Training complete

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.8347884486232371
+      value: 0.8340044742729307
     - name: Recall
       type: recall
-      value: 0.8568474264705882
+      value: 0.8566176470588235
     - name: F1
       type: f1
-      value: 0.8456741127111919
+      value: 0.8451598277034684
     - name: Accuracy
       type: accuracy
-      value: 0.9702609719811555
+      value: 0.9701369947929581
 ---
 
 <!-- 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 [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the conll2002 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1589
-- Precision: 0.8348
-- Recall: 0.8568
-- F1: 0.8457
-- Accuracy: 0.9703
+- Loss: 0.1588
+- Precision: 0.8340
+- Recall: 0.8566
+- F1: 0.8452
+- Accuracy: 0.9701
 
 ## Model description
 
@@ -80,8 +80,8 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
 | 0.0499        | 1.0   | 521  | 0.1304          | 0.8278    | 0.8536 | 0.8405 | 0.9704   |
-| 0.0272        | 2.0   | 1042 | 0.1510          | 0.8355    | 0.8486 | 0.8420 | 0.9687   |
-| 0.0153        | 3.0   | 1563 | 0.1589          | 0.8348    | 0.8568 | 0.8457 | 0.9703   |
+| 0.0272        | 2.0   | 1042 | 0.1509          | 0.8351    | 0.8483 | 0.8417 | 0.9686   |
+| 0.0153        | 3.0   | 1563 | 0.1588          | 0.8340    | 0.8566 | 0.8452 | 0.9701   |
 
 
 ### Framework versions