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Your word
measured
“the local seemed to take forever to get to New York”
Definitions come from WordNet, a hand-curated dictionary.
public transport consisting of a bus or train that stops at all stations or stops
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“the local seemed to take forever to get to New York”
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 locals. 1 of 5 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to locals, 1 of 8 also appear in the dictionary. 1 of 5 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to locals, 1 of 8 also appear in the dictionary. 1 of 5 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 locals. 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 locals. 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 locals. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.