README.md

metadata

license: cc-by-nc-4.0
language:
  - en
  - pbv
metrics:
  - bleu
  - chrf
  - ter
base_model:
  - facebook/nllb-200-distilled-600M
pipeline_tag: translation
library_name: transformers
tags:
  - translation
  - pnar
  - jaintia
  - english-pnar
  - pnar-translation
  - low-resource-language
  - fine-tuned
  - nllb
  - neural-machine-translation
  - indic-languages
  - meghalaya
  - north-east-india
model-index:
  - name: nllb-english-pnar
    results:
      - task:
          type: translation
          name: Machine Translation
        dataset:
          type: custom
          name: English-Pnar Parallel Corpus
        metrics:
          - type: bleu
            name: BLEU
            value: 40.32
          - type: chrf
            name: chrF++
            value: 58.58
          - type: ter
            name: TER
            value: 52.07

English to Pnar Machine Translation Model: NLLB-200-Distilled-600M (Fine-Tuned)

This model is a fine-tuned version of facebook/nllb-200-distilled-600M for English to Pnar (Jaintia) translation. It has been trained on a custom dataset specifically curated for this low-resource language spoken in Meghalaya, India.

Summary

Property	Value
Base Model	facebook/nllb-200-distilled-600M
Type	Seq2Seq MT
Languages	English → Pnar (eng_Latn → pbv_Latn)
Technique	LoRA fine-tuning + Continuation Training
License	CC-BY-NC-4.0 (inherits from Meta)
Training Data	Custom English–Pnar parallel corpus
Max Sequence Length	128 tokens (truncation enabled)

---

Training Overview

The training utilized the LoRA (Low-Rank Adaptation) technique on a substantial corpus of parallel sentences.

Final Test Metrics:

• Validation Loss: 0.520427
• BLEU Score: 40.32
• chrF++ Score: 58.58
• TER Score: 52.07

---

Limitations

While this model achieves impressive scores, users should be aware of the following limitations:

• Directionality: Only supports English to Pnar. The reverse direction (Pnar to English) is not optimized in this specific checkpoint.
• Context Window: Sentences longer than 128 tokens may be truncated.
• Domain Specificity: The model was trained on a general-purpose corpus and may not perform well on highly technical medical, legal, or archaic religious texts not covered during training.

---

How to Use

import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "toiar/nllb-finetuned-english-pnar"
device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id).to(device)

def translate(text):
    inputs = tokenizer(text, return_tensors="pt").to(device)
    
    output = model.generate(
        **inputs,
        forced_bos_token_id=tokenizer.convert_tokens_to_ids("pbv_Latn"),
        max_length=128,
        num_beams=5,
        early_stopping=True
    )
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
text = "They are learning new skills to improve their future."
translation = translate(text)

print(f"English: {text}")
print(f"Pnar:    {translation}")