---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-es
tags:
- generated_from_trainer
- translation
- en
- es
datasets:
- custom
language:
- en
- es
pipeline_tag: translation
model-index:
- name: fine-tuned-opus-mt-en-es
  results:
  - task:
      name: Translation
      type: translation
    dataset:
      name: Custom English-Spanish Dataset
      type: custom
    metrics:
    - name: BLEU
      type: bleu
      value: 65.8
---

# Fine-tuned English-Spanish Translation Model

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-es](https://huggingface.co/Helsinki-NLP/opus-mt-en-es) on a custom English-Spanish parallel corpus.

## Model Description

- **Base Model**: Helsinki-NLP/opus-mt-en-es
- **Task**: Machine Translation (English to Spanish)
- **Language**: English → Spanish
- **Fine-tuning Data**: Custom parallel corpus with educational and general domain text

## Performance

The model achieves a BLEU score of **65.8** on the test set, indicating high-quality translation performance.

### Sample Translations

| English | Spanish (Reference) | Spanish (Model) |
|---------|-------------------|-----------------|
| "The London and Rio de Janeiro buildings will be less than 540 meters and the Tokyo building will be taller than 540 meters." | "Los edificios de Londres y Río de Janeiro tendrán menos de 540 metros y el de Tokio será más alto de 540 metros." | "Los edificios de Londres y Río de Janeiro serán menos que 540 metros y el edificio de Tokio será más alto que 540 metros." |
| "What are some other ways to think about quantities in this problem?" | "¿De qué otras maneras puedes pensar en las cantidades de este problema?" | "¿Qué otras maneras de pensar en las cantidades de este problema?" |

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("omkarhurix/fine-tuned-opus-mt-en-es")
model = AutoModelForSeq2SeqLM.from_pretrained("omkarhurix/fine-tuned-opus-mt-en-es")

# Translate text
text = "Hello, how are you?"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs)
translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(translation)  # "Hola, ¿cómo estás?"
```

## Training Details

- **Training Framework**: Hugging Face Transformers
- **Training Method**: Fine-tuning with custom parallel data
- **Evaluation Metric**: BLEU score
- **Hardware**: GPU-accelerated training

## Citation

If you use this model, please cite:

```bibtex
@misc{fine-tuned-opus-mt-en-es,
  title={Fine-tuned English-Spanish Translation Model},
  author={omkarhurix},
  year={2025},
  howpublished={\url{https://huggingface.co/omkarhurix/fine-tuned-opus-mt-en-es}},
}
```

## License

This model is released under the Apache 2.0 License, same as the base model.