CommunityJev Guides & Articles
Jev Classifier
A browser tool where you paste a Telegram channel JSON export and each post gets four Jev labels: category, quality, sentiment and reaction tone, written out as result.json.
- Format
- Article
- Published by
- Community
- Last verified
- Oct 2026
Works forJev for Classification
Used inJev for Software & DevTools
Highlights
- Four questions against typesafe-ai/jev, listed in Settings: category, quality, sentiment and reaction tone.
- The category list is a fixed option set including tech, quote, job, event, news, promo, education and humor.
- Every option shows a probability readout, so a result is a distribution rather than one hard label.
- Output is a result.json drop target, alongside an Export labels action and a 3-post sample to try.
Watch out
The captured page is the tool's interface, not documentation, so what it does not state matters as much as what it does. Unknown: whether the Telegram JSON is processed in the browser or uploaded, where the Jev key from the LLM Settings key field is stored, what a run costs, whether posts are sent one request at a time or batched, what happens at rate limits, and what the Conversation, Duel and Group controls do. All four questions are choice-style over fixed option lists, which caps what it can label: a post matching none of the categories must still land in other, quality is limited to low, medium or high, and there is no free-text or multi-label output. Accuracy appears nowhere on the page, with no test set and no comparison against another model. typesafe-ai/jev is the model string shown, but whether it is changeable is not visible. Copy reviewed against the linked page on 2026-10-04.
Questions about this entry
Short answers drawn from the linked project or page.
The page does not say. It shows an area to paste the channel JSON, a Load 3-post sample action and a drag-and-drop target for result.json, but no statement about where processing happens, whether the export is stored or how long it is kept. If data handling matters to you, you would need to inspect the page itself, which is not part of what was captured.
There is a Key field under LLM Settings and the model string shown is typesafe-ai/jev, but the page does not say whether the key is kept in local storage, sent to a server or masked, and no alternative model appears as an option. It is also unclear whether the four questions are editable; the page only says a run uses the saved Settings questions.
Neither number is on the page. There is no accuracy figure, no test set and no comparison against another model, and no price or token count for a run. The one thing you can do yourself is read the probability readouts the UI already shows per option, which tells you where the model is unsure on your own data instead of relying on a published score.
It has to land somewhere, because every question is a fixed set of labelled options with percentages. The category set includes tech, quote, job, event, spiritual, personal, news, promo, education, humor and other, and quality is only low, medium or high. The page describes no escape hatch, no custom option and no free-text output for genuinely ambiguous posts.
Category covers tech, quote, job, event, spiritual, personal, news, promo, education, humor and other. Quality is low, medium or high. Post sentiment is positive, neutral or negative. Reaction tone is none, funny, love, supportive, angry, sad, wow or mixed.
Paste a Telegram channel JSON export into the page or use the Load 3-post sample action, set the run, then classify. The output goes to a result.json drop target, with an Export labels action alongside.
Not documented. The page does not say whether posts are sent to Jev one request at a time or in batches, nor what happens at rate limits, so the request pattern behind a large export is not visible from the capture.
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