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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;