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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_maveriq_tobacco3482_2023-07-04 |
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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_maveriq_tobacco3482_2023-07-04 |
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This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2641 |
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- Accuracy: 0.955 |
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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: 16 |
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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: 256 |
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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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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 0.96 | 3 | 2.0434 | 0.265 | |
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| No log | 1.96 | 6 | 1.9976 | 0.31 | |
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| No log | 2.96 | 9 | 1.7970 | 0.335 | |
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| No log | 3.96 | 12 | 1.6899 | 0.385 | |
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| No log | 4.96 | 15 | 1.6519 | 0.36 | |
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| No log | 5.96 | 18 | 1.5378 | 0.42 | |
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| No log | 6.96 | 21 | 1.4401 | 0.51 | |
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| No log | 7.96 | 24 | 1.3607 | 0.575 | |
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| No log | 8.96 | 27 | 1.2614 | 0.605 | |
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| No log | 9.96 | 30 | 1.1654 | 0.63 | |
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| No log | 10.96 | 33 | 1.0758 | 0.66 | |
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| No log | 11.96 | 36 | 0.9908 | 0.73 | |
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| No log | 12.96 | 39 | 0.9152 | 0.72 | |
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| No log | 13.96 | 42 | 0.8412 | 0.755 | |
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| No log | 14.96 | 45 | 0.7759 | 0.78 | |
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| No log | 15.96 | 48 | 0.7105 | 0.785 | |
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| No log | 16.96 | 51 | 0.6489 | 0.815 | |
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| No log | 17.96 | 54 | 0.6037 | 0.825 | |
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| No log | 18.96 | 57 | 0.5628 | 0.83 | |
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| No log | 19.96 | 60 | 0.5084 | 0.84 | |
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| No log | 20.96 | 63 | 0.4593 | 0.85 | |
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| No log | 21.96 | 66 | 0.4306 | 0.865 | |
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| No log | 22.96 | 69 | 0.4078 | 0.87 | |
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| No log | 23.96 | 72 | 0.3888 | 0.875 | |
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| No log | 24.96 | 75 | 0.3713 | 0.885 | |
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| No log | 25.96 | 78 | 0.3469 | 0.89 | |
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| No log | 26.96 | 81 | 0.3234 | 0.91 | |
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| No log | 27.96 | 84 | 0.3146 | 0.905 | |
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| No log | 28.96 | 87 | 0.3311 | 0.895 | |
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| No log | 29.96 | 90 | 0.3178 | 0.92 | |
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| No log | 30.96 | 93 | 0.3011 | 0.92 | |
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| No log | 31.96 | 96 | 0.2922 | 0.93 | |
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| No log | 32.96 | 99 | 0.2822 | 0.93 | |
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| No log | 33.96 | 102 | 0.2615 | 0.93 | |
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| No log | 34.96 | 105 | 0.2577 | 0.94 | |
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| No log | 35.96 | 108 | 0.2587 | 0.94 | |
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| No log | 36.96 | 111 | 0.2659 | 0.93 | |
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| No log | 37.96 | 114 | 0.2697 | 0.925 | |
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| No log | 38.96 | 117 | 0.2721 | 0.93 | |
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| No log | 39.96 | 120 | 0.2829 | 0.935 | |
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| No log | 40.96 | 123 | 0.2564 | 0.94 | |
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| No log | 41.96 | 126 | 0.2420 | 0.94 | |
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| No log | 42.96 | 129 | 0.2433 | 0.94 | |
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| No log | 43.96 | 132 | 0.2405 | 0.945 | |
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| No log | 44.96 | 135 | 0.2391 | 0.95 | |
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| No log | 45.96 | 138 | 0.2455 | 0.955 | |
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| No log | 46.96 | 141 | 0.2563 | 0.945 | |
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| No log | 47.96 | 144 | 0.2653 | 0.95 | |
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| No log | 48.96 | 147 | 0.2608 | 0.945 | |
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| No log | 49.96 | 150 | 0.2477 | 0.95 | |
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| No log | 50.96 | 153 | 0.2443 | 0.95 | |
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| No log | 51.96 | 156 | 0.2418 | 0.95 | |
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| No log | 52.96 | 159 | 0.2403 | 0.94 | |
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| No log | 53.96 | 162 | 0.2384 | 0.945 | |
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| No log | 54.96 | 165 | 0.2413 | 0.95 | |
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| No log | 55.96 | 168 | 0.2428 | 0.96 | |
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| No log | 56.96 | 171 | 0.2409 | 0.955 | |
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| No log | 57.96 | 174 | 0.2457 | 0.95 | |
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| No log | 58.96 | 177 | 0.2488 | 0.95 | |
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| No log | 59.96 | 180 | 0.2548 | 0.955 | |
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| No log | 60.96 | 183 | 0.2597 | 0.955 | |
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| No log | 61.96 | 186 | 0.2647 | 0.955 | |
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| No log | 62.96 | 189 | 0.2651 | 0.955 | |
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| No log | 63.96 | 192 | 0.2638 | 0.955 | |
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| No log | 64.96 | 195 | 0.2638 | 0.955 | |
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| No log | 65.96 | 198 | 0.2664 | 0.955 | |
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| No log | 66.96 | 201 | 0.2712 | 0.95 | |
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| No log | 67.96 | 204 | 0.2677 | 0.955 | |
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| No log | 68.96 | 207 | 0.2601 | 0.95 | |
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| No log | 69.96 | 210 | 0.2559 | 0.95 | |
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| No log | 70.96 | 213 | 0.2566 | 0.95 | |
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| No log | 71.96 | 216 | 0.2611 | 0.95 | |
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| No log | 72.96 | 219 | 0.2702 | 0.95 | |
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| No log | 73.96 | 222 | 0.2806 | 0.945 | |
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| No log | 74.96 | 225 | 0.2842 | 0.945 | |
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| No log | 75.96 | 228 | 0.2807 | 0.945 | |
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| No log | 76.96 | 231 | 0.2750 | 0.95 | |
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| No log | 77.96 | 234 | 0.2656 | 0.955 | |
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| No log | 78.96 | 237 | 0.2582 | 0.96 | |
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| No log | 79.96 | 240 | 0.2545 | 0.96 | |
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| No log | 80.96 | 243 | 0.2535 | 0.96 | |
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| No log | 81.96 | 246 | 0.2512 | 0.96 | |
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| No log | 82.96 | 249 | 0.2520 | 0.96 | |
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| No log | 83.96 | 252 | 0.2546 | 0.96 | |
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| No log | 84.96 | 255 | 0.2570 | 0.96 | |
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| No log | 85.96 | 258 | 0.2608 | 0.96 | |
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| No log | 86.96 | 261 | 0.2641 | 0.96 | |
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| No log | 87.96 | 264 | 0.2672 | 0.955 | |
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| No log | 88.96 | 267 | 0.2686 | 0.955 | |
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| No log | 89.96 | 270 | 0.2682 | 0.955 | |
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| No log | 90.96 | 273 | 0.2671 | 0.955 | |
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| No log | 91.96 | 276 | 0.2652 | 0.96 | |
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| No log | 92.96 | 279 | 0.2639 | 0.96 | |
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| No log | 93.96 | 282 | 0.2635 | 0.96 | |
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| No log | 94.96 | 285 | 0.2633 | 0.96 | |
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| No log | 95.96 | 288 | 0.2635 | 0.955 | |
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| No log | 96.96 | 291 | 0.2636 | 0.955 | |
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| No log | 97.96 | 294 | 0.2639 | 0.955 | |
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| No log | 98.96 | 297 | 0.2640 | 0.955 | |
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| No log | 99.96 | 300 | 0.2641 | 0.955 | |
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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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