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--- |
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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: dit-tiny_tobacco3482_kd_CEKD_t5.0_a0.7 |
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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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# dit-tiny_tobacco3482_kd_CEKD_t5.0_a0.7 |
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This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.1844 |
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- Accuracy: 0.18 |
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- Brier Loss: 0.8763 |
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- Nll: 6.0873 |
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- F1 Micro: 0.18 |
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- F1 Macro: 0.0306 |
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- Ece: 0.2492 |
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- Aurc: 0.8505 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 256 |
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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: 25 |
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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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| No log | 0.96 | 3 | 3.3625 | 0.145 | 0.8999 | 10.1577 | 0.145 | 0.0253 | 0.2220 | 0.8466 | |
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| No log | 1.96 | 6 | 3.3300 | 0.145 | 0.8947 | 10.5652 | 0.145 | 0.0253 | 0.2237 | 0.8468 | |
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| No log | 2.96 | 9 | 3.2822 | 0.14 | 0.8870 | 8.5877 | 0.14 | 0.0453 | 0.2040 | 0.8325 | |
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| No log | 3.96 | 12 | 3.2442 | 0.16 | 0.8812 | 6.5385 | 0.16 | 0.0327 | 0.2208 | 0.8814 | |
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| No log | 4.96 | 15 | 3.2219 | 0.155 | 0.8784 | 7.1527 | 0.155 | 0.0271 | 0.2352 | 0.8898 | |
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| No log | 5.96 | 18 | 3.2105 | 0.185 | 0.8778 | 8.7319 | 0.185 | 0.0517 | 0.2548 | 0.8944 | |
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| No log | 6.96 | 21 | 3.2032 | 0.18 | 0.8778 | 8.8034 | 0.18 | 0.0308 | 0.2478 | 0.8527 | |
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| No log | 7.96 | 24 | 3.1980 | 0.18 | 0.8779 | 8.1814 | 0.18 | 0.0306 | 0.2635 | 0.8527 | |
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| No log | 8.96 | 27 | 3.1937 | 0.18 | 0.8777 | 7.0314 | 0.18 | 0.0306 | 0.2618 | 0.8529 | |
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| No log | 9.96 | 30 | 3.1915 | 0.18 | 0.8776 | 6.9166 | 0.18 | 0.0306 | 0.2591 | 0.8537 | |
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| No log | 10.96 | 33 | 3.1900 | 0.18 | 0.8774 | 6.8864 | 0.18 | 0.0306 | 0.2551 | 0.8535 | |
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| No log | 11.96 | 36 | 3.1889 | 0.18 | 0.8773 | 6.5148 | 0.18 | 0.0306 | 0.2547 | 0.8532 | |
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| No log | 12.96 | 39 | 3.1881 | 0.18 | 0.8771 | 6.1469 | 0.18 | 0.0306 | 0.2543 | 0.8530 | |
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| No log | 13.96 | 42 | 3.1872 | 0.18 | 0.8769 | 6.1318 | 0.18 | 0.0306 | 0.2538 | 0.8525 | |
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| No log | 14.96 | 45 | 3.1865 | 0.18 | 0.8768 | 6.0783 | 0.18 | 0.0306 | 0.2501 | 0.8525 | |
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| No log | 15.96 | 48 | 3.1859 | 0.18 | 0.8766 | 6.0654 | 0.18 | 0.0306 | 0.2500 | 0.8520 | |
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| No log | 16.96 | 51 | 3.1855 | 0.18 | 0.8766 | 6.0809 | 0.18 | 0.0306 | 0.2459 | 0.8516 | |
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| No log | 17.96 | 54 | 3.1855 | 0.18 | 0.8766 | 6.0610 | 0.18 | 0.0306 | 0.2497 | 0.8515 | |
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| No log | 18.96 | 57 | 3.1854 | 0.18 | 0.8766 | 6.0659 | 0.18 | 0.0306 | 0.2579 | 0.8515 | |
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| No log | 19.96 | 60 | 3.1850 | 0.18 | 0.8764 | 6.0737 | 0.18 | 0.0306 | 0.2656 | 0.8513 | |
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| No log | 20.96 | 63 | 3.1848 | 0.18 | 0.8764 | 6.0869 | 0.18 | 0.0306 | 0.2575 | 0.8510 | |
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| No log | 21.96 | 66 | 3.1846 | 0.18 | 0.8764 | 6.1423 | 0.18 | 0.0306 | 0.2533 | 0.8510 | |
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| No log | 22.96 | 69 | 3.1845 | 0.18 | 0.8763 | 6.1047 | 0.18 | 0.0306 | 0.2532 | 0.8505 | |
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| No log | 23.96 | 72 | 3.1845 | 0.18 | 0.8763 | 6.0895 | 0.18 | 0.0306 | 0.2532 | 0.8504 | |
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| No log | 24.96 | 75 | 3.1844 | 0.18 | 0.8763 | 6.0873 | 0.18 | 0.0306 | 0.2492 | 0.8505 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1.post200 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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