---
tags:
- generated_from_trainer
datasets:
- pierreguillou/DocLayNet-large
metrics:
- precision
- recall
- f1
- accuracy
base_model: microsoft/layoutlmv3-base
model-index:
- name: layoutlmv3-finetuned-doclaynet
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: pierreguillou/DocLayNet-large
      type: pierreguillou/DocLayNet-large
      args: doclaynet
    metrics:
    - type: precision
      value: 0.847
      name: Precision
    - type: recall
      value: 0.893
      name: Recall
    - type: f1
      value: 0.870
      name: F1
    - type: accuracy
      value: 0.957
      name: Accuracy
---

# layoutlmv3-finetuned-funsd

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the pierreguillou/DocLayNet-large using bounding boxes and 
categories for lines (not for for paragraphs).
It achieves the following results on the evaluation set:
- Loss: 0.33888205885887146,
- Precision: 0.8478835766832817,
- Recall: 0.8934488524091807,
- F1: 0.8700700634847538,
- Accuracy: 0.9574140990541197

The script for training can be found here: https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- training_steps: 100000

### Framework versions

- Transformers 4.33.3
- Pytorch 1.11.0+cu115
- Datasets 2.14.5
- Tokenizers 0.13.3