TableGPT2

TableGPT2 is a large-scale multi-modal model that is specially pre-trained and fine-tuned for tabular data to solve the problem of insufficient integration of tabular data in practical applications. The model is pre-trained and fine-tuned on over 593.8K tables and 2.36M high-quality query-table-output tuples, at an unprecedented scale. One of the key innovations of TableGPT2 is its novel table encoder, specifically designed to capture schema-level and cell-level information, enhancing the model's ability to handle ambiguous queries, missing column names, and irregular tables. On 23 benchmark indicators, TableGPT2 has an average performance improvement of 35.20% on the 7B model and 49.32% on the 72B model, while maintaining strong general language and coding capabilities.

TableGPT2的目标受众是数据科学家、开发者和企业，他们需要处理和分析大量的表格数据。该模型能够帮助他们更准确地理解和操作数据库或数据仓库中的数据，尤其是在业务智能领域，提供了适应性强、精确的解决方案，解决了当前大型语言模型可能难以提供的精确度和适应性问题。