mjbommar/opengloss-v2.1-qrels

Superseded by OpenGloss v2.2 (2026-09-08): 148,292 live lexemes and 288,304 senses — tier 5 closes the WordNet gap (38,100 entries imported from Princeton WordNet 3.0 and enriched), inflected-form headwords are folded onto their lemmas, and every inherited field carries a migrate provenance record. v2.1 stays published for reproducibility.

OpenGloss v2.1 — Qrels

A ready-to-score retrieval benchmark built from the release's own graph. The listwise config gives one query with its whole graded candidate list; the docs config is the document corpus those candidate ids address; and qrels.trec at the repo root is the same judgements in standard trec_eval format. Grades are 3 (the query's own sense), 2 (a direct synonym), 1 (a direct hypernym, a co-hyponym, or a polysemous entry's encyclopedia article) and 0 (everything else). The tiers are disjoint by construction: no document is offered twice at two grades for one query.

Part of the OpenGloss v2.1 release family — 16 datasets built from one store of 109,633 lexemes and 250,003 live senses, all joinable on derived ids. See Related datasets for the rest.

What's new in v2.1 vs v1.3

1. Schema v3. Every lexeme carries a kind discriminator (simplex, compound, phrasal verb, idiom, proper noun, abbreviation, affix, function word); every sense carries a controlled domain leaf from a fixed ~160-leaf taxonomy instead of free text; every example carries the character span of the headword occurrence inside it.
2. Renditions, not one string. A definition is a set: the canonical one plus rewrites at four reading levels and in four registers, each produced in a single call from the canonical text so they say the same thing at different altitudes.
3. A sense graph, not a word graph. Typed relations resolve to sense ids wherever the target's entry exists in the release, so bank --hypernym--> financial institution points at a meaning rather than at a string.
4. Retrieval data is first-class. Synthetic per-sense queries in eight styles, grounded QA pairs, mined word-in-context pairs, MS MARCO-style triples with graph-derived hard negatives, and graded TREC qrels — all derivable from, and consistent with, the same entries.
5. Derivable identifiers everywhere. v1.3 published a positional id for lexemes and senses (3d_model_noun_0) and nothing below that. v2.1 gives every rendition, edge, query, QA pair and provenance record an id computable from the row alone, and never renumbers: a retired sense is tombstoned, so the ids after it keep their meaning.
6. Per-field provenance. Which model wrote a field, how many tokens it took, what it cost — published as its own dataset.

What changed since v2.0

v2.0 (2026-09-05) covered the frequency-ranked single words. v2.1 adds tier 4: the function words the core ranking had excluded on purpose, and every remaining v1.3 entry at Wikipedia frequency ≥ 10 — mostly multiword compounds ("natural selection", "catalog number"), plus names and rarer single words. That doubles the lexeme count and changes the mix: v2.0 was 99.8% single words; a third of v2.1 is multiword.

	v2.0 (2026-09-05)	v2.1 (2026-09-07)
Lexemes	54,724	109,633
Live senses	137,314	250,003
Multiword entries (compounds, phrasal verbs, idioms)	86	36,366
Proper nouns	10,365	17,073
Function words	114	462
Gloss renditions	1,129,975	1,684,865
Example sentences	1,398,297	2,163,329
Live relations	735,318	1,574,438
Synthetic queries	1,330,311	1,304,650
QA pairs	750,348	736,010
Pretraining documents	617,175	1,111,044
Pretraining words	196,390,946	331,888,239
Pretraining tokens (cl100k_base)	275,659,096	471,451,693
Judge score, Opus, 40-entry samples	70.2 (core + tier 2), 66.7 (tier 3)	70.2 (core + tier 2), 66.7 (tier 3), 67.0 (tier 4)

Schema. No column was added, removed or retyped in any existing dataset. Three things did change:

• tier gains the value tier4 (it was core, tier2 or tier3).
• One new dataset, opengloss-v2.1-inflections: a flat surface-form → lemma lookup (plural, past tense, participles, comparative, superlative, derivations) built from the morphology that the lexicon already carried nested.
• New provenance note prefixes on tombstones and edges, all reversible and all counted in the store audit: phantom_pos: (a v1.3 part-of-speech block whose glosses defined a component word rather than the compound — 11,440 blocks retired), regen: (relations regenerated for senses that had lost every edge to judging), and retyped: contrast (synonym edges the contrast paragraphs showed to be hypernym or hyponym).

Not row-compatible with v2.0. Lexeme, sense, rendition, edge, query and QA ids are stable for every entry v2.0 had. The derived training sets (retrieval-pairs, retrieval-triples, qrels) re-sample negatives over the larger pool, so their rows differ; and the store-wide quality passes run for v2.1 retired ~3,000 senses of the v2.0 entries (phantom part-of-speech blocks and near-duplicate senses), so those senses are now tombstoned rather than live. Treat v2.1 as a new release, not a delta.

Scope: fewer headwords, far more per headword

v2.1 is not a superset of v1.3. It covers 109,633 of v1.3's 205,988 lexemes — every frequency-ranked single word, plus the compounds and names at Wikipedia frequency ≥ 10 — and spends the difference on depth. If you need breadth of vocabulary, use v1.3; if you need graded renditions, resolved relations, spans, or retrieval supervision, use v2.1.

	v1.3	v2.1
Lexemes	205,988	109,633
Senses	565,604	250,003
Definition renditions per sense	1 canonical	1 canonical + up to 8 graded
Relation targets	bare strings	resolved to sense ids
Retrieval training data	companion sets	queries, QA, triples, qrels
Per-field provenance	no	model, tokens and cost per call

Key statistics

Lexemes	109,633
Live senses	250,003
Rows in this dataset	1,805,540
docs_written	359,618
entries_scanned	109,633
listwise_queries_written	1,445,922
qrels_written	12,016,960
queries_considered	1,445,922
senses_considered	250,003

By tier

• core — top 10K by composite frequency
• tier2 — ranks to ~42K
• tier3 — the rest of the frequency-ranked single words
• tier4 — stopwords, plus compounds and names at Wikipedia frequency ≥ 10

Tier	Lexemes	Live senses
core	10,000	33,405
tier2	31,886	75,326
tier3	12,838	25,593
tier4	54,909	115,679

Coverage by tier

The release was built in 4 frequency-ranked passes (core, tier2, tier3 and tier4) and they did not all receive the same stages. This table is per-field and per-tier so the gaps are visible rather than averaged away.

Field	Of	core	tier2	tier3	tier4
Canonical gloss	sense	100.0%	100.0%	100.0%	100.0%
Controlled domain tag	sense	100.0%	100.0%	100.0%	100.0%
Gloss at 4 reading levels	sense	100.0%	99.9%	99.9%	100.0%
Gloss in 4 registers	sense	100.0%	100.0%	0.0%	0.0%
At least one example	sense	100.0%	99.9%	99.8%	99.5%
Examples at 4 reading levels	sense	99.0%	99.6%	99.7%	99.5%
At least one relation	sense	99.4%	99.2%	99.3%	99.2%
Synthetic retrieval queries	sense	100.0%	100.0%	0.0%	0.0%
Grounded QA pairs	sense	99.8%	99.6%	0.0%	0.0%
Etymology	lexeme	100.0%	100.0%	99.8%	100.0%
Lexical explanation	lexeme	100.0%	100.0%	100.0%	100.0%
Encyclopedia (neutral)	lexeme	100.0%	100.0%	100.0%	100.0%
Encyclopedia at grade 5 + college (core entries also carry grade 1 and grade 10)	lexeme	100.0%	100.0%	100.0%	100.0%
Contrast paragraphs	lexeme	72.8%	55.1%	0.0%	0.0%

Files

Files	Config	Rows	Shards	Size
data/listwise/train-*.parquet	listwise	1,445,922	3	245.9 MB
data/docs/train-*.parquet	docs	359,618	1	109.1 MB

Fields

Config listwise

1,445,922 rows, one row per query, with its whole graded candidate list.

Field	Type	Description
query_id	string	The query's id; matches column 1 of qrels.trec.
query	string	The query text.
query_source	string	generated or gloss_pseudo.
sense_id	string	The sense the query belongs to. Join key.
lexeme_id	string	That sense's entry id.
candidates	list<struct<id, text, grade>>	The graded candidate documents: id into the docs config, text, and grade 0–3.
n_candidates	int32	Length of candidates.
tier	string	Tier of the query's entry.

One real row:

{
  "query_id": "a:determiner:0#grade_5/plain",
  "query": "Use “a” before one countable thing when you mean any single member of a group.",
  "query_source": "gloss_pseudo",
  "sense_id": "a:determiner:0",
  "lexeme_id": "a",
  "candidates": [
    {
      "id": "a:determiner:0",
      "text": "Indefinite article preceding singular count nouns to indicate one unspecified group member.",
      "grade": 3
    },
    {
      "id": "one:determiner:0",
      "text": "A cardinal numeral determiner used to designate a single item in a countable set, modifying the noun that follows.",
      "grade": 2
    },
    {
      "id": "single:adjective:0",
      "text": "Being one in number; not accompanied by or combined with another of the same kind.",
      "grade": 2
    },
    "… 8 more of 11"
  ],
  "n_candidates": 11,
  "tier": "tier4"
}

Config docs

359,618 rows, one row per document in the retrieval corpus.

Field	Type	Description
doc_id	string	A sense id (the document is that sense's canonical gloss) or a {lexeme_id}:encyclopedia id.
text	string	The document text.
lexeme_id	string	The entry the document belongs to. Join key.
tier	string	Tier of that entry.

One real row:

{
  "doc_id": "a:determiner:0",
  "text": "Indefinite article preceding singular count nouns to indicate one unspecified group member.",
  "lexeme_id": "a",
  "tier": "tier4"
}

Loading it

from datasets import load_dataset

# configs: "listwise", "docs"
ds = load_dataset("mjbommar/opengloss-v2.1-qrels", "listwise", split="train")
print(ds)
print(ds[0])

The shards are plain parquet, so nothing forces you through datasets — read them straight, locally or over hf://:

import polars as pl

df = pl.read_parquet("hf://datasets/mjbommar/opengloss-v2.1-qrels/data/listwise/train-*.parquet")
print(df.head())

import duckdb

duckdb.sql("SELECT count(*) FROM 'hf://datasets/mjbommar/opengloss-v2.1-qrels/data/listwise/train-*.parquet'").show()

Beside the parquet shards this repo carries:

• qrels.trec — The graded judgements in standard trec_eval format: query_id 0 doc_id grade, one per line.

Score a run with pytrec_eval

import pytrec_eval
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "mjbommar/opengloss-v2.1-qrels", "qrels.trec", repo_type="dataset"
)
with open(path, encoding="utf-8") as handle:
    qrels = {}
    for line in handle:
        qid, _, docid, grade = line.split()
        qrels.setdefault(qid, {})[docid] = int(grade)

# run = {query_id: {doc_id: score}} from your own retriever
evaluator = pytrec_eval.RelevanceEvaluator(qrels, {"ndcg_cut_10", "recall_100"})
results = evaluator.evaluate(run)

Identifiers, and how they compose

Every id is derived from structure, never randomly minted, so a consumer can recompute one from a row and join across the whole family without a lookup table. Sense positions are stable across regenerations: a retired sense is tombstoned, not removed, so the indices after it never shift.

Id	Shape	Example
Lexeme	slugify(headword)	abseil
Sense	{lexeme_id}:{pos}:{index} (zero-based)	abseil:verb:0
Rendition	{owner_id}#{reading_level}/{register}	abseil:verb:0#grade_5/plain
Entry-level owner	{lexeme_id}:encyclopedia / :explanation	abseil:encyclopedia
Edge	{source_sense_id}-{type}->{target_lexeme_id}	abseil:verb:0-synonym->rappel
Query	{sense_id}#q{n} (zero-based)	abseil:verb:0#q3
QA pair	{sense_id}#qa{n} (zero-based)	abseil:verb:0#qa3
Provenance record	p{n} within its entry (one-based)	p12

An edge id keys on the target's slug, not on the target's sense, so resolving a target never changes the id of the edge that found it.

Reading levels and registers

A rendition is keyed on a (reading_level, register) pair. The canonical rendition of every field is (neutral, plain); everything else is a rewrite of it.

reading_level	Who it is written for	Rough CCSS band
neutral	The canonical text: an adult general reader, no level targeted	—
grade_1	Beginning readers; short sentences, common words	K–1
grade_5	Upper elementary	4–5
grade_10	Secondary	9–10
college	Undergraduate and above; technical vocabulary allowed	11–CCR

register	What changes	Reading it
plain	Nothing — the neutral register	The default
informal	Conversational, contractions, everyday words	How you'd say it to a friend
formal	Full forms, precise hedging, no contractions	How you'd write it in a report
technical	Domain vocabulary, exact conditions	How a specialist would state it
marketing	Benefit-first, persuasive framing	A genre, not a formality level

marketing sits on the register axis for convenience but is a genre value rather than a point on the formality scale — worth remembering if you train a formality classifier on this column.

Related datasets

Everything below is built from the same store and joins on lexeme_id / sense_id.

Dataset	Grain	What it holds
opengloss-v2.1-lexicon	one row per lexeme	One row per lexeme: kind, morphology, etymology, encyclopedia, contrasts, sense ids, provenance summary.
opengloss-v2.1-senses	one row per live sense	One row per live sense: canonical gloss, 8 gloss renditions, examples, resolved relations, synthetic queries, grounded QA pairs.
opengloss-v2.1-definitions	one row per gloss rendition	One row per gloss rendition (canonical included): reading level, register, text, readability grade.
opengloss-v2.1-examples	one row per example rendition	One row per example sentence with the headword's character span, its reading level and register.
opengloss-v2.1-encyclopedia	one row per encyclopedia rendition · one row per lexical-explanation rendition	One row per encyclopedia article rendition, plus an explanation config for the "why this word" prose.
opengloss-v2.1-etymology	one row per entry with an etymology	One row per entry with an etymology: prose summary, ordered language trail, cognates, references.
opengloss-v2.1-inflections	one row per inflected, derived or lemma form	One row per inflected or derived form, plus the lemma itself: a flat form→lemma lookup.
opengloss-v2.1-relations	one row per live relation edge · one row per removed relation edge	One row per semantic edge, resolved to target sense ids; a tombstoned config recovers the edges the reconcile pass removed.
opengloss-v2.1-queries	one row per synthetic query	One row per synthetic retrieval query, across eight query styles, tagged to the sense it should retrieve.
opengloss-v2.1-qa-pairs	one row per question/answer pair	One row per grounded question/answer pair, with the rendition ids the answer cites.
opengloss-v2.1-contrasts	one row per contrast paragraph	One row per "X vs Y" paragraph on a synonym/antonym/confusable edge, with a verdict on the edge.
opengloss-v2.1-provenance	one row per provenance record	One row per recorded generation call: stage, model, tokens, cost, run id — the audit trail.
opengloss-v2.1-retrieval-pairs	one row per mined pair	Word-in-context and doc2query-shaped (text_a, text_b, label) pairs mined from the store for free.
opengloss-v2.1-retrieval-triples	one row per (query, positive, negative) triple	MS MARCO-style (query, positive, negative) triples whose hard negatives come from the graph.
opengloss-v2.1-qrels (this one)	one row per query, with its whole graded candidate list · one row per document in the retrieval corpus	Graded TREC relevance judgements (0–3) plus the document corpus and listwise candidate lists.
opengloss-v2.1-pretrain	one row per rendered document	Entries serialised into plain-prose dictionary, thesaurus, encyclopedia and usage-note documents.

Known limitations

• It is synthetic. Every string here was written by a language model against a schema, not transcribed from a corpus or checked by a lexicographer. It is well-formed and internally consistent; it is not attested usage, and it will contain confident errors. Do not use it as ground truth about what a word means.
• Judge scores 70.2/100 (core + tier 2) and 66.7/100 (tier 3). A different model family (Claude Opus) scored fixed 40-entry stratified samples at the close of each build. Sample statistics, not per-entry guarantees, and the judge is itself a model.
• Relation precision is the weakest axis. Relations were judged for validity and the ones that failed were demoted rather than asserted; symmetric reciprocity finished at 94.3% for synonyms and 95.3% for antonyms, and 1,890 senses were left with no relation at all. Treat a single edge as a hypothesis, not a fact; treat the aggregate graph as usable.
• core, tier2, tier3 and tier4 are deliberately partial. 109,633 lexemes across core, tier2, tier3 and tier4 received the text stages (glosses, examples, encyclopedia) but not the queries, QA pairs, contrasts or register renditions. The coverage table above gives the exact per-field share; nothing is hidden behind an average.
• The encyclopedia is entry-level. One article per headword, about the headword as a whole. On a polysemous entry it is not a description of any one sense, and it is never used as a positive for one (D-71). It is entry-level reference prose, not a specialist article.
• Pseudo-queries. A sense with no written query falls back to its grade_5/plain gloss standing in as one; query_source says which happened, and a pseudo-query is a paraphrase of the document it is supposed to retrieve, which makes it easier than a real query. Filter on query_source == 'generated' for the harder set.

Citation

@misc{bommarito2025opengloss,
  title  = {OpenGloss: A Synthetic Encyclopedic Dictionary and Semantic Knowledge Graph},
  author = {Bommarito, Michael J., II},
  year   = {2025},
  eprint = {2511.18622},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2511.18622}
}

License

Released under Creative Commons Attribution 4.0 International (CC-BY 4.0). Attribution to the OpenGloss project is required; commercial use is permitted.