End of training

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: distilbert-base-uncased
+base_model: bert-base-uncased
 tags:
 - generated_from_trainer
 datasets:
@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9860607282009942
+      value: 0.9948381144840311
     - name: Recall
       type: recall
-      value: 0.9693364297742606
+      value: 0.972891113354671
     - name: F1
       type: f1
-      value: 0.9776270584382788
+      value: 0.9837422213534031
     - name: Accuracy
       type: accuracy
-      value: 0.9882459717748076
+      value: 0.9932984044056051
 ---
 
 <!-- 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-NER
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ner dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0372
-- Precision: 0.9861
-- Recall: 0.9693
-- F1: 0.9776
-- Accuracy: 0.9882
+- Loss: 0.0270
+- Precision: 0.9948
+- Recall: 0.9729
+- F1: 0.9837
+- Accuracy: 0.9933
 
 ## Model description
 
@@ -79,14 +79,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0461        | 1.0   | 858  | 0.0450          | 0.9853    | 0.9602 | 0.9725 | 0.9859   |
-| 0.0408        | 2.0   | 1716 | 0.0400          | 0.9836    | 0.9679 | 0.9757 | 0.9873   |
-| 0.0391        | 3.0   | 2574 | 0.0372          | 0.9861    | 0.9693 | 0.9776 | 0.9882   |
+| 0.0875        | 1.0   | 501  | 0.0328          | 0.9923    | 0.9696 | 0.9808 | 0.9920   |
+| 0.0333        | 2.0   | 1002 | 0.0289          | 0.9935    | 0.9726 | 0.9830 | 0.9929   |
+| 0.0283        | 3.0   | 1503 | 0.0270          | 0.9948    | 0.9729 | 0.9837 | 0.9933   |
 
 
 ### Framework versions
 
-- Transformers 4.34.1
-- Pytorch 2.1.0+cu118
-- Datasets 2.14.6
-- Tokenizers 0.14.1
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1