layoutlmv3-large-model2aa-visit-vs-progress

neuralit/layoutlmv3-large-model2aa-visit-vs-progress

layoutlmv3-large-model2aa-visit-vs-progress 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 20, 2026
SHA: 3c237c0f

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Base models

microsoft/layoutlmv3-large

Model card

From the Hugging Face model README

layoutlmv3-large-model2aa-visit-vs-progress

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: 0.3959
Accuracy: 0.8680
Macro Precision: 0.8680
Macro Recall: 0.8680
Macro F1: 0.8680
Weighted F1: 0.8680
Precision Visit Note Multiple Notes: 0.8644
Recall Visit Note Multiple Notes: 0.8724
F1 Visit Note Multiple Notes: 0.8684
Precision Progress Follow Up Note: 0.8717
Recall Progress Follow Up Note: 0.8636
F1 Progress Follow Up Note: 0.8676

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	Accuracy	F1 Progress Follow Up Note	F1 Visit Note Multiple Notes	Validation Loss	Macro F1	Macro Precision	Macro Recall	Precision Progress Follow Up Note	Precision Visit Note Multiple Notes	Recall Progress Follow Up Note	Recall Visit Note Multiple Notes	Weighted F1
0.4912	0.3168	500	0.7062	0.6104	0.7641	0.5445	0.6873	0.7731	0.7066	0.9087	0.6374	0.4595	0.9537	0.6871
0.3608	0.6335	1000	0.8282	0.8418	0.8120	0.4213	0.8269	0.8378	0.8280	0.7812	0.8945	0.9126	0.7434	0.8269
0.3789	0.9503	1500	0.8485	0.8459	0.8509	0.3537	0.8484	0.8489	0.8485	0.8621	0.8357	0.8303	0.8667	0.8484
0.2658	1.2667	2000	0.8602	0.8596	0.8608	0.3743	0.8602	0.8602	0.8602	0.8648	0.8556	0.8544	0.8660	0.8602
0.3878	1.5835	2500	0.8641	0.8614	0.8667	0.3856	0.8641	0.8648	0.8641	0.8806	0.8489	0.8430	0.8852	0.8640
0.2924	1.9003	3000	0.8541	0.8499	0.8581	0.4044	0.8540	0.8554	0.8542	0.8769	0.8339	0.8246	0.8838	0.8540
0.3093	2.2167	3500	0.8680	0.8677	0.8683	0.3957	0.8680	0.8680	0.8680	0.8712	0.8649	0.8643	0.8717	0.8680
0.171	2.5334	4000	0.5077	0.8620	0.8632	0.8620	0.8619	0.8619	0.8418	0.8909	0.8657	0.8846	0.8331	0.8581
0.1688	2.8502	4500	0.5981	0.8623	0.8641	0.8624	0.8622	0.8622	0.8382	0.8974	0.8668	0.8900	0.8274	0.8576

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 20, 2026