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---
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license: apache-2.0
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language: Spanish Nahuatl
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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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## Model description
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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.
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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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## Evaluation results
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The model is evaluated on 400 validation sentences.
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- Validation loss: 1.56
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- BLEU: 0.13
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_Note: Since the Axolotl corpus contains multiple misalignments, the real BLEU and Validation loss are slightly better. These misalignments also introduce noise into the training._
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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 Morales [(milmor)](https://huggingface.co/milmor)
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- Rodrigo Martínez Arzate
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- Luis Armando Mercado [(luisarmando)](https://huggingface.co/luisar)
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- Jacobo del Valle [(jjdv)](https://huggingface.co/jjdv) |
