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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-small_tobacco3482_simkd_CEKD_t1_aNone
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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_simkd_CEKD_t1_aNone
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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: 0.9876
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- Accuracy: 0.085
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- Brier Loss: 0.8927
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- Nll: 8.3272
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- F1 Micro: 0.085
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- F1 Macro: 0.0461
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- Ece: 0.1645
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- Aurc: 0.7988
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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: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 64
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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 | 12 | 1.0049 | 0.08 | 0.8993 | 5.4663 | 0.08 | 0.0322 | 0.1476 | 0.8883 |
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| No log | 1.96 | 24 | 1.0007 | 0.165 | 0.8988 | 5.5926 | 0.165 | 0.0284 | 0.2066 | 0.8251 |
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| No log | 2.96 | 36 | 0.9994 | 0.16 | 0.8982 | 5.9135 | 0.16 | 0.0277 | 0.2100 | 0.8518 |
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| No log | 3.96 | 48 | 0.9984 | 0.17 | 0.8975 | 6.1195 | 0.17 | 0.0574 | 0.2142 | 0.8153 |
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| No log | 4.96 | 60 | 0.9976 | 0.19 | 0.8970 | 6.2724 | 0.19 | 0.0752 | 0.2294 | 0.8254 |
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| No log | 5.96 | 72 | 0.9967 | 0.09 | 0.8968 | 6.3787 | 0.09 | 0.0315 | 0.1591 | 0.7950 |
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| No log | 6.96 | 84 | 0.9958 | 0.065 | 0.8964 | 6.4218 | 0.065 | 0.0122 | 0.1433 | 0.8333 |
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| No log | 7.96 | 96 | 0.9949 | 0.065 | 0.8960 | 6.5170 | 0.065 | 0.0122 | 0.1543 | 0.8344 |
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| No log | 8.96 | 108 | 0.9941 | 0.065 | 0.8956 | 6.5572 | 0.065 | 0.0123 | 0.1545 | 0.8331 |
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| No log | 9.96 | 120 | 0.9934 | 0.07 | 0.8954 | 6.6362 | 0.07 | 0.0304 | 0.1597 | 0.8313 |
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| No log | 10.96 | 132 | 0.9926 | 0.07 | 0.8951 | 6.6430 | 0.07 | 0.0304 | 0.1576 | 0.8325 |
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| No log | 11.96 | 144 | 0.9920 | 0.07 | 0.8948 | 6.6842 | 0.07 | 0.0304 | 0.1590 | 0.8225 |
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| No log | 12.96 | 156 | 0.9914 | 0.07 | 0.8947 | 6.7731 | 0.07 | 0.0304 | 0.1619 | 0.8155 |
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| No log | 13.96 | 168 | 0.9909 | 0.07 | 0.8944 | 6.8584 | 0.07 | 0.0304 | 0.1522 | 0.8128 |
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| No log | 14.96 | 180 | 0.9904 | 0.07 | 0.8941 | 6.8161 | 0.07 | 0.0304 | 0.1524 | 0.8142 |
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| No log | 15.96 | 192 | 0.9899 | 0.07 | 0.8940 | 7.3169 | 0.07 | 0.0304 | 0.1532 | 0.8109 |
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| No log | 16.96 | 204 | 0.9894 | 0.07 | 0.8937 | 7.8481 | 0.07 | 0.0304 | 0.1531 | 0.8132 |
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| No log | 17.96 | 216 | 0.9890 | 0.08 | 0.8935 | 8.3375 | 0.08 | 0.0439 | 0.1587 | 0.8002 |
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| No log | 18.96 | 228 | 0.9886 | 0.07 | 0.8933 | 8.4250 | 0.07 | 0.0307 | 0.1536 | 0.8132 |
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| No log | 19.96 | 240 | 0.9883 | 0.085 | 0.8931 | 8.4316 | 0.085 | 0.0445 | 0.1618 | 0.8014 |
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| No log | 20.96 | 252 | 0.9880 | 0.075 | 0.8930 | 8.4395 | 0.075 | 0.0392 | 0.1566 | 0.8088 |
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| No log | 21.96 | 264 | 0.9878 | 0.085 | 0.8929 | 8.3319 | 0.085 | 0.0476 | 0.1621 | 0.7956 |
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| No log | 22.96 | 276 | 0.9877 | 0.08 | 0.8928 | 8.3274 | 0.08 | 0.0439 | 0.1594 | 0.8024 |
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| No log | 23.96 | 288 | 0.9876 | 0.08 | 0.8927 | 8.3285 | 0.08 | 0.0440 | 0.1595 | 0.8014 |
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| No log | 24.96 | 300 | 0.9876 | 0.085 | 0.8927 | 8.3272 | 0.085 | 0.0461 | 0.1645 | 0.7988 |
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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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