---
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: 0.6647646219686163
    - name: Recall
      type: recall
      value: 0.6763425253991292
    - name: F1
      type: f1
      value: 0.6705035971223021
    - name: Accuracy
      type: accuracy
      value: 0.8582839474362278
---

<!-- 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: 0.8293
- Precision: 0.6648
- Recall: 0.6763
- F1: 0.6705
- Accuracy: 0.8583

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.5039        | 0.3660 | 250  | 1.1856          | 0.1597    | 0.2785 | 0.2030 | 0.5852   |
| 0.8176        | 0.7321 | 500  | 0.6027          | 0.4143    | 0.5506 | 0.4728 | 0.8651   |
| 0.5533        | 1.0981 | 750  | 0.6755          | 0.5946    | 0.6266 | 0.6102 | 0.8649   |
| 0.4021        | 1.4641 | 1000 | 0.6233          | 0.6017    | 0.6646 | 0.6316 | 0.8804   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1