This model is a fine-tuned version of microsoft/deberta-v3-large on the saiteki-kai/Beavertails-it dataset. It achieves the following results on the evaluation set:

Loss: 0.3246
Accuracy: 0.8645
Unsafe Precision: 0.8883
Unsafe Recall: 0.8653
Unsafe F1: 0.8766
Unsafe Fpr: 0.1366
Unsafe Aucpr: 0.9567
Safe Precision: 0.8363
Safe Recall: 0.8634
Safe F1: 0.8497
Safe Fpr: 0.1347
Safe Aucpr: 0.9241

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: 6e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Unsafe Precision	Unsafe Recall	Unsafe F1	Unsafe Fpr	Unsafe Aucpr	Safe Precision	Safe Recall	Safe F1	Safe Fpr	Safe Aucpr
0.3568	0.3001	5073	0.3409	0.8500	0.8498	0.8871	0.8681	0.1966	0.9480	0.8501	0.8034	0.8261	0.1129	0.9069
0.3161	0.6001	10146	0.3243	0.8592	0.8766	0.8693	0.8729	0.1536	0.9519	0.8377	0.8464	0.8420	0.1307	0.9135
0.304	0.9002	15219	0.3204	0.8609	0.8710	0.8805	0.8757	0.1636	0.9537	0.8480	0.8364	0.8422	0.1195	0.9169
0.3128	1.2002	20292	0.3311	0.8627	0.8932	0.8556	0.8740	0.1283	0.9532	0.8279	0.8717	0.8493	0.1444	0.9191
0.3198	1.5003	25365	0.3146	0.8624	0.8815	0.8695	0.8755	0.1466	0.9555	0.8391	0.8534	0.8462	0.1305	0.9225
0.2852	1.8003	30438	0.3174	0.8647	0.8937	0.8591	0.8761	0.1282	0.9568	0.8314	0.8718	0.8511	0.1409	0.9240
0.2694	2.1004	35511	0.3260	0.8642	0.8895	0.8633	0.8762	0.1346	0.9566	0.8346	0.8654	0.8497	0.1367	0.9231
0.3003	2.4004	40584	0.3265	0.8640	0.8850	0.8683	0.8766	0.1415	0.9568	0.8386	0.8585	0.8484	0.1317	0.9239
0.2723	2.7005	45657	0.3246	0.8644	0.8882	0.8653	0.8766	0.1366	0.9567	0.8363	0.8634	0.8496	0.1347	0.9241

Framework versions

Transformers 4.57.1
Pytorch 2.9.1+cu128
Datasets 4.4.1
Tokenizers 0.22.1

saiteki-kai/QA-DeBERTa-MeanPooling-binary

QA-DeBERTa-MeanPooling-binary

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

saiteki-kai/
QA-DeBERTa-MeanPooling-binary