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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: llm_firewall_distilbert-base-uncased |
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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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# llm_firewall_distilbert-base-uncased |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1218 |
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- Accuracy: 0.9451 |
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# Latest finetune 5 Dec 2023 |
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{'eval_loss': 0.12179878354072571, |
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'eval_accuracy': 0.9450980392156862, |
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'eval_runtime': 5.8053, |
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'eval_samples_per_second': 43.925, |
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'eval_steps_per_second': 2.756, |
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'epoch': 20.0} |
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## Model description |
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Finetuned distilbert-uncased on prompts that are either malicious or benign. |
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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: 16 |
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- eval_batch_size: 16 |
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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: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.3191 | 1.0 | 64 | 0.5996 | 0.7255 | |
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| 0.5065 | 2.0 | 128 | 0.4536 | 0.8 | |
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| 0.4134 | 3.0 | 192 | 0.3856 | 0.8275 | |
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| 0.3294 | 4.0 | 256 | 0.2654 | 0.8824 | |
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| 0.2536 | 5.0 | 320 | 0.1977 | 0.9216 | |
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| 0.2001 | 6.0 | 384 | 0.1671 | 0.9412 | |
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| 0.2144 | 7.0 | 448 | 0.1670 | 0.9373 | |
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| 0.2017 | 8.0 | 512 | 0.1575 | 0.9333 | |
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| 0.1819 | 9.0 | 576 | 0.1866 | 0.9294 | |
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| 0.143 | 10.0 | 640 | 0.1834 | 0.9373 | |
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| 0.153 | 11.0 | 704 | 0.1589 | 0.9412 | |
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| 0.1469 | 12.0 | 768 | 0.1347 | 0.9451 | |
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| 0.1568 | 13.0 | 832 | 0.1425 | 0.9451 | |
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| 0.139 | 14.0 | 896 | 0.1438 | 0.9451 | |
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| 0.1889 | 15.0 | 960 | 0.1330 | 0.9451 | |
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| 0.1185 | 16.0 | 1024 | 0.1323 | 0.9451 | |
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| 0.1166 | 17.0 | 1088 | 0.1280 | 0.9451 | |
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| 0.1475 | 18.0 | 1152 | 0.1233 | 0.9451 | |
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| 0.1145 | 19.0 | 1216 | 0.1225 | 0.9451 | |
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| 0.1121 | 20.0 | 1280 | 0.1218 | 0.9451 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.1 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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