Dhineshk/TestDocumentQuestionAnswering

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: 5.3353

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
0.153	0.22	50	5.3909
0.2793	0.44	100	5.0150
0.2634	0.66	150	4.6620
0.5192	0.88	200	4.7826
0.3096	1.11	250	4.9532
0.2638	1.33	300	5.2584
0.4727	1.55	350	4.0943
0.2763	1.77	400	4.8408
1.0425	1.99	450	5.0344
0.4477	2.21	500	4.9084
0.3266	2.43	550	5.0996
0.3085	2.65	600	4.4858
0.4648	2.88	650	4.0630
0.1845	3.1	700	5.3969
0.1616	3.32	750	4.8225
0.1752	3.54	800	5.2945
0.1877	3.76	850	5.2358
0.3172	3.98	900	5.2205
0.1627	4.2	950	4.9991
0.2548	4.42	1000	4.6917
0.1566	4.65	1050	5.1266
0.2616	4.87	1100	4.3241
0.1199	5.09	1150	4.9821
0.1372	5.31	1200	5.0838
0.1198	5.53	1250	5.0156
0.0558	5.75	1300	4.8638
0.1331	5.97	1350	4.9492
0.0689	6.19	1400	4.6926
0.0912	6.42	1450	4.5153
0.0495	6.64	1500	4.6969
0.0853	6.86	1550	4.7690
0.1072	7.08	1600	4.6783
0.034	7.3	1650	4.7351
0.2999	7.52	1700	4.5185
0.0763	7.74	1750	4.5825
0.0799	7.96	1800	4.7218
0.0343	8.19	1850	5.1508
0.0396	8.41	1900	5.4893
0.033	8.63	1950	5.5167
0.0295	8.85	2000	5.6252
0.2303	9.07	2050	4.7031
0.088	9.29	2100	4.7323
0.0666	9.51	2150	4.8688
0.0597	9.73	2200	5.6007
0.0615	9.96	2250	5.5403
0.1003	10.18	2300	5.3198
0.0457	10.4	2350	5.4828
0.0391	10.62	2400	5.5312
0.0325	10.84	2450	5.7410
0.0147	11.06	2500	5.8749
0.1013	11.28	2550	5.6522
0.001	11.5	2600	5.7776
0.0002	11.73	2650	5.8431
0.03	11.95	2700	5.9751
0.0452	12.17	2750	5.6928
0.0002	12.39	2800	5.6264
0.0109	12.61	2850	5.2688
0.0801	12.83	2900	5.2780
0.0216	13.05	2950	5.3691
0.0002	13.27	3000	5.5237
0.0092	13.5	3050	5.3662
0.0124	13.72	3100	5.4474
0.0515	13.94	3150	5.3623
0.0032	14.16	3200	5.4168
0.0051	14.38	3250	5.2897
0.0002	14.6	3300	5.3205
0.014	14.82	3350	5.2114
0.0004	15.04	3400	5.2342
0.0104	15.27	3450	5.2562
0.0107	15.49	3500	5.1112
0.0002	15.71	3550	5.1515
0.0002	15.93	3600	5.2054
0.0002	16.15	3650	5.1968
0.0003	16.37	3700	5.3196
0.0246	16.59	3750	5.3111
0.0054	16.81	3800	5.3335
0.0001	17.04	3850	5.3488
0.0243	17.26	3900	5.2597
0.0217	17.48	3950	5.2834
0.0002	17.7	4000	5.2947
0.0002	17.92	4050	5.3131
0.0001	18.14	4100	5.3240
0.0016	18.36	4150	5.3129
0.0133	18.58	4200	5.3241
0.0002	18.81	4250	5.3382
0.0159	19.03	4300	5.3764
0.003	19.25	4350	5.3776
0.0516	19.47	4400	5.3389
0.016	19.69	4450	5.3275
0.0105	19.91	4500	5.3353

Framework versions

• Transformers 4.33.2
• Pytorch 2.0.1+cpu
• Datasets 2.14.5
• Tokenizers 0.13.3