Update README.md

README.md

@@ -119,7 +119,7 @@ The following hyperparameters were used during training:
 Several models with differet hyperparameters were trained. The following figures show the training process for three of them.
 ![wer](wandb-wer.png)
 ![loss](wandb-loss.png)
-'20_2000_1e-5_hp-mehrdad' is the current model and it's hyperparameters are:
+**20_2000_1e-5_hp-mehrdad** is the current model (lnxdx/Wav2Vec2-Large-XLSR-Persian-ShEMO) and it's hyperparameters are:
 ```python
 model = Wav2Vec2ForCTC.from_pretrained(
     model_name_or_path if not last_checkpoint else last_checkpoint,
@@ -133,7 +133,7 @@ model = Wav2Vec2ForCTC.from_pretrained(
     ctc_zero_infinity = True,
 )
 ```
-The hyperparameters of '19_2000_1e-5_hp-base' are:
+The hyperparameters of **19_2000_1e-5_hp-base** are:
 ```python
 model = Wav2Vec2ForCTC.from_pretrained(
     model_name_or_path if not last_checkpoint else last_checkpoint,
@@ -148,7 +148,7 @@ model = Wav2Vec2ForCTC.from_pretrained(
 )
 ```
 
-And the hyperparameters of '22_2000_1e-5_hp-masoud' are:
+And the hyperparameters of **22_2000_1e-5_hp-masoud** are:
 ```python
 model = Wav2Vec2ForCTC.from_pretrained(
     model_name_or_path if not last_checkpoint else last_checkpoint,
@@ -163,6 +163,7 @@ model = Wav2Vec2ForCTC.from_pretrained(
 )
 ```
 Learning rate is 1e-5 for all three models.
+
 As you can see this model performs better with WER metric on validation(evaluation) set.
 
 #### Framework versions