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sweta-14
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bert-finetuned-ner

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  1. README.md +12 -12
  2. runs/Jul25_09-32-00_b46e53630630/events.out.tfevents.1721899923.b46e53630630.590.1 +2 -2
README.md CHANGED
@@ -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: 0.9325508348487354
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  - name: Recall
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  type: recall
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- value: 0.9493436553349041
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  - name: F1
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  type: f1
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- value: 0.9408723209073472
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  - name: Accuracy
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  type: accuracy
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- value: 0.9859304173779949
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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: 0.0643
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- - Precision: 0.9326
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- - Recall: 0.9493
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- - F1: 0.9409
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- - Accuracy: 0.9859
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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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- | 0.0762 | 1.0 | 1756 | 0.0665 | 0.9061 | 0.9355 | 0.9206 | 0.9817 |
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- | 0.034 | 2.0 | 3512 | 0.0692 | 0.9269 | 0.9455 | 0.9361 | 0.9840 |
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- | 0.0227 | 3.0 | 5268 | 0.0643 | 0.9326 | 0.9493 | 0.9409 | 0.9859 |
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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: 0.9375
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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: 0.9445465174544847
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9866368399364219
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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: 0.0634
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+ - Precision: 0.9375
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+ - Recall: 0.9517
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+ - F1: 0.9445
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+ - Accuracy: 0.9866
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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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+ | 0.0752 | 1.0 | 1756 | 0.0668 | 0.9016 | 0.9315 | 0.9163 | 0.9815 |
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+ | 0.0356 | 2.0 | 3512 | 0.0683 | 0.9298 | 0.9453 | 0.9375 | 0.9852 |
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+ | 0.0221 | 3.0 | 5268 | 0.0634 | 0.9375 | 0.9517 | 0.9445 | 0.9866 |
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  ### Framework versions
runs/Jul25_09-32-00_b46e53630630/events.out.tfevents.1721899923.b46e53630630.590.1 CHANGED
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