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bert-finetuned-ner

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  1. README.md +11 -11
  2. runs/Aug06_09-02-35_f59daa081399/events.out.tfevents.1722934960.f59daa081399.513.2 +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: 0.937303035329242
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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: 0.9441149444490853
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  - name: Accuracy
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  type: accuracy
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- value: 0.9862836286572084
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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
@@ -44,10 +44,10 @@ 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: 0.0609
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- - Precision: 0.9373
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- - Recall: 0.9510
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- - F1: 0.9441
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  - Accuracy: 0.9863
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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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- | 0.0787 | 1.0 | 1756 | 0.0677 | 0.8985 | 0.9327 | 0.9153 | 0.9813 |
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- | 0.035 | 2.0 | 3512 | 0.0714 | 0.9306 | 0.9455 | 0.9380 | 0.9845 |
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- | 0.0225 | 3.0 | 5268 | 0.0609 | 0.9373 | 0.9510 | 0.9441 | 0.9863 |
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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: 0.9330024813895782
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  - name: Recall
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  type: recall
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+ value: 0.9491753618310333
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  - name: F1
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  type: f1
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+ value: 0.9410194377242012
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9862541943839407
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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: 0.0604
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+ - Precision: 0.9330
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+ - Recall: 0.9492
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+ - F1: 0.9410
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  - Accuracy: 0.9863
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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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+ | 0.0765 | 1.0 | 1756 | 0.0666 | 0.9061 | 0.9367 | 0.9211 | 0.9818 |
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+ | 0.0354 | 2.0 | 3512 | 0.0634 | 0.9262 | 0.9440 | 0.9350 | 0.9845 |
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+ | 0.0219 | 3.0 | 5268 | 0.0604 | 0.9330 | 0.9492 | 0.9410 | 0.9863 |
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  ### Framework versions
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