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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;
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