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Your word
measured
“nutritional information”
Definitions come from WordNet, a hand-curated dictionary.
of or relating to or providing nutrition
nutricional is Spanish for nutritional, and every measurement below was made on that English word. Definitions come from English WordNet; Open Multilingual WordNet supplies the Spanish headword, not a Spanish definition.
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“nutritional information”
Definitions come from WordNet, a hand-curated dictionary.
This panel splits nutricional itself, not nutritional — a tokenizer does not care what language it is fed. It is the only panel here that does.
This is the tokenizer splitting text, not the model understanding it.
Shade shows how close the model puts each word to nutritional. 0 of 1 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to nutritional. 0 of 1 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits nutritional into 3 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 nutritional. 0 of 1 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.
This model splits nutritional into 3 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 nutritional. 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 nutritional. That gap is the model’s own learned association.
This model splits nutritional into 3 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 nutritional. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.