Update README.md

README.md

@@ -1,51 +1,51 @@
----
-license: apache-2.0
-language: 
-- es
-- nah
-tags:
-- translation 
-widget:
-- text: "translate Spanish to Nahuatl: mi hermano es un ajolote"
-
----
-
-# 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).
-
-
+---
+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).