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Your word
measured
“I am promoting the use of computers in the classroom”
Definitions come from WordNet, a hand-curated dictionary.
contribute to the progress or growth of
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“I am promoting the use of computers in the classroom”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
discourage is the dictionary opposite of encourage, yet this model puts it closer than 3 of the 4 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 encourage, 1 of 8 also appear in the dictionary.
discourage is the dictionary opposite of encourage, yet this model puts it closer than every one of the 4 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 encourage, 1 of 8 also appear in the dictionary.
discourage is the dictionary opposite of encourage, yet this model puts it closer than every one of the 4 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 encourage, 1 of 8 also appear in the dictionary.
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.
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 encourage. 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 encourage. 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 encourage. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.