layoutlm-captive-corp-70

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

• Loss: 0.1795
• Precision: 0.9517
• Recall: 0.9680
• F1: 0.9598
• Accuracy: 0.9713

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0311	1.0	23	0.1756	0.9478	0.9658	0.9567	0.9708
0.0282	2.0	46	0.1781	0.9486	0.9649	0.9567	0.9705
0.0296	3.0	69	0.1780	0.9481	0.9635	0.9558	0.9697
0.0252	4.0	92	0.1870	0.9463	0.9608	0.9535	0.9674
0.0255	5.0	115	0.1780	0.9476	0.9622	0.9549	0.9697
0.022	6.0	138	0.1779	0.9469	0.9644	0.9556	0.9700
0.0196	7.0	161	0.1806	0.9503	0.9649	0.9575	0.9695
0.0184	8.0	184	0.1827	0.9524	0.9653	0.9588	0.9705
0.0173	9.0	207	0.1888	0.9489	0.9626	0.9557	0.9687
0.0175	10.0	230	0.1799	0.9500	0.9667	0.9582	0.9708
0.0155	11.0	253	0.1814	0.9486	0.9653	0.9569	0.9710
0.0144	12.0	276	0.1853	0.9473	0.9640	0.9556	0.9702
0.0139	13.0	299	0.1840	0.9469	0.9649	0.9558	0.9697
0.0133	14.0	322	0.1797	0.9504	0.9662	0.9582	0.9718
0.0126	15.0	345	0.1832	0.9508	0.9671	0.9589	0.9710
0.0121	16.0	368	0.1795	0.9517	0.9680	0.9598	0.9713
0.0117	17.0	391	0.1844	0.9504	0.9658	0.9580	0.9705
0.0108	18.0	414	0.1850	0.9500	0.9662	0.9580	0.9702
0.0105	19.0	437	0.1847	0.9517	0.9671	0.9593	0.9713
0.0107	20.0	460	0.1846	0.9482	0.9653	0.9567	0.9702
0.0098	21.0	483	0.1863	0.9495	0.9662	0.9578	0.9702
0.0099	22.0	506	0.1871	0.9473	0.9635	0.9553	0.9692
0.0091	23.0	529	0.1879	0.9482	0.9644	0.9562	0.9700
0.0091	24.0	552	0.1859	0.9517	0.9671	0.9593	0.9713
0.0091	25.0	575	0.1849	0.9479	0.9671	0.9574	0.9713
0.0088	26.0	598	0.1883	0.9495	0.9662	0.9578	0.9702
0.0083	27.0	621	0.1884	0.9495	0.9658	0.9576	0.9700
0.0079	28.0	644	0.1890	0.9499	0.9658	0.9578	0.9710
0.008	29.0	667	0.1921	0.9491	0.9658	0.9574	0.9700
0.0075	30.0	690	0.1904	0.9504	0.9662	0.9582	0.9713
0.0075	31.0	713	0.1907	0.9504	0.9667	0.9585	0.9710
0.0075	32.0	736	0.1904	0.9504	0.9662	0.9582	0.9710
0.008	33.0	759	0.1935	0.9508	0.9653	0.9580	0.9705
0.0071	34.0	782	0.1950	0.9486	0.9644	0.9564	0.9700
0.007	35.0	805	0.1934	0.9478	0.9644	0.9560	0.9705
0.0072	36.0	828	0.1938	0.9486	0.9653	0.9569	0.9702
0.0068	37.0	851	0.1946	0.9482	0.9649	0.9565	0.9697
0.0066	38.0	874	0.1946	0.9486	0.9653	0.9569	0.9700
0.0068	39.0	897	0.1947	0.9508	0.9658	0.9582	0.9705
0.007	40.0	920	0.1942	0.9486	0.9640	0.9562	0.9697
0.0066	41.0	943	0.1937	0.9490	0.9649	0.9569	0.9702
0.0065	42.0	966	0.1943	0.9478	0.9644	0.9560	0.9695
0.0067	43.0	989	0.1936	0.9508	0.9662	0.9584	0.9708
0.0071	44.0	1012	0.1944	0.9521	0.9667	0.9593	0.9710
0.0064	45.0	1035	0.1940	0.9517	0.9671	0.9593	0.9713
0.0063	46.0	1058	0.1938	0.9499	0.9658	0.9578	0.9708
0.0066	47.0	1081	0.1944	0.9499	0.9658	0.9578	0.9708
0.0063	48.0	1104	0.1955	0.9495	0.9653	0.9573	0.9705
0.007	49.0	1127	0.1955	0.9495	0.9653	0.9573	0.9705
0.0063	50.0	1150	0.1954	0.9495	0.9653	0.9573	0.9705

Framework versions

• Transformers 4.49.0.dev0
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0