Update README.md

README.md

@@ -21,49 +21,42 @@ This model card aims to document the capabilities, performance, and intended usa
 ## Model Details
 
 ### Model Description
-
 This model is a fine-tuned version of large-scale language models optimized for tasks such as parsing shell commands and analyzing cybersecurity exploits. The training leverages datasets such as Canstralian/ShellCommands and Canstralian/CyberExploitDB to provide domain-specific knowledge.
 
-- **Developed by:** Canstralian 
-- **Model type:** Transformer-based Language Model for cybersecurity applications
-- **Language(s) (NLP):** English (en)
-- **License:** MIT
-- **Finetuned from model:** WhiteRabbitNeo/WhiteRabbitNeo-13B-v1, replit/replit-code-v1_5-3b
-
-## Uses
+**Developed by**: Canstralian  
+**Model type**: Transformer-based Language Model for cybersecurity applications  
+**Language(s) (NLP)**: English (en)  
+**License**: MIT  
+**Finetuned from model**: WhiteRabbitNeo/WhiteRabbitNeo-13B-v1, replit/replit-code-v1_5-3b
 
-### Direct Use
+### Uses
 
+#### Direct Use
 The model is intended to be used directly for tasks like:
 - Shell command understanding and classification
 - Analyzing and classifying cybersecurity exploit patterns
 - Assisting with code generation and debugging in a cybersecurity context
 
-### Downstream Use 
-
+#### Downstream Use
 When fine-tuned further, the model can be applied to:
 - Automated incident response systems
 - Security tool integration (e.g., for vulnerability scanners)
 - Custom cybersecurity solutions tailored to enterprise needs
 
-### Out-of-Scope Use
-
+#### Out-of-Scope Use
 The model is not designed for general-purpose natural language understanding outside of its specified cybersecurity domain. It may perform poorly or inaccurately for tasks outside of:
 - Shell command parsing
 - Exploit database analysis
 - Code generation for cybersecurity applications
 
-## Bias, Risks, and Limitations
-
+### Bias, Risks, and Limitations
 This model may exhibit bias in the detection of certain exploits or shell commands, particularly if it encounters unfamiliar patterns not covered in the training data. Additionally, the model's predictions may be less accurate on unseen datasets or with edge cases that were not represented in the training data.
 
 ### Recommendations
-
 - Users should be cautious when applying the model to novel or unverified exploits, as it may not handle new attack vectors well.
 - Regular evaluation and testing in real-world environments are recommended before deploying the model in production.
 
-## How to Get Started with the Model
-
+### How to Get Started with the Model
 Use the code below to get started with the model:
 
 ```python
@@ -76,84 +69,4 @@ nlp = pipeline("text-classification", model=model_name)
 # Example usage
 result = nlp("Example shell command or exploit input")
 print(result)
-```
-
-## Training Details
-
-### Training Data
-
-The model was fine-tuned on the following datasets:
-- **Canstralian/ShellCommands**: A collection of shell commands used in cybersecurity contexts.
-- **Canstralian/CyberExploitDB**: A curated set of known exploits and vulnerabilities.
-
-Further details on the preprocessing of these datasets can be found in their respective dataset cards.
-
-### Training Procedure
-
-#### Preprocessing 
-
-The data was preprocessed to remove any sensitive or personally identifiable information. Text normalization and tokenization were applied to ensure consistency across the datasets.
-
-#### Training Hyperparameters
-
-- **Training regime:** fp16 mixed precision
-
-## Evaluation
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-Testing was performed on both synthetic and real-world shell command and exploit datasets, focusing on their ability to correctly parse shell commands and identify exploit signatures.
-
-#### Factors
-
-The evaluation factors included:
-- Model performance across different types of shell commands and exploits.
-- Accuracy, precision, recall, and F1-score in detecting known exploits.
-
-#### Metrics
-
-Metrics used for evaluation include:
-- **Accuracy**: Percentage of correct predictions made by the model.
-- **Precision**: The number of relevant instances among the retrieved instances.
-- **Recall**: The number of relevant instances that were retrieved.
-- **F1-score**: The harmonic mean of precision and recall.
-
-### Results
-
-The model performs well on standard shell command parsing tasks and exploit detection, with high accuracy for common exploits. However, its performance may degrade on newer or less common exploits.
-
-#### Summary
-
-The model is well-suited for cybersecurity applications involving shell command and exploit detection. While it excels in these areas, users should monitor its performance for emerging threats and unusual attack patterns.
-
-## Model Examination [optional]
-
-Relevant interpretability work for this model is ongoing. Future research will focus on understanding why the model makes specific predictions and improving its interpretability.
-
-## 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]
-
-## Glossary [optional]
-
-Terms like "shell command", "exploit", and "cybersecurity" may be used frequently in this model's context. Further definitions will help readers understand model performance.
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-For more information, please contact [Contact Information Needed].
+```