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;