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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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base_model: microsoft/dit-base |
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model-index: |
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- name: dit_base |
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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_base |
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This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the davanstrien/leicester_loaded_annotations dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4527 |
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- Accuracy: 0.8190 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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: 50 |
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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 | 0.89 | 6 | 1.7452 | 0.4095 | |
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| 1.8958 | 1.89 | 12 | 1.6185 | 0.4286 | |
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| 1.8958 | 2.89 | 18 | 1.4731 | 0.4857 | |
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| 1.8466 | 3.89 | 24 | 1.3459 | 0.5524 | |
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| 1.445 | 4.89 | 30 | 1.1766 | 0.5810 | |
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| 1.445 | 5.89 | 36 | 1.0902 | 0.6381 | |
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| 1.2077 | 6.89 | 42 | 0.9331 | 0.6762 | |
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| 1.2077 | 7.89 | 48 | 0.8431 | 0.6762 | |
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| 1.0254 | 8.89 | 54 | 0.8657 | 0.6857 | |
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| 0.8275 | 9.89 | 60 | 0.6801 | 0.7429 | |
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| 0.8275 | 10.89 | 66 | 0.6699 | 0.7810 | |
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| 0.8063 | 11.89 | 72 | 0.6296 | 0.7524 | |
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| 0.8063 | 12.89 | 78 | 0.5498 | 0.7905 | |
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| 0.7127 | 13.89 | 84 | 0.4974 | 0.8381 | |
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| 0.6356 | 14.89 | 90 | 0.6715 | 0.7619 | |
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| 0.6356 | 15.89 | 96 | 0.4602 | 0.8095 | |
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| 0.6438 | 16.89 | 102 | 0.4886 | 0.8095 | |
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| 0.6438 | 17.89 | 108 | 0.4332 | 0.8 | |
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| 0.5329 | 18.89 | 114 | 0.4197 | 0.8095 | |
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| 0.4932 | 19.89 | 120 | 0.4168 | 0.8190 | |
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| 0.4932 | 20.89 | 126 | 0.4691 | 0.8 | |
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| 0.4861 | 21.89 | 132 | 0.4263 | 0.8476 | |
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| 0.4861 | 22.89 | 138 | 0.4464 | 0.8190 | |
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| 0.4935 | 23.89 | 144 | 0.4857 | 0.7905 | |
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| 0.433 | 24.89 | 150 | 0.4873 | 0.7810 | |
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| 0.433 | 25.89 | 156 | 0.4641 | 0.8095 | |
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| 0.4289 | 26.89 | 162 | 0.5316 | 0.8 | |
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| 0.4289 | 27.89 | 168 | 0.3389 | 0.8571 | |
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| 0.4204 | 28.89 | 174 | 0.4272 | 0.8 | |
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| 0.3668 | 29.89 | 180 | 0.3493 | 0.8667 | |
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| 0.3668 | 30.89 | 186 | 0.3861 | 0.8571 | |
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| 0.4101 | 31.89 | 192 | 0.4216 | 0.8381 | |
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| 0.4101 | 32.89 | 198 | 0.4258 | 0.8190 | |
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| 0.3614 | 33.89 | 204 | 0.4409 | 0.8571 | |
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| 0.3267 | 34.89 | 210 | 0.4475 | 0.8190 | |
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| 0.3267 | 35.89 | 216 | 0.4316 | 0.8190 | |
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| 0.3423 | 36.89 | 222 | 0.4095 | 0.8381 | |
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| 0.3423 | 37.89 | 228 | 0.4671 | 0.8286 | |
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| 0.3325 | 38.89 | 234 | 0.3994 | 0.8286 | |
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| 0.3326 | 39.89 | 240 | 0.5004 | 0.8190 | |
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| 0.3326 | 40.89 | 246 | 0.4103 | 0.8381 | |
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| 0.2964 | 41.89 | 252 | 0.4469 | 0.8286 | |
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| 0.2964 | 42.89 | 258 | 0.4774 | 0.8286 | |
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| 0.3435 | 43.89 | 264 | 0.3843 | 0.8381 | |
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| 0.3146 | 44.89 | 270 | 0.3710 | 0.8667 | |
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| 0.3146 | 45.89 | 276 | 0.3392 | 0.8667 | |
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| 0.3168 | 46.89 | 282 | 0.3597 | 0.8667 | |
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| 0.3168 | 47.89 | 288 | 0.4143 | 0.8381 | |
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| 0.3081 | 48.89 | 294 | 0.3579 | 0.8571 | |
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| 0.3103 | 49.89 | 300 | 0.4527 | 0.8190 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.1 |
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