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bert-finetuned-ner

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update model card README.md

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@@ -22,16 +22,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9361244415025649
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  - name: Recall
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  type: recall
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- value: 0.9520363513968361
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  - name: F1
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  type: f1
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- value: 0.9440133500208594
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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
@@ -41,11 +41,11 @@ 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: 0.0642
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- - Precision: 0.9361
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- - Recall: 0.9520
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- - F1: 0.9440
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- - Accuracy: 0.9865
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  ## Model description
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@@ -76,14 +76,14 @@ 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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- | 0.0874 | 1.0 | 1756 | 0.0696 | 0.9177 | 0.9344 | 0.9260 | 0.9818 |
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- | 0.0324 | 2.0 | 3512 | 0.0634 | 0.9362 | 0.9490 | 0.9426 | 0.9856 |
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- | 0.0226 | 3.0 | 5268 | 0.0642 | 0.9361 | 0.9520 | 0.9440 | 0.9865 |
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  ### Framework versions
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- - Transformers 4.16.2
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- - Pytorch 1.10.0a0+0aef44c
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  - Datasets 1.18.3
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- - Tokenizers 0.11.0
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9330024813895782
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  - name: Recall
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  type: recall
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+ value: 0.9491753618310333
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  - name: F1
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  type: f1
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+ value: 0.9410194377242012
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9861511744275033
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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: 0.0599
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+ - Precision: 0.9330
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+ - Recall: 0.9492
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+ - F1: 0.9410
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+ - Accuracy: 0.9862
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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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+ | 0.0852 | 1.0 | 1756 | 0.0647 | 0.9147 | 0.9345 | 0.9245 | 0.9826 |
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+ | 0.0305 | 2.0 | 3512 | 0.0599 | 0.9333 | 0.9463 | 0.9398 | 0.9858 |
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+ | 0.0212 | 3.0 | 5268 | 0.0599 | 0.9330 | 0.9492 | 0.9410 | 0.9862 |
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  ### Framework versions
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+ - Transformers 4.15.0
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+ - Pytorch 1.9.1
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  - Datasets 1.18.3
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+ - Tokenizers 0.10.3