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bert-finetuned-ner

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  1. README.md +12 -12
  2. runs/Sep18_05-30-06_14e93d5cc4db/events.out.tfevents.1726637410.14e93d5cc4db.542.0 +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: 0.8116805721096544
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  - name: Recall
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  type: recall
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- value: 0.8653113087674714
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  - name: F1
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  type: f1
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- value: 0.8376383763837638
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  - name: Accuracy
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  type: accuracy
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- value: 0.9840282291763395
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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 [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the ncbi_disease dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0621
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- - Precision: 0.8117
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- - Recall: 0.8653
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- - F1: 0.8376
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- - Accuracy: 0.9840
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  ## Model description
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@@ -80,9 +80,9 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.1181 | 1.0 | 680 | 0.0523 | 0.7339 | 0.8272 | 0.7778 | 0.9824 |
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- | 0.0386 | 2.0 | 1360 | 0.0554 | 0.8112 | 0.8463 | 0.8284 | 0.9838 |
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- | 0.0145 | 3.0 | 2040 | 0.0621 | 0.8117 | 0.8653 | 0.8376 | 0.9840 |
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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: 0.7952941176470588
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  - name: Recall
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  type: recall
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+ value: 0.8589580686149937
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  - name: F1
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  type: f1
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+ value: 0.8259010384850336
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9841210883090352
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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 [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the ncbi_disease dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0623
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+ - Precision: 0.7953
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+ - Recall: 0.8590
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+ - F1: 0.8259
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+ - Accuracy: 0.9841
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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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+ | 0.1204 | 1.0 | 680 | 0.0536 | 0.7417 | 0.8247 | 0.7810 | 0.9824 |
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+ | 0.0386 | 2.0 | 1360 | 0.0542 | 0.7808 | 0.8463 | 0.8122 | 0.9831 |
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+ | 0.0144 | 3.0 | 2040 | 0.0623 | 0.7953 | 0.8590 | 0.8259 | 0.9841 |
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  ### Framework versions
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