Sanskrit → English — Custom Transformer (trained from scratch)

A compact encoder–decoder Transformer trained from scratch on 10,000 Sanskrit–English pairs (NLU Assignment 2), with a joint SentencePiece BPE vocabulary. It is small and fast — meant to be efficient rather than to match large pretrained models. This is not a 🤗 Transformers architecture, so it ships with a self-contained modeling.py.

Files: pytorch_model.bin (weights), spm.model (tokenizer), config.json (hyperparameters), modeling.py (model + load/translate helpers).

Install

pip install torch sentencepiece huggingface_hub

Inference

from huggingface_hub import snapshot_download
import sys

d = snapshot_download("krpraveen/sanskrit-en-custom-transformer")
sys.path.insert(0, d)
from modeling import load, translate

model, sp, cfg = load(d)                    # add device="cuda" on a GPU
print(translate(model, sp, cfg, ["बाल: भवत्सु प्रेमं प्रकटयति ।"]))
# ['Boy displays love in you.']

Use it from an open-source chat UI (Gradio)

import gradio as gr

def respond(message, history):
    return translate(model, sp, cfg, [message])[0]

gr.ChatInterface(
    respond,
    title="Sanskrit → English (custom Transformer)",
    description="Type a Sanskrit sentence in Devanagari.",
    examples=["बाल: भवत्सु प्रेमं प्रकटयति ।", "अस्तु, इदं सम्यक् दृश्यते ।"],
).launch()

pip install gradio first. To host it, create a Hugging Face Space (SDK: Gradio) with an app.py (the load + respond code) and a requirements.txt of torch sentencepiece huggingface_hub gradio.

Notes

Trained only on the provided dataset — no pretrained weights and no external data. Being a from-scratch model on 10k pairs, quality is modest; a larger model or more data would help. For higher quality see the fine-tuned IndicTrans2 model krpraveen/indictrans2-sanskrit-en-finetuned.