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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-tiny_rvl_cdip_100_examples_per_class_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-tiny_rvl_cdip_100_examples_per_class_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: 0.1502 |
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- Accuracy: 0.0625 |
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- Brier Loss: 0.9374 |
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- Nll: 9.1398 |
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- F1 Micro: 0.0625 |
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- F1 Macro: 0.0074 |
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- Ece: 0.1015 |
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- Aurc: 0.9383 |
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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 | 0.1540 | 0.0625 | 0.9376 | 8.5438 | 0.0625 | 0.0074 | 0.1043 | 0.9530 | |
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| No log | 1.96 | 24 | 0.1519 | 0.0625 | 0.9376 | 8.2831 | 0.0625 | 0.0074 | 0.1008 | 0.9465 | |
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| No log | 2.96 | 36 | 0.1512 | 0.0625 | 0.9375 | 8.4629 | 0.0625 | 0.0074 | 0.1028 | 0.9336 | |
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| No log | 3.96 | 48 | 0.1510 | 0.0625 | 0.9375 | 8.6283 | 0.0625 | 0.0074 | 0.1027 | 0.9365 | |
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| No log | 4.96 | 60 | 0.1509 | 0.0625 | 0.9375 | 8.5065 | 0.0625 | 0.0074 | 0.1030 | 0.9433 | |
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| No log | 5.96 | 72 | 0.1508 | 0.0625 | 0.9375 | 8.4779 | 0.0625 | 0.0074 | 0.1017 | 0.9414 | |
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| No log | 6.96 | 84 | 0.1507 | 0.0625 | 0.9375 | 8.5053 | 0.0625 | 0.0074 | 0.1045 | 0.9438 | |
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| No log | 7.96 | 96 | 0.1507 | 0.0625 | 0.9375 | 8.7396 | 0.0625 | 0.0074 | 0.1032 | 0.9440 | |
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| No log | 8.96 | 108 | 0.1506 | 0.0625 | 0.9375 | 8.6420 | 0.0625 | 0.0074 | 0.1031 | 0.9448 | |
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| No log | 9.96 | 120 | 0.1506 | 0.0625 | 0.9375 | 8.8410 | 0.0625 | 0.0074 | 0.1045 | 0.9438 | |
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| No log | 10.96 | 132 | 0.1506 | 0.0625 | 0.9374 | 8.9438 | 0.0625 | 0.0074 | 0.1042 | 0.9413 | |
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| No log | 11.96 | 144 | 0.1505 | 0.0625 | 0.9374 | 8.9847 | 0.0625 | 0.0074 | 0.1032 | 0.9418 | |
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| No log | 12.96 | 156 | 0.1505 | 0.0625 | 0.9374 | 9.0594 | 0.0625 | 0.0074 | 0.1031 | 0.9397 | |
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| No log | 13.96 | 168 | 0.1504 | 0.0625 | 0.9374 | 9.0748 | 0.0625 | 0.0074 | 0.1045 | 0.9343 | |
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| No log | 14.96 | 180 | 0.1504 | 0.0625 | 0.9374 | 9.0912 | 0.0625 | 0.0074 | 0.1018 | 0.9358 | |
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| No log | 15.96 | 192 | 0.1504 | 0.0625 | 0.9374 | 9.0950 | 0.0625 | 0.0074 | 0.1032 | 0.9331 | |
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| No log | 16.96 | 204 | 0.1503 | 0.0625 | 0.9374 | 9.2141 | 0.0625 | 0.0074 | 0.1015 | 0.9363 | |
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| No log | 17.96 | 216 | 0.1503 | 0.0625 | 0.9374 | 9.0918 | 0.0625 | 0.0074 | 0.1046 | 0.9354 | |
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| No log | 18.96 | 228 | 0.1503 | 0.0625 | 0.9374 | 9.1430 | 0.0625 | 0.0074 | 0.1018 | 0.9385 | |
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| No log | 19.96 | 240 | 0.1503 | 0.0625 | 0.9374 | 9.2149 | 0.0625 | 0.0074 | 0.0991 | 0.9404 | |
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| No log | 20.96 | 252 | 0.1503 | 0.0625 | 0.9374 | 9.0900 | 0.0625 | 0.0074 | 0.1043 | 0.9386 | |
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| No log | 21.96 | 264 | 0.1503 | 0.0625 | 0.9374 | 9.1244 | 0.0625 | 0.0074 | 0.1060 | 0.9395 | |
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| No log | 22.96 | 276 | 0.1503 | 0.0625 | 0.9374 | 9.1353 | 0.0625 | 0.0074 | 0.1005 | 0.9378 | |
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| No log | 23.96 | 288 | 0.1502 | 0.0625 | 0.9374 | 9.2063 | 0.0625 | 0.0074 | 0.1032 | 0.9373 | |
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| No log | 24.96 | 300 | 0.1502 | 0.0625 | 0.9374 | 9.1398 | 0.0625 | 0.0074 | 0.1015 | 0.9383 | |
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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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