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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: 0.9362054681027341
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  - name: Recall
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  type: recall
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- value: 0.9508582968697409
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  - name: F1
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  type: f1
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- value: 0.9434749937379978
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  - name: Accuracy
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  type: accuracy
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- value: 0.986489668570083
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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: 0.0603
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- - Precision: 0.9362
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- - Recall: 0.9509
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- - F1: 0.9435
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- - Accuracy: 0.9865
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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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- | 0.0865 | 1.0 | 1756 | 0.0682 | 0.9233 | 0.9334 | 0.9283 | 0.9821 |
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- | 0.0353 | 2.0 | 3512 | 0.0593 | 0.9287 | 0.9509 | 0.9396 | 0.9859 |
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- | 0.0191 | 3.0 | 5268 | 0.0603 | 0.9362 | 0.9509 | 0.9435 | 0.9865 |
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  ### Framework versions
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- - Transformers 4.30.1
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  - Pytorch 2.0.1+cu118
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- - Datasets 2.12.0
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  - Tokenizers 0.13.3
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9340732151730993
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  - name: Recall
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  type: recall
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+ value: 0.9490070683271625
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  - name: F1
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  type: f1
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+ value: 0.9414809249519994
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9859598516512628
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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: 0.0620
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+ - Precision: 0.9341
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+ - Recall: 0.9490
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+ - F1: 0.9415
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+ - Accuracy: 0.9860
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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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+ | 0.0895 | 1.0 | 1756 | 0.0694 | 0.9148 | 0.9337 | 0.9241 | 0.9823 |
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+ | 0.0345 | 2.0 | 3512 | 0.0657 | 0.9279 | 0.9488 | 0.9383 | 0.9854 |
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+ | 0.0185 | 3.0 | 5268 | 0.0620 | 0.9341 | 0.9490 | 0.9415 | 0.9860 |
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  ### Framework versions
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+ - Transformers 4.30.2
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  - Pytorch 2.0.1+cu118
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+ - Datasets 2.13.0
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  - Tokenizers 0.13.3