| | --- |
| | license: mit |
| | datasets: |
| | - fka/awesome-chatgpt-prompts |
| | - gopipasala/fka-awesome-chatgpt-prompts |
| | - Raiff1982/eval |
| | language: |
| | - en |
| | metrics: |
| | - accuracy |
| | - character |
| | - code_eval |
| | - cer |
| | - bertscore |
| | base_model: |
| | - meta-llama/Llama-3.3-70B-Instruct |
| | - black-forest-labs/FLUX.1-dev |
| | new_version: Raiff1982/CoderTheGoat |
| | library_name: transformers |
| | tags: |
| | - code |
| | pipeline_tag: any-to-any |
| | --- |
| | Model Card for MyBot |
| |
|
| | <!-- Provide a quick summary of what the model is/does. --> |
| | 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 |
| | |
| | <!-- Provide a quick summary of what the model is/does. --> |
| | |
| | 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 |
| | |
| | <!-- Provide a longer summary of what this model is. --> |
| | |
| | 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 |
| | |
| | <!-- Provide the basic links for the model. --> |
| | |
| | - **Repository:** https://github.com/Raiff1982/MyBot.git |
| | |
| | |
| | ## Uses |
| | |
| | <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
| | |
| | ### Direct Use |
| | |
| | <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
| | |
| | Interacting with users to provide insightful responses and enhance user interactions. |
| | |
| | ### Downstream Use [optional] |
| | |
| | <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
| | |
| | [More Information Needed] |
| | |
| | ### Out-of-Scope Use |
| | |
| | <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
| | |
| | [More Information Needed] |
| | |
| | ## Bias, Risks, and Limitations |
| | |
| | <!-- This section is meant to convey both technical and sociotechnical 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 |
| | |
| | <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
| | |
| | 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 |
| | |
| | <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
| | |
| | - fka/awesome-chatgpt-prompts |
| | - gopipasala/fka-awesome-chatgpt-prompts |
| | - O1-OPEN/OpenO1-SFT |
| | |
| | ### Training Procedure |
| | |
| | <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
| | |
| | #### Preprocessing [optional] |
| | |
| | [More Information Needed] |
| | |
| | #### Training Hyperparameters |
| | |
| | - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
| | |
| | #### Speeds, Sizes, Times [optional] |
| | |
| | <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
| | |
| | [More Information Needed] |
| | |
| | ## Evaluation |
| | |
| | <!-- This section describes the evaluation protocols and provides the results. --> |
| | |
| | ### Testing Data, Factors & Metrics |
| | |
| | #### Testing Data |
| | |
| | <!-- This should link to a Dataset Card if possible. --> |
| | |
| | [More Information Needed] |
| | |
| | #### Factors |
| | |
| | <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
| | |
| | [More Information Needed] |
| | |
| | #### Metrics |
| | |
| | <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
| | |
| | - code_eval |
| | - accuracy |
| | - bertscore |
| | - character |
| |
|
| | ### Results |
| |
|
| | [More Information Needed] |
| |
|
| | #### Summary |
| |
|
| | [More Information Needed] |
| |
|
| | ## Model Examination [optional] |
| |
|
| | <!-- Relevant interpretability work for the model goes here --> |
| |
|
| | [More Information Needed] |
| |
|
| | ## Environmental Impact |
| |
|
| | <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
| |
|
| | 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 |
