model documentation

README.md

@@ -1,17 +1,172 @@
-# Password-Model
+---
+language:
+- en
 
-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.
-
-## Model description
+---
+# Model Card for Password-Model
 
+ 
+# Model Details
+ 
+## Model Description
+ 
+ 
+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.
+ 
+- **Developed by:** SAP OSS
+- **Shared by [Optional]:** Hugging Face
+- **Model type:** Text Classification
+- **Language(s) (NLP):** en
+- **License:** Apache-2.0
+- **Related Models:** 
+  - **Parent Model:** RoBERTa
+- **Resources for more information:**
+  - [GitHub Repo](https://github.com/SAP/credential-digger) 
+  - [Associated Paper](https://www.scitepress.org/Papers/2021/102381/102381.pdf) 
+ 
+# Uses
+ 
+ 
+## Direct Use
+The model is directly integrated into Credential Digger and can be used to filter the false positive discoveries of a scan
+ 
+## Downstream Use [Optional]
+ 
+ 
+More information needed.
+ 
+## Out-of-Scope Use
+ 
+ 
+The model should not be used to intentionally create hostile or alienating environments for people. 
+ 
+# Bias, Risks, and Limitations
+ 
+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.
+ 
+ 
+## Recommendations
+ 
+ 
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.
+ 
+ 
+# Training Details
+ 
+## Training Data
+ 
 [CodeBERT-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) fine-tuned on a dataset for leak detection.
+ 
+ 
+## Training Procedure
+ 
+ 
+### Preprocessing
+ 
+More information needed
+ 
+### Speeds, Sizes, Times
+ 
+More information needed
+ 
+# Evaluation
+ 
+More information needed
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+More information needed
+ 
+### Factors
+ 
+More information needed
+ 
+### Metrics
+ 
+More information needed
+ 
+## Results 
+ 
+More information needed
+ 
+ 
+# Model Examination
+More information needed
+ 
+# Environmental Impact
+ 
+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).
+ 
+- **Hardware Type:** More information needed
+- **Hours used:** More information needed
+- **Cloud Provider:** More information needed
+- **Compute Region:** More information needed
+- **Carbon Emitted:** More information needed
+ 
+# Technical Specifications [optional]
+ 
+## Model Architecture and Objective
+ 
+More information needed
+ 
+## Compute Infrastructure
+More information needed
+ 
+### Hardware
+ 
+More information needed
+ 
+### Software
+ 
+More information needed
+ 
+ 
+# Citation
+ 
+**BibTeX:**
+ 
+```
+@InProceedings {lrnto-icissp21,
+    author = {S. Lounici and M. Rosa and C. M. Negri and S. Trabelsi and M. Önen},
+    booktitle = {Proc. of the 8th The International Conference on Information Systems Security and Privacy  (ICISSP)},
+    title = {Optimizing Leak Detection in Open-Source Platforms with Machine Learning Techniques},
+    month = {February},
+    day = {11-13},
+    year = {2021}
+}
+```
+ 
+# Glossary [optional]
+ 
+More information needed
+ 
+# More Information [optional]
+ 
+More information needed
+ 
+# Model Card Authors [optional]
+ 
+SAP OSS in collaboration with Ezi Ozoani and the Hugging Face team.
+ 
+# Model Card Contact
+ 
+More information needed
 
-The aim of this model is to classify whether a code snippet contains a password (i.e., there is a leak) or not. 
-
-## How to use
-
-The model is directly integrated into Credential Digger and can be used to filter the false positive discoveries of a scan.
+# How to Get Started with the Model
+ 
+The model is directly integrated into Credential Digger and can be used to filter the false positive discoveries of a scan
+ 
+<details>
+<summary> Click to expand </summary>
 
-Please refer to Credential Digger for its usage within the [Python library](https://github.com/SAP/credential-digger#python-library-usage),
-the [CLI](https://github.com/SAP/credential-digger/wiki/CLI:--Command-Line-Interface),
-or the UI.
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+ 
+tokenizer = AutoTokenizer.from_pretrained("SAPOSS/password-model")
+ 
+model = AutoModelForSequenceClassification.from_pretrained("SAPOSS/password-model")
+ 
+ ```
+</details>