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mlops-fraud-detection
usamahfirdaa/mlops-fraud-detection
mlops-fraud-detection is a text classification model from usamahfirdaa. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Jan 16, 2025
- Updated
- Jan 16, 2025
- SHA
- d9e3d8e3
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
mlops-fraud-detection
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0021
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 4 | 0.0155 |
| No log | 2.0 | 8 | 0.0088 |
| 0.021 | 3.0 | 12 | 0.0055 |
| 0.021 | 4.0 | 16 | 0.0041 |
| 0.0062 | 5.0 | 20 | 0.0034 |
| 0.0062 | 6.0 | 24 | 0.0028 |
| 0.0062 | 7.0 | 28 | 0.0024 |
| 0.0038 | 8.0 | 32 | 0.0022 |
| 0.0038 | 9.0 | 36 | 0.0021 |
| 0.003 | 10.0 | 40 | 0.0021 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jan 16, 2025
- Updated
- Jan 16, 2025