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Your word
measured
“the ventral aspect of the human body”
Definitions come from WordNet, a hand-curated dictionary.
toward or on or near the belly (front of a primate or lower surface of a lower animal)
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“the ventral aspect of the human body”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
This model splits ventral 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.
Shade shows how close the model puts each word to ventral.
dorsal is the dictionary opposite of ventral, 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 ventral.
This model splits ventral 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.
dorsal is the dictionary opposite of ventral, 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 ventral.
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 ventral 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.
This model splits ventral 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.
This model splits ventral 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 ventral. 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 ventral. That gap is the model’s own learned association.
This model splits ventral 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 ventral. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.