metadata

tags:
  - generated_from_trainer
datasets:
  - nielsr/funsd-layoutlmv3
pipeline_tag: object-detection
widget:
  - src: >-
      https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png
    example_title: invoice
  - src: >-
      https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg
    example_title: contract
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-funsd
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: nielsr/funsd-layoutlmv3
          type: nielsr/funsd-layoutlmv3
          args: funsd
        metrics:
          - name: Precision
            type: precision
            value: 0.9026198714780029
          - name: Recall
            type: recall
            value: 0.913
          - name: F1
            type: f1
            value: 0.9077802634849614
          - name: Accuracy
            type: accuracy
            value: 0.8330271015158475
duplicated_from: nielsr/layoutlmv3-finetuned-funsd

layoutlmv3-finetuned-funsd

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

Loss: 1.1164
Precision: 0.9026
Recall: 0.913
F1: 0.9078
Accuracy: 0.8330

The script for training can be found here: https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3

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: 16
eval_batch_size: 16
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
No log	10.0	100	0.5238	0.8366	0.886	0.8606	0.8410
No log	20.0	200	0.6930	0.8751	0.8965	0.8857	0.8322
No log	30.0	300	0.7784	0.8902	0.908	0.8990	0.8414
No log	40.0	400	0.9056	0.8916	0.905	0.8983	0.8364
0.2429	50.0	500	1.0016	0.8954	0.9075	0.9014	0.8298
0.2429	60.0	600	1.0097	0.8899	0.897	0.8934	0.8294
0.2429	70.0	700	1.0722	0.9035	0.9085	0.9060	0.8315
0.2429	80.0	800	1.0884	0.8905	0.9105	0.9004	0.8269
0.2429	90.0	900	1.1292	0.8938	0.909	0.9013	0.8279
0.0098	100.0	1000	1.1164	0.9026	0.913	0.9078	0.8330

Framework versions

Transformers 4.19.0.dev0
Pytorch 1.11.0+cu113
Datasets 2.0.0
Tokenizers 0.11.6