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---
base_model: SynamicTechnologies/CYBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: anonymouspd/CyBERT-DNRTI
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. -->
# anonymouspd/CyBERT-DNRTI
This model is a fine-tuned version of [SynamicTechnologies/CYBERT](https://huggingface.co/SynamicTechnologies/CYBERT) on the [DNRTI](https://github.com/SCreaMxp/DNRTI-A-Large-scale-Dataset-for-Named-Entity-Recognition-in-Threat-Intelligence) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3378
- Precision: 0.5628
- Recall: 0.6439
- F1: 0.6006
- Accuracy: 0.9077
It achieves the following results on the prediction set:
- Loss: 0.2841
- Precision: 0.6301
- Recall: 0.6926
- F1: 0.6599
- Accuracy: 0.9201
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8529 | 0.76 | 500 | 0.5937 | 0.4470 | 0.3593 | 0.3984 | 0.8508 |
| 0.5566 | 1.52 | 1000 | 0.5027 | 0.4669 | 0.4196 | 0.4420 | 0.8636 |
| 0.4678 | 2.28 | 1500 | 0.4671 | 0.4706 | 0.4832 | 0.4768 | 0.8694 |
| 0.4038 | 3.04 | 2000 | 0.4320 | 0.4629 | 0.5371 | 0.4972 | 0.8739 |
| 0.3572 | 3.81 | 2500 | 0.4002 | 0.5134 | 0.5394 | 0.5261 | 0.8858 |
| 0.3167 | 4.57 | 3000 | 0.4047 | 0.4691 | 0.6094 | 0.5302 | 0.8826 |
| 0.2987 | 5.33 | 3500 | 0.3761 | 0.5158 | 0.5854 | 0.5484 | 0.8948 |
| 0.2706 | 6.09 | 4000 | 0.3558 | 0.5362 | 0.6066 | 0.5693 | 0.9001 |
| 0.2461 | 6.85 | 4500 | 0.3493 | 0.5511 | 0.5735 | 0.5621 | 0.9028 |
| 0.2311 | 7.61 | 5000 | 0.3526 | 0.5334 | 0.6518 | 0.5867 | 0.9024 |
| 0.2171 | 8.37 | 5500 | 0.3418 | 0.5586 | 0.6407 | 0.5969 | 0.9071 |
| 0.2062 | 9.13 | 6000 | 0.3378 | 0.5628 | 0.6439 | 0.6006 | 0.9077 |
| 0.1972 | 9.89 | 6500 | 0.3384 | 0.5648 | 0.6527 | 0.6056 | 0.9087 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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