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--- |
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library_name: transformers |
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license: mit |
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base_model: roberta-base |
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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: roberta-wellness-classifier |
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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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# roberta-wellness-classifier |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8807 |
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- Accuracy: 0.71 |
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- Auc: 0.871 |
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- Precision Class 0: 0.787 |
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- Precision Class 1: 0.857 |
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- Precision Class 2: 0.731 |
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- Precision Class 3: 0.645 |
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- Recall Class 0: 0.698 |
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- Recall Class 1: 0.667 |
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- Recall Class 2: 0.603 |
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- Recall Class 3: 0.796 |
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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: 3e-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: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision Class 0 | Precision Class 1 | Precision Class 2 | Precision Class 3 | Recall Class 0 | Recall Class 1 | Recall Class 2 | Recall Class 3 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:--------------:|:--------------:|:--------------:|:--------------:| |
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| 1.0423 | 1.0 | 140 | 0.7782 | 0.685 | 0.888 | 0.732 | 0.727 | 0.595 | 0.734 | 0.774 | 0.593 | 0.794 | 0.592 | |
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| 0.6558 | 2.0 | 280 | 0.7589 | 0.714 | 0.902 | 0.733 | 0.85 | 0.78 | 0.658 | 0.83 | 0.63 | 0.508 | 0.806 | |
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| 0.4306 | 3.0 | 420 | 1.0251 | 0.73 | 0.89 | 0.738 | 0.857 | 0.64 | 0.795 | 0.849 | 0.667 | 0.873 | 0.592 | |
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| 0.3002 | 4.0 | 560 | 1.2314 | 0.726 | 0.908 | 0.816 | 0.938 | 0.707 | 0.669 | 0.755 | 0.556 | 0.651 | 0.806 | |
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| 0.2117 | 5.0 | 700 | 1.3601 | 0.714 | 0.888 | 0.857 | 0.941 | 0.645 | 0.67 | 0.679 | 0.593 | 0.778 | 0.724 | |
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| 0.1606 | 6.0 | 840 | 1.4648 | 0.718 | 0.887 | 0.784 | 0.933 | 0.682 | 0.679 | 0.755 | 0.519 | 0.714 | 0.755 | |
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| 0.1135 | 7.0 | 980 | 1.6228 | 0.714 | 0.883 | 0.78 | 0.826 | 0.698 | 0.667 | 0.736 | 0.704 | 0.698 | 0.714 | |
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| 0.0686 | 8.0 | 1120 | 1.8947 | 0.71 | 0.866 | 0.809 | 0.857 | 0.745 | 0.635 | 0.717 | 0.667 | 0.556 | 0.816 | |
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| 0.0525 | 9.0 | 1260 | 1.8817 | 0.718 | 0.875 | 0.796 | 0.864 | 0.74 | 0.65 | 0.736 | 0.704 | 0.587 | 0.796 | |
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| 0.0526 | 10.0 | 1400 | 1.8807 | 0.71 | 0.871 | 0.787 | 0.857 | 0.731 | 0.645 | 0.698 | 0.667 | 0.603 | 0.796 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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