1
Your word
measured
“Please delete my name from your list”
Definitions come from WordNet, a hand-curated dictionary.
remove or make invisible
effacer is French for delete, 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.
“Please delete my name from your list”
Definitions come from WordNet, a hand-curated dictionary.
This panel splits effacer itself, not delete — 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 delete. 3 of 4 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to delete, 1 of 8 also appear in the dictionary. 3 of 4 dictionary synonyms are in this build; the rest have no vector to compare yet.
record is the dictionary opposite of delete, yet this model puts it closer than 1 of the 3 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 delete. 3 of 4 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 delete. 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 delete. 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 delete. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.