1
Your word
measured
Definitions come from WordNet, a hand-curated dictionary.
a person trained to compete in sports
atleta is Spanish for athletes, and every measurement below was made on that English word. Definitions come from English WordNet; Open Multilingual WordNet supplies the Spanish headword, not a Spanish definition.
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
Definitions come from WordNet, a hand-curated dictionary.
This panel splits atleta itself, not athletes — a tokenizer does not care what language it is fed. It is the only panel here that does.
This is the tokenizer splitting text, not the model understanding it.
Shade shows how close the model puts each word to athletes, 1 of 8 also appear in the dictionary.
Shade shows how close the model puts each word to athletes, 1 of 8 also appear in the dictionary.
Shade shows how close the model puts each word to athletes, 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 athletes. 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 athletes. 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 athletes. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.