Add demo to zero shot classification (#596)

* add demo to zero shot classification and put it live

* removed redundant header

Author: merveenoyan

tasks/src/tasksData.ts

@@ -29,6 +29,7 @@ import textGeneration from "./text-generation/data";
 import unconditionalImageGeneration from "./unconditional-image-generation/data";
 import videoClassification from "./video-classification/data";
 import visualQuestionAnswering from "./visual-question-answering/data";
+import zeroShotClassification from "./zero-shot-classification/data";
 import zeroShotImageClassification from "./zero-shot-image-classification/data";
 import { TASKS_MODEL_LIBRARIES } from "./const";
 
@@ -75,7 +76,7 @@ export const TASKS_DATA: Record<PipelineType, TaskData | undefined> = {
 	"unconditional-image-generation": getData("unconditional-image-generation", unconditionalImageGeneration),
 	"visual-question-answering":      getData("visual-question-answering", visualQuestionAnswering),
 	"voice-activity-detection":       getData("voice-activity-detection"),
-	"zero-shot-classification":       getData("zero-shot-classification"),
+	"zero-shot-classification":       getData("zero-shot-classification", zeroShotClassification),
 	"zero-shot-image-classification": getData("zero-shot-image-classification", zeroShotImageClassification),
 } as const;
 

tasks/src/zero-shot-classification/about.md

@@ -1,5 +1,3 @@
-# Zero Shot Classification
-
 ## About the Task
 
 Zero Shot Classification is the task of predicting a class that wasn't seen by the model during training. This method, which leverages a pre-trained language model, can be thought of as an instance of [transfer learning](https://www.youtube.com/watch?v=BqqfQnyjmgg) which generally refers to using a model trained for one task in a different application than what it was originally trained for. This is particularly useful for situations where the amount of labeled data is small.

tasks/src/zero-shot-classification/data.ts

@@ -3,9 +3,39 @@ import type { TaskDataCustom } from "../Types";
 const taskData: TaskDataCustom = {
 
 	datasets:     [],
-	demo:     {
-		inputs:  [],
-		outputs: [],
+	demo: {
+		inputs: [
+			{
+				label:   "Text Input",
+				content: "Dune is the best movie ever.",
+				type:    "text",
+			},
+			{
+				label:   "Candidate Labels",
+				content: "CINEMA, ART, MUSIC",
+				type:    "text",
+			},
+			
+		],
+		outputs: [
+			{
+				type: "chart",
+				data: [
+					{
+						label: "CINEMA",
+						score: 0.90,
+					},
+					{
+						label: "ART",
+						score: 0.10,
+					},
+					{
+						label: "MUSIC",
+						score: 0.00,
+					},
+				],
+			},
+		],
 	},
 	metrics:      [],
 	models:       [