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Your word
measured
“He suffered the penalty”
Definitions come from WordNet, a hand-curated dictionary.
undergo or be subjected to
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“He suffered the penalty”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
enjoy is the dictionary opposite of suffers, yet this model puts it closer than 4 of the 16 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 suffers, 1 of 8 also appear in the dictionary. 16 of 19 dictionary synonyms are in this build; the rest have no vector to compare yet.
enjoy is the dictionary opposite of suffers, yet this model puts it closer than 9 of the 16 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 suffers, 1 of 8 also appear in the dictionary. 16 of 19 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits suffers 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.
enjoy is the dictionary opposite of suffers, yet this model puts it closer than 11 of the 16 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 suffers. 16 of 19 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 suffers 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 suffers. 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 suffers. That gap is the model’s own learned association.
This model splits suffers 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 suffers. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.