metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - funsd-layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ft-ms-layoutlmv3-funsd-layoutlmv3
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: funsd-layoutlmv3
          type: funsd-layoutlmv3
          config: funsd
          split: test
          args: funsd
        metrics:
          - name: Precision
            type: precision
            value: 0.89171974522293
          - name: Recall
            type: recall
            value: 0.9041231992051664
          - name: F1
            type: f1
            value: 0.8978786383818451
          - name: Accuracy
            type: accuracy
            value: 0.8377510994888863

ft-ms-layoutlmv3-funsd-layoutlmv3

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

Loss: 1.0021
Precision: 0.8917
Recall: 0.9041
F1: 0.8979
Accuracy: 0.8378

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	0.5222	0.8477	0.8823	0.8647	0.8436
No log	20.0	200	0.6686	0.8736	0.9026	0.8879	0.8357
No log	30.0	300	0.7175	0.8759	0.9151	0.8950	0.8286
No log	40.0	400	0.7636	0.8832	0.8977	0.8904	0.8426
0.2392	50.0	500	0.9518	0.8820	0.9026	0.8922	0.8178
0.2392	60.0	600	0.9803	0.8771	0.8897	0.8834	0.8121
0.2392	70.0	700	1.0956	0.8883	0.9086	0.8983	0.8173
0.2392	80.0	800	0.9517	0.8930	0.9076	0.9002	0.8444
0.2392	90.0	900	1.0337	0.8950	0.9061	0.9005	0.8379
0.0083	100.0	1000	1.0021	0.8917	0.9041	0.8979	0.8378

Framework versions

Transformers 4.35.2
Pytorch 2.0.0
Datasets 2.15.0
Tokenizers 0.15.0