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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: cisco-ai/SecureBERT2.0-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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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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should probably proofread and complete it, then remove this comment. --> |
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# SecureBERT2.0-ner |
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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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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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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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