update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.527336860670194
+      value: 0.5846994535519126
     - name: Recall
       type: recall
-      value: 0.27710843373493976
+      value: 0.39666357738646896
     - name: F1
       type: f1
-      value: 0.36330498177399756
+      value: 0.47266703478741023
     - name: Accuracy
       type: accuracy
-      value: 0.939506647856013
+      value: 0.947714933093925
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ 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 wnut_17 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2825
-- Precision: 0.5273
-- Recall: 0.2771
-- F1: 0.3633
-- Accuracy: 0.9395
+- Loss: 0.2932
+- Precision: 0.5847
+- Recall: 0.3967
+- F1: 0.4727
+- Accuracy: 0.9477
 
 ## Model description
 
@@ -66,9 +66,9 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
@@ -78,8 +78,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 213  | 0.2920          | 0.3733    | 0.2076 | 0.2668 | 0.9352   |
-| No log        | 2.0   | 426  | 0.2825          | 0.5273    | 0.2771 | 0.3633 | 0.9395   |
+| No log        | 1.0   | 107  | 0.2419          | 0.5242    | 0.4319 | 0.4736 | 0.9469   |
+| No log        | 2.0   | 214  | 0.2932          | 0.5847    | 0.3967 | 0.4727 | 0.9477   |
 
 
 ### Framework versions