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--- |
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library_name: transformers |
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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: 0.9672131147540983 |
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- name: Recall |
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type: recall |
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value: 0.9776304888152444 |
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- name: F1 |
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type: f1 |
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value: 0.9723939019365472 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9766697163769442 |
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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: 0.1292 |
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- Precision: 0.9672 |
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- Recall: 0.9776 |
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- F1: 0.9724 |
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- Accuracy: 0.9767 |
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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: 1e-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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| No log | 3.125 | 250 | 0.6018 | 0.8218 | 0.8633 | 0.8420 | 0.8577 | |
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| 1.0098 | 6.25 | 500 | 0.2695 | 0.9205 | 0.9495 | 0.9347 | 0.9451 | |
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| 1.0098 | 9.375 | 750 | 0.1813 | 0.9528 | 0.9693 | 0.9610 | 0.9639 | |
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| 0.1993 | 12.5 | 1000 | 0.1557 | 0.9616 | 0.9743 | 0.9679 | 0.9739 | |
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| 0.1993 | 15.625 | 1250 | 0.1749 | 0.9608 | 0.9743 | 0.9675 | 0.9703 | |
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| 0.0787 | 18.75 | 1500 | 0.1482 | 0.9616 | 0.9743 | 0.9679 | 0.9730 | |
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| 0.0787 | 21.875 | 1750 | 0.1288 | 0.9640 | 0.9751 | 0.9695 | 0.9762 | |
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| 0.0433 | 25.0 | 2000 | 0.1292 | 0.9672 | 0.9776 | 0.9724 | 0.9767 | |
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| 0.0433 | 28.125 | 2250 | 0.1372 | 0.9623 | 0.9735 | 0.9679 | 0.9735 | |
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| 0.031 | 31.25 | 2500 | 0.1408 | 0.9631 | 0.9743 | 0.9687 | 0.9730 | |
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### Framework versions |
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- Transformers 4.44.2 |
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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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