layoutlmv3-finetuned-sroie

Theivaprakasham/layoutlmv3-finetuned-sroie

layoutlmv3-finetuned-sroie is a token classification model from Theivaprakasham. Use it when you need labels on individual words, such as names. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
Model type: layoutlmv3
Access: Public
Created: Jun 7, 2022
Updated: Jun 7, 2022
SHA: 6c8e3d5d

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Datasets

sroie

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Model card

From the Hugging Face model README

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

Task: Token Classification
Library: transformers
Type: layoutlmv3
Created: Jun 7, 2022
Updated: Jun 7, 2022