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sbartlett97
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SecureBERT2.0-ner

Token Classification
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Safetensors
modernbert
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End of training

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  1. README.md +11 -13
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -12,8 +12,6 @@ metrics:
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  model-index:
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  - name: SecureBERT2.0-ner
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  results: []
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- datasets:
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- - mrmoor/cyber-threat-intelligence
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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
@@ -23,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [cisco-ai/SecureBERT2.0-base](https://huggingface.co/cisco-ai/SecureBERT2.0-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2991
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- - Precision: 0.5749
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- - Recall: 0.5350
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- - F1: 0.5543
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- - Accuracy: 0.8895
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  ## Model description
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@@ -61,11 +59,11 @@ 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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- | No log | 1.0 | 487 | 0.3160 | 0.4991 | 0.3324 | 0.3990 | 0.8632 |
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- | 0.4464 | 2.0 | 974 | 0.2617 | 0.4909 | 0.4759 | 0.4832 | 0.8780 |
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- | 0.2277 | 3.0 | 1461 | 0.2647 | 0.5095 | 0.5419 | 0.5252 | 0.8812 |
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- | 0.1469 | 4.0 | 1948 | 0.2784 | 0.5711 | 0.5350 | 0.5525 | 0.8887 |
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- | 0.0922 | 5.0 | 2435 | 0.2991 | 0.5749 | 0.5350 | 0.5543 | 0.8895 |
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  ### Framework versions
@@ -73,4 +71,4 @@ The following hyperparameters were used during training:
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  - Transformers 4.57.3
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  - Pytorch 2.9.1+cu128
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  - Datasets 4.4.2
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- - Tokenizers 0.22.1
 
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  model-index:
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  - name: SecureBERT2.0-ner
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  results: []
 
 
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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 [cisco-ai/SecureBERT2.0-base](https://huggingface.co/cisco-ai/SecureBERT2.0-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2745
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+ - Precision: 0.5641
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+ - Recall: 0.5361
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+ - F1: 0.5497
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+ - Accuracy: 0.8981
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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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+ | No log | 1.0 | 487 | 0.2967 | 0.4305 | 0.3045 | 0.3567 | 0.8696 |
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+ | 0.4569 | 2.0 | 974 | 0.2485 | 0.4620 | 0.4440 | 0.4528 | 0.8852 |
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+ | 0.2305 | 3.0 | 1461 | 0.2569 | 0.5296 | 0.5140 | 0.5217 | 0.8887 |
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+ | 0.1465 | 4.0 | 1948 | 0.2527 | 0.5571 | 0.5197 | 0.5378 | 0.8974 |
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+ | 0.089 | 5.0 | 2435 | 0.2745 | 0.5641 | 0.5361 | 0.5497 | 0.8981 |
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  ### Framework versions
 
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  - Transformers 4.57.3
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  - Pytorch 2.9.1+cu128
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  - Datasets 4.4.2
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+ - Tokenizers 0.22.1
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