1
Your word
measured
“the increase in unemployment”
Definitions come from WordNet, a hand-curated dictionary.
a process of becoming larger or longer or more numerous or more important
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“the increase in unemployment”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
Shade shows how close the model puts each word to increments, 1 of 8 also appear in the dictionary.
decrease is the dictionary opposite of increments, 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 increments, 2 of 8 also appear in the dictionary.
This model splits increments 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.
decrease is the dictionary opposite of increments, 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 increments.
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 increments 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 increments. 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 increments. That gap is the model’s own learned association.
This model splits increments 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 increments. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.