Update README.md

README.md

@@ -9,14 +9,16 @@ tags:
 ## 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 12890 samples collected from the web and 16117 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('hackathon-pln-es/t5-small-spanish-nahuatl')
-tokenizer = AutoTokenizer.from_pretrained('hackathon-pln-es/t5-small-spanish-nahuatl')
+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'
@@ -32,7 +34,7 @@ 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._
+_Note: Since the Axolotl corpus contains multiple misalignments, the real BLEU and Validation loss are slightly better. This misalignments also introduce noise into the training._
 
 
 ## References