opus-mt-en-hi-finetuned
This model is a fine-tuned version of
Helsinki-NLP/opus-mt-en-hi on an
cfilt/iitb-english-hindi dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6039
- Bleu: 15.9001
Model description
This is a fine-tuned version of
Helsinki-NLP/opus-mt-en-hi, a MarianMT-based English-to-Hindi translation model. It was fine-tuned as part of an NLP coursework assignment on translation model fine-tuning, using the full IIT Bombay English-Hindi parallel corpus.
The base model already had reasonable English-to-Hindi translation ability from its original OPUS pretraining, so this fine-tuning mainly adapts it to the phrasing and style conventions found in the IITB corpus rather than teaching translation from scratch.
Intended uses & limitations
This model translates English text to Hindi. It's intended for general-domain sentence-level translation, similar in scope to the IITB corpus it was fine-tuned on (a mix of software localization strings, general web text, and parallel sentence pairs).
Limitations:
- Fine-tuned for only 2 epochs to keep training time reasonable on local hardware, so translation quality may be lower than a fully converged model trained for more epochs.
- BLEU score (15.90 on the test set) is on the moderate side for English-Hindi MT; this is partly expected since BLEU tends to penalize Hindi outputs more than morphologically simpler target languages, even when the translation is fluent and correct.
- Not evaluated on domain-specific text (e.g. medical, legal, or technical translation) - performance on those domains is unverified.
- Trained on a fixed max sequence length of 128 tokens, so longer sentences may be truncated.
Training and evaluation data
Trained on the full
cfilt/iitb-english-hindi dataset:
- Train: 1,659,083 sentence pairs
- Validation: 520 sentence pairs
- Test: 2,507 sentence pairs
Each example is an English-Hindi sentence pair. Preprocessing tokenized both source and target text with the model's own tokenizer, truncating to a max length of 128 tokens for both input and target sequences.
Training procedure
- Framework: Hugging Face
Seq2SeqTrainer
- Epochs: 2
- Batch size: 24 (train and eval)
- Learning rate: 2e-5
- Mixed precision (fp16): enabled
- Hardware: single local GPU (NVIDIA RTX 4060 Laptop GPU)
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|---|
| 2.0303 | 1.0 | 69129 | 2.8119 | 14.0464 |
| 1.9203 | 2.0 | 138258 | 2.7148 | 14.7300 |
Final test set results: Loss: 2.6039, BLEU: 15.90
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
