Training complete

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.5841184387617766
+      value: 0.5422163588390502
     - name: Recall
       type: recall
-      value: 0.40222428174235403
+      value: 0.3809082483781279
     - name: F1
       type: f1
-      value: 0.47639956092206365
+      value: 0.4474686989657049
     - name: Accuracy
       type: accuracy
-      value: 0.9491750021373002
+      value: 0.9475506540138497
 ---
 
 <!-- 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 [bert-base-cased](https://huggingface.co/bert-base-cased) on the wnut_17 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4776
-- Precision: 0.5841
-- Recall: 0.4022
-- F1: 0.4764
-- Accuracy: 0.9492
+- Loss: 0.2964
+- Precision: 0.5422
+- Recall: 0.3809
+- F1: 0.4475
+- Accuracy: 0.9476
 
 ## Model description
 
@@ -79,9 +79,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 425  | 0.4859          | 0.5671    | 0.3642 | 0.4436 | 0.9478   |
-| 0.0029        | 2.0   | 850  | 0.4842          | 0.5832    | 0.4059 | 0.4787 | 0.9495   |
-| 0.0042        | 3.0   | 1275 | 0.4776          | 0.5841    | 0.4022 | 0.4764 | 0.9492   |
+| No log        | 1.0   | 425  | 0.2617          | 0.5380    | 0.3086 | 0.3922 | 0.9427   |
+| 0.1895        | 2.0   | 850  | 0.2944          | 0.5930    | 0.3160 | 0.4123 | 0.9443   |
+| 0.0702        | 3.0   | 1275 | 0.2964          | 0.5422    | 0.3809 | 0.4475 | 0.9476   |
 
 
 ### Framework versions