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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-finetuned-cord_100 |
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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-finetuned-cord_100 |
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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: 0.2879 |
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- Precision: 0.9258 |
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- Recall: 0.9384 |
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- F1: 0.9321 |
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- Accuracy: 0.9474 |
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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: 5 |
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- eval_batch_size: 5 |
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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 | 4.17 | 250 | 0.6982 | 0.7974 | 0.8017 | 0.7995 | 0.8265 | |
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| 0.9999 | 8.33 | 500 | 0.4214 | 0.8603 | 0.8754 | 0.8678 | 0.8936 | |
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| 0.9999 | 12.5 | 750 | 0.2820 | 0.9081 | 0.9164 | 0.9123 | 0.9364 | |
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| 0.1912 | 16.67 | 1000 | 0.2710 | 0.9147 | 0.9293 | 0.9220 | 0.9389 | |
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| 0.1912 | 20.83 | 1250 | 0.2748 | 0.9125 | 0.9271 | 0.9197 | 0.9406 | |
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| 0.061 | 25.0 | 1500 | 0.2612 | 0.9220 | 0.9339 | 0.9279 | 0.9474 | |
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| 0.061 | 29.17 | 1750 | 0.2731 | 0.9300 | 0.9384 | 0.9342 | 0.9478 | |
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| 0.0275 | 33.33 | 2000 | 0.2824 | 0.9279 | 0.9384 | 0.9331 | 0.9487 | |
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| 0.0275 | 37.5 | 2250 | 0.2886 | 0.9242 | 0.9362 | 0.9302 | 0.9457 | |
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| 0.0168 | 41.67 | 2500 | 0.2879 | 0.9258 | 0.9384 | 0.9321 | 0.9474 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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