1
Your word
measured
“he wanted to feel a part of something bigger than himself”
Definitions come from WordNet, a hand-curated dictionary.
something determined in relation to something that includes it
portion is French for part, and every measurement below was made on that English word. Definitions come from English WordNet; Open Multilingual WordNet supplies the French headword, not a French definition.
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“he wanted to feel a part of something bigger than himself”
Definitions come from WordNet, a hand-curated dictionary.
This panel splits portion itself, not part — 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 part, 2 of 8 also appear in the dictionary. 24 of 35 dictionary synonyms are in this build; the rest have no vector to compare yet.
wholly is the dictionary opposite of part, yet this model puts it closer than 2 of the 24 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 part. 24 of 35 dictionary synonyms are in this build; the rest have no vector to compare yet.
wholly is the dictionary opposite of part, yet this model puts it closer than 2 of the 24 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 part, 3 of 8 also appear in the dictionary. 24 of 35 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 part. 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 part. 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 part. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.