Update README.md

README.md

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 # t5-small-spanish-nahuatl
-
 ## Model description
-This model is a T5 Transformer ([t5-small](https://huggingface.co/t5-small)) that was fine-tuned in spanish and nahuatl.
+This model is a T5 Transformer ([t5-small](https://huggingface.co/t5-small)) fine-tuned on 29,007 spanish and nahuatl sentences using 12890 samples collected from the web and 16117 samples from the Axolotl dataset.
+
+
+## Usage
+```python
+from transformers import AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer
+
+model = AutoModelForSeq2SeqLM.from_pretrained('hackathon-pln-es/t5-small-spanish-nahuatl')
+tokenizer = AutoTokenizer.from_pretrained('hackathon-pln-es/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: 1.56 
+- BLEU: 0.13
+
+_Note: Since the Axolotl corpus contains multiple misalignments, the real BLEU and Validation loss are slightly better._
+
+
+## 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.
+
+- Nahuatl: Gutierrez-Vasques, X., Sierra, G., & Pompa, I. H. (2016). Axolotl: a Web Accessible Parallel Corpus for Spanish-Nahuatl. In LREC.
+
 
-> Created by [Emilio Morales](https://huggingface.co/milmor) 
+> Created by [Emilio Morales](https://huggingface.co/milmor).