layoutlm-custom

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1583
• Noise: {'precision': 0.8818897637795275, 'recall': 0.8736349453978159, 'f1': 0.8777429467084641, 'number': 641}
• Signal: {'precision': 0.861198738170347, 'recall': 0.853125, 'f1': 0.8571428571428572, 'number': 640}
• Overall Precision: 0.8716
• Overall Recall: 0.8634
• Overall F1: 0.8675
• Overall Accuracy: 0.9656

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: 3e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Noise	Signal	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.3882	1.0	18	0.2617	{'precision': 0.6654804270462633, 'recall': 0.5834633385335414, 'f1': 0.6217788861180383, 'number': 641}	{'precision': 0.6149732620320856, 'recall': 0.5390625, 'f1': 0.5745212323064114, 'number': 640}	0.6402	0.5613	0.5982	0.8986
0.1694	2.0	36	0.1752	{'precision': 0.7387820512820513, 'recall': 0.719188767550702, 'f1': 0.7288537549407115, 'number': 641}	{'precision': 0.709470304975923, 'recall': 0.690625, 'f1': 0.6999208234362629, 'number': 640}	0.7241	0.7049	0.7144	0.9296
0.1039	3.0	54	0.1356	{'precision': 0.7865168539325843, 'recall': 0.7644305772230889, 'f1': 0.7753164556962026, 'number': 641}	{'precision': 0.77491961414791, 'recall': 0.753125, 'f1': 0.7638668779714739, 'number': 640}	0.7807	0.7588	0.7696	0.9439
0.064	4.0	72	0.1342	{'precision': 0.8220472440944881, 'recall': 0.8143525741029641, 'f1': 0.8181818181818181, 'number': 641}	{'precision': 0.8028391167192429, 'recall': 0.7953125, 'f1': 0.7990580847723705, 'number': 640}	0.8125	0.8048	0.8086	0.9522
0.0433	5.0	90	0.1241	{'precision': 0.8544303797468354, 'recall': 0.8424336973478939, 'f1': 0.8483896307934014, 'number': 641}	{'precision': 0.8320126782884311, 'recall': 0.8203125, 'f1': 0.8261211644374509, 'number': 640}	0.8432	0.8314	0.8373	0.9601
0.0293	6.0	108	0.1274	{'precision': 0.8650793650793651, 'recall': 0.8502340093603744, 'f1': 0.8575924468922109, 'number': 641}	{'precision': 0.8378378378378378, 'recall': 0.8234375, 'f1': 0.830575256107171, 'number': 640}	0.8515	0.8368	0.8441	0.9617
0.0199	7.0	126	0.1372	{'precision': 0.8722397476340694, 'recall': 0.8627145085803433, 'f1': 0.8674509803921568, 'number': 641}	{'precision': 0.8530805687203792, 'recall': 0.84375, 'f1': 0.8483896307934015, 'number': 640}	0.8627	0.8532	0.8579	0.9640
0.0139	8.0	144	0.1386	{'precision': 0.8839427662957074, 'recall': 0.8673946957878315, 'f1': 0.8755905511811023, 'number': 641}	{'precision': 0.856687898089172, 'recall': 0.840625, 'f1': 0.8485804416403785, 'number': 640}	0.8703	0.8540	0.8621	0.9656
0.0126	9.0	162	0.1467	{'precision': 0.8829113924050633, 'recall': 0.8705148205928237, 'f1': 0.8766692851531814, 'number': 641}	{'precision': 0.8541996830427893, 'recall': 0.8421875, 'f1': 0.848151062155783, 'number': 640}	0.8686	0.8564	0.8624	0.9654
0.0114	10.0	180	0.1531	{'precision': 0.8694968553459119, 'recall': 0.8627145085803433, 'f1': 0.8660924040720438, 'number': 641}	{'precision': 0.8472440944881889, 'recall': 0.840625, 'f1': 0.8439215686274509, 'number': 640}	0.8584	0.8517	0.8550	0.9631
0.0099	11.0	198	0.1581	{'precision': 0.8703125, 'recall': 0.8689547581903276, 'f1': 0.8696330991412958, 'number': 641}	{'precision': 0.8450704225352113, 'recall': 0.84375, 'f1': 0.8444096950742768, 'number': 640}	0.8577	0.8564	0.8570	0.9634
0.0064	12.0	216	0.1543	{'precision': 0.8866141732283465, 'recall': 0.8783151326053042, 'f1': 0.8824451410658307, 'number': 641}	{'precision': 0.8643533123028391, 'recall': 0.85625, 'f1': 0.8602825745682888, 'number': 640}	0.8755	0.8673	0.8714	0.9659
0.0059	13.0	234	0.1628	{'precision': 0.8732394366197183, 'recall': 0.8705148205928237, 'f1': 0.871875, 'number': 641}	{'precision': 0.8526645768025078, 'recall': 0.85, 'f1': 0.8513302034428795, 'number': 640}	0.8630	0.8603	0.8616	0.9645
0.0056	14.0	252	0.1587	{'precision': 0.878740157480315, 'recall': 0.8705148205928237, 'f1': 0.8746081504702194, 'number': 641}	{'precision': 0.8580441640378549, 'recall': 0.85, 'f1': 0.8540031397174254, 'number': 640}	0.8684	0.8603	0.8643	0.9651
0.005	15.0	270	0.1583	{'precision': 0.8818897637795275, 'recall': 0.8736349453978159, 'f1': 0.8777429467084641, 'number': 641}	{'precision': 0.861198738170347, 'recall': 0.853125, 'f1': 0.8571428571428572, 'number': 640}	0.8716	0.8634	0.8675	0.9656

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0