1
Your word
measured
Definitions come from WordNet, a hand-curated dictionary.
emotions experienced when not in a state of well-being
malheur is French for sadness, 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.
Definitions come from WordNet, a hand-curated dictionary.
This panel splits malheur itself, not sadness — 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.
happiness is the dictionary opposite of sadness, yet this model puts it closer than every one of the 1 synonym 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 sadness, 1 of 8 also appear in the dictionary. 1 of 5 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to sadness, 1 of 8 also appear in the dictionary. 1 of 5 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits sadness into 2 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 sadness. 1 of 5 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 sadness into 2 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.
These sit just outside the closest neighbours in panel 3, and no dictionary lists any of them as related to sadness. 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 sadness. That gap is the model’s own learned association.
This model splits sadness into 2 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 sadness. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.