Datasets:

kumarabd1996
/

agent-task-classifier-dataset

license: apache-2.0
task_categories:
  - text-classification
language:
  - en
pretty_name: Agent Task Classifier Dataset (laya loop-taxonomy v2)
size_categories:
  - 1K<n<10K
tags:
  - agent
  - routing
  - task-classification
  - decision-model
  - laya

agent-task-classifier-dataset

5,154 labeled English training examples (plus 54 held-out) for classifiers that map a user request (to a personal-assistant agent) to the agent-loop shape it demands — the skeleton of the high-level process a task of that kind must follow — rather than its topic.

Companion model: kumarabd1996/agent-task-classifier (a fine-tuned laya checkpoint; 47/54 on the held-out set here, vs 13/54 for the untuned base).

v2 relabels the v1 corpus onto a nine-label set and adds ~1,500 personal-records and finance scenarios (goals, routines, budgets, trackers and the lookups around them). The v1 files are in this repo's git history.

The nine labels

label	the loop it demands
`conversation`	questions, quick asks, lookups, a reported measurement, a single reminder, chatter — the default, safe fallback
`companion`	inner-life support with no outcome in the world (reflection, therapy)
`learn`	build the user's own ability over sessions (practice, tutoring)
`diagnose`	find the cause of a specific malfunction or anomaly, including in the user's own data
`research`	assemble understanding from investigation (multi-source, comparison, synthesis)
`produce`	make an artifact judged against a standard (document, spreadsheet, script, letter)
`transact`	a commitment or possession in the world: book, buy, pay, transfer, or complete a procedure
`stabilize`	urgent or deteriorating situation — act before full understanding
`plan`	set up or change the person's own standing structure: a goal, routine, budget/limit, or tracker

v1 → v2: make→produce; react→stabilize; arrange/acquire/execute→transact; tend→conversation; plan is new. Two requests share a label iff they demand the same loop skeleton. Topic is irrelevant.

Format

train.jsonl — one JSON object per line, laya's native training format:

{"state": "<the request text>", "questions": {"loop": {"type": "choice", "instructions": "...", "criteria": {...}}}, "expected": {"loop": "<label>"}, "src": "<source>", "id": "<id>"}

Continuations carry the request being continued, in the exact format used at inference:

Earlier request: <prior request>
New request: <follow-up>

eval.jsonl — 54 held-out items: the 24 taxonomy boundary drills (relabelled) plus 30 hand-written personal-records and finance cases (eval_life_finance.json, phrased differently from any template). Never trained on.

Composition

source (`src`)	what	license
`multiwoz`, `multiwoz-cont`	MultiWOZ 2.1 booking utterances + continuations (`transact`)	permissive (research; Budzianowski et al. 2018)
`massive`	MASSIVE en-US takeaway orders, transport bookings, sampled commands	CC-BY-4.0 (Amazon Science)
`oasst`, `oasst-cont`	OASST2 English prompts + continuations, model-labeled with the taxonomy spec, spot-audited	Apache-2.0 (OpenAssistant)
`synth`, `synth-cont`, `hard`	template × slot generation across the personal-assistant domains; labels by construction; hand-written boundary cases	Apache-2.0 (this release)
`lf`, `lf-cont`, `lf-hard`	personal-records and finance scenarios incl. short follow-ups ("All", "yes"); labels by construction	Apache-2.0 (this release)

Label distribution (after per-class caps): conversation 1,300 · transact 1,200 · plan 633 · produce 600 · diagnose 551 · research 333 · learn 226 · companion 187 · stabilize 124.

Provenance and licensing

MASSIVE: Amazon Science, CC-BY-4.0 — attribution required and given.
OASST2: OpenAssistant Conversations Dataset, Apache-2.0.
MultiWOZ 2.1: Budzianowski et al., 2018; distributed for research use.
Synthetic items: generated for this release; scripts included (generate_synthetic.py, generate_life_finance.py, merge_v2.py, taxonomy_labels.py) so the construction is auditable. generate_synthetic.py still emits the v1 label names; merge_v2.py maps them to v2 (its inputs, the v1 corpus files, are in this repo's git history, not in the current tree).

Limitations

A large share is synthetic; template patterns bias the text distribution. The plan examples in particular are template-built, so implicit phrasings ("I keep meaning to exercise more…") are underrepresented.
English only; nine fixed labels; out-of-taxonomy requests resolve to conversation.
OASST2 labels are teacher-model labels (spot-audited), not fully human-verified. The MultiWOZ "find a hotel" requests are labeled transact, a judgment call.
54 held-out items is a small evaluation set.
Privacy: public datasets and synthetic text only. The maintainer's own data is excluded.