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--- |
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base_model: SynamicTechnologies/CYBERT |
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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: our_data |
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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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# our_data |
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This model is a fine-tuned version of [SynamicTechnologies/CYBERT](https://huggingface.co/SynamicTechnologies/CYBERT) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6376 |
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- Precision: 0.1972 |
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- Recall: 0.3585 |
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- F1: 0.2545 |
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- Accuracy: 0.6637 |
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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: 2e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10.0 |
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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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| 2.0809 | 0.4 | 500 | 2.0594 | 0.5 | 0.0066 | 0.0131 | 0.5298 | |
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| 1.8682 | 0.81 | 1000 | 1.8006 | 0.1043 | 0.0807 | 0.0910 | 0.5529 | |
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| 1.6332 | 1.21 | 1500 | 1.8356 | 0.1339 | 0.1495 | 0.1412 | 0.5748 | |
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| 1.468 | 1.61 | 2000 | 1.6261 | 0.1356 | 0.1706 | 0.1511 | 0.5891 | |
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| 1.401 | 2.01 | 2500 | 1.6943 | 0.1563 | 0.1693 | 0.1625 | 0.5986 | |
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| 1.1878 | 2.42 | 3000 | 1.6740 | 0.1194 | 0.2460 | 0.1608 | 0.5976 | |
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| 1.1182 | 2.82 | 3500 | 1.6201 | 0.1589 | 0.2196 | 0.1843 | 0.6227 | |
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| 0.9677 | 3.22 | 4000 | 1.6241 | 0.1393 | 0.2196 | 0.1704 | 0.6176 | |
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| 0.9055 | 3.63 | 4500 | 1.5932 | 0.1317 | 0.2646 | 0.1758 | 0.6158 | |
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| 0.8772 | 4.03 | 5000 | 1.5797 | 0.1654 | 0.2804 | 0.2080 | 0.6254 | |
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| 0.7224 | 4.43 | 5500 | 1.5723 | 0.1587 | 0.2976 | 0.2070 | 0.6413 | |
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| 0.7498 | 4.83 | 6000 | 1.5957 | 0.1794 | 0.2897 | 0.2215 | 0.6496 | |
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| 0.6632 | 5.24 | 6500 | 1.6825 | 0.1864 | 0.2751 | 0.2222 | 0.6427 | |
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| 0.6139 | 5.64 | 7000 | 1.5827 | 0.1769 | 0.3479 | 0.2345 | 0.6508 | |
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| 0.6212 | 6.04 | 7500 | 1.5537 | 0.1778 | 0.3413 | 0.2338 | 0.6526 | |
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| 0.5379 | 6.45 | 8000 | 1.5670 | 0.1792 | 0.3307 | 0.2325 | 0.6536 | |
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| 0.5376 | 6.85 | 8500 | 1.6113 | 0.1844 | 0.3386 | 0.2388 | 0.6530 | |
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| 0.5 | 7.25 | 9000 | 1.6432 | 0.1789 | 0.3214 | 0.2299 | 0.6600 | |
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| 0.4928 | 7.66 | 9500 | 1.6422 | 0.1881 | 0.3373 | 0.2415 | 0.6609 | |
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| 0.4877 | 8.06 | 10000 | 1.6851 | 0.2042 | 0.3360 | 0.254 | 0.6654 | |
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| 0.4339 | 8.46 | 10500 | 1.6376 | 0.1972 | 0.3585 | 0.2545 | 0.6637 | |
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| 0.4303 | 8.86 | 11000 | 1.6364 | 0.1835 | 0.3452 | 0.2397 | 0.6604 | |
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| 0.4509 | 9.27 | 11500 | 1.6448 | 0.1983 | 0.3413 | 0.2509 | 0.6664 | |
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| 0.4114 | 9.67 | 12000 | 1.6494 | 0.1956 | 0.3505 | 0.2511 | 0.6658 | |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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