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Your word
measured
“he feels that you are in the wrong”
Definitions come from WordNet, a hand-curated dictionary.
that which is contrary to the principles of justice or law
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“he feels that you are in the wrong”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
correct is the dictionary opposite of wrong, yet this model puts it closer than every one of the 5 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 wrong, 1 of 8 also appear in the dictionary. 5 of 14 dictionary synonyms are in this build; the rest have no vector to compare yet.
right is the dictionary opposite of wrong, yet this model puts it closer than every one of the 5 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 wrong, 1 of 8 also appear in the dictionary. 5 of 14 dictionary synonyms are in this build; the rest have no vector to compare yet.
right is the dictionary opposite of wrong, yet this model puts it closer than every one of the 5 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 wrong, 2 of 8 also appear in the dictionary. 5 of 14 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 wrong. 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 wrong. 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 wrong. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.