Hi, I am building a custom classifier for coding job descriptions. I have two text inputs that I concatenate, preprocess, and embed using a sentenceTransformer during training (in python) and the feature-extraction pipeline during prediction (transformers.js). At training time, I use tensorflow to train a multilayer classifier that also take another multi-hot encoded input. I save my classifer as an onnx model. I have written a web app that uses my classifier, but I would love to have a pipeline that others could use like: let job_classifier = pipeline('job-classifer','DR/job_classifer_v1') my current code very incomplete looks like this: import { env, pipeline, Pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.3.2'; import * as ort from 'https://cdn.jsdelivr.net/npm/onnxruntime-web@1.19.2/dist/ort.webgpu.bundle.min.mjs'; class JobClassificationPipeline extends Pipeline { constructor(modelPath) { super('job-classification') this.modelPath = modelPath this.initialized = false } async initialize() { this.initialized = true; this.config = await (await fetch(`${this.modelPath}/config.json`)).json(); this.abbreviations = await (await fetch(`${this.modelPath}/abbrev.json`)).json(); this.crosswalks = {} for (const [key, path] of Object.entries(this.config.known_crosswalks)) { console.log(`... loading ${key} crosswalk ...`) this.crosswalks[key] = await (await fetch(path)).json() } this.embeddingPipeline = await pipeline('text-embedding',"") } async _call(jobDescription) { if (!this.initialized) { await this.initialize(); } console.log(this.config) let pp = this.preprocess(jobDescription) let embeddings = console.log(pp) this.crosswalk(jobDescription) } crosswalk(jobDescription) { if (!Array.isArray(jobDescription)) { jobDescription = [jobDescription] } console.log(this.crosswalks) } preprocess(jobDescription) { console.log(jobDescription) if (!Array.isArray(jobDescription)) { jobDescription = [jobDescription] } console.log(this.abbreviations) return jobDescription.map(x => x.JobTitle ?? "") } } let j27 = new JobClassificationPipeline('models/s3_jan27') j27({ "JobTitle": "doctor", "JobTask": "see patients", "soc1980": "261" }) Before I go too far down this rabbit hole, is this the wrong approach? Should I just create an ESM that wraps the huggingface encoder and my onnx model?