1
Your word
measured
“She has $1,000 in the bank”
Definitions come from WordNet, a hand-curated dictionary.
have or possess, either in a concrete or an abstract sense
poseer is Spanish for had, 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.
“She has $1,000 in the bank”
Definitions come from WordNet, a hand-curated dictionary.
This panel splits poseer itself, not had — 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.
lack is the dictionary opposite of had, yet this model puts it closer than 19 of the 23 synonyms it can score. Opposites share the sentences a word lives in, so cosine alone cannot tell them apart - which is why the dictionary seeds this list rather than the geometry.
Shade shows how close the model puts each word to had, 1 of 8 also appear in the dictionary. 23 of 27 dictionary synonyms are in this build; the rest have no vector to compare yet.
lack is the dictionary opposite of had, yet this model puts it closer than 13 of the 23 synonyms it can score. Opposites share the sentences a word lives in, so cosine alone cannot tell them apart - which is why the dictionary seeds this list rather than the geometry.
Shade shows how close the model puts each word to had, 1 of 8 also appear in the dictionary. 23 of 27 dictionary synonyms are in this build; the rest have no vector to compare yet.
lack is the dictionary opposite of had, yet this model puts it closer than 20 of the 23 synonyms it can score. Opposites share the sentences a word lives in, so cosine alone cannot tell them apart - which is why the dictionary seeds this list rather than the geometry.
Shade shows how close the model puts each word to had, 1 of 8 also appear in the dictionary. 23 of 27 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 had. 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 had. 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 had. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.