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--- |
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license: apache-2.0 |
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base_model: jordyvl/vit-base_rvl-cdip |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-base_rvl_cdip-N1K_aAURC_32 |
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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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# vit-base_rvl_cdip-N1K_aAURC_32 |
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This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5215 |
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- Accuracy: 0.888 |
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- Brier Loss: 0.1918 |
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- Nll: 0.9026 |
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- F1 Micro: 0.888 |
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- F1 Macro: 0.8883 |
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- Ece: 0.0880 |
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- Aurc: 0.0205 |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| |
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| 0.1629 | 1.0 | 500 | 0.3779 | 0.8875 | 0.1721 | 1.1899 | 0.8875 | 0.8877 | 0.0531 | 0.0201 | |
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| 0.1234 | 2.0 | 1000 | 0.4074 | 0.8868 | 0.1790 | 1.1333 | 0.8868 | 0.8874 | 0.0647 | 0.0213 | |
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| 0.0616 | 3.0 | 1500 | 0.4257 | 0.888 | 0.1813 | 1.0677 | 0.888 | 0.8879 | 0.0695 | 0.0201 | |
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| 0.0303 | 4.0 | 2000 | 0.4595 | 0.885 | 0.1869 | 1.0256 | 0.885 | 0.8856 | 0.0776 | 0.0222 | |
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| 0.0133 | 5.0 | 2500 | 0.4902 | 0.8848 | 0.1922 | 0.9983 | 0.8848 | 0.8849 | 0.0831 | 0.0228 | |
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| 0.0083 | 6.0 | 3000 | 0.4941 | 0.8862 | 0.1903 | 0.9464 | 0.8862 | 0.8868 | 0.0850 | 0.0211 | |
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| 0.0051 | 7.0 | 3500 | 0.5116 | 0.8875 | 0.1928 | 0.9118 | 0.8875 | 0.8873 | 0.0875 | 0.0207 | |
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| 0.0043 | 8.0 | 4000 | 0.5154 | 0.8882 | 0.1910 | 0.9138 | 0.8882 | 0.8887 | 0.0864 | 0.0205 | |
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| 0.0041 | 9.0 | 4500 | 0.5221 | 0.8865 | 0.1924 | 0.9101 | 0.8865 | 0.8868 | 0.0896 | 0.0206 | |
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| 0.0037 | 10.0 | 5000 | 0.5215 | 0.888 | 0.1918 | 0.9026 | 0.888 | 0.8883 | 0.0880 | 0.0205 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.2.0.dev20231002 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.3 |
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