---
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