layoutlmv3-large-model1-clinical-vs-specialized

neuralit/layoutlmv3-large-model1-clinical-vs-specialized

layoutlmv3-large-model1-clinical-vs-specialized is a text classification model from neuralit. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as cc-by-nc-sa-4.0.

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

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At a glance

Task: Text Classification
Library: transformers
License: cc-by-nc-sa-4.0
Model type: layoutlmv3
Access: Public
Created: Mar 18, 2026
Updated: Mar 18, 2026
SHA: 1fd467f0

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Base models

microsoft/layoutlmv3-large

Model card

From the Hugging Face model README

layoutlmv3-large-model1-clinical-vs-specialized

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

Loss: 0.1348
Accuracy: 0.9771
Macro Precision: 0.9771
Macro Recall: 0.9771
Macro F1: 0.9771
Weighted F1: 0.9771
Precision Specialized Diagnostics Procedures: 0.9776
Recall Specialized Diagnostics Procedures: 0.9766
F1 Specialized Diagnostics Procedures: 0.9771
Precision Clinical Evaluation Progress: 0.9766
Recall Clinical Evaluation Progress: 0.9776
F1 Clinical Evaluation Progress: 0.9771

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: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro Precision	Macro Recall	Macro F1	Weighted F1	Precision Specialized Diagnostics Procedures	Recall Specialized Diagnostics Procedures	F1 Specialized Diagnostics Procedures	Precision Clinical Evaluation Progress	Recall Clinical Evaluation Progress	F1 Clinical Evaluation Progress
0.1797	0.2426	500	0.6136	0.8732	0.8942	0.8732	0.8715	0.8715	0.8033	0.9885	0.8863	0.9851	0.7579	0.8567
0.1666	0.4851	1000	0.2275	0.9558	0.9568	0.9558	0.9558	0.9558	0.9354	0.9793	0.9568	0.9783	0.9324	0.9548
0.1152	0.7277	1500	0.1561	0.9681	0.9683	0.9681	0.9681	0.9681	0.9772	0.9586	0.9678	0.9593	0.9776	0.9684
0.1771	0.9703	2000	0.1491	0.9643	0.9645	0.9643	0.9643	0.9643	0.9733	0.9547	0.9639	0.9556	0.9738	0.9646
0.1685	1.2125	2500	0.1408	0.9719	0.9719	0.9719	0.9719	0.9719	0.9722	0.9716	0.9719	0.9717	0.9722	0.9719
0.1446	1.4551	3000	0.1437	0.9725	0.9725	0.9725	0.9725	0.9725	0.9702	0.9749	0.9725	0.9748	0.9700	0.9724
0.1136	1.6976	3500	0.1414	0.9711	0.9712	0.9711	0.9711	0.9711	0.9768	0.9651	0.9709	0.9655	0.9771	0.9713
0.0852	1.9402	4000	0.1387	0.9733	0.9734	0.9733	0.9733	0.9733	0.9806	0.9656	0.9731	0.9662	0.9809	0.9735
0.0429	2.1824	4500	0.1323	0.9768	0.9768	0.9768	0.9768	0.9768	0.9787	0.9749	0.9768	0.9750	0.9787	0.9769
0.0717	2.4250	5000	0.1373	0.9766	0.9766	0.9766	0.9766	0.9766	0.975	0.9782	0.9766	0.9781	0.9749	0.9765
0.0784	2.6676	5500	0.1257	0.9785	0.9785	0.9785	0.9785	0.9785	0.9787	0.9782	0.9785	0.9782	0.9787	0.9785
0.1109	2.9101	6000	0.1348	0.9771	0.9771	0.9771	0.9771	0.9771	0.9776	0.9766	0.9771	0.9766	0.9776	0.9771

Framework versions

Transformers 4.57.6
Pytorch 2.10.0+cu128
Tokenizers 0.22.2

Task: Text Classification
Library: transformers
Type: layoutlmv3
License: cc-by-nc-sa-4.0
Created: Mar 18, 2026
Updated: Mar 18, 2026