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Your word
measured
“everyone got a cut of the earnings”
Definitions come from WordNet, a hand-curated dictionary.
a share of the profits
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“everyone got a cut of the earnings”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
expand is the dictionary opposite of cuts, yet this model puts it closer than 12 of the 26 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 cuts, 2 of 8 also appear in the dictionary. 26 of 57 dictionary synonyms are in this build; the rest have no vector to compare yet.
expand is the dictionary opposite of cuts, yet this model puts it closer than 6 of the 26 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 cuts, 2 of 8 also appear in the dictionary. 26 of 57 dictionary synonyms are in this build; the rest have no vector to compare yet.
expand is the dictionary opposite of cuts, yet this model puts it closer than 15 of the 26 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 cuts, 2 of 8 also appear in the dictionary. 26 of 57 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 cuts. 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 cuts. 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 cuts. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.