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Your word
measured
“putting them in readiness”
Definitions come from WordNet, a hand-curated dictionary.
the state of having been made ready or prepared for use or action (especially military action)
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“putting them in readiness”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
This model splits preparedness 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.
Shade shows how close the model puts each word to preparedness.
Shade shows how close the model puts each word to preparedness, 2 of 8 also appear in the dictionary.
This model splits preparedness 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.
Shade shows how close the model puts each word to preparedness.
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 preparedness 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.
This model splits preparedness 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.
This model splits preparedness 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 preparedness. 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 preparedness. That gap is the model’s own learned association.
This model splits preparedness 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 preparedness. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.