layoutlmv2-base-uncased_finetuned_docvqa

This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 4.6207

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: 4
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss
5.3769	0.22	50	4.5459
4.3879	0.44	100	4.0099
3.9828	0.66	150	3.8817
3.7387	0.88	200	3.5334
3.4702	1.11	250	3.4670
3.0784	1.33	300	3.5327
2.8014	1.55	350	2.8722
2.673	1.77	400	2.8246
2.4546	1.99	450	2.7050
2.094	2.21	500	2.7639
1.9542	2.43	550	2.3472
1.8966	2.65	600	2.4508
1.7745	2.88	650	2.2813
1.4371	3.1	700	2.5234
1.3257	3.32	750	2.4358
1.3269	3.54	800	2.3044
1.4035	3.76	850	2.3546
1.6189	3.98	900	2.0838
0.9209	4.2	950	2.3836
0.8405	4.42	1000	3.1673
0.9808	4.65	1050	2.8038
0.8978	4.87	1100	2.7652
0.8733	5.09	1150	3.0965
0.7449	5.31	1200	2.9948
0.8173	5.53	1250	2.8631
0.8322	5.75	1300	2.6144
0.7147	5.97	1350	3.2041
0.6495	6.19	1400	3.3711
0.5458	6.42	1450	3.5480
0.5624	6.64	1500	3.3366
0.5736	6.86	1550	2.9356
0.2921	7.08	1600	3.4028
0.3883	7.3	1650	3.4411
0.3614	7.52	1700	3.2267
0.4376	7.74	1750	3.2137
0.4849	7.96	1800	3.6388
0.5035	8.19	1850	4.0089
0.36	8.41	1900	3.7903
0.2238	8.63	1950	3.7131
0.276	8.85	2000	3.8541
0.2661	9.07	2050	3.5220
0.291	9.29	2100	3.9075
0.3767	9.51	2150	3.4267
0.1326	9.73	2200	3.9069
0.2365	9.96	2250	3.5183
0.2676	10.18	2300	3.4975
0.131	10.4	2350	3.8138
0.1818	10.62	2400	3.6213
0.2988	10.84	2450	3.7380
0.0511	11.06	2500	4.1506
0.1909	11.28	2550	3.7369
0.0854	11.5	2600	3.8751
0.1291	11.73	2650	3.7143
0.1896	11.95	2700	4.0645
0.0746	12.17	2750	4.0363
0.1254	12.39	2800	4.0050
0.1588	12.61	2850	4.1739
0.1622	12.83	2900	4.2698
0.047	13.05	2950	4.5366
0.1023	13.27	3000	4.3094
0.1195	13.5	3050	4.3112
0.0753	13.72	3100	4.3137
0.0545	13.94	3150	4.4992
0.0771	14.16	3200	4.4759
0.0648	14.38	3250	4.5531
0.0935	14.6	3300	4.3725
0.1032	14.82	3350	4.3321
0.0199	15.04	3400	4.3527
0.0498	15.27	3450	4.2477
0.0688	15.49	3500	4.2340
0.0271	15.71	3550	4.2386
0.0176	15.93	3600	4.4715
0.0319	16.15	3650	4.5608
0.0061	16.37	3700	4.5767
0.0043	16.59	3750	4.6581
0.071	16.81	3800	4.5622
0.0689	17.04	3850	4.5067
0.0328	17.26	3900	4.4449
0.0784	17.48	3950	4.3684
0.0387	17.7	4000	4.4261
0.0107	17.92	4050	4.5190
0.0056	18.14	4100	4.4963
0.0261	18.36	4150	4.5995
0.0061	18.58	4200	4.6121
0.0354	18.81	4250	4.5794
0.0111	19.03	4300	4.5977
0.022	19.25	4350	4.6151
0.0628	19.47	4400	4.6061
0.0186	19.69	4450	4.6180
0.0077	19.91	4500	4.6207

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3