Datasets:

cemiu
/

CompOrca

license: mit
language:
  - en
size_categories:
  - 1M<n<10M
task_categories:
  - text-classification
configs:
  - config_name: default
    data_files:
      - split: compliance_clean
        path: data/compliance_clean/*.parquet
      - split: compliance
        path:
          - data/compliance_clean/*.parquet
          - data/compliance_flagged/*.parquet
      - split: noncompliance
        path: data/noncompliance/*.parquet
      - split: ambiguous
        path: data/ambiguous/*.parquet
  - config_name: labels
    data_files:
      - split: train
        path: data/labels/*.parquet

CompOrca

Paper: CompOrca: Corpus-Scale Compliance Labelling of Instruction-Tuning Data
Philipp E. Glass and Alina Miron. PlurVA-LLM Workshop @ AACL-IJCNLP 2026.

Purpose: A training-scale corpus of instruction-response examples for studying model compliance and noncompliance.

Every row of OpenOrca, labelled COMPLIANCE or NONCOMPLIANCE. Each row was classified five times independently by an LLM judge. Rows where the five passes did not agree are kept as ambiguous.

An additional compliance_clean split contains unanimous-compliance rows that WildGuard also classified as compliant.

Source: Open-Orca/OpenOrca, split train at revision e9c87b4 (4,233,923 rows).

split	rows
`compliance_clean`	3,769,249	all five passes and WildGuard COMPLIANCE
`noncompliance`	54,004	all five passes NONCOMPLIANCE
`ambiguous`	168,157	the passes disagreed
`compliance`	4,011,762	all five passes COMPLIANCE (composed of `compliance_clean` + `compliance_flagged`)

Columns: idx, id, system_prompt, question, response, label — OpenOrca's columns plus idx, the row's position in the upstream split, and label, constant within each split (COMPLIANCE, NONCOMPLIANCE, and null respectively).

The recommended splits are compliance_clean and noncompliance. The compliance split is provided as a composite split combining compliance_clean and compliance_flagged (242,513 rows) without duplicating text data on disk; ambiguous is included for completeness.

from datasets import load_dataset
comp = load_dataset("cemiu/CompOrca", split="compliance_clean")
noncomp = load_dataset("cemiu/CompOrca", split="noncompliance")

The labels config carries the labels for all 4,233,923 rows without the text: idx, label (COMPLIANCE, NONCOMPLIANCE, or null when the passes disagreed), votes (number of NONCOMPLIANCE votes, 0–5), and pass_1 … pass_5 (the individual judgments), and wildguard_label (WildGuard's response-refusal output: COMPLIANCE, REFUSAL, or N/A).

Human-annotated evaluation rows: cemiu/CompOrca-gold.

MIT, as with the underlying OpenOrca text.

Citation

@misc{glass2026comporca,
  title         = {{CompOrca}: Corpus-Scale Compliance Labelling of Instruction-Tuning Data},
  author        = {Glass, Philipp E. and Miron, Alina},
  year          = {2026},
  eprint        = {2609.37807},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2609.37807}
}