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--- |
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language: |
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- en |
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--- |
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# Model Card for Password-Model |
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# Model Details |
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## Model Description |
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The Password Model is intended to be used with [Credential Digger](https://github.com/SAP/credential-digger) in order to automatically filter false positive password discoveries. |
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- **Developed by:** SAP OSS |
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- **Shared by [Optional]:** Hugging Face |
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- **Model type:** Text Classification |
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- **Language(s) (NLP):** en |
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- **License:** Apache-2.0 |
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- **Related Models:** |
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- **Parent Model:** RoBERTa |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/SAP/credential-digger) |
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- [Associated Paper](https://www.scitepress.org/Papers/2021/102381/102381.pdf) |
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# Uses |
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## Direct Use |
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The model is directly integrated into Credential Digger and can be used to filter the false positive discoveries of a scan |
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## Downstream Use [Optional] |
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More information needed. |
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## Out-of-Scope Use |
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The model should not be used to intentionally create hostile or alienating environments for people. |
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# Bias, Risks, and Limitations |
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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations. |
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# Training Details |
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## Training Data |
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[CodeBERT-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) fine-tuned on a dataset for leak detection. |
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## Training Procedure |
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### Preprocessing |
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More information needed |
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### Speeds, Sizes, Times |
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More information needed |
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# Evaluation |
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More information needed |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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More information needed |
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### Factors |
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More information needed |
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### Metrics |
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More information needed |
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## Results |
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More information needed |
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# Model Examination |
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More information needed |
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# Environmental Impact |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** More information needed |
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- **Hours used:** More information needed |
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- **Cloud Provider:** More information needed |
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- **Compute Region:** More information needed |
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- **Carbon Emitted:** More information needed |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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More information needed |
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## Compute Infrastructure |
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More information needed |
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### Hardware |
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More information needed |
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### Software |
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More information needed |
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# Citation |
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**BibTeX:** |
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``` |
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@InProceedings {lrnto-icissp21, |
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author = {S. Lounici and M. Rosa and C. M. Negri and S. Trabelsi and M. Önen}, |
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booktitle = {Proc. of the 8th The International Conference on Information Systems Security and Privacy (ICISSP)}, |
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title = {Optimizing Leak Detection in Open-Source Platforms with Machine Learning Techniques}, |
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month = {February}, |
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day = {11-13}, |
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year = {2021} |
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} |
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``` |
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# Glossary [optional] |
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More information needed |
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# More Information [optional] |
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More information needed |
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# Model Card Authors [optional] |
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SAP OSS in collaboration with Ezi Ozoani and the Hugging Face team. |
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# Model Card Contact |
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More information needed |
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# How to Get Started with the Model |
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The model is directly integrated into Credential Digger and can be used to filter the false positive discoveries of a scan |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("SAPOSS/password-model") |
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model = AutoModelForSequenceClassification.from_pretrained("SAPOSS/password-model") |
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``` |
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</details> |
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