|
--- |
|
license: mit |
|
language: |
|
- en |
|
- ne |
|
- sa |
|
base_model: |
|
- ai4bharat/indictrans2-indic-en-1B |
|
--- |
|
|
|
# IndicTrans2 Sanskrit ↔ Nepali (LoRA Fine-tuned) |
|
|
|
[](https://huggingface.co/karki-dennish/indictrans2-sanNpi) |
|
|
|
This repository provides a [LoRA](https://arxiv.org/abs/2106.09685)-based fine-tuning of the [IndicTrans2](https://huggingface.co/ai4bharat/indictrans2-indic-indic-dist-320M) model for translation between Sanskrit and Nepali (both in Devanagari script). |
|
|
|
--- |
|
|
|
## Model Details |
|
|
|
- **Base Model**: [ai4bharat/indictrans2-indic-indic-dist-320M](https://huggingface.co/ai4bharat/indictrans2-indic-indic-dist-320M) |
|
- **Fine-tuning Approach**: [LoRA (Low-Rank Adaptation)](https://arxiv.org/abs/2106.09685) |
|
- **Language Pair**: |
|
- Source: Sanskrit (`san_Deva`) |
|
- Target: Nepali (`npi_Deva`) |
|
|
|
> **Note**: In the training script, the source and target might be reversed (`npi_Deva` → `san_Deva`). However, in the example usage code below, we demonstrate translating from Sanskrit to Nepali. Adjust `src_lang` and `tgt_lang` as needed for your specific use case. |
|
|
|
--- |
|
|
|
## Intended Use |
|
|
|
- **Primary Use**: Machine translation from Sanskrit to Nepali (and vice versa if you reverse the language codes). |
|
- **Domains**: General domain, though performance may vary depending on your dataset and domain specifics. |
|
|
|
--- |
|
|
|
## How to Use |
|
|
|
Below is a minimal code snippet demonstrating how to use this LoRA-finetuned model for translation. The snippet uses: |
|
|
|
- [Transformers](https://github.com/huggingface/transformers) library for the model and tokenizer |
|
- [IndicTransToolkit](https://github.com/VarunGumma/IndicTransToolkit) for preprocessing and postprocessing Indic text |
|
|
|
### Installation |
|
|
|
**Install Transformers** (if not already installed): |
|
```bash |
|
pip install transformers |
|
git clone https://github.com/VarunGumma/IndicTransToolkit |
|
cd IndicTransToolkit |
|
pip install --editable ./ |
|
pip install torch |
|
pip install ipython jupyter |
|
|
|
import torch |
|
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
|
from IndicTransToolkit import IndicProcessor |
|
``` |
|
|
|
### Inference Example |
|
```bash |
|
ip = IndicProcessor(inference=True) |
|
model_name = "karki-dennish/indictrans2-sanNpi" # or your Hugging Face repo ID |
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
|
model = AutoModelForSeq2SeqLM.from_pretrained(model_name, trust_remote_code=True) |
|
|
|
def translate_san_to_npi(texts): |
|
# 1. Preprocess |
|
batch = ip.preprocess_batch(texts, src_lang="san_Deva", tgt_lang="npi_Deva") |
|
encoded = tokenizer(batch, padding=True, truncation=True, return_tensors="pt") |
|
|
|
# 2. Generate translations |
|
with torch.inference_mode(): |
|
outputs = model.generate(**encoded, num_beams=5, max_length=256) |
|
|
|
# 3. Decode and Postprocess |
|
translations = tokenizer.batch_decode(outputs, skip_special_tokens=True) |
|
translations = ip.postprocess_batch(translations, lang="npi_Deva") |
|
|
|
return translations |
|
|
|
# Example usage |
|
sanskrit_sentences = [ |
|
"अहं गच्छामि।", # "I am going." |
|
"किं समाचारः?" # "What news?" |
|
] |
|
|
|
translated = translate_san_to_npi(sanskrit_sentences) |
|
for s, t in zip(sanskrit_sentences, translated): |
|
print(f"Sanskrit: {s} --> Nepali: {t}") |
|
``` |
|
|
|
### Citation |
|
|
|
```bibtex |
|
@misc{indictrans2-lora-san-npi, |
|
title = {IndicTrans2 Sanskrit-Nepali LoRA Fine-tuned Model}, |
|
author = {Dennish Karki}, |
|
howpublished = {Hugging Face repository}, |
|
year = {2025}, |
|
url = {https://huggingface.co/karki-dennish/indictrans2-sanNpi} |
|
} |
|
|
