Fine-tuning Complete

README.md

@@ -23,10 +23,10 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.936
+      value: 0.926
     - name: F1
       type: f1
-      value: 0.9362275859495188
+      value: 0.9260875538517123
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -36,9 +36,9 @@ 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 emotion dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1535
-- Accuracy: 0.936
-- F1: 0.9362
+- Loss: 0.2045
+- Accuracy: 0.926
+- F1: 0.9261
 
 ## Model description
 
@@ -58,20 +58,19 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 48
-- eval_batch_size: 48
+- train_batch_size: 56
+- eval_batch_size: 56
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 3
+- num_epochs: 2
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
-| 0.3053        | 1.0   | 334  | 0.2418          | 0.924    | 0.9238 |
-| 0.1823        | 2.0   | 668  | 0.1734          | 0.934    | 0.9347 |
-| 0.1226        | 3.0   | 1002 | 0.1535          | 0.936    | 0.9362 |
+| 0.4094        | 1.0   | 286  | 0.2890          | 0.9125   | 0.9121 |
+| 0.2244        | 2.0   | 572  | 0.2045          | 0.926    | 0.9261 |
 
 
 ### Framework versions

pytorch_model.bin

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