---
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-DocLayNet-small
  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: validation
      args: DocLayNet_2022.08_processed_on_2023.01
    metrics:
    - name: Precision
      type: precision
      value: 0.12834224598930483
    - name: Recall
      type: recall
      value: 0.0759493670886076
    - name: F1
      type: f1
      value: 0.09542743538767395
    - name: Accuracy
      type: accuracy
      value: 0.6476804585348379
---

<!-- 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-DocLayNet-small

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: 1.2781
- Precision: 0.1283
- Recall: 0.0759
- F1: 0.0954
- Accuracy: 0.6477

## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- total_eval_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.51.3
- Pytorch 2.4.1
- Datasets 3.5.1
- Tokenizers 0.21.1