metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - cord-layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: project-ocr
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: cord-layoutlmv3
          type: cord-layoutlmv3
          config: cord
          split: test
          args: cord
        metrics:
          - name: Precision
            type: precision
            value: 0.7515745276417075
          - name: Recall
            type: recall
            value: 0.8038922155688623
          - name: F1
            type: f1
            value: 0.7768535262206148
          - name: Accuracy
            type: accuracy
            value: 0.8102716468590832

project-ocr

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

Loss: 0.9877
Precision: 0.7516
Recall: 0.8039
F1: 0.7769
Accuracy: 0.8103

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: 5
eval_batch_size: 5
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.83	50	2.6184	0.4355	0.5404	0.4823	0.4338
No log	1.67	100	1.8766	0.5912	0.6018	0.5964	0.5620
No log	2.5	150	1.6165	0.5737	0.6347	0.6027	0.6150
No log	3.33	200	1.4317	0.5732	0.6737	0.6194	0.6944
No log	4.17	250	1.2787	0.6190	0.7126	0.6625	0.7347
No log	5.0	300	1.1632	0.6729	0.7560	0.7120	0.7759
No log	5.83	350	1.0990	0.6980	0.7665	0.7306	0.7857
No log	6.67	400	1.0327	0.7125	0.7792	0.7444	0.7946
No log	7.5	450	0.9994	0.7526	0.8016	0.7764	0.8065
1.6589	8.33	500	0.9877	0.7516	0.8039	0.7769	0.8103

Framework versions

Transformers 4.27.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2