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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_MSE
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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_MSE
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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: 6.8328
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- Accuracy: 0.19
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- Brier Loss: 0.8942
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- Nll: 7.0296
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- F1 Micro: 0.19
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- F1 Macro: 0.0703
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- Ece: 0.2429
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- Aurc: 0.8146
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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 | 7.1188 | 0.145 | 0.9003 | 10.1627 | 0.145 | 0.0253 | 0.2218 | 0.8463 |
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| No log | 1.96 | 6 | 7.0608 | 0.145 | 0.8969 | 9.8809 | 0.145 | 0.0253 | 0.2197 | 0.8454 |
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| No log | 2.96 | 9 | 6.9777 | 0.145 | 0.8929 | 8.9712 | 0.145 | 0.0442 | 0.2065 | 0.7921 |
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| No log | 3.96 | 12 | 6.9144 | 0.17 | 0.8908 | 4.9924 | 0.17 | 0.0413 | 0.2325 | 0.7807 |
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| No log | 4.96 | 15 | 6.8797 | 0.145 | 0.8912 | 6.8983 | 0.145 | 0.0399 | 0.2089 | 0.7932 |
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| No log | 5.96 | 18 | 6.8636 | 0.085 | 0.8926 | 6.9917 | 0.085 | 0.0299 | 0.1822 | 0.8755 |
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| No log | 6.96 | 21 | 6.8545 | 0.075 | 0.8946 | 7.0604 | 0.075 | 0.0307 | 0.1849 | 0.8758 |
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| No log | 7.96 | 24 | 6.8486 | 0.06 | 0.8958 | 7.1035 | 0.06 | 0.0230 | 0.1801 | 0.8891 |
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| No log | 8.96 | 27 | 6.8455 | 0.165 | 0.8967 | 7.1315 | 0.165 | 0.0604 | 0.2414 | 0.8438 |
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| No log | 9.96 | 30 | 6.8450 | 0.185 | 0.8973 | 7.1546 | 0.185 | 0.0468 | 0.2477 | 0.8436 |
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| No log | 10.96 | 33 | 6.8438 | 0.18 | 0.8969 | 7.1569 | 0.18 | 0.0308 | 0.2406 | 0.8504 |
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| No log | 11.96 | 36 | 6.8414 | 0.18 | 0.8962 | 7.1492 | 0.18 | 0.0306 | 0.2510 | 0.8501 |
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| No log | 12.96 | 39 | 6.8390 | 0.18 | 0.8958 | 7.1455 | 0.18 | 0.0306 | 0.2374 | 0.8494 |
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| No log | 13.96 | 42 | 6.8365 | 0.18 | 0.8950 | 7.0793 | 0.18 | 0.0306 | 0.2436 | 0.8488 |
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| No log | 14.96 | 45 | 6.8349 | 0.18 | 0.8944 | 7.0591 | 0.18 | 0.0306 | 0.2369 | 0.8486 |
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| No log | 15.96 | 48 | 6.8338 | 0.18 | 0.8942 | 7.0493 | 0.18 | 0.0306 | 0.2396 | 0.8482 |
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| No log | 16.96 | 51 | 6.8335 | 0.18 | 0.8940 | 7.0429 | 0.18 | 0.0309 | 0.2390 | 0.8486 |
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| No log | 17.96 | 54 | 6.8341 | 0.18 | 0.8943 | 7.0410 | 0.18 | 0.0314 | 0.2351 | 0.8514 |
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| No log | 18.96 | 57 | 6.8338 | 0.19 | 0.8943 | 7.0391 | 0.19 | 0.0495 | 0.2480 | 0.8471 |
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| No log | 19.96 | 60 | 6.8335 | 0.205 | 0.8943 | 7.0342 | 0.205 | 0.0722 | 0.2562 | 0.8204 |
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| No log | 20.96 | 63 | 6.8334 | 0.2 | 0.8942 | 7.0308 | 0.2000 | 0.0683 | 0.2541 | 0.8199 |
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| No log | 21.96 | 66 | 6.8332 | 0.195 | 0.8942 | 7.0296 | 0.195 | 0.0714 | 0.2511 | 0.8099 |
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| No log | 22.96 | 69 | 6.8330 | 0.195 | 0.8942 | 7.0297 | 0.195 | 0.0717 | 0.2572 | 0.8123 |
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| No log | 23.96 | 72 | 6.8329 | 0.19 | 0.8942 | 7.0294 | 0.19 | 0.0703 | 0.2459 | 0.8148 |
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| No log | 24.96 | 75 | 6.8328 | 0.19 | 0.8942 | 7.0296 | 0.19 | 0.0703 | 0.2429 | 0.8146 |
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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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