End of training

README.md

@@ -26,16 +26,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.0
+      value: 0.5723577235772358
     - name: Recall
       type: recall
-      value: 0.0
+      value: 0.3262279888785913
     - name: F1
       type: f1
-      value: 0.0
+      value: 0.4155844155844156
     - name: Accuracy
       type: accuracy
-      value: 0.16666666666666666
+      value: 0.9418579795647899
 ---
 
 <!-- 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: 2.5804
-- Precision: 0.0
-- Recall: 0.0
-- F1: 0.0
-- Accuracy: 0.1667
+- Loss: 0.2714
+- Precision: 0.5724
+- Recall: 0.3262
+- F1: 0.4156
+- Accuracy: 0.9419
 
 ## Model description
 
@@ -78,10 +78,10 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
-| No log        | 1.0   | 1    | 2.5822          | 0.0       | 0.0    | 0.0 | 0.1667   |
-| No log        | 2.0   | 2    | 2.5804          | 0.0       | 0.0    | 0.0 | 0.1667   |
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 213  | 0.2815          | 0.5675    | 0.2651 | 0.3613 | 0.9385   |
+| No log        | 2.0   | 426  | 0.2714          | 0.5724    | 0.3262 | 0.4156 | 0.9419   |
 
 
 ### Framework versions