Loka — RDF-star world-model corpus and checkpoints

Loka is a neuro-symbolic world model: an RDF-star triplestore engine plus a small role-aware transformer trained on the same triples, sharing a single SPARQL+ query layer. Generated triples write back into the store with propositionInferredFrom citation edges that point at the curated context the prediction was conditioned on, so every model-emitted fact is auditable, queryable, and filterable in SPARQL.

This dataset repo holds the corpus (label-substituted Wikidata slice) and the trained transformer checkpoints together so a corpus version and a checkpoint version stay aligned. The engine itself lives at https://github.com/EmmaLeonhart/Loka; the paper is at paper/paper.md in that repo.

Latest pinned model: loka-wikidata-v14 (revision tag v14, released 2026-05-15). See Snapshots below for the full version history.

Layout

Path	Contents
corpus/triples.txt	Tab-separated label-substituted triples (subject predicate object) used as training input. Wikidata QIDs/PIDs are substituted with English labels at preprocess time; @lang and ^^<datatype> suffixes are stripped from literals.
corpus/vocab_bpe.json	50 K-piece BPE vocabulary used by v6 and later.
corpus/tokenizer_bpe.json	tokenizers library JSON for the BPE tokenizer. Pass to inference / training scripts via --bpe-tokenizer.
corpus/vocab.json	Word-level vocabulary used by v3–v5 (kept for reproducibility).
corpus/generated_v*.nt	RDF-star inferences emitted by trained models, with propositionInferredFrom provenance back to their citation context.
loka-data/ (optional, ~770 MB)	The live RDF-star Loka store used to extract the corpus. Pull this and loka serve --data-dir loka-data/ to query directly.
checkpoints/wikidata_v*.pt	Role-aware transformer checkpoints, PyTorch .pt format. Same 44.5 M-parameter architecture from v5 onward (d_model 512, 6 layers, 8 heads, 50 K BPE vocab, 8 tokens per role).

Architecture

All current checkpoints (v5 onward) share:

• Role-aware masked S/P/O transformer. Input is a concatenation of three fixed-length slots (subject / predicate / object), each tagged with a role embedding. At training time one role is masked and the model predicts the original tokens; at inference the object slot is masked and decoded greedily with a cumulative repetition penalty.
• 44.5 M parameters. d_model=512, num_layers=6, nhead=8, tokens_per_role=8, max_len=28. Approx. 178 MB on disk per checkpoint.
• 50 K BPE vocabulary (v6+). v5 and earlier used a word-level regex tokenizer; that tokenizer dropped non-ASCII characters and is preserved only for back-comparison.

Datatype filtering policy

The training corpus is built from philippesaade/wikidata by training/preprocess.py in the Loka repo. Decisions per Wikidata datatype:

KEEP (semantic content; ~2,231 properties):

• wikibase-item — entity-to-entity links (label-substituted)
• wikibase-property — property-to-property links
• string — plain string values
• quantity — numeric values; leading + stripped (+1234 → 1234)
• time — dates; leading + stripped, Z dropped, T00:00:00 dropped when zero (+2012-10-15T00:00:00Z → 2012-10-15; BCE keeps the -)
• monolingualtext — value with @lang tag stripped. All languages kept in v7+ (v6 dropped non-English).

DROP (catalog cross-references or specialised noise; ~10,525 properties, about 82.5 % of all Wikidata property types):

• external-id (~10,206) — Freebase, ISNI, GND, LCCN, Dewey, etc. Training on these in v6 produced confident catalog-format hallucinations (ISNI -> "00000000") and a leak of the format shape onto unrelated predicates (instance of -> "+ Ġof - 00 - 03 T 00"). v7 onward exclude them.
• url (120), commonsMedia (91), math (36), wikibase-sense/wikibase-lexeme/wikibase-form/wikibase-entity-schema (47), globe-coordinate (10), geo-shape/musical-notation/tabular-data (15) — rare or non-transferable.

Full per-datatype spec: planning/wikidata-datatype-processing.md in the Loka repo. Property list pinned at training/wikidata_excluded_predicates.json.

Snapshots

Each meaningful checkpoint round is tagged. Pull a specific snapshot:

from huggingface_hub import hf_hub_download
ckpt = hf_hub_download(
    repo_id="EmmaLeonhart/loka",
    repo_type="dataset",
    filename="checkpoints/wikidata_v8.pt",
    revision="v8",
)
# also pull the matching tokenizer + vocab
tok = hf_hub_download(repo_id="EmmaLeonhart/loka", repo_type="dataset",
                      filename="corpus/tokenizer_bpe.json", revision="v8")
vocab = hf_hub_download(repo_id="EmmaLeonhart/loka", repo_type="dataset",
                        filename="corpus/vocab_bpe.json", revision="v8")

Tag	Date	Final ppl	Corpus	Notes
v3	2026-05-08	53.4	757 k noisy	First end-to-end run. Datatype-suffix leakage bug; emitted xmlschema decimal http www w3 org as memorised template.
v4	2026-05-09	92.5	757 k cleaned	16 M params. Datatype-suffix bug fixed. Mode collapse on common connectors (of of of of) addressed with decode-time cumulative repetition penalty.
v5	2026-05-09	84.85	757 k cleaned	44.5 M params (3 × scale-up). Bigger model picks specific entities (halle, 33, kosmos 116) where v4 fell back to fillers.
v6-bpe	2026-05-10	194.98	757 k cleaned	BPE tokenizer added. Same 44.5 M architecture. Final ppl not directly comparable to v5 (BPE has more tokens per role). Catalog hallucinations dominant in post-training behavioural tests — diagnosed as corpus composition.
v7	2026-05-10	192.63	184 k v7-cleaned	Catalog datatypes dropped (~76 % of v6 corpus removed). Same architecture, 5 epochs. Tied ppl with v6 on a 4 ×-smaller corpus, but the catalog-format leak (instance of -> "+ Ġof - 00 - 03 T 00") is gone.
v8	2026-05-10	64.65	184 k v7-cleaned	Same architecture, 20 epochs from scratch on the v7 corpus. 3 × ppl improvement over v7 — the v7 corpus was undersaturated at 5 epochs. Loss still descending at epoch 20, so the next bottleneck is data scale.
v9	2026-05-11	57.15	94 k from fresh 2 M-triple slice	First cron-cycle model. v9 propgen test: 35 emissions, 34 of them on semantic predicates (97 %) — best ratio yet. URL-prefix shape leak on `Template:*
v10	2026-05-11	55.52	94 k from fresh 2 M-triple slice	First fully-automated cron cycle (no manual steps). 100 % semantic-predicate share on Q42 propgen — the cleanest signal of the catalog-hallucination series. Loss still descending at epoch 20.
v11	2026-05-13	279.12	350 k from new normalized-wikidata pipeline	First model on the no-Loka-in-the-loop preprocessing pipeline (tools/preprocess_from_hf.py). Trained 3 of 20 epochs before CUDA OOM at batch 32 on the 4070 Laptop's 8 GB VRAM — epoch-3 checkpoint is the v11 release. Future runs use batch 16. ppl looks worse than v10 because v10 ran 20 clean epochs on 94 k triples while v11 ran only 3 epochs on 350 k. Different operating regime, not directly comparable. Corpus tag on EmmaLeonhart/normalized-wikidata: v11-50k.
v12	2026-05-14	250.82	672 k from v12-100k normalized corpus	Second rung of the normalized-wikidata series. Training was disrupted by an unrelated LLaMA 3.1 8B experiment sharing the GPU — epochs 5–7 diverged from epoch 4's best (226.86) as Adam's momentum state corrupted under contention. Shipped at the epoch-6 snapshot (taken before further degradation). Even with the corruption, v12 beats v11 (250.82 vs 279.12) — the bigger, cleaner corpus shows through. Corpus tag: v12-100k.

For the live latest list, see this dataset's Files and versions tab on Hugging Face. Tag main always tracks the most recent upload.

The normalized-wikidata pipeline (v11 onward)

v11 onward, the training corpus is built by a separate streaming preprocessor that goes directly from philippesaade/wikidata parquet → text triples, without staging into a Loka store. Corpus versions live in a parallel HF dataset at EmmaLeonhart/normalized-wikidata:

Loka model	Corpus tag	Entity rows	Output triples
v11	v11-50k	50 000	350 428
v12	v12-100k	100 000	671 817
v13	v13-500k (in training as of 2026-05-14)	500 000	2 511 771
v14	v14-1M (queued)	1 000 000	~7 M est.

The series exists because the corpus quality lever still hasn't saturated. v10's 55.52 ppl on a 94 k-triple corpus was the best clean-trained result; v11–v14 are scaling the cleaned corpus up to see when the perplexity floor re-establishes itself, and to ship a publicly-useful normalized Wikidata dataset as a side effect.

Generated-triple provenance

Every triple under corpus/generated_*.nt carries an RDF-star annotation block:

<S> <P> "predicted-value" .
<<S P "predicted-value">> loka-prov:propositionGenerated   "true"^^xsd:boolean .
<<S P "predicted-value">> loka-prov:propositionGeneratedBy "loka-wikidata-v8" .
<<S P "predicted-value">> loka-prov:propositionConfidence  "0.67"^^xsd:decimal .
<<S P "predicted-value">> loka-prov:propositionInferredFrom <<S P_existing O_existing>> .
... (one inferredFrom edge per cited context triple)

loka-prov: expands to http://loka.dev/provenance/. Predicates under that namespace are reserved system metadata — the model never sees them, never proposes them as candidate predicates, and never emits them, enforced at three layers (corpus stripping in training/preprocess.py, candidate filtering in training/infer_with_citations.py, emit-time guard before each primary triple is written).

The propositionInferredFrom edges are auditable like any other RDF: SPARQL can query "every generated triple that cites a triple about X" or "remove all v6 generations" with a single pattern match. Generated triples are flagged out of training corpora automatically (the FILTER NOT EXISTS << ?s ?p ?o >> loka-prov:propositionGenerated clause in TRAINING_CORPUS_QUERY).

Versioning policy

• main branch on this dataset always points at the most recent upload.
• Each numbered checkpoint round (v3, v4, v5, v6-bpe, v7, v8, …) is a stable tag. Pull revision="vN" for reproducibility.
• The local 12-hour cycle loop (tools/training_cron.py in the Loka repo) produces a new tagged snapshot each cycle. README is regenerated on every upload so the version history table above stays current.

License

Apache 2.0. Source data is Wikidata under CC0; the upstream philippesaade/wikidata snapshot is used unmodified for the import step.

Citation

If you use these checkpoints or the corpus, please cite:

@misc{loka2026,
  title = {Loka: Generative Citation in a Neuro-Symbolic World Model over RDF-Star Knowledge Graphs},
  author = {Leonhart, Emma},
  year = {2026},
  url = {https://github.com/EmmaLeonhart/Loka},
  note = {Paper at \url{https://github.com/EmmaLeonhart/Loka/blob/main/paper/paper.md}}
}