metadata

license: apache-2.0
base_model: jackaduma/SecBERT
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: aptner_secbert
    results: []

aptner_secbert

This model is a fine-tuned version of jackaduma/SecBERT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3230
Precision: 0.5124
Recall: 0.5356
F1: 0.5237
Accuracy: 0.9142

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: 8
eval_batch_size: 8
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
0.6662	0.59	500	0.3587	0.4744	0.4743	0.4744	0.9113
0.3128	1.19	1000	0.3230	0.5124	0.5356	0.5237	0.9142
0.2374	1.78	1500	0.3429	0.4750	0.5714	0.5188	0.9083
0.1904	2.37	2000	0.3650	0.4945	0.5598	0.5251	0.9090
0.1521	2.97	2500	0.3765	0.4713	0.5783	0.5193	0.9055
0.1101	3.56	3000	0.4023	0.4727	0.5744	0.5186	0.9067
0.1019	4.15	3500	0.4322	0.4726	0.5571	0.5114	0.9056
0.0764	4.74	4000	0.4595	0.4592	0.5897	0.5163	0.9039
0.0619	5.34	4500	0.4755	0.4740	0.5783	0.5210	0.9062
0.059	5.93	5000	0.4514	0.5055	0.5649	0.5335	0.9126
0.0429	6.52	5500	0.5036	0.474	0.5666	0.5162	0.9065
0.0425	7.12	6000	0.5249	0.4767	0.5726	0.5203	0.9064
0.0349	7.71	6500	0.5537	0.4634	0.5744	0.5129	0.9038
0.0338	8.3	7000	0.5301	0.4839	0.5672	0.5223	0.9089
0.0255	8.9	7500	0.5545	0.4731	0.5735	0.5185	0.9059
0.0253	9.49	8000	0.5526	0.4789	0.5702	0.5206	0.9074

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1