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---
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license: apache-2.0
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language:
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- es
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- nah
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tags:
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- translation
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widget:
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- text: "translate Spanish to Nahuatl: mi hermano es un ajolote"
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---
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# t5-small-spanish-nahuatl
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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.
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## Model description
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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).
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## Usage
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```python
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from transformers import AutoModelForSeq2SeqLM
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from transformers import AutoTokenizer
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model = AutoModelForSeq2SeqLM.from_pretrained('hackathon-pln-es/t5-small-spanish-nahuatl')
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tokenizer = AutoTokenizer.from_pretrained('hackathon-pln-es/t5-small-spanish-nahuatl')
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model.eval()
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sentence = 'muchas flores son blancas'
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input_ids = tokenizer('translate Spanish to Nahuatl: ' + sentence, return_tensors='pt').input_ids
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outputs = model.generate(input_ids)
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# outputs = miak xochitl istak
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outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
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```
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## Approach
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### Dataset
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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).
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| Axolotl best aligned books |
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|:-----------------------------------------------------:|
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| Anales de Tlatelolco |
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| Diario |
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| Documentos nauas de la Ciudad de México del siglo XVI |
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| Historia de México narrada en náhuatl y español |
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| La tinta negra y roja (antología de poesía náhuatl) |
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| Memorial Breve (Libro las ocho relaciones) |
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| Método auto-didáctico náhuatl-español |
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| Nican Mopohua |
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| Quinta Relación (Libro las ocho relaciones) |
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| Recetario Nahua de Milpa Alta D.F |
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| Testimonios de la antigua palabra |
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| Trece Poetas del Mundo Azteca |
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| Una tortillita nomás - Se taxkaltsin saj |
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| Vida económica de Tenochtitlan |
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Also, to increase the amount of data, we collected 3,000 extra samples from the web.
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### Model and training
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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).
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### Training-stage 1 (learning Spanish)
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In training stage 1 we first introduce Spanish to the model. The objective 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: ".
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### Training-stage 2 (learning Nahuatl)
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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.
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### Training setup
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We train the models on the same datasets for 660k steps using batch size = 16 and a learning rate of 2e-5.
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## Evaluation results
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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:
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| English-Spanish pretraining | Validation loss | BLEU | Chrf |
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|:----------------------------:|:---------------:|:-----|-------:|
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| False | 1.34 | 6.17 | 26.96 |
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| True | 1.31 | 6.18 | 28.21 |
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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.
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## References
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- Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019. Exploring the limits
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of transfer learning with a unified Text-to-Text transformer.
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- 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).
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## Team members
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- Emilio Alejandro Morales [(milmor)](https://huggingface.co/milmor)
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- Rodrigo Martínez Arzate [(rockdrigoma)](https://huggingface.co/rockdrigoma)
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- Luis Armando Mercado [(luisarmando)](https://huggingface.co/luisarmando)
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- Jacobo del Valle [(jjdv)](https://huggingface.co/jjdv) |
