mjbommar/opengloss-v2.0-pretrain

Superseded by OpenGloss v2.1 (2026-09-07): 109,633 lexemes and 250,003 live senses — twice this release's coverage — plus a new opengloss-v2.1-inflections form→lemma lookup. v2.0 stays published for reproducibility.

OpenGloss v2.0 — Pretrain

The release rendered as continuous prose for language-model pretraining or continued pretraining: four document templates per entry — a dictionary entry, a thesaurus entry, an encyclopedia article and a usage note — written as plain text and light markdown, with no JSON or YAML duplication and no special tokens. A section with nothing to say is left out rather than emitted empty. Requesting a reading level renders every template at that level, falling back to the canonical text where a rendition is missing (level_used says which happened).

Part of the OpenGloss v2.0 release family — 15 datasets built from one store of 54,724 lexemes and 137,314 live senses, all joinable on derived ids. See Related datasets for the rest.

What's new in v2.0 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. v2.0 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.

Scope: fewer headwords, far more per headword

v2.0 is not a superset of v1.3. It covers 54,724 lexemes — a frequency-ranked subset of v1.3's 205,983 — 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 v2.0.

	v1.3	v2.0
Lexemes	205,983	54,724
Senses	565,604	137,314
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	54,724
Live senses	137,314
Rows in this dataset	617,175
documents_written	617,175
entries_scanned	54,724
words_total	196,390,946

By tier

Tier	Lexemes	Live senses
core	10,000	34,015
tier2	31,886	76,855
tier3	12,838	26,444

Coverage by tier

The release was built in three frequency-ranked passes 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
Canonical gloss	sense	100.0%	100.0%	100.0%
Controlled domain tag	sense	100.0%	100.0%	100.0%
Gloss at 4 reading levels	sense	100.0%	99.9%	99.9%
Gloss in 4 registers	sense	100.0%	100.0%	0.0%
At least one example	sense	100.0%	99.9%	99.8%
Examples at 4 reading levels	sense	99.0%	99.6%	99.8%
At least one relation	sense	96.8%	97.3%	98.0%
Synthetic retrieval queries	sense	100.0%	100.0%	0.0%
Grounded QA pairs	sense	99.8%	99.6%	0.0%
Etymology	lexeme	100.0%	100.0%	99.8%
Lexical explanation	lexeme	100.0%	100.0%	100.0%
Encyclopedia (neutral)	lexeme	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%
Contrast paragraphs	lexeme	72.8%	55.1%	0.0%

Files

Files	Config	Rows	Shards	Size
data/train-*.parquet	default	617,175	2	242.8 MB

Fields

617,175 rows, one row per rendered document.

Field	Type	Description
id	string	Derived: {lexeme_id}#pretrain-{template}-{level}, recomputable from the row alone.
lexeme_id	string	The entry the document renders. Join key.
headword	string	That entry's headword.
template	string	dictionary, thesaurus, encyclopedia or usage_note.
level	string	The reading level requested for this document.
level_used	string	neutral when any part of the document fell back to canonical text; otherwise equal to level.
text	string	The document.
n_words	int32	Whitespace-delimited word count.
tier	string	Tier of the entry.

One real row:

{
  "id": "aaa#pretrain-dictionary-neutral",
  "lexeme_id": "aaa",
  "headword": "aaa",
  "template": "dictionary",
  "level": "neutral",
  "level_used": "neutral",
  "text": "# aaa\n## Noun\nForms: plural: aaas.\n1. The American Automobile Association, commonly referred to as the AAA, is a nonprofit federation of motor clubs that provides roadside assistance, travel planning, insurance, and related services.\n   - \"I called AAA when my car wouldn’t start in the grocery store parking lot.\"\n   -  … [truncated for this card]",
  "n_words": 258,
  "tier": "core"
}

Loading it

from datasets import load_dataset

ds = load_dataset("mjbommar/opengloss-v2.0-pretrain", 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-v2.0-pretrain/data/train-*.parquet")
print(df.head())

import duckdb

duckdb.sql("SELECT count(*) FROM 'hf://datasets/mjbommar/opengloss-v2.0-pretrain/data/train-*.parquet'").show()

Stream the corpus without downloading it

from datasets import load_dataset

corpus = load_dataset("mjbommar/opengloss-v2.0-pretrain", split="train", streaming=True)
for doc in corpus.take(3):
    print(f"--- {doc['template']} @ {doc['level']} ({doc['n_words']} words)")
    print(doc["text"][:400])

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-v2.0-lexicon	one row per lexeme	One row per lexeme: kind, morphology, etymology, encyclopedia, contrasts, sense ids, provenance summary.
opengloss-v2.0-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-v2.0-definitions	one row per gloss rendition	One row per gloss rendition (canonical included): reading level, register, text, readability grade.
opengloss-v2.0-examples	one row per example rendition	One row per example sentence with the headword's character span, its reading level and register.
opengloss-v2.0-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-v2.0-etymology	one row per entry with an etymology	One row per entry with an etymology: prose summary, ordered language trail, cognates, references.
opengloss-v2.0-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-v2.0-queries	one row per synthetic query	One row per synthetic retrieval query, across eight query styles, tagged to the sense it should retrieve.
opengloss-v2.0-qa-pairs	one row per question/answer pair	One row per grounded question/answer pair, with the rendition ids the answer cites.
opengloss-v2.0-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-v2.0-provenance	one row per provenance record	One row per recorded generation call: stage, model, tokens, cost, run id — the audit trail.
opengloss-v2.0-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-v2.0-retrieval-triples	one row per (query, positive, negative) triple	MS MARCO-style (query, positive, negative) triples whose hard negatives come from the graph.
opengloss-v2.0-qrels	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-v2.0-pretrain (this one)	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 98.0% for synonyms and 99.1% for antonyms, and 3,709 senses were left with no relation at all. Treat a single edge as a hypothesis, not a fact; treat the aggregate graph as usable.
• Tier 3 is deliberately partial. 12,838 lexemes 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.

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.