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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: muchas flores son blancas" |
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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('milmor/t5-small-spanish-nahuatl') |
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tokenizer = AutoTokenizer.from_pretrained('milmor/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: 1.36 |
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_Note: Since the Axolotl corpus contains multiple misalignments, the real Validation loss is 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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> Created by [Emilio Morales](https://huggingface.co/milmor). |
