update model card README.md

README.md

@@ -19,7 +19,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9180645161290323
+      value: 0.9083870967741936
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -29,12 +29,12 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.7719
-- Accuracy: 0.9181
+- Loss: 0.8080
+- Accuracy: 0.9084
 
 ## 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 distillation loss \\(L_{KD}\\).
+More information needed
 
 ## Intended uses & limitations
 
@@ -42,34 +42,30 @@ 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: 42
+- seed: 8675309
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 318  | 3.2882          | 0.7426   |
-| 3.7861        | 2.0   | 636  | 1.8744          | 0.8381   |
-| 3.7861        | 3.0   | 954  | 1.1567          | 0.8958   |
-| 1.6922        | 4.0   | 1272 | 0.8569          | 0.9132   |
-| 0.9055        | 5.0   | 1590 | 0.7719          | 0.9181   |
+| No log        | 1.0   | 318  | 3.3061          | 0.6681   |
+| 3.8033        | 2.0   | 636  | 1.9122          | 0.8271   |
+| 3.8033        | 3.0   | 954  | 1.1951          | 0.8832   |
+| 1.7323        | 4.0   | 1272 | 0.8907          | 0.9039   |
+| 0.9371        | 5.0   | 1590 | 0.8080          | 0.9084   |
 
 
 ### Framework versions