layoutlmv2-base-uncased_finetuned_docvqa

This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 4.6788

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
5.3193	0.22	50	4.5453
4.5115	0.44	100	4.1632
4.1316	0.66	150	3.8496
3.7911	0.88	200	3.7418
3.5175	1.11	250	3.9454
3.2171	1.33	300	3.0430
3.0377	1.55	350	3.1317
3.1081	1.77	400	2.8709
2.6219	1.99	450	2.9745
2.2922	2.21	500	3.0184
2.2245	2.43	550	2.6649
2.0918	2.65	600	2.3156
2.0339	2.88	650	2.4970
1.7088	3.1	700	2.2817
1.4584	3.32	750	2.3237
1.4296	3.54	800	2.1868
1.4413	3.76	850	2.2775
1.4055	3.98	900	2.6660
1.0251	4.2	950	2.6155
1.1251	4.42	1000	2.9841
1.059	4.65	1050	2.7376
1.0179	4.87	1100	3.7345
1.1128	5.09	1150	2.6704
0.8461	5.31	1200	3.0422
0.86	5.53	1250	3.2093
0.9124	5.75	1300	3.2782
0.8687	5.97	1350	3.1477
0.7039	6.19	1400	2.6896
0.8908	6.42	1450	3.0843
0.7408	6.64	1500	2.9585
0.6026	6.86	1550	3.3629
0.4852	7.08	1600	3.1505
0.5496	7.3	1650	3.6285
0.5578	7.52	1700	3.3481
0.5897	7.74	1750	3.3201
0.4487	7.96	1800	3.1462
0.2182	8.19	1850	3.7251
0.3524	8.41	1900	3.5870
0.4516	8.63	1950	3.6300
0.5658	8.85	2000	3.1085
0.4877	9.07	2050	3.5353
0.2226	9.29	2100	3.6744
0.2544	9.51	2150	4.1244
0.6194	9.73	2200	3.4775
0.3759	9.96	2250	3.7031
0.2718	10.18	2300	3.6076
0.1322	10.4	2350	3.6885
0.2596	10.62	2400	3.9328
0.1675	10.84	2450	4.1439
0.158	11.06	2500	4.4306
0.1462	11.28	2550	4.3744
0.2187	11.5	2600	4.4111
0.264	11.73	2650	3.9780
0.1997	11.95	2700	4.2383
0.1369	12.17	2750	4.1329
0.1204	12.39	2800	4.2738
0.2001	12.61	2850	4.0106
0.2132	12.83	2900	4.1816
0.1472	13.05	2950	4.4600
0.0603	13.27	3000	4.0050
0.0911	13.5	3050	4.1838
0.1016	13.72	3100	4.4429
0.0887	13.94	3150	4.1510
0.0495	14.16	3200	4.2938
0.0677	14.38	3250	4.6133
0.1263	14.6	3300	4.4634
0.1953	14.82	3350	3.9348
0.0212	15.04	3400	4.1726
0.0082	15.27	3450	4.3512
0.0432	15.49	3500	4.2992
0.0975	15.71	3550	4.2274
0.0933	15.93	3600	4.4028
0.024	16.15	3650	4.4662
0.0964	16.37	3700	4.3964
0.0487	16.59	3750	4.4827
0.0147	16.81	3800	4.5577
0.0951	17.04	3850	4.5640
0.0508	17.26	3900	4.4473
0.1163	17.48	3950	4.4565
0.0151	17.7	4000	4.5511
0.0569	17.92	4050	4.5298
0.0639	18.14	4100	4.5417
0.0155	18.36	4150	4.6398
0.0107	18.58	4200	4.7664
0.0044	18.81	4250	4.8119
0.0906	19.03	4300	4.7168
0.0533	19.25	4350	4.7032
0.0496	19.47	4400	4.6918
0.0938	19.69	4450	4.6824
0.0483	19.91	4500	4.6788

Framework versions

• Transformers 4.38.1
• Pytorch 2.2.1
• Datasets 2.17.1
• Tokenizers 0.15.2