End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.8908045977011494
+      value: 0.8885217391304348
     - name: Recall
       type: recall
-      value: 0.9056303116147308
+      value: 0.9045679886685553
     - name: F1
       type: f1
-      value: 0.8981562774363476
+      value: 0.8964730654500789
     - name: Accuracy
       type: accuracy
-      value: 0.9781845590610531
+      value: 0.9781414881016475
 ---
 
 <!-- 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-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1539
-- Precision: 0.8908
-- Recall: 0.9056
-- F1: 0.8982
-- Accuracy: 0.9782
+- Loss: 0.1530
+- Precision: 0.8885
+- Recall: 0.9046
+- F1: 0.8965
+- Accuracy: 0.9781
 
 ## Model description
 
@@ -79,8 +79,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0641        | 1.0   | 3922 | 0.1398          | 0.8868    | 0.9044 | 0.8955 | 0.9776   |
-| 0.0262        | 2.0   | 7844 | 0.1539          | 0.8908    | 0.9056 | 0.8982 | 0.9782   |
+| 0.0651        | 1.0   | 3922 | 0.1483          | 0.8842    | 0.9067 | 0.8953 | 0.9775   |
+| 0.0287        | 2.0   | 7844 | 0.1530          | 0.8885    | 0.9046 | 0.8965 | 0.9781   |
 
 
 ### Framework versions

model.safetensors

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pytorch_model.bin

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training_args.bin

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