metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
  - layoutlmv3
  - token_classifier
  - layout_analysis
datasets:
  - pierreguillou/DocLayNet-small
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-DocLayNet
    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: 0.6178861788617886
          - name: Recall
            type: recall
            value: 0.7238095238095238
          - name: F1
            type: f1
            value: 0.6666666666666667
          - name: Accuracy
            type: accuracy
            value: 0.8719611021069692
language:
  - en
pipeline_tag: token-classification

layoutlmv3-finetuned-DocLayNet

This model is a fine-tuned version of microsoft/layoutlmv3-base on the doc_lay_net-small dataset. It achieves the following results on the evaluation set:

Loss: 0.5644
Precision: 0.6179
Recall: 0.7238
F1: 0.6667
Accuracy: 0.8720

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: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.3383	0.58	200	0.8358	0.3007	0.4381	0.3566	0.7724
0.8308	1.16	400	0.6735	0.4634	0.5429	0.5	0.8084
0.518	1.74	600	0.5706	0.5373	0.6857	0.6025	0.8399
0.3856	2.33	800	0.6303	0.6032	0.7238	0.6580	0.8648
0.2558	2.91	1000	0.5644	0.6179	0.7238	0.6667	0.8720

Framework versions

Transformers 4.27.3
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2

How to Train & Inference:

Check this out this repo: https://github.com/mit1280/Document-AI