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---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dit-tiny_tobacco3482_kd_CEKD_t5.0_a0.5
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_kd_CEKD_t5.0_a0.5
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: 3.8497
- Accuracy: 0.18
- Brier Loss: 0.8788
- Nll: 6.0432
- F1 Micro: 0.18
- F1 Macro: 0.0305
- Ece: 0.2578
- Aurc: 0.8511
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- 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 | 3 | 4.0678 | 0.145 | 0.8999 | 10.1608 | 0.145 | 0.0253 | 0.2221 | 0.8466 |
| No log | 1.96 | 6 | 4.0316 | 0.145 | 0.8948 | 10.5160 | 0.145 | 0.0253 | 0.2239 | 0.8468 |
| No log | 2.96 | 9 | 3.9774 | 0.16 | 0.8871 | 8.6333 | 0.16 | 0.0524 | 0.2217 | 0.8424 |
| No log | 3.96 | 12 | 3.9325 | 0.155 | 0.8813 | 6.5340 | 0.155 | 0.0272 | 0.2161 | 0.8837 |
| No log | 4.96 | 15 | 3.9041 | 0.155 | 0.8787 | 7.1704 | 0.155 | 0.0271 | 0.2296 | 0.8923 |
| No log | 5.96 | 18 | 3.8876 | 0.155 | 0.8782 | 8.7334 | 0.155 | 0.0277 | 0.2325 | 0.8942 |
| No log | 6.96 | 21 | 3.8766 | 0.18 | 0.8785 | 8.8120 | 0.18 | 0.0314 | 0.2476 | 0.8555 |
| No log | 7.96 | 24 | 3.8690 | 0.18 | 0.8791 | 8.8676 | 0.18 | 0.0308 | 0.2643 | 0.8534 |
| No log | 8.96 | 27 | 3.8633 | 0.18 | 0.8793 | 8.5299 | 0.18 | 0.0306 | 0.2594 | 0.8541 |
| No log | 9.96 | 30 | 3.8601 | 0.18 | 0.8796 | 7.4142 | 0.18 | 0.0305 | 0.2622 | 0.8548 |
| No log | 10.96 | 33 | 3.8577 | 0.18 | 0.8797 | 6.6642 | 0.18 | 0.0305 | 0.2720 | 0.8546 |
| No log | 11.96 | 36 | 3.8560 | 0.18 | 0.8797 | 6.2862 | 0.18 | 0.0305 | 0.2723 | 0.8543 |
| No log | 12.96 | 39 | 3.8547 | 0.18 | 0.8796 | 6.2084 | 0.18 | 0.0305 | 0.2678 | 0.8541 |
| No log | 13.96 | 42 | 3.8535 | 0.18 | 0.8794 | 6.1826 | 0.18 | 0.0305 | 0.2631 | 0.8534 |
| No log | 14.96 | 45 | 3.8525 | 0.18 | 0.8793 | 6.1744 | 0.18 | 0.0305 | 0.2593 | 0.8529 |
| No log | 15.96 | 48 | 3.8516 | 0.18 | 0.8792 | 6.1606 | 0.18 | 0.0305 | 0.2680 | 0.8527 |
| No log | 16.96 | 51 | 3.8511 | 0.18 | 0.8791 | 6.1634 | 0.18 | 0.0305 | 0.2724 | 0.8528 |
| No log | 17.96 | 54 | 3.8510 | 0.18 | 0.8791 | 6.0971 | 0.18 | 0.0305 | 0.2676 | 0.8525 |
| No log | 18.96 | 57 | 3.8508 | 0.18 | 0.8790 | 6.0686 | 0.18 | 0.0305 | 0.2630 | 0.8522 |
| No log | 19.96 | 60 | 3.8503 | 0.18 | 0.8789 | 6.0495 | 0.18 | 0.0305 | 0.2581 | 0.8518 |
| No log | 20.96 | 63 | 3.8501 | 0.18 | 0.8789 | 6.0918 | 0.18 | 0.0305 | 0.2581 | 0.8516 |
| No log | 21.96 | 66 | 3.8499 | 0.18 | 0.8788 | 6.0464 | 0.18 | 0.0305 | 0.2536 | 0.8516 |
| No log | 22.96 | 69 | 3.8497 | 0.18 | 0.8788 | 6.0419 | 0.18 | 0.0305 | 0.2535 | 0.8513 |
| No log | 23.96 | 72 | 3.8497 | 0.18 | 0.8788 | 6.0432 | 0.18 | 0.0305 | 0.2578 | 0.8511 |
| No log | 24.96 | 75 | 3.8497 | 0.18 | 0.8788 | 6.0432 | 0.18 | 0.0305 | 0.2578 | 0.8511 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
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