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Your word
measured
“contrary to popular belief, glaucoma is not always caused by elevated intraocular pressure”
Definitions come from WordNet, a hand-curated dictionary.
an eye disease that damages the optic nerve and impairs vision (sometimes progressing to blindness)
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“contrary to popular belief, glaucoma is not always caused by elevated intraocular pressure”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
This model splits glaucoma into 3 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 glaucoma.
WordNet lists none for this word.
Shade shows how close the model puts each word to glaucoma.
This model splits glaucoma 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 glaucoma.
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 glaucoma into 3 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 glaucoma 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.
Scores are projections onto axes we defined from anchor words, not labels the model assigns.
This model splits glaucoma into 3 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 glaucoma. 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 glaucoma. That gap is the model’s own learned association.
This model splits glaucoma 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 glaucoma. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.