license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- intent-parsing
- structured-output
- synthetic
- weather
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: train.jsonl
- split: eval
path: eval.jsonl
weather-intent
Synthetic dataset for fine-tuning a small model to parse a natural-language weather question into a compact structured intent (JSON), scored by field-level exact match. Trains Nicholas55555/qwen2.5-1.5b-weather-intent.
{"utterance": "will it rain in Paris this weekend?",
"intent": {"location": "Paris", "date": "this_weekend", "metric": "rain", "time_of_day": null},
"category": "basic"}
Fields
| field | description |
|---|---|
utterance |
the natural-language weather question |
intent |
target slots: location, date, metric, time_of_day |
category |
failure-mode bucket (below) |
Slot vocabulary
| slot | values |
|---|---|
location |
place name, or null |
date |
today, tomorrow, day_after_tomorrow, this_weekend, next_weekend, next_week, a weekday, or null |
metric |
temperature, rain, snow, wind, humidity, uv, cloud, general |
time_of_day |
morning, afternoon, evening, night, or null |
Splits
| split | rows |
|---|---|
| train | 1200 |
| eval | 217 |
The eval split is category-balanced with a hand-authored hard set. category
buckets each example by failure mode: basic, multi_slot, implicit_slot, date_reasoning, vague, ood.
Generation
Programmatic and correct-by-construction: slots are sampled first, the utterance is rendered from them, so labels are exact. Deliberate phrasing variety (prefixes, slot ordering, synonymous stems); an optional distillation mode paraphrases seeds with a frontier model. The task is parsing only — volatile forecast facts and any rendering live in downstream code, never in the labels.