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Your word
measured
Definitions come from WordNet, a hand-curated dictionary.
Roman Emperor who was the adoptive son of Trajan; travelled throughout his empire to strengthen its frontiers and encourage learning and architecture; on a visit to Britain in 122 he ordered the construction of Hadrian's Wall (76-138)
Edgar Douglas Adrian is Spanish for adrian, 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 Edgar Douglas Adrian itself, not adrian — 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.
This model splits adrian 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 adrian. 0 of 4 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits adrian 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 adrian. 0 of 4 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits adrian 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 adrian. 0 of 4 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 adrian 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 adrian 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 adrian 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 adrian 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 adrian. That gap is the model’s own learned association.
This model splits adrian 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 adrian. That gap is the model’s own learned association.
This model splits adrian 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 adrian. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.