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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv3-base |
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tags: |
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- generated_from_trainer |
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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-cordv2 |
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results: [] |
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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-cordv2 |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3190 |
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- Precision: 0.8830 |
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- Recall: 0.8864 |
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- F1: 0.8847 |
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- Accuracy: 0.9201 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 1500 |
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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 | 0.3333 | 100 | 1.5910 | 0.5469 | 0.4954 | 0.5199 | 0.5926 | |
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| No log | 0.6667 | 200 | 1.0875 | 0.6533 | 0.6685 | 0.6608 | 0.7540 | |
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| No log | 1.0 | 300 | 0.8007 | 0.7629 | 0.7782 | 0.7705 | 0.8144 | |
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| No log | 1.3333 | 400 | 0.6082 | 0.8099 | 0.8130 | 0.8114 | 0.8471 | |
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| 1.2178 | 1.6667 | 500 | 0.5302 | 0.8222 | 0.8184 | 0.8203 | 0.8568 | |
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| 1.2178 | 2.0 | 600 | 0.5046 | 0.8300 | 0.8261 | 0.8280 | 0.8624 | |
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| 1.2178 | 2.3333 | 700 | 0.4537 | 0.8498 | 0.8485 | 0.8492 | 0.8785 | |
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| 1.2178 | 2.6667 | 800 | 0.4270 | 0.8566 | 0.8586 | 0.8576 | 0.8870 | |
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| 1.2178 | 3.0 | 900 | 0.3938 | 0.8680 | 0.8686 | 0.8683 | 0.8980 | |
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| 0.4128 | 3.3333 | 1000 | 0.3926 | 0.8765 | 0.8717 | 0.8741 | 0.9031 | |
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| 0.4128 | 3.6667 | 1100 | 0.3403 | 0.8644 | 0.8717 | 0.8680 | 0.9087 | |
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| 0.4128 | 4.0 | 1200 | 0.3326 | 0.8861 | 0.8841 | 0.8851 | 0.9159 | |
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| 0.4128 | 4.3333 | 1300 | 0.3223 | 0.8824 | 0.8872 | 0.8848 | 0.9201 | |
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| 0.4128 | 4.6667 | 1400 | 0.3175 | 0.8852 | 0.8879 | 0.8866 | 0.9210 | |
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| 0.274 | 5.0 | 1500 | 0.3190 | 0.8830 | 0.8864 | 0.8847 | 0.9201 | |
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### Framework versions |
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- Transformers 4.43.0.dev0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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