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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: 0.7572254335260116 |
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- name: Recall |
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type: recall |
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value: 0.8136645962732919 |
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- name: F1 |
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type: f1 |
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value: 0.784431137724551 |
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- name: Accuracy |
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type: accuracy |
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value: 0.7975382003395586 |
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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: 1.0695 |
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- Precision: 0.7572 |
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- Recall: 0.8137 |
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- F1: 0.7844 |
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- Accuracy: 0.7975 |
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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-06 |
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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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| 2.7217 | 6.25 | 500 | 1.8788 | 0.5168 | 0.6095 | 0.5593 | 0.5883 | |
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| 1.7562 | 12.5 | 1000 | 1.4443 | 0.5337 | 0.6576 | 0.5892 | 0.6795 | |
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| 1.4387 | 18.75 | 1500 | 1.2162 | 0.6981 | 0.7811 | 0.7373 | 0.7746 | |
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| 1.2728 | 25.0 | 2000 | 1.1030 | 0.7473 | 0.8106 | 0.7777 | 0.7941 | |
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| 1.1902 | 31.25 | 2500 | 1.0695 | 0.7572 | 0.8137 | 0.7844 | 0.7975 | |
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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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