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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_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-small_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: 6.7275 |
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- Accuracy: 0.21 |
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- Brier Loss: 0.8834 |
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- Nll: 6.7677 |
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- F1 Micro: 0.2100 |
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- F1 Macro: 0.1146 |
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- Ece: 0.2647 |
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- Aurc: 0.7666 |
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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 | 7.1014 | 0.06 | 0.9055 | 7.9056 | 0.06 | 0.0114 | 0.1732 | 0.9050 | |
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| No log | 1.96 | 6 | 6.9659 | 0.125 | 0.8970 | 10.1253 | 0.125 | 0.0631 | 0.2010 | 0.8465 | |
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| No log | 2.96 | 9 | 6.8528 | 0.075 | 0.8954 | 7.0315 | 0.075 | 0.0258 | 0.1912 | 0.8871 | |
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| No log | 3.96 | 12 | 6.8522 | 0.205 | 0.8955 | 7.0990 | 0.205 | 0.0776 | 0.2426 | 0.7588 | |
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| No log | 4.96 | 15 | 6.8465 | 0.19 | 0.8959 | 7.1340 | 0.19 | 0.0627 | 0.2308 | 0.7536 | |
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| No log | 5.96 | 18 | 6.8246 | 0.205 | 0.8937 | 7.1101 | 0.205 | 0.0867 | 0.2410 | 0.7354 | |
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| No log | 6.96 | 21 | 6.8054 | 0.085 | 0.8918 | 7.0215 | 0.085 | 0.0435 | 0.1847 | 0.8289 | |
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| No log | 7.96 | 24 | 6.8025 | 0.22 | 0.8879 | 6.8272 | 0.22 | 0.0967 | 0.2487 | 0.7438 | |
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| No log | 8.96 | 27 | 6.8045 | 0.21 | 0.8871 | 6.3740 | 0.2100 | 0.0992 | 0.2412 | 0.7634 | |
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| No log | 9.96 | 30 | 6.8013 | 0.22 | 0.8869 | 6.9538 | 0.22 | 0.1016 | 0.2495 | 0.7633 | |
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| No log | 10.96 | 33 | 6.7920 | 0.215 | 0.8865 | 6.9670 | 0.2150 | 0.0968 | 0.2549 | 0.7577 | |
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| No log | 11.96 | 36 | 6.7817 | 0.22 | 0.8867 | 6.9953 | 0.22 | 0.1004 | 0.2455 | 0.7437 | |
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| No log | 12.96 | 39 | 6.7729 | 0.17 | 0.8884 | 6.9738 | 0.17 | 0.0891 | 0.2277 | 0.7865 | |
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| No log | 13.96 | 42 | 6.7632 | 0.2 | 0.8873 | 6.9622 | 0.2000 | 0.0998 | 0.2393 | 0.7413 | |
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| No log | 14.96 | 45 | 6.7548 | 0.215 | 0.8860 | 6.9576 | 0.2150 | 0.1010 | 0.2635 | 0.7189 | |
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| No log | 15.96 | 48 | 6.7489 | 0.22 | 0.8857 | 6.8386 | 0.22 | 0.1024 | 0.2665 | 0.7098 | |
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| No log | 16.96 | 51 | 6.7457 | 0.23 | 0.8855 | 6.8730 | 0.23 | 0.1129 | 0.2506 | 0.7217 | |
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| No log | 17.96 | 54 | 6.7455 | 0.215 | 0.8864 | 6.8688 | 0.2150 | 0.1058 | 0.2576 | 0.7528 | |
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| No log | 18.96 | 57 | 6.7424 | 0.16 | 0.8861 | 6.8631 | 0.16 | 0.0843 | 0.2281 | 0.8036 | |
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| No log | 19.96 | 60 | 6.7380 | 0.155 | 0.8850 | 6.8443 | 0.155 | 0.0871 | 0.2315 | 0.7937 | |
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| No log | 20.96 | 63 | 6.7348 | 0.195 | 0.8841 | 6.7769 | 0.195 | 0.0949 | 0.2501 | 0.7799 | |
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| No log | 21.96 | 66 | 6.7317 | 0.175 | 0.8838 | 6.7692 | 0.175 | 0.1025 | 0.2421 | 0.7797 | |
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| No log | 22.96 | 69 | 6.7293 | 0.175 | 0.8836 | 6.7682 | 0.175 | 0.1012 | 0.2452 | 0.7799 | |
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| No log | 23.96 | 72 | 6.7281 | 0.205 | 0.8834 | 6.7672 | 0.205 | 0.1132 | 0.2566 | 0.7679 | |
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| No log | 24.96 | 75 | 6.7275 | 0.21 | 0.8834 | 6.7677 | 0.2100 | 0.1146 | 0.2647 | 0.7666 | |
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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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