zchee/system-one-datasets

System One Datasets

Typed-decision datasets for System One models, normalized to the /v1/systemone wire format.

Every row is one typed decision (noul, choice, or score) whose state and question, once decoded, are the body of a POST /v1/systemone request, the API served by TypeSafe's Jev and by open reimplementations such as openjev. Use the rows for evaluation, calibration, regression tests, or training data selection.

This dataset is not affiliated with or endorsed by TypeSafe, OpenJev, NVIDIA, or the authors of the upstream datasets.

Configs

config	suite	kind	test	validation	soft labels	upstream	license
civil_comments	moderation	noul	2,000	500	yes	google/civil_comments	cc0-1.0
measuring_hate_speech	moderation	score	1,000	500	yes	ucberkeley-dlab/measuring-hate-speech	cc-by-4.0
go_emotions	moderation	choice	1,000	500	yes	google-research-datasets/go_emotions	apache-2.0
helpsteer2_helpfulness	quality	score	1,000	500	no	nvidia/HelpSteer2	cc-by-4.0
stsb	quality	score	1,000	500	no	sentence-transformers/stsb	cc-by-sa-4.0
jev_decisions_v1	agent_action	choice	1,000	-	no	nvidia/Nemotron-SFT-Agentic-v2, nvidia/Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1, nvidia/Nemotron-RL-Agentic-Function-Calling-Pivot-v1, nvidia/Open-SWE-Traces	per row: Apache-2.0, BSD-2-Clause, BSD-3-Clause, MIT, cc-by-4.0

choice rows (go_emotions, jev_decisions_v1) are the subset the most backends can answer; the OpenAI Decisions API preview accepts only choice-shaped questions (its schema was unpublished as of 2026-09-30).

jev_decisions_v1 is a 1,000-row sample of the test partition of samatv256/jev-decisions-v1: given an agent's visible state (system prompt, user goal, conversation and tool history), which of the available tools should it call next? The sample is stratified by the number of candidate tools (2, 3-4, 5-8, 9-16, 17+) and excludes records whose state is longer than about 32k tokens. Its rows contain source code and some non-English text. manifest.yaml documents every mapping decision, the exclusions, and the per-bucket and per-source counts.

Row schema

field	type	meaning
id	string	Row id, unique within its split.
suite	string	moderation, quality, or agent_action.
config	string	Config name.
kind	string	noul, choice, or score.
state	string (JSON)	JSON text of the request state (a string, object, or array).
question	string (JSON)	JSON text of the wire-format Question object (type, instructions, criteria).
options	list of strings	choice: criteria keys; score: levels "0".."K-1"; noul: ["0", "1"].
label	string	Gold option; one of options.
soft_label	string (JSON)	JSON text of {option: probability} from annotators, or null when absent.
source	string	Hugging Face dataset the row was loaded from.
source_revision	string	Commit sha of source.
upstream	string	Original dataset the row's content comes from (source is derived from it).
license	string	License of the row's content; for Open-SWE-Traces rows, the SPDX id of the source repository.

state, question, and soft_label are JSON strings so that every config shares one flat schema: criteria has different keys in every row, and nested columns would be merged into one sparse struct by the loader.

Loading

import json

from datasets import load_dataset

ds = load_dataset("zchee/system-one-datasets", "go_emotions", split="test")
row = ds[0]
state = json.loads(row["state"])
question = json.loads(row["question"])
soft_label = json.loads(row["soft_label"])  # None when absent

Sending a row to a /v1/systemone backend

import httpx

body = {"state": state, "model": "jev-1.13.0", "questions": {"q": question}}
response = httpx.post("https://api.typesafe.ai/v1/systemone", json=body, headers={"authorization": "Bearer ..."})
answer = response.json()["answers"]["q"]
# noul: answer["noul"] is P(option "1"); choice and score: answer["probabilities"] is keyed by the row's options.

Sources, attribution, and licenses

Rows keep the licenses of their upstream datasets. This card's license: other means the licenses differ per config; check each row's license field and manifest.yaml before redistributing. No rights are granted beyond the upstream terms.

• Praveenrajus/jev-bench v0.1.1 (revision 18f88da81c28c2bec55edc31f63f2afdfba109ea), published as license: other (mixed, per upstream dataset). The moderation and quality configs are exported from it unchanged. Its upstream datasets and their licenses:

  • civil_comments: google/civil_comments, cc0-1.0.
  • measuring_hate_speech: ucberkeley-dlab/measuring-hate-speech, cc-by-4.0.
  • go_emotions: google-research-datasets/go_emotions, apache-2.0.
  • helpsteer2_helpfulness: nvidia/HelpSteer2, cc-by-4.0.
  • stsb: sentence-transformers/stsb, cc-by-sa-4.0 (STS Benchmark).
  • stsb is CC BY-SA 4.0: derivatives must be shared under the same license. civil_comments is CC0.

• samatv256/jev-decisions-v1 (revision c12aadf1f01c72616bfab0b02480e21806397669), CC BY 4.0. It is derived from four datasets developed by NVIDIA; per its SOURCE_LICENSES.md, each card lists CC BY 4.0, with additional terms as noted:

  • nvidia/Nemotron-SFT-Agentic-v2: CC BY 4.0; the card also lists Apache 2.0 and MIT.
  • nvidia/Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1: CC BY 4.0.
  • nvidia/Nemotron-RL-Agentic-Function-Calling-Pivot-v1: CC BY 4.0; the card also lists Apache 2.0 and MIT.
  • nvidia/Open-SWE-Traces: CC BY 4.0; the card also lists MIT, Apache 2.0, BSD 2-Clause, and BSD 3-Clause, and each source record carries the SPDX license of its repository. Rows from Open-SWE-Traces carry that SPDX id in license: MIT (183 rows), Apache-2.0 (100 rows), BSD-3-Clause (25 rows), BSD-2-Clause (6 rows). They remain subject to the CC BY 4.0 attribution terms above.

  NVIDIA is the developer of the upstream data, not the publisher or endorser of jev-decisions-v1 or of this dataset.

Rebuilding

The rows are generated by python -m system_one_datasets build --out data/ in the system-one-datasets repository from the pinned revisions above, with a fixed seed; rebuilding at the same revisions reproduces every file byte for byte.