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: 0.8808265257087938
          - name: Recall
            type: recall
            value: 0.910581222056632
          - name: F1
            type: f1
            value: 0.895456765999023
          - name: Accuracy
            type: accuracy
            value: 0.8507072387970998

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: 0.5799
Precision: 0.8808
Recall: 0.9106
F1: 0.8955
Accuracy: 0.8507

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	1.3333	100	0.6686	0.7452	0.8251	0.7831	0.7535
No log	2.6667	200	0.4724	0.8064	0.8713	0.8376	0.8389
No log	4.0	300	0.4922	0.8612	0.8942	0.8774	0.8481
No log	5.3333	400	0.4632	0.8587	0.8997	0.8787	0.8521
0.544	6.6667	500	0.4850	0.8632	0.9031	0.8827	0.8474
0.544	8.0	600	0.5024	0.8744	0.8992	0.8866	0.8451
0.544	9.3333	700	0.5394	0.8768	0.9155	0.8957	0.8565
0.544	10.6667	800	0.5647	0.8800	0.9146	0.8970	0.8550
0.544	12.0	900	0.5798	0.8847	0.9106	0.8974	0.8545
0.1288	13.3333	1000	0.5799	0.8808	0.9106	0.8955	0.8507

Framework versions

Transformers 4.41.0.dev0
Pytorch 2.1.1+cu118
Datasets 2.15.0
Tokenizers 0.19.1