group2_non_all_zero

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: 2.3325
• Precision: 0.0395
• Recall: 0.182
• F1: 0.0649
• Accuracy: 0.8597

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	43	1.5592	0.0020	0.124	0.0040	0.3311
No log	2.0	86	1.2689	0.0104	0.14	0.0193	0.6247
No log	3.0	129	1.1742	0.0110	0.172	0.0206	0.6614
No log	4.0	172	1.3716	0.0147	0.178	0.0271	0.6468
No log	5.0	215	1.3265	0.0177	0.178	0.0323	0.7203
No log	6.0	258	1.5835	0.0217	0.176	0.0386	0.7574
No log	7.0	301	1.6678	0.0249	0.174	0.0435	0.7952
No log	8.0	344	1.9432	0.0387	0.18	0.0636	0.8551
No log	9.0	387	1.9371	0.0306	0.188	0.0526	0.7962
No log	10.0	430	2.0129	0.0305	0.182	0.0523	0.8187
No log	11.0	473	2.1952	0.0402	0.192	0.0664	0.8595
0.5993	12.0	516	2.1873	0.0369	0.182	0.0614	0.8512
0.5993	13.0	559	2.2653	0.0394	0.18	0.0646	0.8583
0.5993	14.0	602	2.3001	0.0397	0.184	0.0653	0.8553
0.5993	15.0	645	2.3325	0.0395	0.182	0.0649	0.8597

Framework versions

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