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Ella01
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bert-finetuned-ner

Token Classification
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bert
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  1. README.md +14 -14
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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: 0.9338842975206612
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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: 0.9422948632421615
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  - name: Accuracy
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  type: accuracy
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- value: 0.986210042974039
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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: 0.0625
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- - Precision: 0.9339
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- - Recall: 0.9509
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- - F1: 0.9423
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- - Accuracy: 0.9862
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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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- | 0.0763 | 1.0 | 1756 | 0.0717 | 0.8968 | 0.9286 | 0.9124 | 0.9810 |
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- | 0.036 | 2.0 | 3512 | 0.0656 | 0.9268 | 0.9438 | 0.9352 | 0.9848 |
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- | 0.0222 | 3.0 | 5268 | 0.0625 | 0.9339 | 0.9509 | 0.9423 | 0.9862 |
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  ### Framework versions
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- - Transformers 4.40.1
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- - Pytorch 2.2.1+cu121
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  - Datasets 2.20.0
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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: 0.9493392070484582
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  - name: Recall
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  type: recall
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+ value: 0.9577777777777777
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  - name: F1
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  type: f1
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+ value: 0.9535398230088495
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9834710743801653
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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: 0.0572
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+ - Precision: 0.9493
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+ - Recall: 0.9578
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+ - F1: 0.9535
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+ - Accuracy: 0.9835
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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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+ | No log | 1.0 | 125 | 0.0625 | 0.9304 | 0.9511 | 0.9407 | 0.9816 |
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+ | No log | 2.0 | 250 | 0.0662 | 0.9409 | 0.9556 | 0.9482 | 0.9832 |
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+ | No log | 3.0 | 375 | 0.0572 | 0.9493 | 0.9578 | 0.9535 | 0.9835 |
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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