--- license: mit datasets: - fka/awesome-chatgpt-prompts - gopipasala/fka-awesome-chatgpt-prompts language: - en metrics: - accuracy - character - code_eval - cer - bertscore base_model: - deepseek-ai/DeepSeek-V3-Base - meta-llama/Llama-3.3-70B-Instruct - black-forest-labs/FLUX.1-dev new_version: black-forest-labs/FLUX.1-dev library_name: diffusers tags: - code --- Model Card for MyBot Certainly! I'll add more details about the model to provide a comprehensive overview. Here's the updated model card with additional information: ```markdown # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {{ card_data }} --- # Model Card for MyBot MyBot is an intelligent chatbot built using the BotBuilder framework. It leverages various perspectives to generate insightful responses and enhance user interactions. ## Model Details ### Model Description MyBot is an intelligent chatbot built using the BotBuilder framework. It leverages various perspectives to generate insightful responses and enhance user interactions. @misc {jonathan_harrison_2025, author = { {Jonathan Harrison} }, title = { CoderTheGoat (Revision 9dac74a) }, year = 2025, url = { https://huggingface.co/Raiff1982/CoderTheGoat }, doi = { 10.57967/hf/4007 }, publisher = { Hugging Face } } ### Model Sources - **Repository:** https://github.com/Raiff1982/MyBot.git ## Uses ### Direct Use Interacting with users to provide insightful responses and enhance user interactions. ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations ### Bias Detection and Mitigation - **Training Data Review**: The training data is carefully reviewed and curated to minimize biases. Diverse and representative datasets are used to ensure the model learns from a wide range of perspectives. - **Algorithmic Fairness**: Techniques such as re-weighting, re-sampling, and adversarial debiasing are applied to reduce biases in the model's predictions. - **Continuous Monitoring**: The model's outputs are continuously monitored for biased behavior. If biases are detected, corrective measures are taken to retrain or adjust the model. ### Ethical Decision Making - **Ethical Guidelines**: MyBot follows a set of ethical guidelines to ensure its decisions and actions align with ethical standards. These guidelines are integrated into the model's decision-making processes. - **Transparency and Explainability**: MyBot provides explanations for its decisions, allowing users to understand the reasoning behind its actions. This transparency helps build trust and ensures accountability. ### Risk Mitigation - **Context Awareness**: MyBot is designed to be context-aware, understanding the user's environment, activities, and emotional state. This helps it provide more relevant and appropriate responses, reducing the risk of misunderstandings or inappropriate interactions. - **User Feedback**: MyBot encourages user feedback to identify and address any issues or concerns. This feedback loop helps improve the model and mitigate potential risks. - **Out-of-Scope Use**: MyBot clearly defines its intended use cases and limitations. It is designed to recognize and avoid out-of-scope or malicious use, reducing the risk of misuse. ### Sentiment Analysis - **Emotionally Intelligent Responses**: By analyzing user sentiment, MyBot can tailor its responses to be more empathetic and appropriate to the user's emotional state. This helps prevent negative interactions and ensures a positive user experience. ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ```bash git clone https://github.com/yourusername/mybot.git cd mybot python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate` pip install -r requirements.txt echo "AZURE_OPENAI_API_KEY=your_openai_api_key" >> .env echo "AZURE_OPENAI_ENDPOINT=your_openai_endpoint" >> .env echo "LUIS_ENDPOINT=your_luis_endpoint" >> .env echo "LUIS_API_VERSION=your_luis_api_version" >> .env echo "LUIS_API_KEY=your_luis_api_key" >> .env python main.py ``` ## Training Details ### Training Data - fka/awesome-chatgpt-prompts - gopipasala/fka-awesome-chatgpt-prompts - O1-OPEN/OpenO1-SFT ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics - code_eval - accuracy - bertscore - character ### Results [More Information Needed] #### Summary [More Information Needed] ## Model Examination [optional] [More Information Needed] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - **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 MyBot is built using the BotBuilder framework, designed to leverage multiple perspectives to generate insightful responses. It integrates various components such as sentiment analysis, context awareness, ethical decision-making, and dialog management to enhance user interactions. ### Compute Infrastructure MyBot is designed to run on cloud-based infrastructure, ensuring scalability and reliability. It can be deployed on various cloud providers, depending on the user's preference. #### Hardware MyBot can be deployed on standard cloud-based hardware configurations, including virtual machines and containerized environments. #### Software MyBot is built using the BotBuilder framework and integrates with various NLP libraries and APIs, such as Azure OpenAI, LUIS, and BERT. ## Security Capabilities ### Data Encryption - **In-Transit Encryption**: All data transmitted between users and MyBot is encrypted using secure protocols (e.g., HTTPS, TLS) to protect against interception and eavesdropping. - **At-Rest Encryption**: Data stored by MyBot is encrypted to prevent unauthorized access, ensuring that sensitive information remains secure. ### Authentication and Authorization - **User Authentication**: MyBot supports various authentication methods (e.g., OAuth, API keys) to verify the identity of users and ensure that only authorized individuals can access the bot. - **Role-Based Access Control (RBAC)**: MyBot implements RBAC to restrict access to certain features and data based on user roles, ensuring that users only have access to the information and functionalities they need. ### Data Privacy - **Compliance with Regulations**: MyBot adheres to data privacy regulations (e.g., GDPR, CCPA) to ensure that user data is handled responsibly and transparently. - **Data Anonymization**: Personal data is anonymized where possible to protect user privacy and reduce the risk of data breaches. ### Threat Detection and Prevention - **Intrusion Detection Systems (IDS)**: MyBot uses IDS to monitor for suspicious activities and potential security threats, allowing for timely detection and response. - **Regular Security Audits**: MyBot undergoes regular security audits and vulnerability assessments to identify and address potential security weaknesses. ### User Consent and Control - **Informed Consent**: Users are informed about data collection and usage practices, and their consent is obtained before collecting any personal information. - **Data Control**: Users have control over their data, including the ability to access, modify, and delete their information as