update model card README.md

README.md

@@ -20,10 +20,10 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9225
+      value: 0.941
     - name: F1
       type: f1
-      value: 0.9225152799408713
+      value: 0.9407981871026927
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -33,9 +33,9 @@ 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: 0.2100
-- Accuracy: 0.9225
-- F1: 0.9225
+- Loss: 0.2275
+- Accuracy: 0.941
+- F1: 0.9408
 
 ## Model description
 
@@ -55,8 +55,8 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 64
-- eval_batch_size: 64
+- train_batch_size: 2
+- eval_batch_size: 2
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
@@ -64,15 +64,15 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
-| 0.793         | 1.0   | 250  | 0.2946          | 0.912    | 0.9103 |
-| 0.2423        | 2.0   | 500  | 0.2100          | 0.9225   | 0.9225 |
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
+| 0.4546        | 1.0   | 8000  | 0.2665          | 0.933    | 0.9318 |
+| 0.2003        | 2.0   | 16000 | 0.2275          | 0.941    | 0.9408 |
 
 
 ### Framework versions
 
 - Transformers 4.11.3
-- Pytorch 1.11.0+cu113
+- Pytorch 1.11.0
 - Datasets 1.16.1
 - Tokenizers 0.10.3