---
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-test
  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: 0.5207226354941552
    - name: Recall
      type: recall
      value: 0.7111756168359942
    - name: F1
      type: f1
      value: 0.6012269938650306
    - name: Accuracy
      type: accuracy
      value: 0.842051017778923
---

<!-- 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-test

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: 0.5038
- Precision: 0.5207
- Recall: 0.7112
- F1: 0.6012
- Accuracy: 0.8421

## 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: 2
- eval_batch_size: 2
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.5092        | 0.37  | 250  | 0.8072          | 0.1922    | 0.2342 | 0.2111 | 0.8227   |
| 0.8608        | 0.73  | 500  | 0.6402          | 0.3963    | 0.6108 | 0.4807 | 0.8596   |
| 0.6463        | 1.1   | 750  | 0.8042          | 0.5702    | 0.6297 | 0.5985 | 0.8080   |
| 0.4495        | 1.46  | 1000 | 0.8439          | 0.5353    | 0.6234 | 0.5760 | 0.8033   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3