deberta-base

This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1665
• Accuracy: 0.9601
• Precision: 0.9599
• Recall: 0.9601
• F1: 0.9594
• Auroc: 0.9928

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1
• label_smoothing_factor: 0.03

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Auroc
0.4866	0.0988	256	0.2931	0.8845	0.8939	0.8845	0.8876	0.9465
0.2757	0.1977	512	0.3478	0.8898	0.8984	0.8898	0.8765	0.9544
0.2433	0.2965	768	0.2097	0.9404	0.9413	0.9404	0.9408	0.9799
0.2332	0.3953	1024	0.3548	0.8815	0.8907	0.8815	0.8657	0.9690
0.2152	0.4942	1280	0.1942	0.9440	0.9434	0.9440	0.9426	0.9868
0.1907	0.5930	1536	0.1615	0.9649	0.9647	0.9649	0.9647	0.9899
0.1865	0.6918	1792	0.1556	0.9655	0.9654	0.9655	0.9654	0.9922
0.1865	0.7907	2048	0.2322	0.9369	0.9370	0.9369	0.9344	0.9773
0.168	0.8895	2304	0.1653	0.9672	0.9670	0.9672	0.9668	0.9937
0.1732	0.9883	2560	0.1467	0.9702	0.9716	0.9702	0.9706	0.9935

Framework versions

• Transformers 4.45.1
• Pytorch 2.4.0
• Datasets 3.0.1
• Tokenizers 0.20.0