Training complete

README.md

@@ -11,8 +11,6 @@ metrics:
 model-index:
 - name: bert-finetuned-ner
   results: []
-datasets:
-- conll2002
 ---
 
 <!-- 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: 0.1705
-- Precision: 0.7588
-- Recall: 0.8038
-- F1: 0.7806
-- Accuracy: 0.9680
+- Loss: 0.1471
+- Precision: 0.7369
+- Recall: 0.7943
+- F1: 0.7646
+- Accuracy: 0.9666
 
 ## Model description
 
@@ -57,11 +55,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0281        | 1.0   | 521  | 0.1800          | 0.7135    | 0.7578 | 0.7350 | 0.9610   |
-| 0.0413        | 2.0   | 1042 | 0.1418          | 0.7279    | 0.7937 | 0.7594 | 0.9657   |
-| 0.026         | 3.0   | 1563 | 0.1476          | 0.7575    | 0.8077 | 0.7818 | 0.9678   |
-| 0.0172        | 4.0   | 2084 | 0.1660          | 0.7503    | 0.8022 | 0.7753 | 0.9676   |
-| 0.0117        | 5.0   | 2605 | 0.1705          | 0.7588    | 0.8038 | 0.7806 | 0.9680   |
+| 0.1691        | 1.0   | 521  | 0.1438          | 0.6830    | 0.7371 | 0.7090 | 0.9587   |
+| 0.076         | 2.0   | 1042 | 0.1402          | 0.7075    | 0.7670 | 0.7361 | 0.9622   |
+| 0.05          | 3.0   | 1563 | 0.1332          | 0.7536    | 0.7971 | 0.7748 | 0.9672   |
+| 0.0359        | 4.0   | 2084 | 0.1442          | 0.7420    | 0.7845 | 0.7626 | 0.9663   |
+| 0.0265        | 5.0   | 2605 | 0.1471          | 0.7369    | 0.7943 | 0.7646 | 0.9666   |
 
 
 ### Framework versions
@@ -69,4 +67,4 @@ The following hyperparameters were used during training:
 - Transformers 4.40.2
 - Pytorch 2.2.1+cu121
 - Datasets 2.19.1
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1