| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: dit_base_binary_task |
| 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_base_binary_task |
| |
| This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the davanstrien/leicester_loaded_annotations_binary dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0513 |
| - Accuracy: 0.9873 |
| - F1: 0.9600 |
|
|
| ## 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: 4 |
| - 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: 50 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
| | No log | 0.87 | 5 | 0.6816 | 0.5 | 0.2476 | |
| | 0.7387 | 1.87 | 10 | 0.5142 | 0.8354 | 0.0 | |
| | 0.7387 | 2.87 | 15 | 0.4690 | 0.8354 | 0.0 | |
| | 0.4219 | 3.87 | 20 | 0.5460 | 0.8354 | 0.0 | |
| | 0.4219 | 4.87 | 25 | 0.4703 | 0.8354 | 0.0 | |
| | 0.3734 | 5.87 | 30 | 0.4371 | 0.8354 | 0.0 | |
| | 0.3734 | 6.87 | 35 | 0.4147 | 0.8354 | 0.0 | |
| | 0.3261 | 7.87 | 40 | 0.4272 | 0.8354 | 0.0 | |
| | 0.3261 | 8.87 | 45 | 0.4038 | 0.8354 | 0.0 | |
| | 0.3078 | 9.87 | 50 | 0.3418 | 0.8354 | 0.0 | |
| | 0.3078 | 10.87 | 55 | 0.3042 | 0.8354 | 0.0 | |
| | 0.2501 | 11.87 | 60 | 0.2799 | 0.8354 | 0.0 | |
| | 0.2501 | 12.87 | 65 | 0.1419 | 0.9367 | 0.7619 | |
| | 0.1987 | 13.87 | 70 | 0.1224 | 0.9494 | 0.8182 | |
| | 0.1987 | 14.87 | 75 | 0.0749 | 0.9747 | 0.9167 | |
| | 0.1391 | 15.87 | 80 | 0.0539 | 0.9810 | 0.9412 | |
| | 0.1391 | 16.87 | 85 | 0.0830 | 0.9873 | 0.9600 | |
| | 0.1085 | 17.87 | 90 | 0.0443 | 0.9873 | 0.9600 | |
| | 0.1085 | 18.87 | 95 | 0.0258 | 0.9937 | 0.9804 | |
| | 0.1039 | 19.87 | 100 | 0.1025 | 0.9684 | 0.8936 | |
| | 0.1039 | 20.87 | 105 | 0.1597 | 0.9684 | 0.8936 | |
| | 0.1217 | 21.87 | 110 | 0.0278 | 0.9937 | 0.9811 | |
| | 0.1217 | 22.87 | 115 | 0.0458 | 0.9873 | 0.9600 | |
| | 0.0609 | 23.87 | 120 | 0.0478 | 0.9937 | 0.9804 | |
| | 0.0609 | 24.87 | 125 | 0.0671 | 0.9747 | 0.9231 | |
| | 0.1031 | 25.87 | 130 | 0.0751 | 0.9873 | 0.9600 | |
| | 0.1031 | 26.87 | 135 | 0.1963 | 0.9557 | 0.8444 | |
| | 0.0601 | 27.87 | 140 | 0.0870 | 0.9747 | 0.9167 | |
| | 0.0601 | 28.87 | 145 | 0.0890 | 0.9747 | 0.9167 | |
| | 0.0799 | 29.87 | 150 | 0.1017 | 0.9747 | 0.9167 | |
| | 0.0799 | 30.87 | 155 | 0.0041 | 1.0 | 1.0 | |
| | 0.0441 | 31.87 | 160 | 0.0332 | 0.9873 | 0.9615 | |
| | 0.0441 | 32.87 | 165 | 0.0839 | 0.9747 | 0.9167 | |
| | 0.0757 | 33.87 | 170 | 0.0722 | 0.9873 | 0.9600 | |
| | 0.0757 | 34.87 | 175 | 0.0168 | 0.9937 | 0.9804 | |
| | 0.0555 | 35.87 | 180 | 0.0443 | 0.9937 | 0.9804 | |
| | 0.0555 | 36.87 | 185 | 0.0227 | 0.9873 | 0.9615 | |
| | 0.0336 | 37.87 | 190 | 0.0128 | 0.9937 | 0.9804 | |
| | 0.0336 | 38.87 | 195 | 0.0169 | 0.9937 | 0.9811 | |
| | 0.0405 | 39.87 | 200 | 0.0193 | 0.9937 | 0.9804 | |
| | 0.0405 | 40.87 | 205 | 0.1216 | 0.9810 | 0.9388 | |
| | 0.0578 | 41.87 | 210 | 0.0307 | 0.9937 | 0.9804 | |
| | 0.0578 | 42.87 | 215 | 0.0539 | 0.9873 | 0.9600 | |
| | 0.0338 | 43.87 | 220 | 0.0573 | 0.9937 | 0.9804 | |
| | 0.0338 | 44.87 | 225 | 0.0086 | 1.0 | 1.0 | |
| | 0.0417 | 45.87 | 230 | 0.0491 | 0.9873 | 0.9600 | |
| | 0.0417 | 46.87 | 235 | 0.0089 | 1.0 | 1.0 | |
| | 0.0538 | 47.87 | 240 | 0.0846 | 0.9810 | 0.9388 | |
| | 0.0538 | 48.87 | 245 | 0.0452 | 0.9810 | 0.9388 | |
| | 0.0364 | 49.87 | 250 | 0.0513 | 0.9873 | 0.9600 | |
|
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|
| ### Framework versions |
|
|
| - Transformers 4.25.1 |
| - Pytorch 1.12.1 |
| - Datasets 2.7.1 |
| - Tokenizers 0.13.1 |
|
|
