helloboyn/translate-marathi-hindi

NLLB-200-distilled-600M fine-tuned: Marathi → Hindi

This model is a fine-tuned version of facebook/nllb-200-distilled-600M for Marathi (mar_Deva) to Hindi (hin_Deva) machine translation.

Model description

• Base model: facebook/nllb-200-distilled-600M (distilled NLLB-200, 600M parameters)
• Architecture: M2M100ForConditionalGeneration (encoder-decoder Transformer)
• Source language: Marathi — FLORES-200 code mar_Deva
• Target language: Hindi — FLORES-200 code hin_Deva
• Tokenizer: original NLLB-200 SentencePiece tokenizer (sentencepiece.bpe.model), unchanged from the base model

Intended uses

Single-direction machine translation from Marathi to Hindi. For general-purpose multi-language translation, use the original facebook/nllb-200-distilled-600M instead — this checkpoint is specialized for the mar_Deva → hin_Deva direction only.

How to use

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_id = "helloboyn/translate-marathi-hindi"
tokenizer = AutoTokenizer.from_pretrained(model_id, src_lang="mar_Deva")
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

text = "your Marathi input text here"
inputs = tokenizer(text, return_tensors="pt")

translated_tokens = model.generate(
    **inputs,
    forced_bos_token_id=tokenizer.convert_tokens_to_ids("hin_Deva"),
    max_length=200,
)
print(tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0])

Training procedure

Fine-tuned from facebook/nllb-200-distilled-600M with the Hugging Face Trainer.

Training hyperparameters

• learning_rate: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: AdamW (torch fused)
• lr_scheduler_type: linear
• num_epochs: 3
• mixed_precision_training: fp16

Framework versions

• Transformers 4.38.2
• PyTorch

Limitations and bias

This model is trained for a single translation direction (mar_Deva → hin_Deva) and has not been evaluated on out-of-domain text. Translation quality for low-resource languages such as Hindi can vary significantly by domain and sentence length. As with the base NLLB-200 model, it may reflect biases present in its training data.

License

This model is derived from facebook/nllb-200-distilled-600M, which is released under the CC-BY-NC-4.0 license. This fine-tuned checkpoint is distributed under the same license and is intended for non-commercial use.