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Your word
measured
Definitions come from WordNet, a hand-curated dictionary.
a growth in strength or number or importance
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
fall is the dictionary opposite of rises, yet this model puts it closer than 22 of the 25 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 rises, 3 of 8 also appear in the dictionary. 25 of 44 dictionary synonyms are in this build; the rest have no vector to compare yet.
fall is the dictionary opposite of rises, yet this model puts it closer than 17 of the 25 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 rises, 3 of 8 also appear in the dictionary. 25 of 44 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits rises 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.
fall is the dictionary opposite of rises, yet this model puts it closer than 16 of the 25 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 rises. 25 of 44 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 rises 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 rises. 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 rises. That gap is the model’s own learned association.
This model splits rises 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 rises. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.