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bert-finetuned-ner

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  1. README.md +12 -12
  2. runs/Nov13_16-34-14_989e9d525a69/events.out.tfevents.1731515669.989e9d525a69.1152.0 +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: 0.9331789612967251
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  - name: Recall
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  type: recall
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- value: 0.9495119488387749
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  - name: F1
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  type: f1
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- value: 0.9412746079412746
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  - name: Accuracy
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  type: accuracy
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- value: 0.9857685288750221
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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: 0.0656
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- - Precision: 0.9332
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- - Recall: 0.9495
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- - F1: 0.9413
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- - Accuracy: 0.9858
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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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- | 0.0744 | 1.0 | 1756 | 0.0716 | 0.8906 | 0.9273 | 0.9086 | 0.9804 |
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- | 0.0337 | 2.0 | 3512 | 0.0732 | 0.9276 | 0.9423 | 0.9349 | 0.9840 |
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- | 0.0208 | 3.0 | 5268 | 0.0656 | 0.9332 | 0.9495 | 0.9413 | 0.9858 |
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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: 0.9338186631369954
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  - name: Recall
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  type: recall
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+ value: 0.9498485358465163
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  - name: F1
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  type: f1
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+ value: 0.9417653929584515
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9865338199799847
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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: 0.0599
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+ - Precision: 0.9338
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+ - Recall: 0.9498
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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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+ | 0.0736 | 1.0 | 1756 | 0.0684 | 0.9025 | 0.9317 | 0.9169 | 0.9807 |
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+ | 0.0325 | 2.0 | 3512 | 0.0642 | 0.9290 | 0.9463 | 0.9376 | 0.9853 |
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+ | 0.0205 | 3.0 | 5268 | 0.0599 | 0.9338 | 0.9498 | 0.9418 | 0.9865 |
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  ### Framework versions
runs/Nov13_16-34-14_989e9d525a69/events.out.tfevents.1731515669.989e9d525a69.1152.0 CHANGED
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