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| import { InferenceOutputError } from "../../lib/InferenceOutputError"; | |
| import type { BaseArgs, Options } from "../../types"; | |
| import { toArray } from "../../utils/toArray"; | |
| import { request } from "../custom/request"; | |
| export type ZeroShotClassificationArgs = BaseArgs & { | |
| /** | |
| * a string or list of strings | |
| */ | |
| inputs: string | string[]; | |
| parameters: { | |
| /** | |
| * a list of strings that are potential classes for inputs. (max 10 candidate_labels, for more, simply run multiple requests, results are going to be misleading if using too many candidate_labels anyway. If you want to keep the exact same, you can simply run multi_label=True and do the scaling on your end. | |
| */ | |
| candidate_labels: string[]; | |
| /** | |
| * (Default: false) Boolean that is set to True if classes can overlap | |
| */ | |
| multi_label?: boolean; | |
| }; | |
| }; | |
| export interface ZeroShotClassificationOutputValue { | |
| labels: string[]; | |
| scores: number[]; | |
| sequence: string; | |
| } | |
| export type ZeroShotClassificationOutput = ZeroShotClassificationOutputValue[]; | |
| /** | |
| * This task is super useful to try out classification with zero code, you simply pass a sentence/paragraph and the possible labels for that sentence, and you get a result. Recommended model: facebook/bart-large-mnli. | |
| */ | |
| export async function zeroShotClassification( | |
| args: ZeroShotClassificationArgs, | |
| options?: Options | |
| ): Promise<ZeroShotClassificationOutput> { | |
| const res = toArray( | |
| await request<ZeroShotClassificationOutput[number] | ZeroShotClassificationOutput>(args, { | |
| ...options, | |
| taskHint: "zero-shot-classification", | |
| }) | |
| ); | |
| const isValidOutput = | |
| Array.isArray(res) && | |
| res.every( | |
| (x) => | |
| Array.isArray(x.labels) && | |
| x.labels.every((_label) => typeof _label === "string") && | |
| Array.isArray(x.scores) && | |
| x.scores.every((_score) => typeof _score === "number") && | |
| typeof x.sequence === "string" | |
| ); | |
| if (!isValidOutput) { | |
| throw new InferenceOutputError("Expected Array<{labels: string[], scores: number[], sequence: string}>"); | |
| } | |
| return res; | |
| } | |