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bert-finetuned-ner

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update model card README.md

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@@ -18,20 +18,22 @@ model-index:
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  dataset:
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  name: conll2003
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  type: conll2003
 
 
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  args: conll2003
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9315407456285054
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  - name: Recall
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  type: recall
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- value: 0.9503534163581285
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  - name: F1
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  type: f1
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- value: 0.9408530489836722
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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
@@ -41,11 +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: 0.0615
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- - Precision: 0.9315
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- - Recall: 0.9504
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- - F1: 0.9409
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- - Accuracy: 0.9862
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  ## Model description
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@@ -76,14 +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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- | 0.084 | 1.0 | 1756 | 0.0683 | 0.9173 | 0.9347 | 0.9259 | 0.9826 |
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- | 0.0342 | 2.0 | 3512 | 0.0602 | 0.9312 | 0.9470 | 0.9390 | 0.9856 |
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- | 0.0236 | 3.0 | 5268 | 0.0615 | 0.9315 | 0.9504 | 0.9409 | 0.9862 |
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  ### Framework versions
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- - Transformers 4.15.0
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- - Pytorch 1.10.0+cu111
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- - Datasets 1.18.0
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- - Tokenizers 0.10.3
 
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  dataset:
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  name: conll2003
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  type: conll2003
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+ config: conll2003
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+ split: train
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  args: conll2003
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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
 
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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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  | 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