---
license: apache-2.0
language: 
- es
- nah 
tags:
- translation
widget:
- text: "translate Spanish to Nahuatl: Mi hermano es un ajolote"

---

# t5-small-spanish-nahuatl
Nahuatl is the most widely spoken indigenous language in Mexico. However, training a neural network for the task of neural machine tranlation is hard due to the lack of structured data. The most popular datasets such as the Axolot dataset and the bible-corpus only consist of ~16,000 and ~7,000 samples respectivly. Moreover, there are multiple variants of Nahuatl, which makes this task even more difficult. For example, a single word from the Axolot dataset can be found written in more than three different ways. Therefore, in this work we leverage the T5 text-to-text sufix training strategy to compensate the lack of data. We first teach the  multilingual model Spanish using English, then we make the transition to Spanish-Nahuatl. The resulting model successfully translates short sentences from Spanish to Nahuatl. We report Chrf and BLEU results.


## Model description
This model is a T5 Transformer ([t5-small](https://huggingface.co/t5-small)) fine-tuned on spanish and nahuatl sentences collected from the web. 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('hackathon-pln-es/t5-small-spanish-nahuatl')
tokenizer = AutoTokenizer.from_pretrained('hackathon-pln-es/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]
```

## Approach
### Dataset
Since the Axolotl corpus contains misaligments, we just select the best samples (12,207 samples). We also use the [bible-corpus](https://github.com/christos-c/bible-corpus) (7,821 samples). 

| Axolotl best aligned books                            | 
|:-----------------------------------------------------:|
| Anales de Tlatelolco                                  | 
| Diario                                                |  
| Documentos nauas de la Ciudad de México del siglo XVI |  
| Historia de México narrada en náhuatl y español       |  
| La tinta negra y roja (antología de poesía náhuatl)   |  
| Memorial Breve (Libro las ocho relaciones)            |  
| Método auto-didáctico náhuatl-español                 |  
| Nican Mopohua                                         | 
| Quinta Relación (Libro las ocho relaciones)           |   
| Recetario Nahua de Milpa Alta D.F                     | 
| Testimonios de la antigua palabra                     |
| Trece Poetas del Mundo Azteca                         |
| Una tortillita nomás - Se taxkaltsin saj              |
| Vida económica de Tenochtitlan                        |

Also, to increase the amount of data, we collected 3,000 extra samples from the web.

### Model and training
We employ two training-stages using a multilingual T5-small. This model was chosen because it can handle different vocabularies and suffixes. T5-small is pretrained on different tasks and languages (French, Romanian, English, German).

### Training-stage 1 (learning Spanish)
In training stage 1 we first introduce Spanish to the model. The goal is to learn a new language rich in data (Spanish) and not lose the previous knowledge acquired. We use the English-Spanish [Anki](https://www.manythings.org/anki/) dataset, which consists of 118.964 text pairs. We train the model till convergence adding the suffix "Translate Spanish to English: ".

### Training-stage 2 (learning Nahuatl)
We use the pretrained Spanish-English model to learn Spanish-Nahuatl. Since the amount of Nahuatl pairs is limited, we also add to our dataset 20,000 samples from the English-Spanish Anki dataset. This two-task-training avoids overfitting end makes the model more robust.

### Training setup
We train the models on the same datasets for 660k steps using batch size = 16 and a learning rate of 2e-5.


## Evaluation results
For a fair comparison, the models are evaluated on the same 505 validation  Nahuatl sentences. We report the results using chrf and sacrebleu hugging face metrics:

| English-Spanish pretraining  | Validation loss | BLEU | Chrf   |
|:----------------------------:|:---------------:|:-----|-------:|
| False                        | 1.34            | 6.17 | 26.96  | 
| True                         | 1.31            | 6.18 | 28.21  | 

The English-Spanish pretraining improves BLEU and Chrf, and leads to faster convergence. You can reproduce the evaluation on the [eval.ipynb](https://github.com/milmor/spanish-nahuatl-translation/blob/main/eval.ipynb) notebook. 

## 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).


## Team members
- Emilio Alejandro Morales [(milmor)](https://huggingface.co/milmor)
- Rodrigo Martínez Arzate  [(rockdrigoma)](https://huggingface.co/rockdrigoma)
- Luis Armando Mercado [(luisarmando)](https://huggingface.co/luisarmando)
- Jacobo del Valle [(jjdv)](https://huggingface.co/jjdv)