Training complete

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.0
+      value: 0.006369426751592357
     - name: Recall
       type: recall
-      value: 0.0
+      value: 0.030303030303030304
     - name: F1
       type: f1
-      value: 0.0
+      value: 0.010526315789473686
     - name: Accuracy
       type: accuracy
-      value: 0.5919540229885057
+      value: 0.4482758620689655
 ---
 
 <!-- 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 conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.7842
-- Precision: 0.0
-- Recall: 0.0
-- F1: 0.0
-- Accuracy: 0.5920
+- Loss: 1.8836
+- Precision: 0.0064
+- Recall: 0.0303
+- F1: 0.0105
+- Accuracy: 0.4483
 
 ## 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   | 2    | 1.9938          | 0.008     | 0.0606 | 0.0141 | 0.2385   |
-| No log        | 2.0   | 4    | 1.8448          | 0.0       | 0.0    | 0.0    | 0.4856   |
-| No log        | 3.0   | 6    | 1.7842          | 0.0       | 0.0    | 0.0    | 0.5920   |
+| No log        | 1.0   | 2    | 2.1049          | 0.0037    | 0.0303 | 0.0066 | 0.1753   |
+| No log        | 2.0   | 4    | 1.9468          | 0.0054    | 0.0303 | 0.0092 | 0.3793   |
+| No log        | 3.0   | 6    | 1.8836          | 0.0064    | 0.0303 | 0.0105 | 0.4483   |
 
 
 ### Framework versions