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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: deepvk/bert-base-uncased |
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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: bert-base-uncased-finetuned-spam-detection |
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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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# bert-base-uncased-finetuned-spam-detection |
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This model is a fine-tuned version of [deepvk/bert-base-uncased](https://huggingface.co/deepvk/bert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0508 |
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- Accuracy: 0.9862 |
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- F1: 0.9862 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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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 | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.0626 | 1.0 | 967 | 0.0504 | 0.9838 | 0.9838 | |
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| 0.0258 | 2.0 | 1934 | 0.0508 | 0.9862 | 0.9862 | |
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### Framework versions |
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- Transformers 4.46.0 |
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- Pytorch 2.4.1 |
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- Datasets 3.0.2 |
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- Tokenizers 0.20.1 |
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