layoutlm-custom

uttam333/layoutlm-custom

layoutlm-custom is a token classification model from uttam333. 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: layoutlm
Access: Public
Created: Jan 9, 2024
Updated: Jan 9, 2024
SHA: 89a613b5

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Model card

From the Hugging Face model README

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

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Jan 9, 2024
Updated: Jan 9, 2024