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---
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license: apache-2.0
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language: es
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tags:
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- translation Spanish Nahuatl
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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 consists 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. In this work we leverage the T5 text-to-text training strategy to compensate for the lack of data. 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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Since the Axolotl corpus contains misaligments, we just select the best samples (~10,000 samples). We use the [bible-sorpus](https://github.com/christos-c/bible-corpus) (7,821 samples) to compensate the lack of nahuatl data.
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## Evaluation results
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The model is evaluated on 505 validation sentences. We report the results using chrf and sacrebleu hugging face metrics:
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- Validation loss: 1.31
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- BLEU: 6.18
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- Chrf: 28.21
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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) |
