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Your word
measured
“She detected high levels of lead in her drinking water”
Definitions come from WordNet, a hand-curated dictionary.
discover or determine the existence, presence, or fact of
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“She detected high levels of lead in her drinking water”
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 observed, 1 of 8 also appear in the dictionary. 17 of 22 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to observed, 1 of 8 also appear in the dictionary. 17 of 22 dictionary synonyms are in this build; the rest have no vector to compare yet.
break is the dictionary opposite of observed, yet this model puts it closer than 1 of the 17 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 observed, 1 of 8 also appear in the dictionary. 17 of 22 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 observed. 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 observed. 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 observed. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.