---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- doc_lay_net-small
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-DocLayNet
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: doc_lay_net-small
      type: doc_lay_net-small
      config: DocLayNet_2022.08_processed_on_2023.01
      split: test
      args: DocLayNet_2022.08_processed_on_2023.01
    metrics:
    - name: Precision
      type: precision
      value: 0.876231416801003
    - name: Recall
      type: recall
      value: 0.876231416801003
    - name: F1
      type: f1
      value: 0.876231416801003
    - name: Accuracy
      type: accuracy
      value: 0.876231416801003
---

<!-- 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-finetuned-DocLayNet

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the doc_lay_net-small dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4878
- Precision: 0.8762
- Recall: 0.8762
- F1: 0.8762
- Accuracy: 0.8762

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.1244        | 2.9070  | 250  | 0.7630          | 0.7337    | 0.7337 | 0.7337 | 0.7337   |
| 0.2934        | 5.8140  | 500  | 0.4878          | 0.8762    | 0.8762 | 0.8762 | 0.8762   |
| 0.1028        | 8.7209  | 750  | 0.5626          | 0.8752    | 0.8752 | 0.8752 | 0.8752   |
| 0.0539        | 11.6279 | 1000 | 0.6090          | 0.8719    | 0.8719 | 0.8719 | 0.8719   |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1