---
base_model: facebook/mbart-large-50-many-to-many-mmt
library_name: transformers
pipeline_tag: translation
license: mit
tags:
- facebook/mbart-large-50-many-to-many-mmt
- transformers
- translation
- wuxia
- chinese-to-english
---

# HSC Wuxia mBART-50 Model

This model is a fine-tuned sequence-to-sequence model based on **facebook/mbart-large-50-many-to-many-mmt** for Chinese-to-English translation of *Wuxia* and *Xianxia* literature. It was developed as part of the Bachelor's Thesis (TFG):
> **"Enfoques de traducción automática con modelos de lenguaje en obras wuxia"**
> (Degree in Data Science and Engineering, Universidade da Coruña)

## Model Details

* **Base Model**: [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt)
* **Training Method**: Full architectural fine-tuning
* **Training Dataset**: [HSilvosa/hsc-wuxia-100k](https://huggingface.co/datasets/HSilvosa/hsc-wuxia-100k) (100,000 stratified parallel sentence pairs)
* **Target Domain**: Wuxia/Xianxia web novels

## Intended Use

This model can be used directly for Chinese-to-English translation of literary text in the Wuxia domain.

### Quick Start (Inference)

```python
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast

model_name = "HSilvosa/hsc-wuxia-mbart-large-50"
model = MBartForConditionalGeneration.from_pretrained(model_name)
tokenizer = MBart50TokenizerFast.from_pretrained(model_name)

# Set source and target language
tokenizer.src_lang = "zh_CN"
text = "一念成沧海，一念化桑田。"

encoded_zh = tokenizer(text, return_tensors="pt")
generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
print(tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0])
```

## Evaluation Results

The model was evaluated on the Wuxia parallel test set, comparing the base model and the fine-tuned version:

| Metric | Base Model (`mbart-large-50-...`) | Fine-tuned Model (`hsc-wuxia-mbart-large-50`) | Delta (Improvement) |
| :--- | :---: | :---: | :---: |
| **SacreBLEU** | 6.33 | **37.73** | +31.40 |
| **chrF** | 26.73 | **56.22** | +29.49 |
| **ROUGE-L** | 29.26 | **62.45** | +33.19 |
| **METEOR** | 25.79 | **61.91** | +36.12 |
| **COMET** | 60.86 | **79.96** | +19.10 |

## Training Hyperparameters

The model was fine-tuned using the following architectural training parameters:
- **Optimizer**: AdamW
- **Learning Rate**: 2e-5 (0.00002)
- **Weight Decay**: 0.01
- **Epochs**: Max 10 (with early stopping patience of 3 epochs based on validation loss)
- **Batch Size**: 16 (per device/GPU)
- **Max Sequence Length**: 128 tokens
- **Seed**: 42

## Project Repository

This model and the associated training/evaluation pipelines were developed in the following project repository:
* **GitHub Repository**: [HugoSilvosa/hsc-mtl-wuxia](https://github.com/HugoSilvosa/hsc-mtl-wuxia)