Multitabqa Base

🚀 MultiTabQA (base-sized model)

MultiTabQA is a table question - answering model that generates tabular answers for multi - table questions, addressing real - world complex queries over multiple tables.

🚀 Quick Start

MultiTabQA was proposed in MultiTabQA: Generating Tabular Answers for Multi - Table Question Answering by Vaishali Pal, Andrew Yates, Evangelos Kanoulas, Maarten de Rijke. The original repo can be found here.

✨ Features

Model description

MultiTabQA is a tableQA model which generates the answer table from multiple - input tables. It can handle multi - table operators such as UNION, INTERSECT, EXCEPT, JOINS, etc.

MultiTabQA is based on the TAPEX(BART) architecture, which is a bidirectional (BERT - like) encoder and an autoregressive (GPT - like) decoder.

Intended Uses

You can use the raw model SQL execution over multiple input tables. The model has been finetuned on Spider dataset where it answers natural language questions over multiple input tables.

📦 Installation

No specific installation steps are provided in the original README.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import pandas as pd

tokenizer = AutoTokenizer.from_pretrained("vaishali/multitabqa-base")
model = AutoModelForSeq2SeqLM.from_pretrained("vaishali/multitabqa-base")

question = "How many departments are led by heads who are not mentioned?"
table_names = ['department', 'management']
tables=[{"columns":["Department_ID","Name","Creation","Ranking","Budget_in_Billions","Num_Employees"],
                  "index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],
                  "data":[
                          [1,"State","1789",1,9.96,30266.0],
                          [2,"Treasury","1789",2,11.1,115897.0],
                          [3,"Defense","1947",3,439.3,3000000.0],
                          [4,"Justice","1870",4,23.4,112557.0],
                          [5,"Interior","1849",5,10.7,71436.0],
                          [6,"Agriculture","1889",6,77.6,109832.0],
                          [7,"Commerce","1903",7,6.2,36000.0],
                          [8,"Labor","1913",8,59.7,17347.0],
                          [9,"Health and Human Services","1953",9,543.2,67000.0],
                          [10,"Housing and Urban Development","1965",10,46.2,10600.0],
                          [11,"Transportation","1966",11,58.0,58622.0],
                          [12,"Energy","1977",12,21.5,116100.0],
                          [13,"Education","1979",13,62.8,4487.0],
                          [14,"Veterans Affairs","1989",14,73.2,235000.0],
                          [15,"Homeland Security","2002",15,44.6,208000.0]
                        ]
                  },
                  {"columns":["department_ID","head_ID","temporary_acting"],
                    "index":[0,1,2,3,4],
                    "data":[
                            [2,5,"Yes"],
                            [15,4,"Yes"],
                            [2,6,"Yes"],
                            [7,3,"No"],
                            [11,10,"No"]
                          ]
                  }]

input_tables = [pd.read_json(table, orient="split") for table in tables]

# flatten the model inputs in the format: query + " " + <table_name> : table_name1 + flattened_table1 + <table_name> : table_name2 + flattened_table2 + ...  
#flattened_input = question + " " + [f"<table_name> : {table_name} linearize_table(table) for table_name, table in zip(table_names, tables)]
model_input_string = """How many departments are led by heads who are not mentioned? <table_name> : department col : Department_ID | Name | Creation | Ranking | Budget_in_Billions | Num_Employees row 1 : 1 | State | 1789 | 1 | 9.96 | 30266 row 2 : 2 | Treasury | 1789 | 2 | 11.1 | 115897 row 3 : 3 | Defense | 1947 | 3 | 439.3 | 3000000 row 4 : 4 | Justice | 1870 | 4 | 23.4 | 112557 row 5 : 5 | Interior | 1849 | 5 | 10.7 | 71436 row 6 : 6 | Agriculture | 1889 | 6 | 77.6 | 109832 row 7 : 7 | Commerce | 1903 | 7 | 6.2 | 36000 row 8 : 8 | Labor | 1913 | 8 | 59.7 | 17347 row 9 : 9 | Health and Human Services | 1953 | 9 | 543.2 | 67000 row 10 : 10 | Housing and Urban Development | 1965 | 10 | 46.2 | 10600 row 11 : 11 | Transportation | 1966 | 11 | 58.0 | 58622 row 12 : 12 | Energy | 1977 | 12 | 21.5 | 116100 row 13 : 13 | Education | 1979 | 13 | 62.8 | 4487 row 14 : 14 | Veterans Affairs | 1989 | 14 | 73.2 | 235000 row 15 : 15 | Homeland Security | 2002 | 15 | 44.6 | 208000 <table_name> : management col : department_ID | head_ID | temporary_acting row 1 : 2 | 5 | Yes row 2 : 15 | 4 | Yes row 3 : 2 | 6 | Yes row 4 : 7 | 3 | No row 5 : 11 | 10 | No"""
inputs = tokenizer(model_input_string, return_tensors="pt")

outputs = model.generate(**inputs)

print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# 'col : count(*) row 1 : 11'

Advanced Usage

There is no advanced usage example provided in the original README.

How to Fine - tuning

Please find the fine - tuning script here.

📚 Documentation

BibTeX entry and citation info

@inproceedings{pal-etal-2023-multitabqa,
    title = "{M}ulti{T}ab{QA}: Generating Tabular Answers for Multi-Table Question Answering",
    author = "Pal, Vaishali  and
      Yates, Andrew  and
      Kanoulas, Evangelos  and
      de Rijke, Maarten",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.348",
    doi = "10.18653/v1/2023.acl-long.348",
    pages = "6322--6334",
    abstract = "Recent advances in tabular question answering (QA) with large language models are constrained in their coverage and only answer questions over a single table. However, real-world queries are complex in nature, often over multiple tables in a relational database or web page. Single table questions do not involve common table operations such as set operations, Cartesian products (joins), or nested queries. Furthermore, multi-table operations often result in a tabular output, which necessitates table generation capabilities of tabular QA models. To fill this gap, we propose a new task of answering questions over multiple tables. Our model, MultiTabQA, not only answers questions over multiple tables, but also generalizes to generate tabular answers. To enable effective training, we build a pre-training dataset comprising of 132,645 SQL queries and tabular answers. Further, we evaluate the generated tables by introducing table-specific metrics of varying strictness assessing various levels of granularity of the table structure. MultiTabQA outperforms state-of-the-art single table QA models adapted to a multi-table QA setting by finetuning on three datasets: Spider, Atis and GeoQuery.",
}

📄 License

This project is licensed under the MIT license.