metadata

library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: test
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: layoutlmv3
          type: layoutlmv3
          config: InvoiceExtraction
          split: test
          args: InvoiceExtraction
        metrics:
          - name: Precision
            type: precision
            value: 0.8860759493670886
          - name: Recall
            type: recall
            value: 0.9210526315789473
          - name: F1
            type: f1
            value: 0.9032258064516129
          - name: Accuracy
            type: accuracy
            value: 0.94375

test

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

Loss: 0.3028
Precision: 0.8861
Recall: 0.9211
F1: 0.9032
Accuracy: 0.9437

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	4.3478	100	0.6867	0.6842	0.6842	0.6842	0.8063
No log	8.6957	200	0.2381	0.8625	0.9079	0.8846	0.9313
No log	13.0435	300	0.2598	0.8846	0.9079	0.8961	0.9313
No log	17.3913	400	0.2165	0.8625	0.9079	0.8846	0.9375
0.3281	21.7391	500	0.2037	0.8625	0.9079	0.8846	0.9375
0.3281	26.0870	600	0.2571	0.8861	0.9211	0.9032	0.9437
0.3281	30.4348	700	0.2735	0.8861	0.9211	0.9032	0.9437
0.3281	34.7826	800	0.2993	0.8861	0.9211	0.9032	0.9437
0.3281	39.1304	900	0.3044	0.8861	0.9211	0.9032	0.9437
0.012	43.4783	1000	0.3028	0.8861	0.9211	0.9032	0.9437

Framework versions

Transformers 4.47.0.dev0
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.20.3