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bert-finetuned-ner

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  1. README.md +12 -12
  2. runs/Sep15_06-18-52_3cd17a1e55cc/events.out.tfevents.1726381229.3cd17a1e55cc.1138.0 +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: 0.9331020812685827
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  - name: Recall
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  type: recall
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- value: 0.9506900033658701
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  - name: F1
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  type: f1
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- value: 0.9418139379793263
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  - name: Accuracy
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  type: accuracy
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- value: 0.986489668570083
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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: 0.0617
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- - Precision: 0.9331
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- - Recall: 0.9507
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- - F1: 0.9418
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- - Accuracy: 0.9865
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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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- | 0.0751 | 1.0 | 1756 | 0.0675 | 0.9012 | 0.9317 | 0.9162 | 0.9804 |
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- | 0.0363 | 2.0 | 3512 | 0.0681 | 0.9293 | 0.9440 | 0.9366 | 0.9846 |
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- | 0.0212 | 3.0 | 5268 | 0.0617 | 0.9331 | 0.9507 | 0.9418 | 0.9865 |
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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: 0.9362443964801593
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  - name: Recall
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  type: recall
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+ value: 0.9490070683271625
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  - name: F1
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  type: f1
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+ value: 0.9425825323861261
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9861364572908695
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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: 0.0636
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+ - Precision: 0.9362
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+ - Recall: 0.9490
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+ - F1: 0.9426
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+ - Accuracy: 0.9861
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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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+ | 0.0759 | 1.0 | 1756 | 0.0677 | 0.9006 | 0.9302 | 0.9151 | 0.9812 |
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+ | 0.0348 | 2.0 | 3512 | 0.0738 | 0.9297 | 0.9435 | 0.9365 | 0.9841 |
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+ | 0.0228 | 3.0 | 5268 | 0.0636 | 0.9362 | 0.9490 | 0.9426 | 0.9861 |
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  ### Framework versions
runs/Sep15_06-18-52_3cd17a1e55cc/events.out.tfevents.1726381229.3cd17a1e55cc.1138.0 CHANGED
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