transformers-js 是做什麼的？
使用 Transformers.js 直接在 JavaScript/TypeScript 中執行最先進的機器學習模型。支援自然語言處理（文字分類、翻譯、摘要）、電腦視覺（圖像分類、物件偵測）、音訊（語音辨識、音訊分類）及多模態任務。可在瀏覽器和伺服器端執行環境（Node.js、Bun、Deno）中運作，透過 WebGPU/WASM 使用來自 Hugging Face Hub 的預訓練模型。
使用 Transformers.js 直接在 JavaScript/TypeScript 中執行最先進的機器學習模型。支援自然語言處理（文字分類、翻譯、摘要）、電腦視覺（圖像分類、物件偵測）、音訊（語音辨識、音訊分類）及多模態任務。可在瀏覽器和伺服器端執行環境（Node.js、Bun、Deno）中運作，透過 WebGPU/WASM 使用來自 Hugging Face Hub 的預訓練模型。
這個技能讓你能夠直接在 JavaScript 或 TypeScript 專案中，使用 Transformers.js 執行 Hugging Face Hub 上的預訓練機器學習模型。它涵蓋多種任務類型，包括自然語言處理（如文字分類、翻譯、摘要）、電腦視覺（如圖像分類、物件偵測）、音訊處理（如語音辨識、音訊分類），以及結合多種模態的任務。支援的執行環境非常廣泛，不僅可在瀏覽器中運行，也相容於 Node.js、Bun、Deno 等伺服器端執行環境。底層透過 WebGPU 和 WebAssembly 技術進行高效能推理，無需後端服務或 Python 運行環境。開發者只需安裝 @xenova/transformers 套件，即可輕鬆整合 AI 功能，非常適合用於全端開發、瀏覽器擴充功能或邊緣運算場景。
npx skills add https://github.com/huggingface/skills/tree/main/skills/transformers-jsInstalled? Explore more 影像 skills: steipete/songsee, affaan-m/frontend-design, mattpocock/improve-codebase-architecture · View all 6 →
使用 Transformers.js 直接在 JavaScript/TypeScript 中執行最先進的機器學習模型。支援自然語言處理（文字分類、翻譯、摘要）、電腦視覺（圖像分類、物件偵測）、音訊（語音辨識、音訊分類）及多模態任務。可在瀏覽器和伺服器端執行環境（Node.js、Bun、Deno）中運作，透過 WebGPU/WASM 使用來自 Hugging Face Hub 的預訓練模型。
This repo contains 5 individual skills — each has its own dedicated page.
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.
Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.
Generate spectrograms and feature-panel visualizations from audio with the songsee CLI.
Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
Add documentation for a new AI provider — usage docs, env vars, Docker config, image resources.
Tests in real browsers via Chrome DevTools MCP. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data. Requires the chrome-devtools MCP server to be configured.
This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.