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sports knowledge

Sports Model is a chatbot specifically designed to assist users with sports-related inquiries.

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Description

Sports Model is a chatbot specifically designed to assist users with sports-related inquiries. By integrating data about various sports, teams, and players into a large language model (LLM), this chatbot provides insightful information and advice about sports events, trends, and updates.

Features Interactive chatbot interface for answering sports-related questions. Extensive database of sports information, including teams, leagues, athletes, scores, and more. Personalized advice and insights based on user queries about specific sports, teams, or players. Installation To get started with Sports Model, follow these steps:

Clone the repository:

bash Copy code git clone https://github.com/sports-ai/sports-model.git cd SportsModel Install the Gaia Node:

bash Copy code curl -sSfL 'https://github.com/GaiaNet-AI/gaianet-node/releases/latest/download/install.sh' | bash Update your config.json to run with a sports-focused language model:

bash Copy code gaianet init --config https://raw.githubusercontent.com/sports-ai/config/main/config_sports.json Start the node:

bash Copy code gaianet start How to Use Once the node is started, you will receive a generated link. Open the link in your web browser. Start interacting with the chatbot by asking any sports-related questions. For example, you can inquire about live scores, player stats, upcoming matches, or sports trivia. License This project is licensed under the MIT License. See the LICENSE file for more details.

Progress During Hackathon

Development Process 1. Dataset Creation: We began by curating a comprehensive dataset consisting of over 200 prompts and responses related to various sports. These included basic questions like "How long is a soccer match?" to more complex ones like "What is a grand slam in tennis?" The dataset covered not only rules and technical aspects of different sports but also player roles, scoring methods, and game strategies. 2. Model Training: To power the chatbot, we leveraged a large language model (LLM), fine-tuned with the sports-related dataset.

Team LeaderSsanyamjain660
Sector
SocialFiAI