Table Question Answering
Table Question Answering (Table QA) is the answering a question about an information on a given table.
Rank | Name | No.of reigns | Combined days |
---|---|---|---|
1 | lou Thesz | 3 | 3749 |
2 | Ric Flair | 8 | 3103 |
3 | Harley Race | 7 | 1799 |
Question
What is the number of reigns for Harley Race?
Result
7
About Table Question Answering
Use Cases
SQL execution
You can use the Table Question Answering models to simulate SQL execution by inputting a table.
Table Question Answering
Table Question Answering models are capable of answering questions based on a table.
Task Variants
This place can be filled with variants of this task if there's any.
Inference
You can infer with TableQA models using the 🤗 Transformers library.
from transformers import pipeline
import pandas as pd
# prepare table + question
data = {"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]}
table = pd.DataFrame.from_dict(data)
question = "how many movies does Leonardo Di Caprio have?"
# pipeline model
# Note: you must to install torch-scatter first.
tqa = pipeline(task="table-question-answering", model="google/tapas-large-finetuned-wtq")
# result
print(tqa(table=table, query=question)['cells'][0])
#53
Useful Resources
In this area, you can insert useful resources about how to train or use a model for this task.
This task page is complete thanks to the efforts of Hao Kim Tieu. 🦸
Compatible libraries
Note A table question answering model that is capable of neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table.
Note A robust table question answering model.
No example dataset is defined for this task.
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No example Space is defined for this task.
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- Denotation Accuracy
- Checks whether the predicted answer(s) is the same as the ground-truth answer(s).