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dit-small_rvl_cdip_100_examples_per_class_simkd_CEKD_t1_aNone

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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_rvl_cdip_100_examples_per_class_simkd_CEKD_t1_aNone
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+ results: []
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+ ---
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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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+
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+ # dit-small_rvl_cdip_100_examples_per_class_simkd_CEKD_t1_aNone
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+
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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: 0.1481
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+ - Accuracy: 0.08
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+ - Brier Loss: 0.9369
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+ - Nll: 9.2883
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+ - F1 Micro: 0.08
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+ - F1 Macro: 0.0357
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+ - Ece: 0.1153
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+ - Aurc: 0.8531
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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 | 0.1528 | 0.0625 | 0.9377 | 9.9656 | 0.0625 | 0.0074 | 0.1025 | 0.9319 |
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+ | No log | 1.96 | 24 | 0.1507 | 0.06 | 0.9377 | 9.9434 | 0.06 | 0.0074 | 0.1036 | 0.9537 |
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+ | No log | 2.96 | 36 | 0.1500 | 0.0625 | 0.9376 | 8.6216 | 0.0625 | 0.0074 | 0.1019 | 0.9383 |
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+ | No log | 3.96 | 48 | 0.1498 | 0.0625 | 0.9376 | 9.2776 | 0.0625 | 0.0074 | 0.1032 | 0.9438 |
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+ | No log | 4.96 | 60 | 0.1496 | 0.0625 | 0.9375 | 9.3105 | 0.0625 | 0.0074 | 0.1017 | 0.9421 |
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+ | No log | 5.96 | 72 | 0.1495 | 0.0625 | 0.9375 | 9.7276 | 0.0625 | 0.0074 | 0.1029 | 0.9380 |
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+ | No log | 6.96 | 84 | 0.1494 | 0.0625 | 0.9374 | 9.6348 | 0.0625 | 0.0074 | 0.1017 | 0.9347 |
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+ | No log | 7.96 | 96 | 0.1493 | 0.0625 | 0.9374 | 9.6145 | 0.0625 | 0.0074 | 0.1008 | 0.9359 |
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+ | No log | 8.96 | 108 | 0.1492 | 0.0625 | 0.9374 | 9.5748 | 0.0625 | 0.0074 | 0.1019 | 0.9371 |
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+ | No log | 9.96 | 120 | 0.1491 | 0.0625 | 0.9373 | 9.5551 | 0.0625 | 0.0074 | 0.1005 | 0.9372 |
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+ | No log | 10.96 | 132 | 0.1490 | 0.065 | 0.9373 | 9.5267 | 0.065 | 0.0122 | 0.1047 | 0.9315 |
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+ | No log | 11.96 | 144 | 0.1489 | 0.065 | 0.9373 | 9.5165 | 0.065 | 0.0122 | 0.1043 | 0.9284 |
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+ | No log | 12.96 | 156 | 0.1488 | 0.065 | 0.9372 | 9.5162 | 0.065 | 0.0123 | 0.1068 | 0.9302 |
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+ | No log | 13.96 | 168 | 0.1488 | 0.07 | 0.9372 | 9.5139 | 0.07 | 0.0213 | 0.1070 | 0.9275 |
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+ | No log | 14.96 | 180 | 0.1487 | 0.0725 | 0.9371 | 9.4579 | 0.0725 | 0.0253 | 0.1095 | 0.9174 |
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+ | No log | 15.96 | 192 | 0.1486 | 0.075 | 0.9371 | 9.3950 | 0.075 | 0.0286 | 0.1106 | 0.9161 |
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+ | No log | 16.96 | 204 | 0.1485 | 0.075 | 0.9371 | 9.3347 | 0.075 | 0.0280 | 0.1055 | 0.9014 |
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+ | No log | 17.96 | 216 | 0.1484 | 0.0775 | 0.9370 | 9.3157 | 0.0775 | 0.0315 | 0.1089 | 0.8695 |
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+ | No log | 18.96 | 228 | 0.1483 | 0.08 | 0.9370 | 9.3125 | 0.08 | 0.0362 | 0.1133 | 0.8526 |
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+ | No log | 19.96 | 240 | 0.1483 | 0.08 | 0.9370 | 9.2915 | 0.08 | 0.0360 | 0.1113 | 0.8554 |
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+ | No log | 20.96 | 252 | 0.1482 | 0.0775 | 0.9370 | 9.2937 | 0.0775 | 0.0374 | 0.1118 | 0.8475 |
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+ | No log | 21.96 | 264 | 0.1482 | 0.08 | 0.9369 | 9.2903 | 0.08 | 0.0357 | 0.1167 | 0.8526 |
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+ | No log | 22.96 | 276 | 0.1482 | 0.08 | 0.9369 | 9.2888 | 0.08 | 0.0357 | 0.1099 | 0.8540 |
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+ | No log | 23.96 | 288 | 0.1481 | 0.08 | 0.9369 | 9.2877 | 0.08 | 0.0357 | 0.1126 | 0.8531 |
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+ | No log | 24.96 | 300 | 0.1481 | 0.08 | 0.9369 | 9.2883 | 0.08 | 0.0357 | 0.1153 | 0.8531 |
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+
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+
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+ ### Framework versions
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+
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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