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---
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
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