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| import type { TaskDataCustom } from "../Types"; | |
| const taskData: TaskDataCustom = { | |
| datasets: [ | |
| { | |
| description: "A widely used dataset used to benchmark multiple variants of text classification.", | |
| id: "glue", | |
| }, | |
| { | |
| description: "A text classification dataset used to benchmark natural language inference models", | |
| id: "snli", | |
| }, | |
| ], | |
| demo: { | |
| inputs: [ | |
| { | |
| label: "Input", | |
| content: "I love Hugging Face!", | |
| type: "text", | |
| }, | |
| ], | |
| outputs: [ | |
| { | |
| type: "chart", | |
| data: [ | |
| { | |
| label: "POSITIVE", | |
| score: 0.9, | |
| }, | |
| { | |
| label: "NEUTRAL", | |
| score: 0.1, | |
| }, | |
| { | |
| label: "NEGATIVE", | |
| score: 0.0, | |
| }, | |
| ], | |
| }, | |
| ], | |
| }, | |
| metrics: [ | |
| { | |
| description: "", | |
| id: "accuracy", | |
| }, | |
| { | |
| description: "", | |
| id: "recall", | |
| }, | |
| { | |
| description: "", | |
| id: "precision", | |
| }, | |
| { | |
| description: | |
| "The F1 metric is the harmonic mean of the precision and recall. It can be calculated as: F1 = 2 * (precision * recall) / (precision + recall)", | |
| id: "f1", | |
| }, | |
| ], | |
| models: [ | |
| { | |
| description: "A robust model trained for sentiment analysis.", | |
| id: "distilbert-base-uncased-finetuned-sst-2-english", | |
| }, | |
| { | |
| description: "Multi-genre natural language inference model.", | |
| id: "roberta-large-mnli", | |
| }, | |
| ], | |
| spaces: [ | |
| { | |
| description: "An application that can classify financial sentiment.", | |
| id: "IoannisTr/Tech_Stocks_Trading_Assistant", | |
| }, | |
| { | |
| description: "A dashboard that contains various text classification tasks.", | |
| id: "miesnerjacob/Multi-task-NLP", | |
| }, | |
| { | |
| description: "An application that analyzes user reviews in healthcare.", | |
| id: "spacy/healthsea-demo", | |
| }, | |
| ], | |
| summary: | |
| "Text Classification is the task of assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness.", | |
| widgetModels: ["distilbert-base-uncased-finetuned-sst-2-english"], | |
| youtubeId: "leNG9fN9FQU", | |
| }; | |
| export default taskData; | |