Training complete

README.md

@@ -11,8 +11,6 @@ metrics:
 model-index:
 - name: bert-finetuned-ner
   results: []
-datasets:
-- tner/conll2003
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -22,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0592
-- Precision: 0.9332
-- Recall: 0.9497
-- F1: 0.9414
-- Accuracy: 0.9862
+- Loss: 0.0605
+- Precision: 0.9341
+- Recall: 0.9495
+- F1: 0.9417
+- Accuracy: 0.9863
 
 ## Model description
 
@@ -57,14 +55,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0779        | 1.0   | 1756 | 0.0688          | 0.9155    | 0.9371 | 0.9261 | 0.9824   |
-| 0.0401        | 2.0   | 3512 | 0.0550          | 0.9328    | 0.9483 | 0.9405 | 0.9860   |
-| 0.0267        | 3.0   | 5268 | 0.0592          | 0.9332    | 0.9497 | 0.9414 | 0.9862   |
+| 0.0745        | 1.0   | 1756 | 0.0688          | 0.9081    | 0.9300 | 0.9189 | 0.9812   |
+| 0.0361        | 2.0   | 3512 | 0.0606          | 0.9336    | 0.9472 | 0.9404 | 0.9855   |
+| 0.0231        | 3.0   | 5268 | 0.0605          | 0.9341    | 0.9495 | 0.9417 | 0.9863   |
 
 
 ### Framework versions
 
-- Transformers 4.31.0
-- Pytorch 2.0.1+cu117
-- Datasets 2.18.0
-- Tokenizers 0.13.3
+- Transformers 4.40.0
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1