1
Your word
measured
“the red light gave the central figure increased emphasis”
Definitions come from WordNet, a hand-curated dictionary.
special importance or significance
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“the red light gave the central figure increased emphasis”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
Shade shows how close the model puts each word to emphasis. 2 of 3 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to emphasis. 2 of 3 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to emphasis. 2 of 3 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 emphasis. 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 emphasis. 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 emphasis. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.