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---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dit-tiny_tobacco3482_simkd_CEKD_t1_aNone
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dit-tiny_tobacco3482_simkd_CEKD_t1_aNone
This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9983
- Accuracy: 0.18
- Brier Loss: 0.8965
- Nll: 6.7849
- F1 Micro: 0.18
- F1 Macro: 0.0305
- Ece: 0.2195
- Aurc: 0.8182
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 0.96 | 12 | 1.0062 | 0.18 | 0.8980 | 6.1518 | 0.18 | 0.0309 | 0.2213 | 0.7838 |
| No log | 1.96 | 24 | 1.0034 | 0.18 | 0.8987 | 5.7795 | 0.18 | 0.0305 | 0.2273 | 0.8165 |
| No log | 2.96 | 36 | 1.0025 | 0.18 | 0.8984 | 6.4819 | 0.18 | 0.0305 | 0.2249 | 0.8306 |
| No log | 3.96 | 48 | 1.0018 | 0.18 | 0.8982 | 6.8521 | 0.18 | 0.0306 | 0.2205 | 0.8505 |
| No log | 4.96 | 60 | 1.0015 | 0.16 | 0.8980 | 6.6853 | 0.16 | 0.0324 | 0.2089 | 0.8798 |
| No log | 5.96 | 72 | 1.0011 | 0.175 | 0.8979 | 6.8349 | 0.175 | 0.0314 | 0.2134 | 0.8345 |
| No log | 6.96 | 84 | 1.0008 | 0.18 | 0.8976 | 6.8293 | 0.18 | 0.0313 | 0.2249 | 0.8208 |
| No log | 7.96 | 96 | 1.0005 | 0.18 | 0.8975 | 6.9400 | 0.18 | 0.0305 | 0.2230 | 0.8140 |
| No log | 8.96 | 108 | 1.0003 | 0.18 | 0.8974 | 6.5877 | 0.18 | 0.0306 | 0.2230 | 0.8246 |
| No log | 9.96 | 120 | 1.0000 | 0.18 | 0.8973 | 6.5454 | 0.18 | 0.0306 | 0.2188 | 0.8188 |
| No log | 10.96 | 132 | 0.9998 | 0.18 | 0.8972 | 6.5555 | 0.18 | 0.0306 | 0.2274 | 0.8151 |
| No log | 11.96 | 144 | 0.9996 | 0.18 | 0.8971 | 6.5819 | 0.18 | 0.0306 | 0.2254 | 0.8131 |
| No log | 12.96 | 156 | 0.9994 | 0.18 | 0.8970 | 6.7150 | 0.18 | 0.0305 | 0.2255 | 0.8162 |
| No log | 13.96 | 168 | 0.9993 | 0.18 | 0.8969 | 6.6542 | 0.18 | 0.0305 | 0.2213 | 0.8220 |
| No log | 14.96 | 180 | 0.9991 | 0.18 | 0.8968 | 6.6025 | 0.18 | 0.0305 | 0.2213 | 0.8125 |
| No log | 15.96 | 192 | 0.9990 | 0.18 | 0.8968 | 7.0424 | 0.18 | 0.0305 | 0.2301 | 0.8201 |
| No log | 16.96 | 204 | 0.9988 | 0.18 | 0.8967 | 6.6676 | 0.18 | 0.0305 | 0.2258 | 0.8153 |
| No log | 17.96 | 216 | 0.9987 | 0.18 | 0.8967 | 6.6621 | 0.18 | 0.0305 | 0.2270 | 0.8145 |
| No log | 18.96 | 228 | 0.9986 | 0.18 | 0.8967 | 7.0058 | 0.18 | 0.0305 | 0.2259 | 0.8214 |
| No log | 19.96 | 240 | 0.9985 | 0.18 | 0.8966 | 6.8777 | 0.18 | 0.0305 | 0.2194 | 0.8183 |
| No log | 20.96 | 252 | 0.9984 | 0.18 | 0.8966 | 6.7612 | 0.18 | 0.0305 | 0.2282 | 0.8131 |
| No log | 21.96 | 264 | 0.9984 | 0.18 | 0.8966 | 6.7811 | 0.18 | 0.0305 | 0.2282 | 0.8145 |
| No log | 22.96 | 276 | 0.9983 | 0.18 | 0.8965 | 6.7044 | 0.18 | 0.0305 | 0.2239 | 0.8167 |
| No log | 23.96 | 288 | 0.9983 | 0.18 | 0.8965 | 6.7813 | 0.18 | 0.0305 | 0.2217 | 0.8183 |
| No log | 24.96 | 300 | 0.9983 | 0.18 | 0.8965 | 6.7849 | 0.18 | 0.0305 | 0.2195 | 0.8182 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
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