metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - nielsr/funsd-layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: pasha
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: nielsr/funsd-layoutlmv3
          type: nielsr/funsd-layoutlmv3
          config: pasha
          split: test
          args: pasha
        metrics:
          - name: Precision
            type: precision
            value: 0.986704994610133
          - name: Recall
            type: recall
            value: 0.989193083573487
          - name: F1
            type: f1
            value: 0.9879474725670084
          - name: Accuracy
            type: accuracy
            value: 0.9905978784956606

pasha

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

Loss: 0.0585
Precision: 0.9867
Recall: 0.9892
F1: 0.9879
Accuracy: 0.9906

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	2.13	100	0.2664	0.9534	0.9438	0.9486	0.9571
No log	4.26	200	0.1044	0.9756	0.9802	0.9779	0.9838
No log	6.38	300	0.0672	0.9853	0.9899	0.9876	0.9904
No log	8.51	400	0.0634	0.9824	0.9860	0.9842	0.9884
0.2958	10.64	500	0.0585	0.9867	0.9892	0.9879	0.9906
0.2958	12.77	600	0.0511	0.9889	0.9928	0.9908	0.9928
0.2958	14.89	700	0.0503	0.9871	0.9921	0.9896	0.9925
0.2958	17.02	800	0.0529	0.9860	0.9903	0.9881	0.9913
0.2958	19.15	900	0.0581	0.9842	0.9892	0.9867	0.9904
0.0256	21.28	1000	0.0571	0.9849	0.9888	0.9869	0.9901

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.12.1
Datasets 2.6.1
Tokenizers 0.13.2