Update README.md

README.md

@@ -25,8 +25,20 @@ A value between 0 and 1 is predicted for each signal.
 The model can be used directly with a text-classification pipeline:
 
 ```python
-text = "Es usted un auténtico impresentable, su señoría."
+>>> from transformers import pipeline
+>>> text = "Es usted un auténtico impresentable, su señoría."
+>>> pipe = pipeline("text-classification", model="Newtral/xlm-r-finetuned-toxic-political-tweets-es")
+>>> pipe(text, return_all_scores=True)
 
-pipe = pipeline("text-classification", model="Newtral/xlm-r-finetuned-toxic-political-tweets-es")
-pipe(text, return_all_scores=True)
-```
+[[{'label': 'toxic', 'score': 0.92560875415802},
+  {'label': 'very toxic', 'score': 0.8310967683792114}]]
+```
+
+### Training procedure
+The pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:
+
+* Batch size = 32
+* Learning rate = 2e-5
+* Epochs = 5
+
+The optimizer used was AdamW and the loss optimized was binary cross-entropy with class weights proportional to the class imbalance.