1
Your word
measured
Definitions come from WordNet, a hand-curated dictionary.
someone who is under suspicion
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.
Shade shows how close the model puts each word to suspects, 1 of 8 also appear in the dictionary. 2 of 5 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to suspects, 1 of 8 also appear in the dictionary. 2 of 5 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits suspects 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.
trust is the dictionary opposite of suspects, yet this model puts it closer than 1 of the 2 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 suspects, 1 of 8 also appear in the dictionary. 2 of 5 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 suspects 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 suspects. 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 suspects. That gap is the model’s own learned association.
This model splits suspects 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 suspects. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.