readme: ---
  license: apache-2.0
  language: 
  - es
  - nah
  tags:
  - translation 
  widget:
  - text: "translate Spanish to Nahuatl: muchas flores son blancas"
  
  ---
  
  # t5-small-spanish-nahuatl
  ## Model description
  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.
  
  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('milmor/t5-small-spanish-nahuatl')
  tokenizer = AutoTokenizer.from_pretrained('milmor/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]
  ```
  
  
  ## Evaluation results
  The model is evaluated on 400 validation sentences. 
  - Validation loss: 1.36
  
  _Note: Since the Axolotl corpus contains multiple misalignments, the real Validation loss is slightly better. These misalignments also introduce noise into the training._
  
  
  ## 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).
  
  
  > Created by [Emilio Alejandro Morales](https://huggingface.co/milmor).