layoutlm-sroie_synthetic

pabloma09/layoutlm-sroie_synthetic

layoutlm-sroie_synthetic is a token classification model from pabloma09. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

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

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: layoutlm
Access: Public
Created: Mar 19, 2025
Updated: Mar 25, 2025
SHA: 9c924c57

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Base models

microsoft/layoutlm-base-uncased

Model card

From the Hugging Face model README

layoutlm-sroie_synthetic

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: 0.1204
Ate: {'precision': 0.7636363636363637, 'recall': 0.8571428571428571, 'f1': 0.8076923076923076, 'number': 49}
Ddress: {'precision': 0.8653846153846154, 'recall': 0.9, 'f1': 0.8823529411764707, 'number': 50}
Ompany: {'precision': 0.9230769230769231, 'recall': 0.96, 'f1': 0.9411764705882353, 'number': 50}
Otal: {'precision': 0.9215686274509803, 'recall': 0.94, 'f1': 0.9306930693069307, 'number': 50}
Overall Precision: 0.8667
Overall Recall: 0.9146
Overall F1: 0.8900
Overall Accuracy: 0.9778

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: 16
eval_batch_size: 8
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: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Ate	Ddress	Ompany	Otal	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.4783	1.0	43	0.1302	{'precision': 0.72, 'recall': 0.7346938775510204, 'f1': 0.7272727272727272, 'number': 49}	{'precision': 0.8113207547169812, 'recall': 0.86, 'f1': 0.8349514563106797, 'number': 50}	{'precision': 0.9423076923076923, 'recall': 0.98, 'f1': 0.9607843137254902, 'number': 50}	{'precision': 0.7142857142857143, 'recall': 0.8, 'f1': 0.7547169811320756, 'number': 50}	0.7962	0.8442	0.8195	0.9682
0.0285	2.0	86	0.1126	{'precision': 0.75, 'recall': 0.8571428571428571, 'f1': 0.7999999999999999, 'number': 49}	{'precision': 0.7962962962962963, 'recall': 0.86, 'f1': 0.826923076923077, 'number': 50}	{'precision': 0.9230769230769231, 'recall': 0.96, 'f1': 0.9411764705882353, 'number': 50}	{'precision': 0.9215686274509803, 'recall': 0.94, 'f1': 0.9306930693069307, 'number': 50}	0.8451	0.9045	0.8738	0.9764
0.0119	3.0	129	0.1177	{'precision': 0.7962962962962963, 'recall': 0.8775510204081632, 'f1': 0.8349514563106796, 'number': 49}	{'precision': 0.8846153846153846, 'recall': 0.92, 'f1': 0.9019607843137256, 'number': 50}	{'precision': 0.9423076923076923, 'recall': 0.98, 'f1': 0.9607843137254902, 'number': 50}	{'precision': 0.9215686274509803, 'recall': 0.94, 'f1': 0.9306930693069307, 'number': 50}	0.8852	0.9296	0.9069	0.9797
0.0078	4.0	172	0.1204	{'precision': 0.7636363636363637, 'recall': 0.8571428571428571, 'f1': 0.8076923076923076, 'number': 49}	{'precision': 0.8653846153846154, 'recall': 0.9, 'f1': 0.8823529411764707, 'number': 50}	{'precision': 0.9230769230769231, 'recall': 0.96, 'f1': 0.9411764705882353, 'number': 50}	{'precision': 0.9215686274509803, 'recall': 0.94, 'f1': 0.9306930693069307, 'number': 50}	0.8667	0.9146	0.8900	0.9778

Framework versions

Transformers 4.50.0
Pytorch 2.1.0+cu118
Datasets 3.4.1
Tokenizers 0.21.1

Task: Token Classification
Library: transformers
Type: layoutlm
License: mit
Created: Mar 19, 2025
Updated: Mar 25, 2025