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: 0.8876459143968871
          - name: Recall
            type: recall
            value: 0.9066070541480378
          - name: F1
            type: f1
            value: 0.8970262963873188
          - name: Accuracy
            type: accuracy
            value: 0.86009746820397

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: 0.6568
Precision: 0.8876
Recall: 0.9066
F1: 0.8970
Accuracy: 0.8601

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	0.6157	0.7621	0.8400	0.7991	0.8051
No log	2.67	200	0.4834	0.7915	0.8902	0.8380	0.8334
No log	4.0	300	0.4929	0.8484	0.8922	0.8697	0.8493
No log	5.33	400	0.5191	0.8746	0.9006	0.8874	0.8556
0.5561	6.67	500	0.5553	0.8671	0.9041	0.8852	0.8487
0.5561	8.0	600	0.5766	0.8723	0.9091	0.8903	0.8388
0.5561	9.33	700	0.6486	0.8816	0.8917	0.8866	0.8511
0.5561	10.67	800	0.6188	0.8861	0.9086	0.8972	0.8608
0.5561	12.0	900	0.6317	0.8890	0.9071	0.8980	0.8630
0.1298	13.33	1000	0.6568	0.8876	0.9066	0.8970	0.8601

Framework versions

Transformers 4.34.0.dev0
Pytorch 2.0.1+cpu
Datasets 2.12.0
Tokenizers 0.13.3