Reviewable Jev lead intent for ERPNext
Classification & labelling — page 2
Classifiers for support tickets, documents, content and user intent. Jev returns a label and a confidence instead of generated text.
59 active projects match this task, ordered by traction.
Say what you're working on; get a gentle nudge when you drift to pages that aren't part of it. Chrome extension powered by Jev.
Local-first SEC 8-K research lab: score filings point-in-time with Jev, OpenAI or local LLMs, and label which results can count as evidence. Never places orders.
Jev intent classification for Mautic form submissions
Turns raw smartphone reviews into per-topic star ratings (Camera, Battery, Display, etc.) using Jev LLM analysis. Real-time text-to-rating pipeline, no manual tagging needed.
Models interpret, rules decide: a fail-closed neuro-symbolic router in which language models only understand the text and a state machine carries out what the rules allow.
Sort your Gmail inbox into categories you write in plain English. TypeSafe's Jev makes each call with calibrated probabilities; uncertain mail gets a Review label. Built on One, runs with zero keys.
Do you need TypeSafe's Jev, or is cosine similarity enough? A $0, reproducible benchmark on Banking77 intent routing.
An example repository using the TypeSafe Jev model to do sentiment analysis
Reproducible Laya-CoreML vs Jev benchmark for zero-shot intent classification on Banking77, ArBanking77, and CLINC150. Includes paired accuracy and per-dataset results.
A jev intelligence layer for event stream to classify the app traffic into human vs ai_agent vs seo_tool vs scraper vs other_automation with bad_intent probability
Android app that reads visible Bilibili comments and shows a floating sentiment distribution, powered by TypeSafe Jev.
Head-to-head evaluation of Laya (open weights) and TypeSafe Jev on email intent classification
Non-intrusive QQ chat intent & risk HUD powered by TypeSafe Jev (System One) - 无侵入的 QQ 聊天意图/风险悬浮卡
Reusable TypeSafe Jev template for classifying and tagging incoming email.
Type-safe intent classification guard for agentic workflows
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Compare TypeSafe Jev vs Codex/Claude Code for inbox triage — tokens, latency, and label agreement.
Measured benchmarks of TypeSafe's Jev decision model vs LLMs: ticket triage, voice-agent decision layer, bulk tagging
Prioritize human labels with active-learning strategies, durable review history and clean holdout evaluation.
Control your own Chrome by voice. Jev (TypeSafe System One) decides intent + target from typed probabilities in ~300 ms per spoken word; the extension acts before you finish the sentence. MV3 port of moritzkremb/jev-voice-browser.
Turn source text into standalone, evidence-backed claims with integrity checks, bounded recovery and entity tagging.
How good is Jev? TypeSafe Jev vs an LLM on the same intent-routing task.
Crypto screening: quant signals in code (CoinGecko), news judgment via Jev (TypeSafe System One) — sentiment/catalyst/confirmed, not a trading bot
Benchmarks of TypeSafe Jev on Finnish text: reranking, code search without an index, intent classification, citation checking and lecture-transcript reranking.