group3_non_all_zero_notEqualWeights

This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.3167
• Precision: 0.0476
• Recall: 0.2642
• F1: 0.0807
• Accuracy: 0.9145

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: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	55	1.3844	0.0068	0.2579	0.0133	0.6506
No log	2.0	110	1.1245	0.0107	0.2342	0.0205	0.7285
No log	3.0	165	1.2261	0.0103	0.2120	0.0196	0.7286
No log	4.0	220	1.1828	0.0099	0.1693	0.0188	0.7551
No log	5.0	275	1.2474	0.0141	0.2152	0.0265	0.8008
No log	6.0	330	1.4395	0.0264	0.2516	0.0478	0.8601
No log	7.0	385	1.5667	0.0253	0.2278	0.0456	0.8614
No log	8.0	440	1.6080	0.0286	0.2468	0.0512	0.8756
No log	9.0	495	1.7798	0.0289	0.2358	0.0515	0.8849
0.6462	10.0	550	1.9265	0.0364	0.2579	0.0638	0.8933
0.6462	11.0	605	2.0633	0.0347	0.2468	0.0608	0.8911
0.6462	12.0	660	2.2610	0.0458	0.2690	0.0783	0.9138
0.6462	13.0	715	2.1700	0.0435	0.2595	0.0745	0.9044
0.6462	14.0	770	2.3153	0.0480	0.2690	0.0814	0.9127
0.6462	15.0	825	2.3167	0.0476	0.2642	0.0807	0.9145

Framework versions

• Transformers 4.30.0
• Pytorch 2.2.2+cu121
• Datasets 2.19.0
• Tokenizers 0.13.3