End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.5224489795918368
+      value: 0.45807770961145194
     - name: Recall
       type: recall
-      value: 0.23725671918443003
+      value: 0.20759962928637626
     - name: F1
       type: f1
-      value: 0.3263224984066284
+      value: 0.2857142857142857
     - name: Accuracy
       type: accuracy
-      value: 0.9379248428882904
+      value: 0.9365995468342525
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ 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: 0.2913
-- Precision: 0.5224
-- Recall: 0.2373
-- F1: 0.3263
-- Accuracy: 0.9379
+- Loss: 0.2883
+- Precision: 0.4581
+- Recall: 0.2076
+- F1: 0.2857
+- Accuracy: 0.9366
 
 ## Model description
 
@@ -79,8 +79,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 107  | 0.3016          | 0.4783    | 0.1325 | 0.2075 | 0.9331   |
-| No log        | 2.0   | 214  | 0.2913          | 0.5224    | 0.2373 | 0.3263 | 0.9379   |
+| No log        | 1.0   | 107  | 0.2985          | 0.3836    | 0.1557 | 0.2215 | 0.9332   |
+| No log        | 2.0   | 214  | 0.2883          | 0.4581    | 0.2076 | 0.2857 | 0.9366   |
 
 
 ### Framework versions