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import type { TaskDataCustom } from "../Types";
const taskData: TaskDataCustom = {
datasets: [
{
description: "A widely used dataset useful to benchmark named entity recognition models.",
id: "conll2003",
},
{
description:
"A multilingual dataset of Wikipedia articles annotated for named entity recognition in over 150 different languages.",
id: "wikiann",
},
],
demo: {
inputs: [
{
label: "Input",
content: "My name is Omar and I live in Zürich.",
type: "text",
},
],
outputs: [
{
text: "My name is Omar and I live in Zürich.",
tokens: [
{
type: "PERSON",
start: 11,
end: 15,
},
{
type: "GPE",
start: 30,
end: 36,
},
],
type: "text-with-tokens",
},
],
},
metrics: [
{
description: "",
id: "accuracy",
},
{
description: "",
id: "recall",
},
{
description: "",
id: "precision",
},
{
description: "",
id: "f1",
},
],
models: [
{
description:
"A robust performance model to identify people, locations, organizations and names of miscellaneous entities.",
id: "dslim/bert-base-NER",
},
{
description: "Flair models are typically the state of the art in named entity recognition tasks.",
id: "flair/ner-english",
},
],
spaces: [
{
description:
"An application that can recognizes entities, extracts noun chunks and recognizes various linguistic features of each token.",
id: "spacy/gradio_pipeline_visualizer",
},
],
summary:
"Token classification is a natural language understanding task in which a label is assigned to some tokens in a text. Some popular token classification subtasks are Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. NER models could be trained to identify specific entities in a text, such as dates, individuals and places; and PoS tagging would identify, for example, which words in a text are verbs, nouns, and punctuation marks.",
widgetModels: ["dslim/bert-base-NER"],
youtubeId: "wVHdVlPScxA",
};
export default taskData;
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