Update README.md

README.md

@@ -48,21 +48,6 @@ It's a straightforward and fast solution that creates a positive impact in the c
 
 By using gender inclusive models we can help reducing gender bias in a language corpus by, for instance, adding data augmentation and creating different examples
 
-## Model specs
-
-This model is a fine-tuned version of [spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the data described below.
-It achieves the following results on the evaluation set:
-- 'eval_bleu': 93.8347,
-- 'eval_f1': 0.9904,
-
-## Training procedure
-### Training hyperparameters
-The following hyperparameters were used during training:
-- learning_rate: 1e-04
-- train_batch_size: 32
-- seed: 42
-- num_epochs: 10
-- weight_decay: 0,01
 
 ## Training and evaluation data
 
@@ -97,6 +82,23 @@ The data used for the model training has been manually created form a compilatio
 [Nombra.en.red. En femenino y en masculino](https://www.inmujeres.gob.es/areasTematicas/educacion/publicaciones/serieLenguaje/docs/Nombra_en_red.pdf)
 
 
+## Model specs
+
+This model is a fine-tuned version of [spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the data described below.
+It achieves the following results on the evaluation set:
+- 'eval_bleu': 93.8347,
+- 'eval_f1': 0.9904,
+
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 1e-04
+- train_batch_size: 32
+- seed: 42
+- num_epochs: 10
+- weight_decay: 0,01
+
+
 ## Metrics
 
 For training, we used both Blue (sacrebleu implementation in HF) and BertScore. The first one, a standard in Machine Translation processes, has been added for ensuring robustness of the newly generated data, while the second one is kept for keeping the expected semantic similarity.