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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_t5.0_a0.9
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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_a0.9
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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: 2.5147
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- Accuracy: 0.18
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- Brier Loss: 0.8746
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- Nll: 6.7241
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- F1 Micro: 0.18
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- F1 Macro: 0.0306
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- Ece: 0.2451
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- Aurc: 0.8494
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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 | 2.6571 | 0.145 | 0.8999 | 10.1542 | 0.145 | 0.0253 | 0.2220 | 0.8466 |
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| No log | 1.96 | 6 | 2.6281 | 0.145 | 0.8947 | 10.5635 | 0.145 | 0.0253 | 0.2236 | 0.8461 |
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| No log | 2.96 | 9 | 2.5865 | 0.14 | 0.8870 | 8.5822 | 0.14 | 0.0433 | 0.2063 | 0.8040 |
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| No log | 3.96 | 12 | 2.5552 | 0.19 | 0.8811 | 6.5445 | 0.19 | 0.0552 | 0.2421 | 0.8576 |
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| No log | 4.96 | 15 | 2.5387 | 0.155 | 0.8782 | 7.1184 | 0.155 | 0.0277 | 0.2280 | 0.8892 |
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| No log | 5.96 | 18 | 2.5317 | 0.18 | 0.8774 | 8.7285 | 0.18 | 0.0319 | 0.2392 | 0.8538 |
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| No log | 6.96 | 21 | 2.5274 | 0.18 | 0.8770 | 8.2533 | 0.18 | 0.0306 | 0.2476 | 0.8514 |
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| No log | 7.96 | 24 | 2.5238 | 0.18 | 0.8767 | 6.9903 | 0.18 | 0.0306 | 0.2465 | 0.8523 |
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| No log | 8.96 | 27 | 2.5205 | 0.18 | 0.8762 | 6.9049 | 0.18 | 0.0306 | 0.2473 | 0.8528 |
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| No log | 9.96 | 30 | 2.5189 | 0.18 | 0.8758 | 6.8830 | 0.18 | 0.0306 | 0.2515 | 0.8526 |
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| No log | 10.96 | 33 | 2.5180 | 0.18 | 0.8756 | 6.8133 | 0.18 | 0.0306 | 0.2469 | 0.8522 |
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| No log | 11.96 | 36 | 2.5175 | 0.18 | 0.8754 | 6.7422 | 0.18 | 0.0306 | 0.2500 | 0.8519 |
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| No log | 12.96 | 39 | 2.5173 | 0.18 | 0.8753 | 6.5762 | 0.18 | 0.0306 | 0.2533 | 0.8515 |
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| No log | 13.96 | 42 | 2.5168 | 0.18 | 0.8751 | 6.5666 | 0.18 | 0.0306 | 0.2528 | 0.8516 |
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| No log | 14.96 | 45 | 2.5164 | 0.18 | 0.8750 | 6.7246 | 0.18 | 0.0306 | 0.2532 | 0.8512 |
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| No log | 15.96 | 48 | 2.5160 | 0.18 | 0.8750 | 6.7221 | 0.18 | 0.0306 | 0.2456 | 0.8507 |
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| No log | 16.96 | 51 | 2.5157 | 0.18 | 0.8749 | 6.7242 | 0.18 | 0.0306 | 0.2457 | 0.8507 |
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| No log | 17.96 | 54 | 2.5158 | 0.18 | 0.8749 | 6.7241 | 0.18 | 0.0306 | 0.2417 | 0.8503 |
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| No log | 18.96 | 57 | 2.5157 | 0.18 | 0.8749 | 6.7259 | 0.18 | 0.0306 | 0.2455 | 0.8503 |
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| No log | 19.96 | 60 | 2.5153 | 0.18 | 0.8748 | 6.7248 | 0.18 | 0.0306 | 0.2452 | 0.8495 |
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| No log | 20.96 | 63 | 2.5151 | 0.18 | 0.8748 | 6.7250 | 0.18 | 0.0306 | 0.2414 | 0.8494 |
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| No log | 21.96 | 66 | 2.5149 | 0.18 | 0.8747 | 6.7250 | 0.18 | 0.0306 | 0.2452 | 0.8495 |
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| No log | 22.96 | 69 | 2.5147 | 0.18 | 0.8747 | 6.7247 | 0.18 | 0.0306 | 0.2451 | 0.8495 |
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| No log | 23.96 | 72 | 2.5147 | 0.18 | 0.8747 | 6.7246 | 0.18 | 0.0306 | 0.2451 | 0.8495 |
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| No log | 24.96 | 75 | 2.5147 | 0.18 | 0.8746 | 6.7241 | 0.18 | 0.0306 | 0.2451 | 0.8494 |
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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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