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bert-finetuned-ner

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  1. README.md +13 -13
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@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9358953122411794
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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: 0.9433174722430921
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  - name: Accuracy
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  type: accuracy
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- value: 0.9867104256195914
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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
@@ -44,11 +44,11 @@ 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: 0.0602
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- - Precision: 0.9359
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  - Recall: 0.9509
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- - F1: 0.9433
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- - Accuracy: 0.9867
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  ## Model description
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@@ -79,14 +79,14 @@ 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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- | 0.073 | 1.0 | 1756 | 0.0651 | 0.9098 | 0.9354 | 0.9224 | 0.9813 |
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- | 0.034 | 2.0 | 3512 | 0.0602 | 0.9353 | 0.9483 | 0.9418 | 0.9866 |
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- | 0.0231 | 3.0 | 5268 | 0.0602 | 0.9359 | 0.9509 | 0.9433 | 0.9867 |
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  ### Framework versions
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- - Transformers 4.40.2
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- - Pytorch 2.2.1+cu121
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- - Datasets 2.19.1
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  - Tokenizers 0.19.1
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.935275616619765
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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: 0.9430025869982476
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9868281627126626
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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: 0.0595
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+ - Precision: 0.9353
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  - Recall: 0.9509
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+ - F1: 0.9430
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+ - Accuracy: 0.9868
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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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+ | 0.0793 | 1.0 | 1756 | 0.0648 | 0.9069 | 0.9360 | 0.9212 | 0.9825 |
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+ | 0.0352 | 2.0 | 3512 | 0.0645 | 0.9320 | 0.9458 | 0.9389 | 0.9850 |
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+ | 0.0205 | 3.0 | 5268 | 0.0595 | 0.9353 | 0.9509 | 0.9430 | 0.9868 |
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  ### Framework versions
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+ - Transformers 4.41.2
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.20.0
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  - Tokenizers 0.19.1