jimfhahn/bf-vibe-bibframe-corrections

BF Vibe BIBFRAME Corrections (v3)

Training data for the single open model behind BF Vibe, a desktop assistant that helps catalogers create and repair valid BIBFRAME RDF/XML. The core task is BIBFRAME correction — (corrupted → conforming) record pairs verified against SHACL shapes — supplemented by two smaller task families that teach the model to operate the application: routing free-text requests to BF Vibe's commands and calling its grounding tools.

• Created by: Jim Hahn
• License: Apache 2.0 (source records: id.loc.gov, Library of Congress Linked Data Service, US public domain)
• Generator code: developed in the (currently private) bibframe-olmo repository; generator scripts are named below so the pipeline is auditable if/when the repository is opened
• Model trained on this data: jimfhahn/bibframe-olmo3-v3
• Predecessor (frozen): jimfhahn/bibframe-corrections — kept unchanged exactly as cited in associated publication(s) corruption-repair experiments; this v3 dataset supersedes it for training but does not replace it as a citation target.

Composition

task family	generator (in repo)	base rows (train/val)	repeat in mix	train rows
BIBFRAME correction	repair_targets.py → corrupt_v2.py → prepare_dataset_v2.py	4,801 / 534	×1	4,801
intent routing	data/intent_dataset.py	568 / 68	×2	1,136
grounded tool traces	data/tooltrace_dataset.py	153 / 17	×4	612
total	mixed by data/mix_dataset_v3.py			6,549

Validation splits are concatenated un-repeated (619 rows). Final training shares ≈ 78% correction / 11% intent / 11% tool traces. Repetition is capped to avoid memorizing synthetic text.

Construction and verification

Correction pairs. Real Library of Congress BIBFRAME Work/Instance records are mechanically repaired into conforming targets (provenance-derived repairs, 72.2% yield), then corrupted with parse-safe, genuinely failing errors (missing required properties, wrong namespaces, literal-for-resource, missing rdf:type, etc.). Every target is verified conforming with an in-process copy of the SHACL validator behind the public validate.bibframe.app service (exact parity with the hosted validator) — no pair enters the dataset on a single unverified hosted-validator verdict.

Intent routing. Free-text cataloger utterances mapped to BF Vibe's closed command registry with strict-JSON outputs, byte-compatible with the application's parser. The generator hard-blocklists all 46 utterances of the held-out classifier evaluation panel and refuses to run if it cannot parse the full panel — routing evaluation results cannot be contaminated by training data.

Tool traces. Multi-turn traces of the model calling BF Vibe's real grounding tools (term lookup, LoC example search, MARC↔BIBFRAME crosswalk, DCTAP rules, validation) against live mcp4rdf-core services. Traces are skipped on service outage, never fabricated.

Format

Each row is {id, text} where text is a complete ChatML conversation (system prompt, user turn, assistant completion). Correction rows carry the grounded refine-loop prompt used by BF Vibe's /fix path.

Held-out evaluation discipline

• The corrector evaluation reported for the trained model uses only the correction validation split (records never seen in training).
• The intent-routing evaluation uses the 46-utterance panel that is hard-blocklisted from generation (see above).

Citation

If you use this dataset, please cite this repository page and the frozen predecessor dataset (jimfhahn/bibframe-corrections) for the dissertation experiments it supports.