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sarahflan
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bert-finetuned-ner

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  1. README.md +14 -14
README.md CHANGED
@@ -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: 0.9335652750165673
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  - name: Recall
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  type: recall
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- value: 0.9483338943116796
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  - name: F1
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  type: f1
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- value: 0.9408916346635499
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  - name: Accuracy
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  type: accuracy
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- value: 0.9859745687878966
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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: 0.0619
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- - Precision: 0.9336
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- - Recall: 0.9483
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- - F1: 0.9409
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- - Accuracy: 0.9860
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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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- | 0.0794 | 1.0 | 1756 | 0.0729 | 0.9090 | 0.9342 | 0.9214 | 0.9805 |
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- | 0.0407 | 2.0 | 3512 | 0.0590 | 0.9302 | 0.9493 | 0.9397 | 0.9862 |
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- | 0.0244 | 3.0 | 5268 | 0.0619 | 0.9336 | 0.9483 | 0.9409 | 0.9860 |
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  ### Framework versions
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- - Transformers 4.31.0
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  - Pytorch 2.0.1+cu118
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- - Datasets 2.13.1
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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: 0.9361138695796094
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  - name: Recall
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  type: recall
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+ value: 0.9518680578929654
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  - name: F1
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  type: f1
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+ value: 0.9439252336448599
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  - name: Accuracy
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  type: accuracy
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+ value: 0.986342497203744
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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: 0.0624
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+ - Precision: 0.9361
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+ - Recall: 0.9519
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+ - F1: 0.9439
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+ - Accuracy: 0.9863
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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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+ | 0.0775 | 1.0 | 1756 | 0.0831 | 0.9068 | 0.9352 | 0.9208 | 0.9791 |
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+ | 0.0411 | 2.0 | 3512 | 0.0578 | 0.9232 | 0.9492 | 0.9360 | 0.9853 |
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+ | 0.024 | 3.0 | 5268 | 0.0624 | 0.9361 | 0.9519 | 0.9439 | 0.9863 |
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  ### Framework versions
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+ - Transformers 4.32.1
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  - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.4
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  - Tokenizers 0.13.3