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Your word
measured
“he searched through piles of letters and clippings”
Definitions come from WordNet, a hand-curated dictionary.
an excerpt cut from a newspaper or magazine
Transform is French for clippings, 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 searched through piles of letters and clippings”
Definitions come from WordNet, a hand-curated dictionary.
This panel splits Transform itself, not clippings — 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.
This model splits clippings 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 clippings. 3 of 9 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits clippings 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 clippings. 3 of 9 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits clippings 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 clippings, 1 of 8 also appear in the dictionary. 3 of 9 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 clippings 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.
This model splits clippings 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.
This model splits clippings 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.
This model splits clippings 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 clippings. That gap is the model’s own learned association.
This model splits clippings 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 clippings. That gap is the model’s own learned association.
This model splits clippings 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 clippings. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.