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Your word
measured
“his back was nicely tanned”
Definitions come from WordNet, a hand-curated dictionary.
the posterior part of a human (or animal) body from the neck to the end of the spine
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“his back was nicely tanned”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
front is the dictionary opposite of backs, yet this model puts it closer than 7 of the 15 synonyms 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 backs, 1 of 8 also appear in the dictionary. 15 of 24 dictionary synonyms are in this build; the rest have no vector to compare yet.
front is the dictionary opposite of backs, yet this model puts it closer than 12 of the 15 synonyms 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 backs, 1 of 8 also appear in the dictionary. 15 of 24 dictionary synonyms are in this build; the rest have no vector to compare yet.
front is the dictionary opposite of backs, yet this model puts it closer than 10 of the 15 synonyms 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 backs, 1 of 8 also appear in the dictionary. 15 of 24 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.
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 backs. 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 backs. 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 backs. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.