ocr-scanner-v2 – AI Model by nifras365 | AlphaNeural AI

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nifras365/
ocr-scanner-v2

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token-classification

generated_from_trainer

microsoft/layoutlm-base-uncased

endpoints_compatible

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layoutlm-receipts

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2190
Precision: 0.2222
Recall: 0.4
F1: 0.2857

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: 5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
0.7275	1.0	8	0.8111	0.0	0.0	0.0
0.6014	2.0	16	0.6757	0.0	0.0	0.0
0.5061	3.0	24	0.5598	0.0	0.0	0.0
0.4025	4.0	32	0.4736	0.0	0.0	0.0
0.3486	5.0	40	0.4236	0.0571	0.1	0.0727
0.3318	6.0	48	0.3784	0.0377	0.1	0.0548
0.2649	7.0	56	0.3338	0.1064	0.25	0.1493
0.1982	8.0	64	0.2808	0.25	0.4	0.3077
0.1658	9.0	72	0.2388	0.1778	0.4	0.2462
0.1637	10.0	80	0.2190	0.2222	0.4	0.2857

Framework versions

Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0