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bert-finetuned-ner

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bert
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  1. README.md +12 -12
  2. runs/Sep12_05-58-54_160bf7950b94/events.out.tfevents.1726120736.160bf7950b94.957.2 +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: 0.8066429418742586
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  - name: Recall
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  type: recall
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- value: 0.8640406607369758
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  - name: F1
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  type: f1
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- value: 0.8343558282208589
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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: 0.0626
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- - Precision: 0.8066
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- - Recall: 0.8640
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- - F1: 0.8344
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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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- | 0.1219 | 1.0 | 680 | 0.0523 | 0.7248 | 0.8132 | 0.7665 | 0.9820 |
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- | 0.0387 | 2.0 | 1360 | 0.0572 | 0.7904 | 0.8335 | 0.8114 | 0.9831 |
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- | 0.0139 | 3.0 | 2040 | 0.0626 | 0.8066 | 0.8640 | 0.8344 | 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: 0.805952380952381
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  - name: Recall
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  type: recall
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+ value: 0.8602287166454892
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  - name: F1
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  type: f1
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+ value: 0.8322065150583897
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9841520413532671
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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: 0.0621
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+ - Precision: 0.8060
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+ - Recall: 0.8602
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+ - F1: 0.8322
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+ - Accuracy: 0.9842
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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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+ | 0.1232 | 1.0 | 680 | 0.0541 | 0.7129 | 0.8234 | 0.7642 | 0.9813 |
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+ | 0.0403 | 2.0 | 1360 | 0.0515 | 0.7887 | 0.8539 | 0.8200 | 0.9842 |
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+ | 0.0142 | 3.0 | 2040 | 0.0621 | 0.8060 | 0.8602 | 0.8322 | 0.9842 |
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  ### Framework versions
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