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Your word
measured
Definitions come from WordNet, a hand-curated dictionary.
incompetence of the gonads (especially in the male with low testosterone); results in deficient development of secondary sex characteristics and (in prepubertal males) a body with long legs and a short trunk
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.
This model splits hypogonadism into 5 pieces, so it has no vector of its own here: this is the average of its fragments, and the results below are correspondingly rough.
WordNet lists none for this word.
Shade shows how close the model puts each word to hypogonadism.
This model splits hypogonadism 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.
WordNet lists none for this word.
Shade shows how close the model puts each word to hypogonadism.
This model splits hypogonadism into 5 pieces, so it has no vector of its own here: this is the average of its fragments, and the results below are correspondingly rough.
WordNet lists none for this word.
Shade shows how close the model puts each word to hypogonadism.
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 hypogonadism into 5 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 hypogonadism 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 hypogonadism into 5 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 hypogonadism into 5 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 hypogonadism. That gap is the model’s own learned association.
This model splits hypogonadism 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 hypogonadism. That gap is the model’s own learned association.
This model splits hypogonadism into 5 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 hypogonadism. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.