End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.8794544654641443
+      value: 0.9253070709306186
     - name: Recall
       type: recall
-      value: 0.8945072155722117
+      value: 0.9354513927732409
     - name: F1
       type: f1
-      value: 0.8869169763185625
+      value: 0.9303515798842902
     - name: Accuracy
       type: accuracy
-      value: 0.9731996759178356
+      value: 0.9834305050280394
 ---
 
 <!-- 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 conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1902
-- Precision: 0.8795
-- Recall: 0.8945
-- F1: 0.8869
-- Accuracy: 0.9732
+- Loss: 0.0619
+- Precision: 0.9253
+- Recall: 0.9355
+- F1: 0.9304
+- Accuracy: 0.9834
 
 ## Model description
 
@@ -79,10 +79,10 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 439  | 0.1942          | 0.8818    | 0.8931 | 0.8874 | 0.9732   |
-| 0.0009        | 2.0   | 878  | 0.1902          | 0.8817    | 0.8933 | 0.8875 | 0.9729   |
-| 0.001         | 3.0   | 1317 | 0.1894          | 0.8794    | 0.8952 | 0.8872 | 0.9733   |
-| 0.0009        | 4.0   | 1756 | 0.1902          | 0.8795    | 0.8945 | 0.8869 | 0.9732   |
+| No log        | 1.0   | 439  | 0.0877          | 0.8779    | 0.8955 | 0.8866 | 0.9754   |
+| 0.2182        | 2.0   | 878  | 0.0626          | 0.9193    | 0.9299 | 0.9245 | 0.9820   |
+| 0.0557        | 3.0   | 1317 | 0.0612          | 0.9252    | 0.9323 | 0.9287 | 0.9829   |
+| 0.0346        | 4.0   | 1756 | 0.0619          | 0.9253    | 0.9355 | 0.9304 | 0.9834   |
 
 
 ### Framework versions

model.safetensors

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