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.