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--- |
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base_model: microsoft/codebert-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: codebert-base-password-strength-classifier-normal-weight-balancing |
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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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# codebert-base-password-strength-classifier-normal-weight-balancing |
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This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0083 |
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- Accuracy: 0.9977 |
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- Weighted f1: 0.9977 |
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- Micro f1: 0.9977 |
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- Macro f1: 0.9966 |
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- Weighted recall: 0.9977 |
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- Micro recall: 0.9977 |
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- Macro recall: 0.9979 |
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- Weighted precision: 0.9977 |
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- Micro precision: 0.9977 |
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- Macro precision: 0.9953 |
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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: 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: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
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| 0.0345 | 1.0 | 37667 | 0.0522 | 0.9825 | 0.9829 | 0.9825 | 0.9755 | 0.9825 | 0.9825 | 0.9915 | 0.9844 | 0.9825 | 0.9619 | |
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| 0.0099 | 2.0 | 75334 | 0.0083 | 0.9977 | 0.9977 | 0.9977 | 0.9966 | 0.9977 | 0.9977 | 0.9979 | 0.9977 | 0.9977 | 0.9953 | |
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### Framework versions |
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- Transformers 4.34.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.6.dev0 |
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- Tokenizers 0.13.3 |
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