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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-tiny_tobacco3482_kd_CEKD_t2.5_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-tiny_tobacco3482_kd_CEKD_t2.5_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: 3.2510
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- Accuracy: 0.18
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- Brier Loss: 0.8767
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- Nll: 6.8039
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- F1 Micro: 0.18
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- F1 Macro: 0.0306
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- Ece: 0.2513
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- Aurc: 0.8508
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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 | 3.4586 | 0.145 | 0.8999 | 10.1587 | 0.145 | 0.0253 | 0.2221 | 0.8467 |
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| No log | 1.96 | 6 | 3.4232 | 0.145 | 0.8946 | 10.5824 | 0.145 | 0.0253 | 0.2242 | 0.8475 |
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| No log | 2.96 | 9 | 3.3704 | 0.16 | 0.8867 | 8.6135 | 0.16 | 0.0503 | 0.2171 | 0.8440 |
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| No log | 3.96 | 12 | 3.3273 | 0.155 | 0.8807 | 6.5471 | 0.155 | 0.0274 | 0.2248 | 0.8831 |
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| No log | 4.96 | 15 | 3.3006 | 0.155 | 0.8779 | 6.8045 | 0.155 | 0.0271 | 0.2331 | 0.8918 |
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| No log | 5.96 | 18 | 3.2856 | 0.16 | 0.8773 | 8.2046 | 0.16 | 0.0329 | 0.2361 | 0.8956 |
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| No log | 6.96 | 21 | 3.2758 | 0.18 | 0.8774 | 8.0738 | 0.18 | 0.0308 | 0.2561 | 0.8544 |
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| No log | 7.96 | 24 | 3.2688 | 0.18 | 0.8778 | 7.1046 | 0.18 | 0.0308 | 0.2647 | 0.8524 |
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| No log | 8.96 | 27 | 3.2630 | 0.18 | 0.8778 | 6.9910 | 0.18 | 0.0306 | 0.2591 | 0.8530 |
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| No log | 9.96 | 30 | 3.2597 | 0.18 | 0.8778 | 6.9680 | 0.18 | 0.0306 | 0.2736 | 0.8538 |
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| No log | 10.96 | 33 | 3.2573 | 0.18 | 0.8776 | 6.9547 | 0.18 | 0.0306 | 0.2698 | 0.8536 |
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| No log | 11.96 | 36 | 3.2557 | 0.18 | 0.8775 | 6.9491 | 0.18 | 0.0306 | 0.2653 | 0.8533 |
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| No log | 12.96 | 39 | 3.2546 | 0.18 | 0.8773 | 6.8987 | 0.18 | 0.0306 | 0.2606 | 0.8526 |
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| No log | 13.96 | 42 | 3.2536 | 0.18 | 0.8771 | 6.8204 | 0.18 | 0.0306 | 0.2601 | 0.8523 |
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| No log | 14.96 | 45 | 3.2528 | 0.18 | 0.8771 | 6.8141 | 0.18 | 0.0306 | 0.2521 | 0.8519 |
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| No log | 15.96 | 48 | 3.2522 | 0.18 | 0.8769 | 6.8074 | 0.18 | 0.0306 | 0.2606 | 0.8517 |
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| No log | 16.96 | 51 | 3.2519 | 0.18 | 0.8769 | 6.8077 | 0.18 | 0.0306 | 0.2607 | 0.8515 |
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| No log | 17.96 | 54 | 3.2520 | 0.18 | 0.8769 | 6.8050 | 0.18 | 0.0306 | 0.2561 | 0.8510 |
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| No log | 18.96 | 57 | 3.2520 | 0.18 | 0.8769 | 6.8057 | 0.18 | 0.0306 | 0.2519 | 0.8509 |
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| No log | 19.96 | 60 | 3.2515 | 0.18 | 0.8768 | 6.8046 | 0.18 | 0.0306 | 0.2556 | 0.8507 |
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| No log | 20.96 | 63 | 3.2514 | 0.18 | 0.8768 | 6.8048 | 0.18 | 0.0306 | 0.2515 | 0.8506 |
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| No log | 21.96 | 66 | 3.2512 | 0.18 | 0.8767 | 6.8048 | 0.18 | 0.0306 | 0.2556 | 0.8508 |
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| No log | 22.96 | 69 | 3.2510 | 0.18 | 0.8767 | 6.8045 | 0.18 | 0.0306 | 0.2513 | 0.8509 |
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| No log | 23.96 | 72 | 3.2510 | 0.18 | 0.8767 | 6.8043 | 0.18 | 0.0306 | 0.2513 | 0.8508 |
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| No log | 24.96 | 75 | 3.2510 | 0.18 | 0.8767 | 6.8039 | 0.18 | 0.0306 | 0.2513 | 0.8508 |
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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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