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bert-finetuned-ner

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update model card README.md

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@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9379549966909332
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  - name: Recall
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  type: recall
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- value: 0.9540558734432851
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  - name: F1
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  type: f1
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- value: 0.9459369264141498
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  - name: Accuracy
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  type: accuracy
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- value: 0.9868575969859305
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0604
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- - Precision: 0.9380
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- - Recall: 0.9541
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- - F1: 0.9459
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- - Accuracy: 0.9869
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  ## Model description
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@@ -78,14 +78,14 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.087 | 1.0 | 1756 | 0.0718 | 0.9233 | 0.9317 | 0.9275 | 0.9815 |
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- | 0.0342 | 2.0 | 3512 | 0.0632 | 0.9322 | 0.9507 | 0.9413 | 0.9859 |
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- | 0.017 | 3.0 | 5268 | 0.0604 | 0.9380 | 0.9541 | 0.9459 | 0.9869 |
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  ### Framework versions
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  - Transformers 4.24.0
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  - Pytorch 1.12.1+cu113
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- - Datasets 2.6.1
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  - Tokenizers 0.13.2
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9317169717961405
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  - name: Recall
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  type: recall
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+ value: 0.9506900033658701
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  - name: F1
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  type: f1
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+ value: 0.9411078717201166
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9865632542532525
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0623
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+ - Precision: 0.9317
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+ - Recall: 0.9507
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+ - F1: 0.9411
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+ - Accuracy: 0.9866
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.088 | 1.0 | 1756 | 0.0712 | 0.9107 | 0.9290 | 0.9198 | 0.9818 |
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+ | 0.033 | 2.0 | 3512 | 0.0683 | 0.9238 | 0.9467 | 0.9351 | 0.9856 |
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+ | 0.0183 | 3.0 | 5268 | 0.0623 | 0.9317 | 0.9507 | 0.9411 | 0.9866 |
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  ### Framework versions
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  - Transformers 4.24.0
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  - Pytorch 1.12.1+cu113
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+ - Datasets 2.7.0
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  - Tokenizers 0.13.2