---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-large
tags:
- layoutlmv3
- document-classification
- medical-documents
- model2aa
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: layoutlmv3-large-model2aa-visit-vs-progress
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# layoutlmv3-large-model2aa-visit-vs-progress

This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/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