1
Your word
measured
“what option did I have?”
Definitions come from WordNet, a hand-curated dictionary.
one of a number of things from which only one can be chosen
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“what option did I have?”
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 alternative, 3 of 8 also appear in the dictionary.
Shade shows how close the model puts each word to alternative, 2 of 8 also appear in the dictionary.
Shade shows how close the model puts each word to alternative, 3 of 8 also appear in the dictionary.
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 alternative. 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 alternative. 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 alternative. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.