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TableGPT2: A Multimodal Model for Tabular Data Integration

General Introduction

TableGPT2 is a multimodal model developed by a team from Zhejiang University, focusing on the integration and processing of tabular data. The model is pre-trained and fine-tuned to excel in tabular data related tasks while maintaining strong general-purpose language and coding capabilities.TableGPT2's innovation lies in its unique tabular encoder, which is capable of handling common problems in real-world applications such as fuzzy queries, missing column names and irregular tables.

 

Function List

  • Tabular Data Processing: Ability to efficiently process and analyze structured tabular data.
  • multimodal integration: Combines text and tabular data to provide more comprehensive analytical capabilities.
  • Advanced Query Processing: Handles complex queries, supports fuzzy queries and missing data.
  • Business Intelligence Applications: Applicable to various business intelligence scenarios, providing accurate data analysis and decision support.
  • open source (computing): Open source code and detailed documentation are provided for easy integration and secondary development.

 

Using Help

Installation and Configuration

  1. Installation of dependencies: Ensure that the latest version of the transformers library is installed.
    pip install transformers>=4.37.0
    
  2. Loading Models: Use the following code to load the TableGPT2 model.
    from transformers import AutoModelForCausalLM, AutoTokenizer
    model = AutoModelForCausalLM.from_pretrained("tablegpt/TableGPT2-7B")
    tokenizer = AutoTokenizer.from_pretrained("tablegpt/TableGPT2-7B")
    

usage example

  1. Reading table data: Use pandas to read CSV files.
    import pandas as pd
    data = pd.read_csv("example.csv")
    
  2. Generating Queries: Build the query and generate the response.
    query = "show sales data for 2023"
    inputs = tokenizer(query, return_tensors="pt")
    outputs = model.generate(**inputs)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print(response)
    

Advanced Features

  1. Handling fuzzy queries: TableGPT2 is able to handle incomplete or fuzzy queries and provide accurate results.
  2. Missing data processing: The model intelligently fills in missing data columns to ensure data integrity.
  3. Irregular Forms Processing: TableGPT2 can effectively parse and process tables even if they are not formatted correctly.

Business Intelligence Applications

  1. data analysis: With TableGPT2, users can quickly analyze large amounts of tabular data to generate valuable business insights.
  2. Decision support: The model provides accurate data analysis results to help companies make informed decisions.
  3. Automated report generation: With TableGPT2, users can automatically generate detailed business reports, saving time and labor.

Open Source and Community Support

  1. open source: The code for TableGPT2 is open source on GitHub and users are free to download and use it.
  2. Community Support: The Hugging Face community provides a wealth of resources and support, and users can communicate and get help in the forums.

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