update model card README.md

README.md

@@ -34,10 +34,7 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-This is subsequent example of knowledge-distillation used [`transformers.Trainer.hyperparameter_search`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Trainer.hyperparameter_search) with the default Optuna back to find optimal values for the following hyperparameters:
-- `num_train_epochs`
-- `alpha`
-- `temperature`
+More information needed
 
 ## Intended uses & limitations
 
@@ -45,27 +42,20 @@ More information needed
 
 ## Training and evaluation data
 
-The training and evaluation data come straight from the `train` and `validation` splits in the clinc_oos dataset, respectively; and tokenized using the `distilbert-base-uncased` tokenization.
+More information needed
 
 ## Training procedure
 
-Hyperparameter-search was done via default backend Optuna, leading to the values below.
-
-Please see page 228 in Chapter 8: Making Transformers Efficient in Production, Natural Language Processing with Transformers, May 2022.
-
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- num_epochs: 10
-- alpha: 0.5858821400787321
-- temperature: 4.917005721212045
 - learning_rate: 2e-05
 - train_batch_size: 48
 - eval_batch_size: 48
 - seed: 8675309
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-
+- num_epochs: 10
 
 ### Training results