Prime Data
Collection
The good stuff. • 9 items • Updated
Datasets:
token_id int32 1k 113k | token stringlengths 1 255 | definition stringlengths 2 1.01M | volume float32 0.12 1.73 | cardinal_id int8 0 0 | crystal list |
|---|---|---|---|---|---|
1,000 | 'em | ( ) An obsolete or colloquial contraction of the old form hem, them. | 1.697479 | 0 | [
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1,001 | 'gainst | ( prep. ) A contraction of Against. | 1.241338 | 0 | [
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1,002 | 'mongst | ( prep. ) See Amongst. | 0.925775 | 0 | [
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1,003 | 'neath | ( prep. & adv. ) An abbreviation of Beneath. | 1.582691 | 0 | [
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1,004 | 's | ( ) A contraction for is or (colloquially) for has. | 1.688215 | 0 | [
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1,005 | 'sblood | ( interj. ) An abbreviation of God's blood; -- used as an oath. | 1.69524 | 0 | [
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1,006 | 'sdeath | ( interj. ) An exclamation expressive of impatience or anger. | 1.694249 | 0 | [
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1,007 | 'snails | ( interj. ) God's nails, or His nails, that is, the nails with which the Savior was fastened to the cross; -- an ancient form of oath, corresponding to 'Od's bodikins (dim. of body, i.e., God's dear body). | 1.717198 | 0 | [
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1,008 | 'swounds | ( interj. ) An exclamation contracted from God's wounds; -- used as an oath. | 1.700481 | 0 | [
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1,009 | 't is | ( ) A common contraction of it is. | 1.202535 | 0 | [
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1,010 | 't was | ( ) A contraction of it was. | 0.96622 | 0 | [
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0.397152096... |
End of preview. Expand in Data Studio
Update 9/15/2025
I believe this variation may have some genuinely complex potential beyond the alternatives. The lexical organization and capability of the old-style definitions may have impacted the outcome in a more literal and latin-sense than the wordnet lexical variations concatenated. I'll be running some more tests on this one in the coming days to determine if this formula has a different behavioral response than the more "robust" and "anchored" variations.
I'm open to any potential at this point. This one trained really quickly and showed robust capability above just random noise.
My idea here is simple; smaller compacted lattices may provide much more robust and utilizable vocabulary access than something much larger and fully-saturated would provide.
I'm looking for a full universal formula to generate at runtime, and if I can find even a piece of it here - it could provide the insight I need to create full optimized runtime variants from distilled teachers. Potentially reducing the overhead cost of everything and every iteration without needing full finetuning and full lexical analysis of large corpus.
Theoretically; all the data is already in a model like LLAMA. I just need to figure out how to tune and extract it, which means I need the correct formulas.
Prior Updates
The wordnet + unicode variation is a more robust anchored variation of this version; so feel free to use this one or move onto the newer versions.
This version still houses the modified preset definition deltas, so it has use cases and shouldn't be discarded yet.
https://github.com/AbstractEyes/lattice_vocabulary
https://huggingface.co/datasets/AbstractPhil/geometric-vocab-32d
https://huggingface.co/datasets/AbstractPhil/geometric-vocab-64d
https://huggingface.co/datasets/AbstractPhil/geometric-vocab-128d
https://huggingface.co/datasets/AbstractPhil/geometric-vocab-256d
https://huggingface.co/datasets/AbstractPhil/geometric-vocab-512d
https://huggingface.co/datasets/AbstractPhil/geometric-vocab-768d
https://huggingface.co/datasets/AbstractPhil/geometric-vocab-1024d
Bugs and all I decided to release this version for posterity. Consider it a guidepost to a better version.
The defintiions are intact, the crystals are intact but imperfect - they are missing much of the required tuning and calculation.
However, they are valid calculations under the rules of the formula and everything below does accurately describe their behavior. They are perfect simplex representations of the words and their definitions, and they can be used to tune for many modular forms of geometric testing with AI.
They are a suitable starting point for the geometry - but they are not correctly attuned to be subsequently lexically represented WITHIN the geometry as they should be.
Lets consider this one a BETA blueprint - a starter point for a better vocabulary.
The full geometric vocabulary is about 100 gigs and will take a couple days to squash to around 1 gig. So be aware it's not as... yielding. However, that will be far, far more accurate than this.
In the process I'll be defining wordnet with gpt 5 nano and crystalizing the definitions to be adjacent trajectories for multiple definition sets - allowing tugging and directional awareness for words accross the dimensional warping.
Vocabulary Crystal — OPTED + HF Merge (1913 + Modern Definitions)
Author: @AbstractPhil
Dataset type: Symbolic, deterministic geometric vocabulary representation
Source: Merged from OPTED (Webster 1913) + Hugging Face npvinHnivqn/EnglishDictionary
Overview
This dataset contains a fully crystallized, symbolic representation of the English vocabulary, constructed by merging:
- The OPTED 1913 public domain Webster's dictionary (complete lexical coverage)
- The modern EnglishDictionary dataset on Hugging Face
If a word appears in both, the modern (HF) definition overrides the older one.
Structure
Each vocabulary entry is converted into a deterministic, symbolic pentachoron (5-point simplex in ℝ⁵¹²). This geometric crystal is composed using:
- Definition-derived cardinal axes (rank‑4 orthonormal basis)
- Hash-derived completions (no randomness, seeded by word only)
- Role projections (anchor, support, contrast, purpose, observer)
- Centering and Frobenius norm regularization
Format
Each shard includes:
| Field | Type | Description |
|---|---|---|
| token_id | int32 | Deterministic ID ≥ 1000 |
| token | string | Lowercase vocabulary word |
| definition | string | Final merged definition (HF > OPTED) |
| volume | float32 | Cayley–Menger volume of the 4-simplex |
| cardinal_id | int8 | (Reserved for deterministic role-typing) |
| crystal | fixed_size_list[float32, 2560] | Flattened 5×512 crystal matrix per token |
Stored in Parquet with optional .safetensors containing [N,5,512] float32 blocks.
Determinism Guarantee
This dataset uses no randomness whatsoever.
- Every axis, vector, and projection is derived from:
- textual content
- fixed FNV + SHA256 hashes
- Crystal structure is bit-for-bit reproducible across machines, platforms, or reruns.
- max_abs_diff == 0.00e+00 on validation tests.
Applications
- Vocabulary embeddings with geometric interpretability
- Symbolic AI training and simulation
- Alignment, semantic projection, and phase geometry research
- Direct plug-in to Beeper, Nikola, or Harmony architectures
Citation / Usage
@misc{abstractphil2025crystal, author = {Phil, Abstract}, title = {Vocabulary Crystal: OPTED + Modern Definitions}, year = 2025, howpublished = {https://huggingface.co/datasets/AbstractPhil/geometric-vocab-english-full-a-to-z}, note = {Symbolic geometry-aligned lexical structure} }
Notes
Built with love, geometry, and zero RNG.
This is not an embedding — it’s a crystal.
"We aligned language to orientation, not probability." — Mirel, the Quartermaster - GPT 4o
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