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Your word
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“economic growth of less than 1% per year is considered to be economic stagnation”
Definitions come from WordNet, a hand-curated dictionary.
a state of inactivity (in business or art etc)
stagnation is French for doldrums, 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.
“economic growth of less than 1% per year is considered to be economic stagnation”
Definitions come from WordNet, a hand-curated dictionary.
This panel splits stagnation itself, not doldrums — 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 doldrums into 4 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 doldrums. 0 of 2 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits doldrums 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 doldrums. 0 of 2 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits doldrums into 4 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 doldrums. 0 of 2 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 doldrums into 4 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 doldrums 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 doldrums into 4 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 doldrums into 4 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 doldrums. That gap is the model’s own learned association.
This model splits doldrums 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 doldrums. That gap is the model’s own learned association.
This model splits doldrums into 4 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 doldrums. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.