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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 |
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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 |
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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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- Model Preparation Time: 0.0013 |
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- Accuracy: 0.9922 |
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- F1: 0.9924 |
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- Iou: 0.9856 |
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- Loss: 0.0214 |
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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: 8 |
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- eval_batch_size: 8 |
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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 | Model Preparation Time | | Validation Loss | |
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|:-------------:|:-----:|:------:|:----------------------:|:------:|:---------------:| |
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| 0.4994 | 5.0 | 25630 | 0.0013 | 0.9812 | 0.0328 | |
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| 0.4423 | 10.0 | 51260 | 0.0013 | 0.9806 | 0.0356 | |
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| 0.4365 | 15.0 | 76890 | 0.0013 | 0.9836 | 0.0309 | |
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| 0.3913 | 20.0 | 102520 | 0.0013 | 0.1924 | 0.9382 | |
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| 0.3415 | 25.0 | 128150 | 0.0013 | 0.9851 | 0.0250 | |
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| 0.3952 | 30.0 | 153780 | 0.0013 | 0.9860 | 0.0236 | |
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| 0.3746 | 35.0 | 179410 | 0.0013 | 0.9818 | 0.0288 | |
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| 0.3569 | 40.0 | 205040 | 0.0013 | 0.1850 | 0.8815 | |
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| 0.3612 | 45.0 | 230670 | 0.0013 | 0.9856 | 0.0214 | |
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| 0.3501 | 50.0 | 256300 | 0.0013 | 0.9848 | 0.0241 | |
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| 0.3374 | 55.0 | 281930 | 0.0013 | 0.9847 | 0.0232 | |
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| 0.3742 | 60.0 | 307560 | 0.0013 | 0.1883 | 0.9705 | |
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### Framework versions |
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- Transformers 4.45.0 |
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- Pytorch 2.5.1 |
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- Datasets 3.2.0 |
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- Tokenizers 0.20.3 |
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