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Your word
measured
“we tried to collect the money he owed us”
Definitions come from WordNet, a hand-curated dictionary.
the most common medium of exchange; functions as legal tender
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“we tried to collect the money he owed us”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
WordNet lists none for this word.
Shade shows how close the model puts each word to money.
WordNet lists none for this word.
Shade shows how close the model puts each word to money.
WordNet lists none for this word.
Shade shows how close the model puts each word to money.
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 money. 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 money. 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 money. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.