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: test
    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: 0.8889970788704966
          - name: Recall
            type: recall
            value: 0.907103825136612
          - name: F1
            type: f1
            value: 0.8979591836734693
          - name: Accuracy
            type: accuracy
            value: 0.8665161060263877

test

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: 0.5474
Precision: 0.8890
Recall: 0.9071
F1: 0.8980
Accuracy: 0.8665

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: 2
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	1.33	100	0.5976	0.7412	0.8296	0.7829	0.8001
No log	2.67	200	0.5019	0.8259	0.8698	0.8473	0.8269
No log	4.0	300	0.4829	0.8701	0.8982	0.8839	0.8540
No log	5.33	400	0.4490	0.8829	0.9141	0.8982	0.8725
0.5303	6.67	500	0.5120	0.8721	0.9046	0.8881	0.8574
0.5303	8.0	600	0.5212	0.8802	0.9011	0.8905	0.8644
0.5303	9.33	700	0.5447	0.8918	0.9086	0.9001	0.8559
0.5303	10.67	800	0.5304	0.8875	0.9056	0.8965	0.8713
0.5303	12.0	900	0.5496	0.8878	0.9081	0.8978	0.8630
0.1291	13.33	1000	0.5474	0.8890	0.9071	0.8980	0.8665

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2