transformers pytorch t5 text2text-generation translation es nah license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
language:
- es
- nah
license: apache-2.0
tags:
- translation
widget:
- text: 'translate Spanish to Nahuatl: muchas flores son blancas'
---

# t5-small-spanish-nahuatl
## Model description
This model is a T5 Transformer ([t5-small](https://huggingface.co/t5-small)) fine-tuned on 29,007 spanish and nahuatl sentences using 12,890 samples collected from the web and 16,117 samples from the Axolotl dataset.

The dataset is normalized using 'sep' normalization from [py-elotl](https://github.com/ElotlMX/py-elotl).


## Usage
```python
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]
```


## Evaluation results
The model is evaluated on 400 validation sentences. 
- Validation loss: 1.36

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


## 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](https://huggingface.co/milmor).