TableGPT-agent
T
Tablegpt Agent
Overview :
TableGPT-agent is a pre-built agent model based on TableGPT2, designed for question-answering tasks involving tabular data. Developed using the Langgraph library, it offers a user-friendly interface and efficiently handles complex table-related questions. TableGPT2 is a large multimodal model that combines tabular data with natural language processing, providing powerful support for data analysis and knowledge extraction. This model is suitable for scenarios requiring fast and accurate processing of tabular data, such as data analysis, business intelligence, and academic research.
Target Users :
TableGPT-agent is designed for developers, data analysts, and researchers who need to process tabular data. It helps users quickly extract information from tables, improving data analysis efficiency, particularly in scenarios requiring knowledge discovery and question answering within complex tabular data.
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Use Cases
Data analysts use TableGPT-agent to quickly extract key data from financial reports.
Researchers use the model to analyze experimental data tables, accelerating the research process.
Company employees use TableGPT-agent to query sales data and gain business insights.
Features
Provides question answering functionality based on tabular data, understanding table structure and generating accurate answers.
Supports input from various table formats, including CSV and Excel.
Integrates the Langgraph library, providing efficient table processing and analysis capabilities.
Includes evaluation scripts for testing model performance on various tabular benchmark datasets.
Supports custom loading and processing of tabular data, adapting to diverse application scenarios.
How to Use
1. Clone the TableGPT-agent repository to your local machine.
2. Install required dependencies, such as Langgraph and the TableGPT2 model.
3. Prepare your tabular data file (e.g., CSV or Excel format).
4. Load the tabular data using the TableGPT-agent interface.
5. Input your question and invoke the model for question answering to obtain results.
6. Adjust model parameters as needed to optimize performance.
7. Use the evaluation scripts to test the model's performance on different datasets.
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