T5 Small Spanish Nahuatl

🚀 t5-small-spanish-nahuatl

This is a fine - tuned T5 Transformer model for Spanish - Nahuatl translation, offering high - quality translation services.

🚀 Quick Start

This model is designed for Spanish - Nahuatl translation tasks. It is based on the T5 Transformer architecture and fine - tuned on a large number of Spanish and Nahuatl sentence pairs.

✨ Features

• Fine - tuned on Diverse Data: The model is fine - tuned on 29,007 Spanish and Nahuatl sentences, including 12,890 samples collected from the web and 16,117 samples from the Axolotl dataset.
• Normalized Dataset: The dataset is normalized using 'sep' normalization from [py - elotl](https://github.com/ElotlMX/py - elotl).

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForSeq2SeqLM
from transformers import AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained('milmor/t5-small-spanish-nahuatl')
tokenizer = AutoTokenizer.from_pretrained('milmor/t5-small-spanish-nahuatl')

model.eval()
sentence = 'muchas flores son blancas'
input_ids = tokenizer('translate Spanish to Nahuatl: ' + sentence, return_tensors='pt').input_ids
outputs = model.generate(input_ids)
# outputs = miak xochitl istak
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]

📚 Documentation

Evaluation results

The model is evaluated on 400 validation sentences.

• Validation loss: 1.36

⚠️ Important Note

Since the Axolotl corpus contains multiple misalignments, the real Validation loss is slightly better. These misalignments also introduce noise into the training.

🔧 Technical Details

This model is a T5 Transformer ([t5 - small](https://huggingface.co/t5 - small)) fine - tuned on a specific dataset. The dataset comes from both web - collected samples and the Axolotl dataset, and is normalized using 'sep' normalization from [py - elotl](https://github.com/ElotlMX/py - elotl).

📄 License

This project is licensed under the Apache - 2.0 license.

📚 References

• Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019. Exploring the limits of transfer learning with a unified Text - to - Text transformer.
• Ximena Gutierrez - Vasques, Gerardo Sierra, and Hernandez Isaac. 2016. Axolotl: a web accessible parallel corpus for Spanish - Nahuatl. In International Conference on Language Resources and Evaluation (LREC).

Created by Emilio Morales.