ericmey/five-lane-router

Five-Lane Request Router

A small, hand-labelled dataset for training a text classifier that decides what kind of answer a request needs, before any larger model runs:

label	meaning
chat	answer in words (conversation, explanations, opinions, text chores, and requests with nothing concrete to act on)
image	make a picture
search	look something up (facts, current information, or an existing public photo/clip)
audio	a voice note in the assistant's own voice (speech, singing, humming, whispering)
video	make an animation, clip, loop or gif

It is the training data for a fine-tuned ModernBERT-large router that runs on an NVIDIA Jetson Orin Nano (see the model card). It is published so you can train your own router, or fork the labels for your own lanes. The step-by-step tutorial is Train Your Own Request Router.

Format

JSON Lines, one object per row:

{"id": "t5-000123", "text": "user: hey, what should i wear to that party on saturday? i dont know what to pick.\nassistant: Since it's a party, comfort is key unless it's super formal. Maybe that blue shirt you liked? It looks great on you.\nuser: yeah i guess. show me", "label": "image"}

text is the conversation as the router sees it: the last up-to-8 turns, one per line, formatted role: text for user and assistant turns only. The last line is the request being routed. Most rows (1,914 of 2,334 in train, about 82%) are a single user turn; the other 420 include earlier context.

split	rows	chat	image	search	audio	video
train	2,334	806	604	247	263	414
validation	19	9	5	3	1	1

The validation split is small and was used for development feedback during training. It is not a benchmark. The model was judged on a separate sealed blind test set, which is deliberately not published (a published test set stops being blind).

Release export

These files are an export of the files the published model was trained on. They keep the same rows, in the same order, with identical id, text and label values; seven internal columns (labelling audit trail, generator slot, near-duplicate score and similar) were removed. The public files therefore hash differently from the training manifest:

	trained file SHA-256	public file SHA-256
train	82ae7c96cc7b2717235c37872206a70311020c9171976b923dd2c34a38947310	8b8d563e78f9abdb2d9dd2c1635f5a10a38d245455246ec6c97b8d1a97c6a72f
validation	6eb8be247dd71f5aed31f3b8e8b862204b14f635419c625c55ba1b546b9b85a6	314c49daa7e6dde37f03ecd088963c489eebe4fdb890c3e495f4d70a5b206599

How it was made

• Generated, then hand-labelled. Candidate requests were drafted by a large open-weight language model (a Qwen-family 27B model) from structured prompts: minimal contrast pairs (two requests that differ in one thing, like "describe it in words" vs "draw it"), single-line direct asks per lane, and short multi-turn conversations. Every row was then read and labelled by hand; the generator's proposed label was only a suggestion. Rows that were garbled, ambiguous, or too close to an evaluation case were dropped.
• No real user data. All rows are synthetic. They were scanned for personal names and private details before release.
• Leak-checked. Every row was checked against the evaluation sets by exact hash and by near-duplicate similarity (difflib ratio ≥ 0.70) before training.
• Targeted revision. After a first blind test exposed two failure shapes (advice requests that mention drawing were routed to image; explicit animation verbs were routed to still image), 253 contrast rows were added for those shapes, written without seeing the failing test prompts.

Labelling rules (short version)

The core rule: act on the best usable reading. If nothing in the request or the conversation gives a medium to act on, it is chat, where asking "one what?" is just a normal reply.

• No find cue → create. "Send me a gif of a cat" → video; "a pic of a jaguar" → image.
• A find cue (find, look up, search, "the actual/real photo from…", a link) → search. Read the operation, not the adjective: "make a realistic photo" is image.
• "Draw me" / "a selfie of me" → image.
• "Show me my cat" → image (a best-effort depiction). Retrieving the user's own private file ("the actual photo of my party") → chat, because there is no private-asset access.
• audio is the assistant's voice only. Non-voice sounds (thunder, a heartbeat, a door knock) were dropped, not labelled.
• Talking about media ("my friend sent me a voice note", "any tips for drawing hands?") → chat.
• "Find it / look it up" with nothing named → chat (nothing to search for).

Your product's lanes will be different. Change the rules, relabel, and keep the discipline: write the rules down before you label, and label every row by reading it.

Known limitations

• English only, informal chat register, and a companion-app flavour of request. It will not transfer as-is to, say, enterprise ticket triage.
• Lane boundaries are product decisions. Another team could reasonably label some rows differently (for example "show me my cat").
• 2,334 rows is small. It was enough for this five-way decision on this distribution; it is not evidence of how a model does on live traffic.

License

Apache-2.0. The text was generated with an open-weight model and labelled by us; check your own policy on synthetic data before using it commercially.