rankprofile[].name "default" rankprofile[].fef.property[].name "vespa.type.attribute.tokens" rankprofile[].fef.property[].value "tensor(d0[128])" rankprofile[].name "unranked" rankprofile[].fef.property[].name "vespa.rank.firstphase" rankprofile[].fef.property[].value "value(0)" rankprofile[].fef.property[].name "vespa.hitcollector.heapsize" rankprofile[].fef.property[].value "0" rankprofile[].fef.property[].name "vespa.hitcollector.arraysize" rankprofile[].fef.property[].value "0" rankprofile[].fef.property[].name "vespa.dump.ignoredefaultfeatures" rankprofile[].fef.property[].value "true" rankprofile[].fef.property[].name "vespa.type.attribute.tokens" rankprofile[].fef.property[].value "tensor(d0[128])" rankprofile[].name "using_model" rankprofile[].fef.property[].name "vespa.type.feature.attribute(tokens)" rankprofile[].fef.property[].value "tensor(d0[128])" rankprofile[].fef.property[].name "rankingExpression(__token_length@4d7c1b66085df918).rankingScript" rankprofile[].fef.property[].value "reduce(map(query(input), f(x)(x > 0)), sum)" rankprofile[].fef.property[].name "rankingExpression(__token_length@a16087c578950aea).rankingScript" rankprofile[].fef.property[].value "reduce(map(attribute(tokens), f(x)(x > 0)), sum)" rankprofile[].fef.property[].name "rankingExpression(input_ids).rankingScript" rankprofile[].fef.property[].value "tensor(d0[1],d1[128])((if (d1 < 1.0, 101.0, if (d1 < 1.0 + rankingExpression(__token_length@4d7c1b66085df918), query(input){d0:(d1 - (1.0))}, if (d1 < (1.0 + rankingExpression(__token_length@4d7c1b66085df918) + 1.0), 102.0, if (d1 < (1.0 + rankingExpression(__token_length@4d7c1b66085df918) + 1.0 + rankingExpression(__token_length@a16087c578950aea)), attribute(tokens){d0:(d1 - (1.0 + rankingExpression(__token_length@4d7c1b66085df918) + 1.0))}, if (d1 < (1.0 + rankingExpression(__token_length@4d7c1b66085df918) + 1.0 + rankingExpression(__token_length@a16087c578950aea) + 1.0), 102.0, 0.0)))))))" rankprofile[].fef.property[].name "rankingExpression(input_ids).type" rankprofile[].fef.property[].value "tensor(d0[1],d1[128])" rankprofile[].fef.property[].name "rankingExpression(token_type_ids).rankingScript" rankprofile[].fef.property[].value "tensor(d0[1],d1[128])((if (d1 < (1.0 + rankingExpression(__token_length@4d7c1b66085df918) + 1.0), 0.0, if (d1 < (1.0 + rankingExpression(__token_length@4d7c1b66085df918) + 1.0 + rankingExpression(__token_length@a16087c578950aea) + 1.0), 1.0, 0.0))))" rankprofile[].fef.property[].name "rankingExpression(token_type_ids).type" rankprofile[].fef.property[].value "tensor(d0[1],d1[128])" rankprofile[].fef.property[].name "rankingExpression(attention_mask).rankingScript" rankprofile[].fef.property[].value "tensor(d0[1],d1[128])((if (d1 < (1.0 + rankingExpression(__token_length@4d7c1b66085df918) + 1.0 + rankingExpression(__token_length@a16087c578950aea) + 1.0), 1.0, 0.0)))" rankprofile[].fef.property[].name "rankingExpression(attention_mask).type" rankprofile[].fef.property[].value "tensor(d0[1],d1[128])" rankprofile[].fef.property[].name "vespa.rank.globalphase" rankprofile[].fef.property[].value "rankingExpression(globalphase)" rankprofile[].fef.property[].name "rankingExpression(globalphase).rankingScript" rankprofile[].fef.property[].value "onnx(my_ranking_model).score{d0:(attribute(outputidx))}" rankprofile[].fef.property[].name "vespa.match.feature" rankprofile[].fef.property[].value "attribute(tokens)" rankprofile[].fef.property[].name "vespa.match.feature" rankprofile[].fef.property[].value "attribute(outputidx)" rankprofile[].fef.property[].name "vespa.hidden.matchfeature" rankprofile[].fef.property[].value "attribute(tokens)" rankprofile[].fef.property[].name "vespa.hidden.matchfeature" rankprofile[].fef.property[].value "attribute(outputidx)" rankprofile[].fef.property[].name "vespa.globalphase.rerankcount" rankprofile[].fef.property[].value "1000" rankprofile[].fef.property[].name "vespa.type.attribute.tokens" rankprofile[].fef.property[].value "tensor(d0[128])" rankprofile[].fef.property[].name "vespa.type.query.input" rankprofile[].fef.property[].value "tensor(d0[32])" rankprofile[].name "with-fun" rankprofile[].fef.property[].name "rankingExpression(use_model).rankingScript" rankprofile[].fef.property[].value "attribute(outputidx) + 1.0" rankprofile[].fef.property[].name "vespa.rank.globalphase" rankprofile[].fef.property[].value "rankingExpression(use_model)" rankprofile[].fef.property[].name "vespa.match.feature" rankprofile[].fef.property[].value "attribute(outputidx)" rankprofile[].fef.property[].name "vespa.hidden.matchfeature" rankprofile[].fef.property[].value "attribute(outputidx)" rankprofile[].fef.property[].name "vespa.type.attribute.tokens" rankprofile[].fef.property[].value "tensor(d0[128])" rankprofile[].name "with-fun-mf" rankprofile[].fef.property[].name "rankingExpression(use_model).rankingScript" rankprofile[].fef.property[].value "attribute(outputidx) + 1.0" rankprofile[].fef.property[].name "vespa.rank.firstphase" rankprofile[].fef.property[].value "nativeRank" rankprofile[].fef.property[].name "vespa.rank.globalphase" rankprofile[].fef.property[].value "rankingExpression(use_model)" rankprofile[].fef.property[].name "vespa.match.feature" rankprofile[].fef.property[].value "rankingExpression(use_model)" rankprofile[].fef.property[].name "vespa.feature.rename" rankprofile[].fef.property[].value "rankingExpression(use_model)" rankprofile[].fef.property[].name "vespa.feature.rename" rankprofile[].fef.property[].value "use_model" rankprofile[].fef.property[].name "vespa.type.attribute.tokens" rankprofile[].fef.property[].value "tensor(d0[128])" rankprofile[].name "fun-mf-child" rankprofile[].fef.property[].name "rankingExpression(use_model).rankingScript" rankprofile[].fef.property[].value "attribute(outputidx) + 1.0" rankprofile[].fef.property[].name "vespa.rank.firstphase" rankprofile[].fef.property[].value "rankingExpression(firstphase)" rankprofile[].fef.property[].name "rankingExpression(firstphase).rankingScript" rankprofile[].fef.property[].value "42 * attribute(outputidx)" rankprofile[].fef.property[].name "vespa.rank.globalphase" rankprofile[].fef.property[].value "rankingExpression(use_model)" rankprofile[].fef.property[].name "vespa.match.feature" rankprofile[].fef.property[].value "rankingExpression(use_model)" rankprofile[].fef.property[].name "vespa.feature.rename" rankprofile[].fef.property[].value "rankingExpression(use_model)" rankprofile[].fef.property[].name "vespa.feature.rename" rankprofile[].fef.property[].value "use_model" rankprofile[].fef.property[].name "vespa.type.attribute.tokens" rankprofile[].fef.property[].value "tensor(d0[128])"