group3_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: 2.0497
• Precision: 0.0638
• Recall: 0.2421
• F1: 0.1009
• Accuracy: 0.9339

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	1.1877	0.0140	0.25	0.0265	0.7339
No log	2.0	110	0.9789	0.0219	0.2041	0.0395	0.8081
No log	3.0	165	1.0274	0.0385	0.2437	0.0665	0.8703
No log	4.0	220	1.1138	0.0225	0.1820	0.0401	0.8343
No log	5.0	275	1.1690	0.0335	0.2184	0.0581	0.8702
No log	6.0	330	1.3425	0.0421	0.2310	0.0712	0.8972
No log	7.0	385	1.5089	0.0445	0.2342	0.0748	0.9079
No log	8.0	440	1.5614	0.0466	0.2453	0.0783	0.9119
No log	9.0	495	1.7200	0.0534	0.2453	0.0876	0.9220
0.5787	10.0	550	1.7086	0.0447	0.2453	0.0756	0.9098
0.5787	11.0	605	1.8784	0.0553	0.2342	0.0895	0.9263
0.5787	12.0	660	1.9659	0.0589	0.2421	0.0947	0.9299
0.5787	13.0	715	1.9472	0.0600	0.2437	0.0963	0.9297
0.5787	14.0	770	2.0058	0.0605	0.2373	0.0964	0.9310
0.5787	15.0	825	2.0497	0.0638	0.2421	0.1009	0.9339

Framework versions

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