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--- |
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base_model: layoutlmv3 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mp-02/cord |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: layoutlmv3-finetuned-cord |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: mp-02/cord |
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type: mp-02/cord |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9640397857689365 |
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- name: Recall |
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type: recall |
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value: 0.9782608695652174 |
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- name: F1 |
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type: f1 |
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value: 0.9710982658959537 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9741086587436333 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# layoutlmv3-finetuned-cord |
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This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/cord dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1874 |
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- Precision: 0.9640 |
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- Recall: 0.9783 |
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- F1: 0.9711 |
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- Accuracy: 0.9741 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 10 |
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- eval_batch_size: 10 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 2500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.4341 | 6.25 | 500 | 0.1703 | 0.9601 | 0.9720 | 0.9660 | 0.9656 | |
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| 0.0487 | 12.5 | 1000 | 0.1762 | 0.9662 | 0.9759 | 0.9710 | 0.9703 | |
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| 0.0185 | 18.75 | 1500 | 0.1913 | 0.9609 | 0.9720 | 0.9664 | 0.9682 | |
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| 0.0091 | 25.0 | 2000 | 0.1846 | 0.9693 | 0.9821 | 0.9757 | 0.9758 | |
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| 0.0038 | 31.25 | 2500 | 0.1874 | 0.9640 | 0.9783 | 0.9711 | 0.9741 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu118 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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