1
Your word
measured
“She lost her purse when she left it unattended on her seat”
Definitions come from WordNet, a hand-curated dictionary.
fail to keep or to maintain; cease to have, either physically or in an abstract sense
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“She lost her purse when she left it unattended on her seat”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
win is the dictionary opposite of lose, yet this model puts it closer than every one of the 3 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 lose. 3 of 9 dictionary synonyms are in this build; the rest have no vector to compare yet.
gain is the dictionary opposite of lose, yet this model puts it closer than every one of the 3 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 lose. 3 of 9 dictionary synonyms are in this build; the rest have no vector to compare yet.
win is the dictionary opposite of lose, yet this model puts it closer than every one of the 3 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 lose. 3 of 9 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 lose. 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 lose. 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 lose. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.