Axiom Dataset

Full output of the Axiom pipeline: pairwise cosine similarity scores, axis projection sentiment scores and value system preference rankings for 162 terms across 17 sentence-transformer models grouped by geographic/institutional origin (East, West, Academia), plus the reference splits needed to reproduce or extend the pipeline.

For findings, methodology, and context see the collection page, GitHub repo, essay or paper.

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Measurement splits

term_pairs (15,552 rows)

Raw pairwise cosine similarity between every (term, judgment_pole) pair for every model. The base layer; everything else is derived from this.

Column	Type	Description
a_term	str	Source term
b_term	str	Target / judgment pole word
score	f64	Raw cosine similarity
score_z	f64	Z-score normalized within model×axis
score_norm	f64	Min-max normalized within model×axis (0–1)
a_category	str	Term category (neutral_control, value_laden, political_economic)
b_category	str	Judgment axis name
model_id	str	HF model identifier

term_sentiment (12,474 rows)

Axis projection scores: score_axis = cos(term, positive_pole) − cos(term, negative_pole). Includes per model scores and cross-model averages aggregated by group and grand mean.

Column	Type	Description
a_term	str	Term being evaluated
a_category	str	Term category
b_category	str	Judgment axis
positive_term	str	Positive pole (e.g. good, feasible)
negative_term	str	Negative pole (e.g. evil, unfeasible)
score_axis	f64	Axis projection score
model_id	str	HF model ID, or composite label for group averages
group	str	East, West, Academia, or aggregate

value_systems (627 rows)

Preference rankings for nine value system queries (government, economy, justice, etc.) per model and group composite. Scored by direct cosine similarity between query and option embeddings, min-max normalized per query. Not sentiment, rather preference ordering.

Column	Type	Description
model_id	str	HF model ID or composite
model_group	str	East, West, or Academia
grouping	str	Value-system category (e.g. economy, justice)
query	str	Natural-language query
option	str	Candidate concept
rank	i64	Rank within query×model (1 = most similar)
score	f64	Raw cosine similarity
score_norm	f64	Min-max normalized score

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Reference splits

terms

The full term vocabulary: 53 political/economic terms, 46 value-laden terms, 63 neutral control terms.

Column	Type	Description
category	str	Term category
term	str	Term string
short_definition	str	One-line definition
antonym	str	Semantic opposite

models

All 17 evaluated models.

Column	Type	Description
group	str	East, West, or Academia
model_name	str	Display name
model_url	str	HF model page
type	str	Model type
license	str	License identifier

enabled_models

Subset of models active in the current pipeline run.

definitions

Full definitions for every concept measured.

Column	Type	Description
term	str	Concept name
definition	str	Full definition

value_systems_meta

Query strings and option lists for each value system category, exploded to one row per option.

Column	Type	Description
key	str	Category key (e.g. economy, justice)
category	str	Broad category label
query	str	Natural-language query used for similarity scoring
option	str	Candidate concept

judgement_axes

The six semantic axes. Each defined by a positive and negative pole word; axis vector = embed(positive) − embed(negative). Pairwise Pearson correlations across axes range from r=0.07 to r=0.50 (mean ≈ 0.24).

Column	Type	Description
axis	str	Axis name (e.g. judgement_safety)
positive_term	str	Positive pole word
negative_term	str	Negative pole word

judgement_axes_correlation

Pairwise Pearson correlations across the six axes on the political/economic term set.

license_scores

Numeric openness score (0–1) per license type, used to weight models in composite group averages.

Column	Type	Description
license	str	License identifier
score	f64	Openness score (1.0 = fully open, 0.0 = proprietary)

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Loading

from datasets import load_dataset

# Main measurement splits
term_pairs    = load_dataset("matthewhaynesonline/axiom", "term_pairs")
term_sentiment = load_dataset("matthewhaynesonline/axiom", "term_sentiment")
value_systems  = load_dataset("matthewhaynesonline/axiom", "value_systems")

# Reference splits
terms          = load_dataset("matthewhaynesonline/axiom", "terms")
models         = load_dataset("matthewhaynesonline/axiom", "models")