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cotysong113
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bert-finetuned-ner

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  1. README.md +11 -11
  2. runs/Nov10_01-24-46_p16/events.out.tfevents.1731173118.p16.1139893.0 +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: 0.9363711681855841
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  - name: Recall
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  type: recall
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- value: 0.9510265903736116
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  - name: F1
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  type: f1
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- value: 0.9436419804625532
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  - name: Accuracy
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  type: accuracy
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- value: 0.9866809913463237
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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
@@ -45,10 +45,10 @@ 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: 0.0628
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- - Precision: 0.9364
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- - Recall: 0.9510
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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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- | 0.0799 | 1.0 | 1756 | 0.0664 | 0.9072 | 0.9325 | 0.9197 | 0.9823 |
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- | 0.0354 | 2.0 | 3512 | 0.0655 | 0.9334 | 0.9480 | 0.9406 | 0.9860 |
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- | 0.0222 | 3.0 | 5268 | 0.0628 | 0.9364 | 0.9510 | 0.9436 | 0.9867 |
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9387755102040817
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  - name: Recall
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  type: recall
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+ value: 0.9522046449007069
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  - name: F1
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  type: f1
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+ value: 0.9454423928481912
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9867398598928593
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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: 0.0597
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+ - Precision: 0.9388
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+ - Recall: 0.9522
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+ - F1: 0.9454
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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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+ | 0.0775 | 1.0 | 1756 | 0.0689 | 0.9093 | 0.9325 | 0.9207 | 0.9810 |
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+ | 0.0365 | 2.0 | 3512 | 0.0630 | 0.9357 | 0.9500 | 0.9428 | 0.9862 |
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+ | 0.0228 | 3.0 | 5268 | 0.0597 | 0.9388 | 0.9522 | 0.9454 | 0.9867 |
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  ### Framework versions
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