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cotysong113
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bert-finetuned-ner

Token Classification
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bert
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  1. README.md +13 -13
  2. runs/Oct19_15-25-24_p16/events.out.tfevents.1729322731.p16.79367.0 +2 -2
README.md CHANGED
@@ -26,16 +26,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9348652669862787
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  - name: Recall
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  type: recall
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- value: 0.9516997643890945
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  - name: F1
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  type: f1
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- value: 0.9432074055541656
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  - name: Accuracy
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  type: accuracy
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- value: 0.9870342026255372
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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
@@ -45,11 +45,11 @@ 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: 0.0606
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- - Precision: 0.9349
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- - Recall: 0.9517
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- - F1: 0.9432
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- - Accuracy: 0.9870
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  ## Model description
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@@ -80,14 +80,14 @@ 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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- | 0.0779 | 1.0 | 1756 | 0.0624 | 0.9084 | 0.9342 | 0.9211 | 0.9832 |
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- | 0.0342 | 2.0 | 3512 | 0.0660 | 0.9324 | 0.9472 | 0.9397 | 0.9858 |
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- | 0.0225 | 3.0 | 5268 | 0.0606 | 0.9349 | 0.9517 | 0.9432 | 0.9870 |
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  ### Framework versions
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  - Transformers 4.45.2
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- - Pytorch 2.4.1+cu121
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  - Datasets 3.0.1
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  - Tokenizers 0.20.0
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9363606231355651
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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: 0.9435537742150969
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9867251427562254
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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: 0.0616
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+ - Precision: 0.9364
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+ - Recall: 0.9509
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+ - F1: 0.9436
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+ - Accuracy: 0.9867
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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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+ | 0.0771 | 1.0 | 1756 | 0.0683 | 0.9056 | 0.9297 | 0.9175 | 0.9816 |
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+ | 0.0346 | 2.0 | 3512 | 0.0669 | 0.9318 | 0.9448 | 0.9382 | 0.9850 |
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+ | 0.0231 | 3.0 | 5268 | 0.0616 | 0.9364 | 0.9509 | 0.9436 | 0.9867 |
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  ### Framework versions
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  - Transformers 4.45.2
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+ - Pytorch 2.5.0
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  - Datasets 3.0.1
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  - Tokenizers 0.20.0
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