update model card README.md

README.md

@@ -4,9 +4,36 @@ tags:
 - generated_from_trainer
 datasets:
 - rotten_tomatoes_movie_review
+metrics:
+- accuracy
+- f1
+- precision
+- recall
 model-index:
 - name: my_distilbert_model
-  results: []
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: rotten_tomatoes_movie_review
+      type: rotten_tomatoes_movie_review
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8480300187617261
+    - name: F1
+      type: f1
+      value: 0.8480214592727926
+    - name: Precision
+      type: precision
+      value: 0.8481084411583488
+    - name: Recall
+      type: recall
+      value: 0.8480300187617261
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -15,6 +42,12 @@ should probably proofread and complete it, then remove this comment. -->
 # my_distilbert_model
 
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes_movie_review dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4452
+- Accuracy: 0.8480
+- F1: 0.8480
+- Precision: 0.8481
+- Recall: 0.8480
 
 ## Model description
 
@@ -39,13 +72,15 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 1
+- num_epochs: 3
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| No log        | 1.0   | 267  | 0.3889          | 0.8321   | 0.8321 | 0.8321    | 0.8321 |
+| No log        | 1.0   | 267  | 0.4094          | 0.8246   | 0.8241 | 0.8281    | 0.8246 |
+| 0.3518        | 2.0   | 534  | 0.4000          | 0.8508   | 0.8508 | 0.8510    | 0.8508 |
+| 0.3518        | 3.0   | 801  | 0.4452          | 0.8480   | 0.8480 | 0.8481    | 0.8480 |
 
 
 ### Framework versions