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--- |
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library_name: transformers |
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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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- f1 |
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model-index: |
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- name: windowz_test-022625 |
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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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# windowz_test-022625 |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.9907 |
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- F1: 0.9909 |
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- Iou: 0.9830 |
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- Per Class Metrics: {0: {'f1': 0.99734, 'iou': 0.9947, 'accuracy': 0.99602}, 1: {'f1': 0.9807, 'iou': 0.96214, 'accuracy': 0.99071}, 2: {'f1': 0.74699, 'iou': 0.59616, 'accuracy': 0.99465}} |
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- Loss: 0.0222 |
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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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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | | Class Metrics | Validation Loss | |
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|:-------------:|:-----:|:------:|:------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:| |
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| 0.486 | 5.0 | 12815 | 0.9750 | {0: {'f1': 0.99477, 'iou': 0.9896, 'accuracy': 0.99218}, 1: {'f1': 0.97507, 'iou': 0.95135, 'accuracy': 0.98779}, 2: {'f1': 0.59112, 'iou': 0.41956, 'accuracy': 0.99417}} | 0.1010 | |
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| 0.4424 | 10.0 | 25630 | 0.9841 | {0: {'f1': 0.99787, 'iou': 0.99575, 'accuracy': 0.99682}, 1: {'f1': 0.98309, 'iou': 0.96675, 'accuracy': 0.99173}, 2: {'f1': 0.67276, 'iou': 0.50689, 'accuracy': 0.99485}} | 0.0388 | |
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| 0.398 | 15.0 | 38445 | 0.9800 | {0: {'f1': 0.99635, 'iou': 0.99272, 'accuracy': 0.99454}, 1: {'f1': 0.97804, 'iou': 0.95702, 'accuracy': 0.98935}, 2: {'f1': 0.71599, 'iou': 0.55762, 'accuracy': 0.99474}} | 0.0339 | |
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| 0.3887 | 20.0 | 51260 | 0.9832 | {0: {'f1': 0.99697, 'iou': 0.99395, 'accuracy': 0.99546}, 1: {'f1': 0.98169, 'iou': 0.96404, 'accuracy': 0.99117}, 2: {'f1': 0.76483, 'iou': 0.61921, 'accuracy': 0.99548}} | 0.0228 | |
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| 0.3765 | 25.0 | 64075 | 0.9830 | {0: {'f1': 0.99734, 'iou': 0.9947, 'accuracy': 0.99602}, 1: {'f1': 0.9807, 'iou': 0.96214, 'accuracy': 0.99071}, 2: {'f1': 0.74699, 'iou': 0.59616, 'accuracy': 0.99465}} | 0.0222 | |
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| 0.4094 | 30.0 | 76890 | 0.9848 | {0: {'f1': 0.99775, 'iou': 0.99551, 'accuracy': 0.99663}, 1: {'f1': 0.98255, 'iou': 0.9657, 'accuracy': 0.9916}, 2: {'f1': 0.7705, 'iou': 0.62667, 'accuracy': 0.99492}} | 0.0345 | |
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| 0.371 | 35.0 | 89705 | 0.9836 | {0: {'f1': 0.99757, 'iou': 0.99515, 'accuracy': 0.99636}, 1: {'f1': 0.98094, 'iou': 0.9626, 'accuracy': 0.99085}, 2: {'f1': 0.75391, 'iou': 0.60502, 'accuracy': 0.99445}} | 0.0224 | |
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| 0.3752 | 40.0 | 102520 | 0.9826 | {0: {'f1': 0.99777, 'iou': 0.99555, 'accuracy': 0.99666}, 1: {'f1': 0.97899, 'iou': 0.95885, 'accuracy': 0.98995}, 2: {'f1': 0.72023, 'iou': 0.56278, 'accuracy': 0.99326}} | 0.0243 | |
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### Framework versions |
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- Transformers 4.45.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 2.21.0 |
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- Tokenizers 0.20.3 |
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