Training_02 complete

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: bert-base-cased
+base_model: MarcosAutuori/bert-finetuned-ner
 tags:
 - generated_from_trainer
 datasets:
@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9383823285168577
+      value: 0.9379036264282166
     - name: Recall
       type: recall
-      value: 0.9508582968697409
+      value: 0.9532144059239314
     - name: F1
       type: f1
-      value: 0.9445791189500962
+      value: 0.9454970369752107
     - name: Accuracy
       type: accuracy
-      value: 0.9864013657502796
+      value: 0.9869900512156354
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -42,13 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-finetuned-ner
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
+This model is a fine-tuned version of [MarcosAutuori/bert-finetuned-ner](https://huggingface.co/MarcosAutuori/bert-finetuned-ner) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0608
-- Precision: 0.9384
-- Recall: 0.9509
-- F1: 0.9446
-- Accuracy: 0.9864
+- Loss: 0.0639
+- Precision: 0.9379
+- Recall: 0.9532
+- F1: 0.9455
+- Accuracy: 0.9870
 
 ## Model description
 
@@ -73,15 +73,13 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 3
+- num_epochs: 1
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.076         | 1.0   | 1756 | 0.0633          | 0.9118    | 0.9357 | 0.9236 | 0.9827   |
-| 0.0354        | 2.0   | 3512 | 0.0631          | 0.9360    | 0.9505 | 0.9432 | 0.9864   |
-| 0.0211        | 3.0   | 5268 | 0.0608          | 0.9384    | 0.9509 | 0.9446 | 0.9864   |
+| 0.0123        | 1.0   | 1756 | 0.0639          | 0.9379    | 0.9532 | 0.9455 | 0.9870   |
 
 
 ### Framework versions