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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-tiny_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix
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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_kd_MSE_lr_fix
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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: 1.4358
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- Accuracy: 0.195
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- Brier Loss: 0.9035
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- Nll: 12.0550
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- F1 Micro: 0.195
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- F1 Macro: 0.1471
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- Ece: 0.1675
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- Aurc: 0.6988
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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: 0.0001
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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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 | 1.0 | 25 | 1.5167 | 0.07 | 0.9368 | 20.8948 | 0.07 | 0.0305 | 0.1106 | 0.8850 |
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| No log | 2.0 | 50 | 1.5246 | 0.08 | 0.9362 | 21.4368 | 0.08 | 0.0346 | 0.1200 | 0.8659 |
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| No log | 3.0 | 75 | 1.5053 | 0.1 | 0.9340 | 23.7241 | 0.1000 | 0.0522 | 0.1280 | 0.8087 |
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| No log | 4.0 | 100 | 1.5097 | 0.0975 | 0.9322 | 17.3004 | 0.0975 | 0.0487 | 0.1220 | 0.8220 |
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| No log | 5.0 | 125 | 1.4926 | 0.12 | 0.9296 | 16.3893 | 0.12 | 0.0600 | 0.1284 | 0.7752 |
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| No log | 6.0 | 150 | 1.4838 | 0.105 | 0.9273 | 19.3692 | 0.1050 | 0.0356 | 0.1254 | 0.7955 |
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| No log | 7.0 | 175 | 1.4729 | 0.0975 | 0.9229 | 18.6899 | 0.0975 | 0.0411 | 0.1134 | 0.7963 |
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| No log | 8.0 | 200 | 1.4754 | 0.125 | 0.9196 | 17.7842 | 0.125 | 0.0676 | 0.1238 | 0.7778 |
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| No log | 9.0 | 225 | 1.4725 | 0.1125 | 0.9193 | 16.6572 | 0.1125 | 0.0505 | 0.1254 | 0.7839 |
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| No log | 10.0 | 250 | 1.4702 | 0.1175 | 0.9168 | 16.3975 | 0.1175 | 0.0556 | 0.1183 | 0.7638 |
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| No log | 11.0 | 275 | 1.4648 | 0.1175 | 0.9169 | 18.4274 | 0.1175 | 0.0558 | 0.1219 | 0.7806 |
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| No log | 12.0 | 300 | 1.4660 | 0.155 | 0.9166 | 15.6492 | 0.155 | 0.0791 | 0.1411 | 0.7512 |
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| No log | 13.0 | 325 | 1.4684 | 0.16 | 0.9164 | 17.1698 | 0.16 | 0.1140 | 0.1519 | 0.7285 |
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| No log | 14.0 | 350 | 1.4662 | 0.1175 | 0.9158 | 17.6999 | 0.1175 | 0.0501 | 0.1269 | 0.7637 |
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| No log | 15.0 | 375 | 1.4602 | 0.1675 | 0.9143 | 13.2540 | 0.1675 | 0.1153 | 0.1515 | 0.7223 |
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| No log | 16.0 | 400 | 1.4556 | 0.1325 | 0.9138 | 13.3868 | 0.1325 | 0.0881 | 0.1323 | 0.7558 |
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| No log | 17.0 | 425 | 1.4527 | 0.175 | 0.9128 | 11.1983 | 0.175 | 0.1334 | 0.1596 | 0.7153 |
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| No log | 18.0 | 450 | 1.4535 | 0.1625 | 0.9111 | 17.6046 | 0.1625 | 0.1021 | 0.1435 | 0.7379 |
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| No log | 19.0 | 475 | 1.4453 | 0.1825 | 0.9086 | 11.8948 | 0.1825 | 0.1228 | 0.1594 | 0.7098 |
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| 1.4614 | 20.0 | 500 | 1.4431 | 0.1525 | 0.9078 | 14.2631 | 0.1525 | 0.1115 | 0.1410 | 0.7293 |
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| 1.4614 | 21.0 | 525 | 1.4392 | 0.1825 | 0.9063 | 10.7664 | 0.1825 | 0.1378 | 0.1567 | 0.7058 |
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| 1.4614 | 22.0 | 550 | 1.4469 | 0.1775 | 0.9055 | 13.4724 | 0.1775 | 0.1212 | 0.1483 | 0.7107 |
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| 1.4614 | 23.0 | 575 | 1.4356 | 0.17 | 0.9039 | 11.8141 | 0.17 | 0.1232 | 0.1515 | 0.7091 |
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| 1.4614 | 24.0 | 600 | 1.4370 | 0.1875 | 0.9039 | 12.9338 | 0.1875 | 0.1384 | 0.1539 | 0.7017 |
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| 1.4614 | 25.0 | 625 | 1.4358 | 0.195 | 0.9035 | 12.0550 | 0.195 | 0.1471 | 0.1675 | 0.6988 |
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### Framework versions
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- Transformers 4.28.0.dev0
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- Pytorch 1.12.1+cu113
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- Datasets 2.12.0
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- Tokenizers 0.12.1
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