QA-DeBERTa-IGPooling-binary

saiteki-kai/QA-DeBERTa-IGPooling-binary

QA-DeBERTa-IGPooling-binary is a text classification model from saiteki-kai. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: deberta-v2
Access: Public
Created: Jun 23, 2026
Updated: Jun 24, 2026
SHA: 750bc942

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Base models

microsoft/deberta-v3-large

Datasets

beavertails

Model card

From the Hugging Face model README

QA-DeBERTa-IGPooling-binary

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: 0.4138
Accuracy: 0.8652
Unsafe Precision: 0.8925
Unsafe Recall: 0.8616
Unsafe F1: 0.8768
Unsafe Fpr: 0.1302
Unsafe Aucpr: 0.9567
Safe Precision: 0.8336
Safe Recall: 0.8698
Safe F1: 0.8513
Safe Fpr: 0.1384
Safe Aucpr: 0.9230

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
0.4537	0.3001	5073	0.4406	0.8504	0.8537	0.8822	0.8678	0.1896	0.9479	0.8458	0.8104	0.8277	0.1178	0.9067
0.4184	0.6001	10146	0.4195	0.8592	0.8815	0.8631	0.8722	0.1456	0.9519	0.8326	0.8544	0.8434	0.1369	0.9133
0.4022	0.9002	15219	0.4160	0.8613	0.8783	0.8717	0.8749	0.1516	0.9536	0.8405	0.8484	0.8444	0.1283	0.9172
0.3991	1.2002	20292	0.4309	0.8618	0.8945	0.8521	0.8728	0.1261	0.9534	0.8248	0.8739	0.8487	0.1479	0.9193
0.4094	1.5003	25365	0.4102	0.8634	0.8847	0.8675	0.8760	0.1418	0.9554	0.8377	0.8582	0.8478	0.1325	0.9220
0.3822	1.8003	30438	0.4139	0.8652	0.8924	0.8616	0.8767	0.1303	0.9567	0.8336	0.8697	0.8512	0.1384	0.9230
0.3505	2.1004	35511	0.4229	0.8639	0.8871	0.8656	0.8762	0.1382	0.9565	0.8363	0.8618	0.8489	0.1344	0.9226
0.4044	2.4004	40584	0.4270	0.8634	0.8824	0.8705	0.8764	0.1455	0.9566	0.8403	0.8545	0.8473	0.1295	0.9236
0.3484	2.7005	45657	0.4222	0.8638	0.8860	0.8668	0.8763	0.1399	0.9567	0.8373	0.8601	0.8485	0.1332	0.9238

Framework versions

Transformers 4.57.1
Pytorch 2.9.1+cu128
Datasets 4.4.1
Tokenizers 0.22.1

Task: Text Classification
Library: transformers
Type: deberta-v2
License: mit
Created: Jun 23, 2026
Updated: Jun 24, 2026