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README.md
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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-small_tobacco3482_kd_CEKD_t1.5_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-small_tobacco3482_kd_CEKD_t1.5_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: 2.5836
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- Accuracy: 0.185
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- Brier Loss: 0.8652
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- Nll: 6.4546
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- F1 Micro: 0.185
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- F1 Macro: 0.0488
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- Ece: 0.2424
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- Aurc: 0.7342
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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 | 2.8093 | 0.06 | 0.9041 | 9.2868 | 0.06 | 0.0114 | 0.1752 | 0.9033 |
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| No log | 1.96 | 6 | 2.7245 | 0.18 | 0.8884 | 6.2166 | 0.18 | 0.0305 | 0.2292 | 0.8036 |
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| No log | 2.96 | 9 | 2.6443 | 0.18 | 0.8760 | 6.9627 | 0.18 | 0.0305 | 0.2437 | 0.8179 |
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| No log | 3.96 | 12 | 2.6356 | 0.185 | 0.8785 | 6.9306 | 0.185 | 0.0488 | 0.2534 | 0.7877 |
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| No log | 4.96 | 15 | 2.6338 | 0.185 | 0.8768 | 6.8870 | 0.185 | 0.0488 | 0.2605 | 0.7787 |
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| No log | 5.96 | 18 | 2.6325 | 0.185 | 0.8740 | 6.2086 | 0.185 | 0.0490 | 0.2453 | 0.7699 |
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| No log | 6.96 | 21 | 2.6322 | 0.185 | 0.8721 | 5.9554 | 0.185 | 0.0488 | 0.2474 | 0.7629 |
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| No log | 7.96 | 24 | 2.6293 | 0.185 | 0.8712 | 5.9359 | 0.185 | 0.0488 | 0.2550 | 0.7576 |
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| No log | 8.96 | 27 | 2.6221 | 0.185 | 0.8701 | 5.9468 | 0.185 | 0.0488 | 0.2436 | 0.7536 |
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| No log | 9.96 | 30 | 2.6171 | 0.185 | 0.8697 | 6.6875 | 0.185 | 0.0488 | 0.2497 | 0.7541 |
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| No log | 10.96 | 33 | 2.6126 | 0.185 | 0.8697 | 6.7549 | 0.185 | 0.0488 | 0.2512 | 0.7517 |
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| No log | 11.96 | 36 | 2.6084 | 0.185 | 0.8697 | 6.7827 | 0.185 | 0.0488 | 0.2476 | 0.7489 |
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| No log | 12.96 | 39 | 2.6037 | 0.185 | 0.8692 | 6.7652 | 0.185 | 0.0488 | 0.2557 | 0.7476 |
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| No log | 13.96 | 42 | 2.5986 | 0.185 | 0.8683 | 6.6847 | 0.185 | 0.0488 | 0.2513 | 0.7446 |
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| No log | 14.96 | 45 | 2.5940 | 0.185 | 0.8676 | 6.6600 | 0.185 | 0.0488 | 0.2572 | 0.7447 |
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| No log | 15.96 | 48 | 2.5910 | 0.185 | 0.8669 | 6.6410 | 0.185 | 0.0488 | 0.2448 | 0.7424 |
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| No log | 16.96 | 51 | 2.5897 | 0.185 | 0.8667 | 6.6371 | 0.185 | 0.0488 | 0.2402 | 0.7402 |
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| No log | 17.96 | 54 | 2.5898 | 0.185 | 0.8664 | 6.5096 | 0.185 | 0.0488 | 0.2549 | 0.7371 |
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| No log | 18.96 | 57 | 2.5897 | 0.185 | 0.8664 | 6.5160 | 0.185 | 0.0488 | 0.2504 | 0.7363 |
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| No log | 19.96 | 60 | 2.5877 | 0.185 | 0.8660 | 6.4661 | 0.185 | 0.0488 | 0.2416 | 0.7346 |
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| No log | 20.96 | 63 | 2.5865 | 0.185 | 0.8658 | 6.4833 | 0.185 | 0.0488 | 0.2459 | 0.7347 |
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| No log | 21.96 | 66 | 2.5852 | 0.185 | 0.8655 | 6.4690 | 0.185 | 0.0488 | 0.2460 | 0.7343 |
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| No log | 22.96 | 69 | 2.5843 | 0.185 | 0.8654 | 6.4625 | 0.185 | 0.0488 | 0.2461 | 0.7340 |
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| No log | 23.96 | 72 | 2.5838 | 0.185 | 0.8653 | 6.4568 | 0.185 | 0.0488 | 0.2424 | 0.7342 |
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| No log | 24.96 | 75 | 2.5836 | 0.185 | 0.8652 | 6.4546 | 0.185 | 0.0488 | 0.2424 | 0.7342 |
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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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