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import type { TaskDataCustom } from "../Types"; | |
const taskData: TaskDataCustom = { | |
datasets: [ | |
{ | |
description: | |
"The WikiTableQuestions dataset is a large-scale dataset for the task of question answering on semi-structured tables.", | |
id: "wikitablequestions", | |
}, | |
{ | |
description: | |
"WikiSQL is a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables from Wikipedia.", | |
id: "wikisql", | |
}, | |
], | |
demo: { | |
inputs: [ | |
{ | |
table: [ | |
["Rank", "Name", "No.of reigns", "Combined days"], | |
["1", "lou Thesz", "3", "3749"], | |
["2", "Ric Flair", "8", "3103"], | |
["3", "Harley Race", "7", "1799"], | |
], | |
type: "tabular", | |
}, | |
{ label: "Question", content: "What is the number of reigns for Harley Race?", type: "text" }, | |
], | |
outputs: [{ label: "Result", content: "7", type: "text" }], | |
}, | |
metrics: [ | |
{ | |
description: "Checks whether the predicted answer(s) is the same as the ground-truth answer(s).", | |
id: "Denotation Accuracy", | |
}, | |
], | |
models: [ | |
{ | |
description: | |
"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.", | |
id: "microsoft/tapex-base", | |
}, | |
{ | |
description: "A robust table question answering model.", | |
id: "google/tapas-base-finetuned-wtq", | |
}, | |
], | |
spaces: [ | |
{ | |
description: "An application that answers questions based on table CSV files.", | |
id: "katanaml/table-query", | |
}, | |
], | |
summary: "Table Question Answering (Table QA) is the answering a question about an information on a given table.", | |
widgetModels: ["google/tapas-base-finetuned-wtq"], | |
}; | |
export default taskData; | |