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Run Hugging Face models in JavaScript or TypeScript with Transformers.js in Node.js or the browser.
Passed 23 of 23 test cases with the skill, and 20 of 23 without it.
3 fixed20 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
I'm building a client-side web application where users upload images, and I need to identify specific objects (like 'dog', 'car', 'hat') using Hugging Face's JS library directly in the browser. Could you write a JavaScript snippet that sets up zero-shot object detection, includes a download progress listener function, executes the detection on an image URL with candidate target labels, and properly frees model resources once finished?
| Checked | Without skill | With skill |
|---|---|---|
| Imports from @huggingface/transformers rather than @xenova/transformers | Missed | Passed |
| Selects zero-shot-object-detection task type for pipeline creation | Passed | Passed |
| Calls the dispose method on the pipeline instance | Passed | Passed |
The instructions your model reads when the skill loads.
Transformers.js enables running state-of-the-art machine learning models directly in JavaScript across browsers and server-side runtimes (Node.js, Bun, Deno), with no Python server required.
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
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test cases passed, vs 20 / 23 without
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