---
library_name: transformers
license: apache-2.0
base_model: cisco-ai/SecureBERT2.0-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SecureBERT2.0-ner
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# SecureBERT2.0-ner

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.
It achieves the following results on the evaluation set:
- Loss: 0.2745
- Precision: 0.5641
- Recall: 0.5361
- F1: 0.5497
- Accuracy: 0.8981

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 487  | 0.2967          | 0.4305    | 0.3045 | 0.3567 | 0.8696   |
| 0.4569        | 2.0   | 974  | 0.2485          | 0.4620    | 0.4440 | 0.4528 | 0.8852   |
| 0.2305        | 3.0   | 1461 | 0.2569          | 0.5296    | 0.5140 | 0.5217 | 0.8887   |
| 0.1465        | 4.0   | 1948 | 0.2527          | 0.5571    | 0.5197 | 0.5378 | 0.8974   |
| 0.089         | 5.0   | 2435 | 0.2745          | 0.5641    | 0.5361 | 0.5497 | 0.8981   |


### Framework versions

- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.2
- Tokenizers 0.22.1