Update README.md

README.md

@@ -23,6 +23,7 @@ This model is intended to be used for automatic podcast summarisation. Given the
 ## Training and evaluation data
 In our solution, an extractive module is developed to select salient chunks from the transcript, which serve as the input to an abstractive summarizer.
 An extensive pre-processing on the creator-provided descriptions is performed selecting a subset of the corpus that is suitable for the training supervised model.
+
 We split the filtered dataset into train/dev sets of 69,336/7,705 episodes. 
 The test set consists of 1,027 episodes. Only 1025 have been used because two of them did not contain an episode description.
 
@@ -41,8 +42,12 @@ print(summary[0]['summary_text'])
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
-- training_precision: float32
+- ```python
+optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
+```
+- ```python
+training_precision: float32
+```
 
 ### Training results