M** model. It has been cloned here for custom personal access and application deployment.

### 🌟 Model Details
- **Original Developer:** Meta AI (Facebook)
- **Model Type:** Seq2Seq Language Model (Machine Translation)
- **Model Size:** 600 Million parameters
- **License:** CC-BY-NC-4.0 (Non-commercial use only)

### 🌍 Language Support
This model supports direct translation between 200+ languages. For example:
- English: `eng_Latn`
- Telugu: `tel_Telu`
- Hindi: `hin_Deva`
- French: `fra_Latn`

### 🚀 How to Get Started

You can use this model directly with the Hugging Face `transformers` library:

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Replace with your actual repository path
model_name = "YOUR_USERNAME/YOUR_REPO_NAME"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Set source language
tokenizer.src_lang = "eng_Latn"

text = "Hello, how are you today?"
inputs = tokenizer(text, return_tensors="pt")

# Target translation (Example: Telugu)
translated_tokens = model.generate(
    **inputs,
    forced_bos_token_id=tokenizer.convert_tokens_to_ids("tel_Telu"),
    max_length=50
)

output = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
print("Translation:", output)

## Citation
@article{nllbteam2022neglected,
  title={No Language Left Behind: Scaling Human-Centered Machine Translation},
  author={NLLB Team and Marta R. Costa-jussà and James Cross and Onur Çelebi and Maha Elbayad and Kenneth Heafield and others},
  journal={arXiv preprint arXiv:2207.04672},
  year={2022}
}