ViT-Base-Document-Classifier

This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0415
• Accuracy: 0.9889
• F1: 0.9888
• Precision: 0.9888
• Recall: 0.9888

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: 5e-05
• train_batch_size: 512
• eval_batch_size: 512
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 2048
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0696	1.25	50	0.0566	0.9852	0.9851	0.9852	0.9852
0.0673	2.0	51	0.0549	0.9870	0.9870	0.9870	0.9870
0.0599	2.02	52	0.0545	0.9864	0.9863	0.9863	0.9864
0.0639	2.02	53	0.0551	0.9876	0.9875	0.9875	0.9875
0.0694	2.04	54	0.0539	0.9864	0.9863	0.9863	0.9864
0.0655	2.04	55	0.0528	0.9879	0.9878	0.9878	0.9879
0.0629	2.06	56	0.0519	0.9877	0.9876	0.9876	0.9876
0.0761	2.06	57	0.0532	0.9872	0.9871	0.9871	0.9871
0.0741	2.08	58	0.0524	0.9865	0.9864	0.9864	0.9865
0.0585	2.08	59	0.0519	0.9879	0.9878	0.9878	0.9878
0.0534	2.1	60	0.0504	0.9881	0.9880	0.9880	0.9880
0.056	2.1	61	0.0497	0.9876	0.9875	0.9875	0.9875
0.0588	2.12	62	0.0485	0.9878	0.9877	0.9877	0.9877
0.0554	2.12	63	0.0482	0.9872	0.9871	0.9871	0.9872
0.0674	2.13	64	0.0491	0.9870	0.9870	0.9870	0.9869
0.0613	2.15	65	0.0480	0.9877	0.9876	0.9876	0.9876
0.0688	2.15	66	0.0468	0.9877	0.9876	0.9876	0.9876
0.0677	2.17	67	0.0476	0.9874	0.9874	0.9873	0.9874
0.0598	2.17	68	0.0471	0.9874	0.9873	0.9873	0.9873
0.0658	2.19	69	0.0462	0.9877	0.9876	0.9876	0.9876
0.051	2.19	70	0.0467	0.9880	0.9879	0.9879	0.9879
0.0601	2.21	71	0.0456	0.9881	0.9880	0.9880	0.9880
0.0619	2.21	72	0.0460	0.9879	0.9878	0.9878	0.9879
0.0459	2.23	73	0.0458	0.9883	0.9882	0.9882	0.9883
0.0705	2.23	74	0.0447	0.9884	0.9883	0.9883	0.9883
0.0606	2.25	75	0.0447	0.9878	0.9878	0.9878	0.9878
0.0599	3.0	76	0.0441	0.9887	0.9886	0.9887	0.9886
0.0489	3.01	77	0.0438	0.9886	0.9885	0.9885	0.9885
0.0533	3.02	78	0.0442	0.9883	0.9882	0.9882	0.9883
0.0573	3.03	79	0.0438	0.9880	0.9879	0.9879	0.9880
0.0622	3.04	80	0.0439	0.9886	0.9885	0.9885	0.9885
0.0625	3.05	81	0.0434	0.9881	0.9880	0.9880	0.9880
0.0577	3.06	82	0.0431	0.9886	0.9885	0.9885	0.9885
0.0688	3.07	83	0.0435	0.9887	0.9886	0.9886	0.9887
0.0478	3.08	84	0.0434	0.9889	0.9888	0.9888	0.9888
0.0516	3.09	85	0.0436	0.9888	0.9887	0.9887	0.9887
0.0588	3.1	86	0.0426	0.9889	0.9888	0.9888	0.9888
0.0563	3.11	87	0.0422	0.9889	0.9888	0.9888	0.9888
0.0463	3.12	88	0.0422	0.9886	0.9886	0.9885	0.9886
0.0582	3.13	89	0.0421	0.9887	0.9886	0.9886	0.9887
0.0643	3.14	90	0.0419	0.9891	0.9890	0.9890	0.9891
0.0706	3.15	91	0.0417	0.9892	0.9891	0.9891	0.9891
0.0554	3.16	92	0.0417	0.9892	0.9891	0.9891	0.9891
0.0644	3.17	93	0.0416	0.9890	0.9890	0.9890	0.9890
0.0624	3.18	94	0.0415	0.9893	0.9892	0.9892	0.9892
0.0555	3.19	95	0.0416	0.9886	0.9886	0.9885	0.9886
0.0507	3.2	96	0.0415	0.9889	0.9888	0.9888	0.9888
0.0443	3.21	97	0.0415	0.9889	0.9888	0.9888	0.9888
0.0527	3.22	98	0.0415	0.9889	0.9888	0.9888	0.9888
0.0589	3.23	99	0.0415	0.9889	0.9888	0.9888	0.9888
0.0647	3.24	100	0.0415	0.9889	0.9888	0.9888	0.9888

Framework versions

• Transformers 4.39.3
• Pytorch 2.2.2
• Datasets 2.18.0
• Tokenizers 0.15.2