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Your word
measured
“a concern with inward reflections”
Definitions come from WordNet, a hand-curated dictionary.
relating to or existing in the mind or thoughts
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“a concern with inward reflections”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
outward is the dictionary opposite of inward, yet this model puts it closer than every one of the 1 synonym it can score. Opposites share the sentences a word lives in, so cosine alone cannot tell them apart - which is why the dictionary seeds this list rather than the geometry.
Shade shows how close the model puts each word to inward, 1 of 8 also appear in the dictionary. 1 of 3 dictionary synonyms are in this build; the rest have no vector to compare yet.
outward is the dictionary opposite of inward, yet this model puts it closer than every one of the 1 synonym it can score. Opposites share the sentences a word lives in, so cosine alone cannot tell them apart - which is why the dictionary seeds this list rather than the geometry.
Shade shows how close the model puts each word to inward, 1 of 8 also appear in the dictionary. 1 of 3 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits inward into 2 pieces, so it has no vector of its own here: this is the average of its fragments, and the results below are correspondingly rough.
outward is the dictionary opposite of inward, yet this model puts it closer than every one of the 1 synonym it can score. Opposites share the sentences a word lives in, so cosine alone cannot tell them apart - which is why the dictionary seeds this list rather than the geometry.
Shade shows how close the model puts each word to inward. 1 of 3 dictionary synonyms are in this build; the rest have no vector to compare yet.
Model neighbours are distributional, not dictionary synonyms. Two words can be close because they appear in similar sentences, which is why an antonym can outscore a synonym here.
This model splits inward into 2 pieces, so it has no vector of its own here: this is the average of its fragments, and the results below are correspondingly rough.
Scores are projections onto axes we defined from anchor words, not labels the model assigns.
These sit just outside the closest neighbours in panel 3, and no dictionary lists any of them as related to inward. That gap is the model’s own learned association.
These sit just outside the closest neighbours in panel 3, and no dictionary lists any of them as related to inward. That gap is the model’s own learned association.
This model splits inward into 2 pieces, so it has no vector of its own here: this is the average of its fragments, and the results below are correspondingly rough.
These sit just outside the closest neighbours in panel 3, and no dictionary lists any of them as related to inward. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.