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PrimWong/
layoutlm_qa

Name: layoutlm_qa
Rating: 2.5 (11 reviews)

document-question-answering

generated_from_trainer

microsoft/layoutlmv2-base-uncased

finetune

cc-by-nc-sa-4.0

endpoints_compatible

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layoutlm_qa

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: 4.7055

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
5.2983	0.22	50	4.5220
4.4844	0.44	100	4.1165
4.1775	0.66	150	3.8581
3.8202	0.88	200	3.5512
3.5174	1.11	250	3.9044
3.3304	1.33	300	3.3451
3.1339	1.55	350	3.0255
2.9657	1.77	400	2.9532
2.7647	1.99	450	3.0166
2.3376	2.21	500	2.9174
1.9903	2.43	550	2.7034
1.9975	2.65	600	2.4877
1.8642	2.88	650	2.3439
1.6613	3.1	700	2.3873
1.4884	3.32	750	2.1284
1.3033	3.54	800	2.3192
1.3821	3.76	850	3.0033
1.4121	3.98	900	2.3074
1.0226	4.2	950	2.5772
0.8721	4.42	1000	2.8909
1.1364	4.65	1050	2.6966
1.1504	4.87	1100	2.7247
0.7333	5.09	1150	3.3075
0.7097	5.31	1200	3.2459
0.7138	5.53	1250	3.2652
0.6852	5.75	1300	3.0537
0.6396	5.97	1350	3.1964
0.6756	6.19	1400	3.3380
0.5771	6.42	1450	3.4396
0.6753	6.64	1500	3.0820
0.5361	6.86	1550	3.3736
0.5659	7.08	1600	3.3211
0.6637	7.3	1650	3.2642
0.5321	7.52	1700	3.3275
0.3525	7.74	1750	3.5490
0.4964	7.96	1800	3.5147
0.4882	8.19	1850	3.4210
0.3879	8.41	1900	3.9024
0.4991	8.63	1950	3.5269
0.5084	8.85	2000	3.7400
0.3502	9.07	2050	3.6098
0.2492	9.29	2100	3.8580
0.2889	9.51	2150	3.6365
0.2672	9.73	2200	3.5260
0.4289	9.96	2250	3.1862
0.1803	10.18	2300	3.9092
0.2014	10.4	2350	3.8147
0.3197	10.62	2400	3.7593
0.1503	10.84	2450	3.8731
0.1766	11.06	2500	3.6034
0.3074	11.28	2550	3.6639
0.1637	11.5	2600	3.9461
0.2674	11.73	2650	3.6418
0.2074	11.95	2700	3.7350
0.1034	12.17	2750	4.0971
0.1438	12.39	2800	3.8840
0.0739	12.61	2850	3.9797
0.2329	12.83	2900	4.0602
0.2348	13.05	2950	3.9343
0.1119	13.27	3000	4.2030
0.0955	13.5	3050	4.3291
0.0787	13.72	3100	4.1507
0.1446	13.94	3150	4.1370
0.0202	14.16	3200	4.2964
0.1201	14.38	3250	4.3851
0.0783	14.6	3300	4.2924
0.0536	14.82	3350	4.2803
0.1042	15.04	3400	4.2722
0.1374	15.27	3450	4.3609
0.096	15.49	3500	4.3868
0.0223	15.71	3550	4.3771
0.0573	15.93	3600	4.4002
0.0688	16.15	3650	4.4771
0.0052	16.37	3700	4.5400
0.0128	16.59	3750	4.5740
0.0913	16.81	3800	4.6113
0.0783	17.04	3850	4.2686
0.0344	17.26	3900	4.3120
0.0064	17.48	3950	4.4239
0.1358	17.7	4000	4.5027
0.0299	17.92	4050	4.5290
0.0157	18.14	4100	4.6270
0.0141	18.36	4150	4.6847
0.0382	18.58	4200	4.6527
0.0069	18.81	4250	4.5969
0.0698	19.03	4300	4.6249
0.0303	19.25	4350	4.6679
0.0076	19.47	4400	4.7096
0.0161	19.69	4450	4.7129
0.0572	19.91	4500	4.7055

Framework versions

Transformers 4.35.2
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0

PrimWong/layoutlm_qa

layoutlm_qa

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

PrimWong/
layoutlm_qa