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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_a0.9 |
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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-small_tobacco3482_kd_CEKD_t1.5_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: 2.2890 |
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- Accuracy: 0.19 |
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- Brier Loss: 0.8648 |
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- Nll: 6.4150 |
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- F1 Micro: 0.19 |
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- F1 Macro: 0.0641 |
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- Ece: 0.2450 |
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- Aurc: 0.7332 |
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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 | 2.4806 | 0.06 | 0.9041 | 9.2838 | 0.06 | 0.0114 | 0.1750 | 0.9034 | |
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| No log | 1.96 | 6 | 2.4041 | 0.18 | 0.8884 | 6.3227 | 0.18 | 0.0305 | 0.2317 | 0.8027 | |
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| No log | 2.96 | 9 | 2.3381 | 0.18 | 0.8760 | 6.9952 | 0.18 | 0.0305 | 0.2424 | 0.8118 | |
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| No log | 3.96 | 12 | 2.3362 | 0.185 | 0.8771 | 6.9040 | 0.185 | 0.0488 | 0.2544 | 0.7841 | |
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| No log | 4.96 | 15 | 2.3345 | 0.185 | 0.8747 | 6.8515 | 0.185 | 0.0488 | 0.2476 | 0.7768 | |
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| No log | 5.96 | 18 | 2.3339 | 0.185 | 0.8725 | 6.0111 | 0.185 | 0.0490 | 0.2457 | 0.7670 | |
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| No log | 6.96 | 21 | 2.3348 | 0.185 | 0.8718 | 5.9199 | 0.185 | 0.0488 | 0.2328 | 0.7596 | |
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| No log | 7.96 | 24 | 2.3310 | 0.185 | 0.8711 | 5.9008 | 0.185 | 0.0488 | 0.2443 | 0.7536 | |
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| No log | 8.96 | 27 | 2.3231 | 0.185 | 0.8699 | 5.8793 | 0.185 | 0.0488 | 0.2337 | 0.7516 | |
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| No log | 9.96 | 30 | 2.3181 | 0.185 | 0.8694 | 6.6980 | 0.185 | 0.0488 | 0.2507 | 0.7500 | |
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| No log | 10.96 | 33 | 2.3139 | 0.185 | 0.8692 | 6.7350 | 0.185 | 0.0488 | 0.2481 | 0.7488 | |
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| No log | 11.96 | 36 | 2.3099 | 0.185 | 0.8690 | 6.7557 | 0.185 | 0.0488 | 0.2484 | 0.7463 | |
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| No log | 12.96 | 39 | 2.3057 | 0.185 | 0.8684 | 6.6765 | 0.185 | 0.0488 | 0.2598 | 0.7441 | |
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| No log | 13.96 | 42 | 2.3014 | 0.185 | 0.8676 | 6.6313 | 0.185 | 0.0488 | 0.2478 | 0.7420 | |
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| No log | 14.96 | 45 | 2.2978 | 0.185 | 0.8669 | 6.6142 | 0.185 | 0.0488 | 0.2496 | 0.7412 | |
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| No log | 15.96 | 48 | 2.2955 | 0.185 | 0.8664 | 6.5990 | 0.185 | 0.0488 | 0.2379 | 0.7399 | |
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| No log | 16.96 | 51 | 2.2947 | 0.185 | 0.8662 | 6.4895 | 0.185 | 0.0488 | 0.2452 | 0.7375 | |
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| No log | 17.96 | 54 | 2.2949 | 0.185 | 0.8661 | 6.4730 | 0.185 | 0.0488 | 0.2438 | 0.7354 | |
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| No log | 18.96 | 57 | 2.2949 | 0.185 | 0.8661 | 6.4244 | 0.185 | 0.0488 | 0.2435 | 0.7356 | |
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| No log | 19.96 | 60 | 2.2930 | 0.185 | 0.8657 | 6.3676 | 0.185 | 0.0490 | 0.2389 | 0.7341 | |
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| No log | 20.96 | 63 | 2.2918 | 0.19 | 0.8654 | 6.4233 | 0.19 | 0.0641 | 0.2446 | 0.7336 | |
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| No log | 21.96 | 66 | 2.2905 | 0.19 | 0.8651 | 6.4742 | 0.19 | 0.0641 | 0.2485 | 0.7334 | |
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| No log | 22.96 | 69 | 2.2897 | 0.19 | 0.8649 | 6.4243 | 0.19 | 0.0641 | 0.2448 | 0.7332 | |
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| No log | 23.96 | 72 | 2.2893 | 0.19 | 0.8648 | 6.4174 | 0.19 | 0.0641 | 0.2450 | 0.7332 | |
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| No log | 24.96 | 75 | 2.2890 | 0.19 | 0.8648 | 6.4150 | 0.19 | 0.0641 | 0.2450 | 0.7332 | |
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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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