End of training

README.md

@@ -26,16 +26,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.5930434782608696
+      value: 0.589018302828619
     - name: Recall
       type: recall
-      value: 0.3160333642261353
+      value: 0.32808155699721964
     - name: F1
       type: f1
-      value: 0.4123337363966143
+      value: 0.42142857142857143
     - name: Accuracy
       type: accuracy
-      value: 0.942242742935317
+      value: 0.9428840152195289
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -45,11 +45,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2703
-- Precision: 0.5930
-- Recall: 0.3160
-- F1: 0.4123
-- Accuracy: 0.9422
+- Loss: 0.2656
+- Precision: 0.5890
+- Recall: 0.3281
+- F1: 0.4214
+- Accuracy: 0.9429
 
 ## Model description
 
@@ -80,8 +80,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1817        | 1.0   | 213  | 0.2754          | 0.5680    | 0.2437 | 0.3411 | 0.9386   |
-| 0.1124        | 2.0   | 426  | 0.2703          | 0.5930    | 0.3160 | 0.4123 | 0.9422   |
+| 0.1881        | 1.0   | 213  | 0.2730          | 0.5805    | 0.2873 | 0.3844 | 0.9399   |
+| 0.1084        | 2.0   | 426  | 0.2656          | 0.5890    | 0.3281 | 0.4214 | 0.9429   |
 
 
 ### Framework versions