wikidyk-scope-clf-deberta-v3-large-semantic_3_clusters

This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5560
• Accuracy: 0.9132
• F1: 0.7401
• Precision: 0.8592
• Recall: 0.6501

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: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 128
• total_eval_batch_size: 128
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0494	1.0	902	0.3398	0.8673	0.5190	0.8346	0.3766
0.0428	2.0	1804	0.3267	0.8902	0.6328	0.8696	0.4973
0.0292	3.0	2706	0.3243	0.9071	0.7333	0.8077	0.6714
0.0208	4.0	3608	0.4366	0.8926	0.6603	0.8284	0.5488
0.0096	5.0	4510	0.4438	0.8943	0.6829	0.7948	0.5986
0.0182	6.0	5412	0.5225	0.8950	0.6594	0.86	0.5346
0.0062	7.0	6314	0.4717	0.9078	0.7117	0.8776	0.5986
0.0021	8.0	7216	0.5226	0.9095	0.7254	0.8571	0.6288
0.0026	9.0	8118	0.5323	0.9173	0.7528	0.8715	0.6625
0.0008	10.0	9020	0.5560	0.9132	0.7401	0.8592	0.6501

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.1
• Tokenizers 0.21.1