import type { TaskDataCustom } from "../Types";

const taskData: TaskDataCustom = {
	datasets: [
		{
			description: "A common dataset that is used to train models for many languages.",
			id: "wikipedia",
		},
		{
			description: "A large English dataset with text crawled from the web.",
			id: "c4",
		},
	],
	demo: {
		inputs: [
			{
				label: "Input",
				content: "The <mask> barked at me",
				type: "text",
			},
		],
		outputs: [
			{
				type: "chart",
				data: [
					{
						label: "wolf",
						score: 0.487,
					},
					{
						label: "dog",
						score: 0.061,
					},
					{
						label: "cat",
						score: 0.058,
					},
					{
						label: "fox",
						score: 0.047,
					},
					{
						label: "squirrel",
						score: 0.025,
					},
				],
			},
		],
	},
	metrics: [
		{
			description:
				"Cross Entropy is a metric that calculates the difference between two probability distributions. Each probability distribution is the distribution of predicted words",
			id: "cross_entropy",
		},
		{
			description:
				"Perplexity is the exponential of the cross-entropy loss. It evaluates the probabilities assigned to the next word by the model. Lower perplexity indicates better performance",
			id: "perplexity",
		},
	],
	models: [
		{
			description: "A faster and smaller model than the famous BERT model.",
			id: "distilbert-base-uncased",
		},
		{
			description: "A multilingual model trained on 100 languages.",
			id: "xlm-roberta-base",
		},
	],
	spaces: [],
	summary:
		"Masked language modeling is the task of masking some of the words in a sentence and predicting which words should replace those masks. These models are useful when we want to get a statistical understanding of the language in which the model is trained in.",
	widgetModels: ["distilroberta-base"],
	youtubeId: "mqElG5QJWUg",
};

export default taskData;