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---
base_model: SynamicTechnologies/CYBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: our_data
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. -->
# our_data
This model is a fine-tuned version of [SynamicTechnologies/CYBERT](https://huggingface.co/SynamicTechnologies/CYBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6376
- Precision: 0.1972
- Recall: 0.3585
- F1: 0.2545
- Accuracy: 0.6637
## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.0809 | 0.4 | 500 | 2.0594 | 0.5 | 0.0066 | 0.0131 | 0.5298 |
| 1.8682 | 0.81 | 1000 | 1.8006 | 0.1043 | 0.0807 | 0.0910 | 0.5529 |
| 1.6332 | 1.21 | 1500 | 1.8356 | 0.1339 | 0.1495 | 0.1412 | 0.5748 |
| 1.468 | 1.61 | 2000 | 1.6261 | 0.1356 | 0.1706 | 0.1511 | 0.5891 |
| 1.401 | 2.01 | 2500 | 1.6943 | 0.1563 | 0.1693 | 0.1625 | 0.5986 |
| 1.1878 | 2.42 | 3000 | 1.6740 | 0.1194 | 0.2460 | 0.1608 | 0.5976 |
| 1.1182 | 2.82 | 3500 | 1.6201 | 0.1589 | 0.2196 | 0.1843 | 0.6227 |
| 0.9677 | 3.22 | 4000 | 1.6241 | 0.1393 | 0.2196 | 0.1704 | 0.6176 |
| 0.9055 | 3.63 | 4500 | 1.5932 | 0.1317 | 0.2646 | 0.1758 | 0.6158 |
| 0.8772 | 4.03 | 5000 | 1.5797 | 0.1654 | 0.2804 | 0.2080 | 0.6254 |
| 0.7224 | 4.43 | 5500 | 1.5723 | 0.1587 | 0.2976 | 0.2070 | 0.6413 |
| 0.7498 | 4.83 | 6000 | 1.5957 | 0.1794 | 0.2897 | 0.2215 | 0.6496 |
| 0.6632 | 5.24 | 6500 | 1.6825 | 0.1864 | 0.2751 | 0.2222 | 0.6427 |
| 0.6139 | 5.64 | 7000 | 1.5827 | 0.1769 | 0.3479 | 0.2345 | 0.6508 |
| 0.6212 | 6.04 | 7500 | 1.5537 | 0.1778 | 0.3413 | 0.2338 | 0.6526 |
| 0.5379 | 6.45 | 8000 | 1.5670 | 0.1792 | 0.3307 | 0.2325 | 0.6536 |
| 0.5376 | 6.85 | 8500 | 1.6113 | 0.1844 | 0.3386 | 0.2388 | 0.6530 |
| 0.5 | 7.25 | 9000 | 1.6432 | 0.1789 | 0.3214 | 0.2299 | 0.6600 |
| 0.4928 | 7.66 | 9500 | 1.6422 | 0.1881 | 0.3373 | 0.2415 | 0.6609 |
| 0.4877 | 8.06 | 10000 | 1.6851 | 0.2042 | 0.3360 | 0.254 | 0.6654 |
| 0.4339 | 8.46 | 10500 | 1.6376 | 0.1972 | 0.3585 | 0.2545 | 0.6637 |
| 0.4303 | 8.86 | 11000 | 1.6364 | 0.1835 | 0.3452 | 0.2397 | 0.6604 |
| 0.4509 | 9.27 | 11500 | 1.6448 | 0.1983 | 0.3413 | 0.2509 | 0.6664 |
| 0.4114 | 9.67 | 12000 | 1.6494 | 0.1956 | 0.3505 | 0.2511 | 0.6658 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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