Update README.md

README.md

@@ -35,8 +35,48 @@ model-index:
 ---
 ## Model objective
 
-## Data used
+TBF
+
+## 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
+
+TBF
 
 ## Metrics
 
-## Enjoy
+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.
+
+However, given the actual use case, we expect generated segments to be very close to input segments and to label segments in training. As an example, we can take the following:
+
+inputSegment = 'De acuerdo con las informaciones anteriores , las alumnas se han quejado de la actitud de los profesores en los exámenes finales. Los representantes estudiantiles son los alumnos Juanju y Javi.'
+expectedOutput (label) = 'De acuerdo con las informaciones anteriores, el alumnado se ha quejado de la actitud del profesorado en los exámenes finales. Los representantes estudiantiles son los alumnos Juanju y Javi.'
+actualOutput = 'De acuerdo con las informaciones anteriores, el alumnado se ha quejado de la actitud del profesorado en los exámenes finales. Los representantes estudiantiles son el alumnado Juanju y Javi.'
+
+As you can see, segments are pretty similar. So, instead of measuring Bleu or BertScore here, we propose an alternate metric that would be DiffBleu:
+
+$$DiffBleu = BLEU(actualOutput - inputSegment, labels - inputSegment)$$
+
+Where the minuses as in set notation. This way, we also evaluate DiffBleu after the model has been trained.
+
+
+## Usage example
+
+
+
+
+Enjoy!