update model card README.md

README.md

@@ -4,9 +4,22 @@ tags:
 - generated_from_trainer
 datasets:
 - clinc_oos
+metrics:
+- accuracy
 model-index:
 - name: distilbert-base-uncased-finetuned-clinc
-  results: []
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: clinc_oos
+      type: clinc_oos
+      args: plus
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9180645161290323
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -14,11 +27,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 # distilbert-base-uncased-finetuned-clinc
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [clinc_oos](https://huggingface.co/datasets/clinc_oos) dataset.
+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
 
 ## 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
 
@@ -26,17 +42,13 @@ 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:
-- alpha: 1.0
-- temperature: 2.0
 - learning_rate: 2e-05
 - train_batch_size: 48
 - eval_batch_size: 48
@@ -45,6 +57,17 @@ The following hyperparameters were used during training:
 - 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   |
+
+
 ### Framework versions
 
 - Transformers 4.16.2