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

Loss: 0.3241
Accuracy: 0.8609
Unsafe Precision: 0.8855
Unsafe Recall: 0.8615
Unsafe F1: 0.8733
Unsafe Fpr: 0.1398
Unsafe Aucpr: 0.9529
Safe Precision: 0.8320
Safe Recall: 0.8602
Safe F1: 0.8458
Safe Fpr: 0.1385
Safe Aucpr: 0.9143

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: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 10

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
0.3283	0.2501	2114	0.4026	0.8181	0.9079	0.7491	0.8209	0.0954	0.9303	0.7418	0.9046	0.8152	0.2509	0.8630
0.353	0.5001	4228	0.3597	0.8432	0.8658	0.8499	0.8578	0.1653	0.9402	0.8159	0.8347	0.8252	0.1501	0.8882
0.3248	0.7502	6342	0.3476	0.8484	0.8928	0.8268	0.8585	0.1245	0.9450	0.8011	0.8755	0.8367	0.1732	0.8972
0.3591	1.0002	8456	0.3414	0.8512	0.8822	0.8455	0.8635	0.1416	0.9468	0.8158	0.8584	0.8365	0.1545	0.9011
0.3167	1.2503	10570	0.3428	0.8537	0.8837	0.8488	0.8659	0.1402	0.9482	0.8192	0.8598	0.8390	0.1512	0.9046
0.2944	1.5004	12684	0.3422	0.8549	0.8864	0.8480	0.8668	0.1364	0.9491	0.8191	0.8636	0.8408	0.1520	0.9072
0.2875	1.7504	14798	0.3345	0.8577	0.8800	0.8617	0.8708	0.1474	0.9504	0.8309	0.8526	0.8416	0.1383	0.9101
0.346	2.0005	16912	0.3275	0.8583	0.8899	0.8507	0.8698	0.1321	0.9517	0.8225	0.8679	0.8446	0.1493	0.9122
0.3394	2.2505	19026	0.3280	0.8579	0.8770	0.8661	0.8715	0.1524	0.9516	0.8346	0.8476	0.8410	0.1339	0.9121
0.2935	2.5006	21140	0.3269	0.8589	0.8761	0.8693	0.8727	0.1543	0.9522	0.8376	0.8457	0.8417	0.1307	0.9133
0.2999	2.7507	23254	0.3241	0.8609	0.8855	0.8615	0.8733	0.1398	0.9529	0.8320	0.8602	0.8458	0.1385	0.9143
0.3016	3.0007	25368	0.3243	0.8608	0.8889	0.8570	0.8727	0.1344	0.9530	0.8283	0.8656	0.8465	0.1430	0.9153
0.3097	3.2508	27482	0.3271	0.8601	0.8832	0.8628	0.8729	0.1432	0.9525	0.8327	0.8568	0.8446	0.1372	0.9142
0.2794	3.5008	29596	0.3300	0.8604	0.8942	0.8496	0.8713	0.1261	0.9531	0.8224	0.8739	0.8474	0.1504	0.9156
0.3129	3.7509	31710	0.3282	0.8585	0.9033	0.8351	0.8679	0.1121	0.9532	0.8111	0.8879	0.8477	0.1649	0.9161

Framework versions

Transformers 4.57.3
Pytorch 2.7.1+cu118
Datasets 4.4.1
Tokenizers 0.22.1

saiteki-kai/QA-mDeBERTa-v3-large-binary

QA-mDeBERTa-v3-large-binary

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

saiteki-kai/
QA-mDeBERTa-v3-large-binary