Fine-tuned student model training completed

README.md

@@ -22,7 +22,7 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-This is an initial example of knowledge-distillation where the student loss is all cross-entropy loss \\(L_{CE}\\) of the ground-truth labels and none of the knowledge-distillation loss \\(L_{KD}\\).
+More information needed
 
 ## Intended uses & limitations
 
@@ -30,24 +30,20 @@ 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
 
-Please see page 224 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: 5
-- alpha: 1.0
-- temperature: 2.0
 - 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: 5
 
 ### Training results
 

model.safetensors

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