metadata

library_name: transformers
base_model: layoutlmv3
tags:
  - generated_from_trainer
datasets:
  - mp-02/cord-sroie
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-cord-sroie
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: mp-02/cord-sroie
          type: mp-02/cord-sroie
        metrics:
          - name: Precision
            type: precision
            value: 0.9551958714520291
          - name: Recall
            type: recall
            value: 0.9647003079838901
          - name: F1
            type: f1
            value: 0.9599245638849601
          - name: Accuracy
            type: accuracy
            value: 0.9859886297506603

layoutlmv3-finetuned-cord-sroie

This model is a fine-tuned version of layoutlmv3 on the mp-02/cord-sroie dataset. It achieves the following results on the evaluation set:

Loss: 0.0731
Precision: 0.9552
Recall: 0.9647
F1: 0.9599
Accuracy: 0.9860

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: 10
eval_batch_size: 10
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 2500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.7483	250	0.3308	0.8186	0.7941	0.8062	0.9427
0.7339	3.4965	500	0.1450	0.9013	0.9195	0.9103	0.9722
0.7339	5.2448	750	0.1009	0.9314	0.9386	0.9350	0.9791
0.1726	6.9930	1000	0.0841	0.9445	0.9562	0.9503	0.9839
0.1726	8.7413	1250	0.0776	0.9563	0.9533	0.9548	0.9850
0.0918	10.4895	1500	0.0782	0.9450	0.9611	0.9530	0.9844
0.0918	12.2378	1750	0.0699	0.9539	0.9602	0.9570	0.9856
0.0587	13.9860	2000	0.0743	0.9550	0.9611	0.9581	0.9859
0.0587	15.7343	2250	0.0726	0.9596	0.9633	0.9615	0.9863
0.046	17.4825	2500	0.0731	0.9552	0.9647	0.9599	0.9860

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1