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Your word
measured
“he gave the ball a powerful kick”
Definitions come from WordNet, a hand-curated dictionary.
the act of delivering a blow with the foot
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“he gave the ball a powerful kick”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
cheer is the dictionary opposite of kicks, yet this model puts it closer than 2 of the 12 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 kicks, 2 of 8 also appear in the dictionary. 12 of 20 dictionary synonyms are in this build; the rest have no vector to compare yet.
cheer is the dictionary opposite of kicks, yet this model puts it closer than 3 of the 12 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 kicks, 2 of 8 also appear in the dictionary. 12 of 20 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits kicks 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.
cheer is the dictionary opposite of kicks, yet this model puts it closer than 2 of the 12 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 kicks. 12 of 20 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 kicks 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 kicks. 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 kicks. That gap is the model’s own learned association.
This model splits kicks 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 kicks. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.