hsc-wuxia-m2m100-418m

HSC Wuxia M2M100 418M Model

This model is a fine-tuned sequence-to-sequence model based on facebook/m2m100_418M 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/m2m100_418M
• Training Method: Full architectural fine-tuning
• Training Dataset: 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)

from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer

model_name = "HSilvosa/hsc-wuxia-m2m100-418m"
model = M2M100ForConditionalGeneration.from_pretrained(model_name)
tokenizer = M2M100Tokenizer.from_pretrained(model_name)

# Set source and target language
tokenizer.src_lang = "zh"
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.get_lang_id("en"))
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:

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