Classifier_30k

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

• Loss: 0.1296
• Accuracy: 0.9876

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-06
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3588	0.9994	831	0.3084	0.9091
0.1252	2.0	1663	0.2260	0.9453
0.1123	2.9994	2494	0.1241	0.9604
0.0896	4.0	3326	0.1372	0.9655
0.0749	4.9994	4157	0.1541	0.9708
0.0743	6.0	4989	0.1127	0.9715
0.0596	6.9994	5820	0.1782	0.9672
0.0494	8.0	6652	0.1352	0.9749
0.0443	8.9994	7483	0.1232	0.9681
0.0405	10.0	8315	0.0756	0.9838
0.0383	10.9994	9146	0.2025	0.9600
0.0361	12.0	9978	0.1130	0.9796
0.0288	12.9994	10809	0.0906	0.9855
0.0249	14.0	11641	0.1122	0.9827
0.0222	14.9994	12472	0.0713	0.9862
0.0239	16.0	13304	0.0552	0.9876
0.0234	16.9994	14135	0.0728	0.9864
0.0258	18.0	14967	0.0558	0.9891
0.0208	18.9994	15798	0.0715	0.9879
0.0199	20.0	16630	0.0753	0.9885
0.0143	20.9994	17461	0.0812	0.9872
0.0255	22.0	18293	0.1661	0.9744
0.0156	22.9994	19124	0.0751	0.9883
0.013	24.0	19956	0.0718	0.9862
0.0126	24.9994	20787	0.0829	0.9853
0.0123	26.0	21619	0.0848	0.9857
0.0109	26.9994	22450	0.0913	0.9864
0.0095	28.0	23282	0.1607	0.9774
0.0096	28.9994	24113	0.0958	0.9853
0.0074	30.0	24945	0.1264	0.9857
0.0091	30.9994	25776	0.1030	0.9881
0.0096	32.0	26608	0.0954	0.9879
0.0074	32.9994	27439	0.1103	0.9885
0.0067	34.0	28271	0.1803	0.9791
0.0044	34.9994	29102	0.1597	0.9817
0.0045	36.0	29934	0.0878	0.9894
0.0034	36.9994	30765	0.1680	0.9806
0.0066	38.0	31597	0.1114	0.9870
0.0041	38.9994	32428	0.0910	0.9896
0.0043	40.0	33260	0.1435	0.9840
0.0037	40.9994	34091	0.1233	0.9881
0.0046	42.0	34923	0.1347	0.9864
0.0029	42.9994	35754	0.1134	0.9883
0.0017	44.0	36586	0.1125	0.9879
0.0025	44.9994	37417	0.1400	0.9859
0.0023	46.0	38249	0.1228	0.9879
0.0017	46.9994	39080	0.1445	0.9862
0.0011	48.0	39912	0.1375	0.9876
0.0013	48.9994	40743	0.1323	0.9876
0.0021	49.9699	41550	0.1296	0.9876

Framework versions

• Transformers 4.40.0
• Pytorch 2.2.2+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1