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Your word
measured
“a diet high in protein”
Definitions come from WordNet, a hand-curated dictionary.
any of a large group of nitrogenous organic compounds that are essential constituents of living cells; consist of polymers of amino acids; essential in the diet of animals for growth and for repair of tissues; can be obtained from meat and eggs and milk and legumes
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“a diet high in protein”
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 proteins, 1 of 8 also appear in the dictionary.
Shade shows how close the model puts each word to proteins, 1 of 8 also appear in the dictionary.
This model splits proteins into 2 pieces, so it has no vector of its own here: this is the average of its fragments, and the results below are correspondingly rough.
Shade shows how close the model puts each word to proteins.
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.
This model splits proteins into 2 pieces, so it has no vector of its own here: this is the average of its fragments, and the results below are correspondingly rough.
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 proteins. 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 proteins. That gap is the model’s own learned association.
This model splits proteins into 2 pieces, so it has no vector of its own here: this is the average of its fragments, and the results below are correspondingly rough.
These sit just outside the closest neighbours in panel 3, and no dictionary lists any of them as related to proteins. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.