README.md

metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - generated
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: document-data-extraction-layoutlmv3
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: generated
          type: generated
          config: sroie
          split: test
          args: sroie
        metrics:
          - name: Precision
            type: precision
            value: 1
          - name: Recall
            type: recall
            value: 1
          - name: F1
            type: f1
            value: 1
          - name: Accuracy
            type: accuracy
            value: 1

document-data-extraction-layoutlmv3

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

• Loss: 0.0015
• Precision: 1.0
• Recall: 1.0
• F1: 1.0
• Accuracy: 1.0

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: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	100	0.1114	0.95	0.9635	0.9567	0.9947
No log	2.0	200	0.0286	0.972	0.9858	0.9789	0.9971
No log	3.0	300	0.0184	0.972	0.9858	0.9789	0.9971
No log	4.0	400	0.0163	0.972	0.9858	0.9789	0.9971
0.1385	5.0	500	0.0141	0.972	0.9858	0.9789	0.9971
0.1385	6.0	600	0.0123	0.972	0.9858	0.9789	0.9971
0.1385	7.0	700	0.0122	0.972	0.9858	0.9789	0.9971
0.1385	8.0	800	0.0108	0.972	0.9858	0.9789	0.9971
0.1385	9.0	900	0.0104	0.972	0.9858	0.9789	0.9971
0.0119	10.0	1000	0.0113	0.972	0.9858	0.9789	0.9971
0.0119	11.0	1100	0.0080	0.974	0.9878	0.9809	0.9973
0.0119	12.0	1200	0.0089	0.9856	0.9736	0.9796	0.9973
0.0119	13.0	1300	0.0034	0.9959	0.9959	0.9959	0.9994
0.0119	14.0	1400	0.0037	0.9980	0.9939	0.9959	0.9994
0.006	15.0	1500	0.0024	0.9960	0.9980	0.9970	0.9996
0.006	16.0	1600	0.0019	0.9980	1.0	0.9990	0.9998
0.006	17.0	1700	0.0022	0.9960	0.9980	0.9970	0.9996
0.006	18.0	1800	0.0017	1.0	1.0	1.0	1.0
0.006	19.0	1900	0.0015	1.0	1.0	1.0	1.0
0.0027	20.0	2000	0.0015	1.0	1.0	1.0	1.0

Framework versions

• Transformers 4.36.0.dev0
• Pytorch 2.1.1+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0