End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.5026595744680851
+      value: 0.4609164420485175
     - name: Recall
       type: recall
-      value: 0.35032437442076
+      value: 0.15848007414272475
     - name: F1
       type: f1
-      value: 0.4128891316220644
+      value: 0.23586206896551726
     - name: Accuracy
       type: accuracy
-      value: 0.9433542815612842
+      value: 0.9349322388953016
 ---
 
 <!-- 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: 0.2928
-- Precision: 0.5027
-- Recall: 0.3503
-- F1: 0.4129
-- Accuracy: 0.9434
+- Loss: 0.3042
+- Precision: 0.4609
+- Recall: 0.1585
+- F1: 0.2359
+- Accuracy: 0.9349
 
 ## 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  | 0.2738          | 0.4273    | 0.4004 | 0.4134 | 0.9407   |
-| No log        | 2.0   | 214  | 0.2928          | 0.5027    | 0.3503 | 0.4129 | 0.9434   |
+| No log        | 1.0   | 107  | 0.3166          | 0.1639    | 0.0093 | 0.0175 | 0.9275   |
+| No log        | 2.0   | 214  | 0.3042          | 0.4609    | 0.1585 | 0.2359 | 0.9349   |
 
 
 ### Framework versions