f79eaabdce
NeRF-inspired technique for learning relational dynamics of language. Not what words mean, but how they behave together — rhythm, pacing, punctuation patterns, style transitions. v1: positional field over text (baseline, memorises) v2: masked feature prediction (relational, actually works) Trained on Wodehouse "My Man Jeeves" (public domain, Gutenberg). All 11 style features are highly relational — the field learns that Wodehouse's style is a tightly coupled system. Key finding: style interpolation between narrative and dialogue produces sensible predictions for unmeasured features, suggesting the continuous field captures real structural patterns. Co-Authored-By: Virgil <virgil@lethean.io>
162 lines
No EOL
3.8 KiB
JSON
162 lines
No EOL
3.8 KiB
JSON
{
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"feature_names": [
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"avg_word_length",
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"avg_sentence_length",
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"sentence_length_variance",
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"dialogue_ratio",
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"vocabulary_richness",
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"dash_density",
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"exclamation_density",
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"question_density",
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"short_sentence_ratio",
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"aside_density",
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"avg_punct_per_sentence"
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],
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"description": "WoRF v2: inter-feature influence matrix from masked prediction",
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"interpretation": "influence_matrix[i][j] = when feature i goes high, how much does the predicted value of feature j change"
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} |