layoutlmv3-finetuned-sroie

This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset. It achieves the following results on the evaluation set:

• Loss: 0.0426
• Precision: 0.9371
• Recall: 0.9438
• F1: 0.9404
• Accuracy: 0.9945

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: 1e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 5000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.32	100	0.1127	0.6466	0.6102	0.6279	0.9729
No log	0.64	200	0.0663	0.8215	0.7428	0.7802	0.9821
No log	0.96	300	0.0563	0.8051	0.8718	0.8371	0.9855
No log	1.28	400	0.0470	0.8766	0.8595	0.8680	0.9895
0.1328	1.6	500	0.0419	0.8613	0.9128	0.8863	0.9906
0.1328	1.92	600	0.0338	0.8888	0.9099	0.8993	0.9926
0.1328	2.24	700	0.0320	0.8690	0.9467	0.9062	0.9929
0.1328	2.56	800	0.0348	0.8960	0.9438	0.9193	0.9931
0.1328	2.88	900	0.0300	0.9169	0.9460	0.9312	0.9942
0.029	3.19	1000	0.0281	0.9080	0.9452	0.9262	0.9942
0.029	3.51	1100	0.0259	0.9174	0.9438	0.9304	0.9945
0.029	3.83	1200	0.0309	0.9207	0.9532	0.9366	0.9944
0.029	4.15	1300	0.0366	0.9195	0.9388	0.9291	0.9940
0.029	4.47	1400	0.0302	0.9343	0.9424	0.9383	0.9949
0.0174	4.79	1500	0.0349	0.9142	0.9517	0.9326	0.9939
0.0174	5.11	1600	0.0327	0.9322	0.9510	0.9415	0.9950
0.0174	5.43	1700	0.0317	0.9215	0.9561	0.9385	0.9938
0.0174	5.75	1800	0.0385	0.9282	0.9316	0.9299	0.9940
0.0174	6.07	1900	0.0342	0.9235	0.9481	0.9357	0.9944
0.0117	6.39	2000	0.0344	0.9287	0.9474	0.9379	0.9944
0.0117	6.71	2100	0.0388	0.9232	0.9445	0.9338	0.9941
0.0117	7.03	2200	0.0325	0.9269	0.9496	0.9381	0.9949
0.0117	7.35	2300	0.0343	0.9225	0.9438	0.9330	0.9941
0.0117	7.67	2400	0.0372	0.9216	0.9481	0.9347	0.9944
0.0081	7.99	2500	0.0385	0.9192	0.9589	0.9386	0.9944
0.0081	8.31	2600	0.0376	0.9293	0.9467	0.9379	0.9944
0.0081	8.63	2700	0.0425	0.9261	0.9474	0.9366	0.9941
0.0081	8.95	2800	0.0407	0.9266	0.9452	0.9358	0.9941
0.0081	9.27	2900	0.0403	0.9280	0.9467	0.9372	0.9941
0.0055	9.58	3000	0.0364	0.9287	0.9474	0.9379	0.9948
0.0055	9.9	3100	0.0427	0.9122	0.9510	0.9312	0.9941
0.0055	10.22	3200	0.0394	0.9223	0.9488	0.9354	0.9943
0.0055	10.54	3300	0.0393	0.9247	0.9561	0.9401	0.9945
0.0055	10.86	3400	0.0413	0.9334	0.9496	0.9414	0.9945
0.0049	11.18	3500	0.0400	0.9290	0.9517	0.9402	0.9945
0.0049	11.5	3600	0.0412	0.9317	0.9539	0.9427	0.9945
0.0049	11.82	3700	0.0419	0.9314	0.9481	0.9397	0.9947
0.0049	12.14	3800	0.0452	0.9243	0.9503	0.9371	0.9941
0.0049	12.46	3900	0.0412	0.9334	0.9496	0.9414	0.9947
0.0039	12.78	4000	0.0438	0.9294	0.9481	0.9387	0.9941
0.0039	13.1	4100	0.0416	0.9326	0.9467	0.9396	0.9944
0.0039	13.42	4200	0.0418	0.9327	0.9488	0.9407	0.9948
0.0039	13.74	4300	0.0423	0.9345	0.9460	0.9402	0.9946
0.0039	14.06	4400	0.0419	0.9286	0.9467	0.9376	0.9947
0.0022	14.38	4500	0.0426	0.9371	0.9438	0.9404	0.9945
0.0022	14.7	4600	0.0424	0.9371	0.9445	0.9408	0.9947
0.0022	15.02	4700	0.0427	0.9372	0.9467	0.9419	0.9947
0.0022	15.34	4800	0.0431	0.9339	0.9460	0.9399	0.9945
0.0022	15.65	4900	0.0431	0.9346	0.9467	0.9406	0.9946
0.0015	15.97	5000	0.0434	0.9324	0.9445	0.9384	0.9945

Framework versions

• Transformers 4.20.0.dev0
• Pytorch 1.11.0+cu113
• Datasets 2.2.2
• Tokenizers 0.12.1