ajrayman/Vulnerability_binary

1---2library_name: transformers3license: mit4base_model: microsoft/deberta-v3-base5tags:6- generated_from_trainer7metrics:8- accuracy9- precision10- recall11- f112model-index:13- name: Vulnerability_binary14  results: []15---16 17<!-- This model card has been generated automatically according to the information the Trainer had access to. You18should probably proofread and complete it, then remove this comment. -->19 20# Vulnerability_binary21 22This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.23It achieves the following results on the evaluation set:24- Loss: 1.073625- Accuracy: 0.671226- Precision: 0.692727- Recall: 0.616928- F1: 0.652629- Auc: 0.722830 31## Model description32 33More information needed34 35## Intended uses & limitations36 37More information needed38 39## Training and evaluation data40 41More information needed42 43## Training procedure44 45### Training hyperparameters46 47The following hyperparameters were used during training:48- learning_rate: 2e-0549- train_batch_size: 3250- eval_batch_size: 3251- seed: 123452- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0853- lr_scheduler_type: linear54- lr_scheduler_warmup_ratio: 0.0655- num_epochs: 856 57### Training results58 59| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Auc    |60|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|61| No log        | 1.0   | 118  | 0.6261          | 0.6687   | 0.7024    | 0.5871 | 0.6396 | 0.7236 |62| No log        | 2.0   | 236  | 0.5943          | 0.6936   | 0.7549    | 0.5746 | 0.6525 | 0.7619 |63| No log        | 3.0   | 354  | 0.6206          | 0.6936   | 0.7042    | 0.6692 | 0.6862 | 0.7537 |64| No log        | 4.0   | 472  | 0.6992          | 0.6899   | 0.7192    | 0.6244 | 0.6684 | 0.7458 |65| 0.5353        | 5.0   | 590  | 0.8100          | 0.6849   | 0.6822    | 0.6940 | 0.6880 | 0.7303 |66| 0.5353        | 6.0   | 708  | 0.9372          | 0.6712   | 0.6835    | 0.6393 | 0.6607 | 0.7217 |67| 0.5353        | 7.0   | 826  | 1.0736          | 0.6712   | 0.6927    | 0.6169 | 0.6526 | 0.7228 |68 69 70### Framework versions71 72- Transformers 4.44.173- Pytorch 1.11.074- Datasets 2.12.075- Tokenizers 0.19.176