diff options
Diffstat (limited to 'config-model/src/test/derived/neuralnet/rank-profiles.cfg')
| -rw-r--r-- | config-model/src/test/derived/neuralnet/rank-profiles.cfg | 198 |
1 files changed, 0 insertions, 198 deletions
diff --git a/config-model/src/test/derived/neuralnet/rank-profiles.cfg b/config-model/src/test/derived/neuralnet/rank-profiles.cfg deleted file mode 100644 index 4530bff2e20..00000000000 --- a/config-model/src/test/derived/neuralnet/rank-profiles.cfg +++ /dev/null @@ -1,198 +0,0 @@ -rankprofile[].name "default" -rankprofile[].fef.property[].name "vespa.type.query.b_out" -rankprofile[].fef.property[].value "tensor(out[1])" -rankprofile[].fef.property[].name "vespa.type.query.W_out" -rankprofile[].fef.property[].value "tensor(out[9])" -rankprofile[].fef.property[].name "vespa.type.query.b_0" -rankprofile[].fef.property[].value "tensor(hidden[9])" -rankprofile[].fef.property[].name "vespa.type.query.b_1" -rankprofile[].fef.property[].value "tensor(out[9])" -rankprofile[].fef.property[].name "vespa.type.query.W_1" -rankprofile[].fef.property[].value "tensor(hidden[9],out[9])" -rankprofile[].fef.property[].name "vespa.type.query.W_0" -rankprofile[].fef.property[].value "tensor(hidden[9],x[9])" -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.query.b_out" -rankprofile[].fef.property[].value "tensor(out[1])" -rankprofile[].fef.property[].name "vespa.type.query.W_out" -rankprofile[].fef.property[].value "tensor(out[9])" -rankprofile[].fef.property[].name "vespa.type.query.b_0" -rankprofile[].fef.property[].value "tensor(hidden[9])" -rankprofile[].fef.property[].name "vespa.type.query.b_1" -rankprofile[].fef.property[].value "tensor(out[9])" -rankprofile[].fef.property[].name "vespa.type.query.W_1" -rankprofile[].fef.property[].value "tensor(hidden[9],out[9])" -rankprofile[].fef.property[].name "vespa.type.query.W_0" -rankprofile[].fef.property[].value "tensor(hidden[9],x[9])" -rankprofile[].name "defaultRankProfile" -rankprofile[].fef.property[].name "rankingExpression(log10_1p).rankingScript" -rankprofile[].fef.property[].value "log10(x + 1)" -rankprofile[].fef.property[].name "rankingExpression(textScoreToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(normalizedTextScore)) == 1, 0, attribute(normalizedTextScore))" -rankprofile[].fef.property[].name "rankingExpression(rCountToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(rCount)) == 1, 0, if (attribute(rCount) < 0, 0, attribute(rCount)))" -rankprofile[].fef.property[].name "rankingExpression(uniqueRCountToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(uniqueRCount)) == 1, 0, if (attribute(uniqueRACount) < 0, 0, attribute(uniqueRACount)))" -rankprofile[].fef.property[].name "rankingExpression(uvCountToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(uvCount)) == 1, 0, if (attribute(uvCount) < 0, 0, attribute(uvCount)))" -rankprofile[].fef.property[].name "rankingExpression(dvCountToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(dvCount)) == 1, 0, if (attribute(dvCount) < 0, 0, attribute(dvCount)))" -rankprofile[].fef.property[].name "rankingExpression(aVoteCountToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(aVoteCount)) == 1, 0, if (attribute(aVoteCount) < 0, 0, attribute(aVoteCount)))" -rankprofile[].fef.property[].name "rankingExpression(totalPR).rankingScript" -rankprofile[].fef.property[].value "rankingExpression(uniqueRCountToUse) + query(voteToRRatio) * (rankingExpression(uvCountToUse) - rankingExpression(dvCountToUse)) - rankingExpression(aVoteCountToUse)" -rankprofile[].fef.property[].name "rankingExpression(totalvote).rankingScript" -rankprofile[].fef.property[].value "query(reportaweight) * rankingExpression(aVoteCountToUse) + rankingExpression(dvCountToUse) + query(rweight) * rankingExpression(uniqueRCountToUse) + rankingExpression(uvCountToUse)" -rankprofile[].fef.property[].name "rankingExpression(phat).rankingScript" -rankprofile[].fef.property[].value "if (rankingExpression(totalvote) == 0, 0, (query(rweight) * rankingExpression(uniqueRCountToUse) + rankingExpression(uvCountToUse)) / rankingExpression(totalvote))" -rankprofile[].fef.property[].name "rankingExpression(nCScoreToUse).rankingScript" -rankprofile[].fef.property[].value "if (rankingExpression(totalPR) > 0, log10(rankingExpression(totalPR)), 0)" -rankprofile[].fef.property[].name "rankingExpression(hsScoreToUse).rankingScript" -rankprofile[].fef.property[].value "attribute(hsScore)" -rankprofile[].fef.property[].name "rankingExpression(tScoreToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(t)) == 1, 0.6, attribute(t))" -rankprofile[].fef.property[].name "rankingExpression(relevanceScoreToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(relevance)) == 1, 0.254, attribute(relevance))" -rankprofile[].fef.property[].name "rankingExpression(freshnessToUse).rankingScript" -rankprofile[].fef.property[].value "if (freshness(createdAt).logscale < 0.01, 0.01, freshness(createdAt).logscale)" -rankprofile[].fef.property[].name "rankingExpression(rankedAt).rankingScript" -rankprofile[].fef.property[].value "now" -rankprofile[].fef.property[].name "rankingExpression(createdAtToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(createdAt)) == 1, rankingExpression(rankedAt), attribute(createdAt))" -rankprofile[].fef.property[].name "rankingExpression(laAtToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(laAt)) == 1, attribute(createdAt), attribute(laAt))" -rankprofile[].fef.property[].name "rankingExpression(markedAsAAtToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(markedAsAAt)) == 1, 9.223372036854776E18, attribute(markedAsAAt))" -rankprofile[].fef.property[].name "rankingExpression(tdToUse).rankingScript" -rankprofile[].fef.property[].value "pow(2,0 - ((rankingExpression(rankedAt) - rankingExpression(createdAtToUse)) / query(decay)))" -rankprofile[].fef.property[].name "rankingExpression(commentOverallScore).rankingScript" -rankprofile[].fef.property[].value "query(textweight) * rankingExpression(textScoreToUse) + query(communityweight) * rankingExpression(nCScoreToUse)" -rankprofile[].fef.property[].name "rankingExpression(pinScore).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(pinned)) == 1, 0, query(pinweight) * attribute(pinned))" -rankprofile[].fef.property[].name "rankingExpression(freshnessRank).rankingScript" -rankprofile[].fef.property[].value "nativeRank + freshness(createdAt)" -rankprofile[].fef.property[].name "vespa.rank.firstphase" -rankprofile[].fef.property[].value "nativeRank" -rankprofile[].fef.property[].name "vespa.type.query.b_out" -rankprofile[].fef.property[].value "tensor(out[1])" -rankprofile[].fef.property[].name "vespa.type.query.W_out" -rankprofile[].fef.property[].value "tensor(out[9])" -rankprofile[].fef.property[].name "vespa.type.query.b_0" -rankprofile[].fef.property[].value "tensor(hidden[9])" -rankprofile[].fef.property[].name "vespa.type.query.b_1" -rankprofile[].fef.property[].value "tensor(out[9])" -rankprofile[].fef.property[].name "vespa.type.query.W_1" -rankprofile[].fef.property[].value "tensor(hidden[9],out[9])" -rankprofile[].fef.property[].name "vespa.type.query.W_0" -rankprofile[].fef.property[].value "tensor(hidden[9],x[9])" -rankprofile[].name "neuralNetworkProfile" -rankprofile[].fef.property[].name "rankingExpression(log10_1p).rankingScript" -rankprofile[].fef.property[].value "log10(x + 1)" -rankprofile[].fef.property[].name "rankingExpression(textScoreToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(normalizedTextScore)) == 1, 0, attribute(normalizedTextScore))" -rankprofile[].fef.property[].name "rankingExpression(rCountToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(rCount)) == 1, 0, if (attribute(rCount) < 0, 0, attribute(rCount)))" -rankprofile[].fef.property[].name "rankingExpression(uniqueRCountToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(uniqueRCount)) == 1, 0, if (attribute(uniqueRACount) < 0, 0, attribute(uniqueRACount)))" -rankprofile[].fef.property[].name "rankingExpression(uvCountToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(uvCount)) == 1, 0, if (attribute(uvCount) < 0, 0, attribute(uvCount)))" -rankprofile[].fef.property[].name "rankingExpression(dvCountToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(dvCount)) == 1, 0, if (attribute(dvCount) < 0, 0, attribute(dvCount)))" -rankprofile[].fef.property[].name "rankingExpression(aVoteCountToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(aVoteCount)) == 1, 0, if (attribute(aVoteCount) < 0, 0, attribute(aVoteCount)))" -rankprofile[].fef.property[].name "rankingExpression(totalPR).rankingScript" -rankprofile[].fef.property[].value "rankingExpression(uniqueRCountToUse) + query(voteToRRatio) * (rankingExpression(uvCountToUse) - rankingExpression(dvCountToUse)) - rankingExpression(aVoteCountToUse)" -rankprofile[].fef.property[].name "rankingExpression(totalvote).rankingScript" -rankprofile[].fef.property[].value "query(reportaweight) * rankingExpression(aVoteCountToUse) + rankingExpression(dvCountToUse) + query(rweight) * rankingExpression(uniqueRCountToUse) + rankingExpression(uvCountToUse)" -rankprofile[].fef.property[].name "rankingExpression(phat).rankingScript" -rankprofile[].fef.property[].value "if (rankingExpression(totalvote) == 0, 0, (query(rweight) * rankingExpression(uniqueRCountToUse) + rankingExpression(uvCountToUse)) / rankingExpression(totalvote))" -rankprofile[].fef.property[].name "rankingExpression(nCScoreToUse).rankingScript" -rankprofile[].fef.property[].value "if (rankingExpression(totalPR) > 0, log10(rankingExpression(totalPR)), 0)" -rankprofile[].fef.property[].name "rankingExpression(hsScoreToUse).rankingScript" -rankprofile[].fef.property[].value "attribute(hsScore)" -rankprofile[].fef.property[].name "rankingExpression(tScoreToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(t)) == 1, 0.6, attribute(t))" -rankprofile[].fef.property[].name "rankingExpression(relevanceScoreToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(relevance)) == 1, 0.254, attribute(relevance))" -rankprofile[].fef.property[].name "rankingExpression(freshnessToUse).rankingScript" -rankprofile[].fef.property[].value "if (freshness(createdAt).logscale < 0.01, 0.01, freshness(createdAt).logscale)" -rankprofile[].fef.property[].name "rankingExpression(rankedAt).rankingScript" -rankprofile[].fef.property[].value "now" -rankprofile[].fef.property[].name "rankingExpression(createdAtToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(createdAt)) == 1, rankingExpression(rankedAt), attribute(createdAt))" -rankprofile[].fef.property[].name "rankingExpression(laAtToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(laAt)) == 1, attribute(createdAt), attribute(laAt))" -rankprofile[].fef.property[].name "rankingExpression(markedAsAAtToUse).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(markedAsAAt)) == 1, 9.223372036854776E18, attribute(markedAsAAt))" -rankprofile[].fef.property[].name "rankingExpression(tdToUse).rankingScript" -rankprofile[].fef.property[].value "pow(2,0 - ((rankingExpression(rankedAt) - rankingExpression(createdAtToUse)) / query(decay)))" -rankprofile[].fef.property[].name "rankingExpression(commentOverallScore).rankingScript" -rankprofile[].fef.property[].value "query(textweight) * rankingExpression(textScoreToUse) + query(communityweight) * rankingExpression(nCScoreToUse)" -rankprofile[].fef.property[].name "rankingExpression(pinScore).rankingScript" -rankprofile[].fef.property[].value "if (isNan(attribute(pinned)) == 1, 0, query(pinweight) * attribute(pinned))" -rankprofile[].fef.property[].name "rankingExpression(freshnessRank).rankingScript" -rankprofile[].fef.property[].value "nativeRank + freshness(createdAt)" -rankprofile[].fef.property[].name "rankingExpression(log10_1p@af9a8c53ba738798).rankingScript" -rankprofile[].fef.property[].value "log10(rankingExpression(aVoteCountToUse) + 1)" -rankprofile[].fef.property[].name "rankingExpression(log10_1p@6ad21b437fe95dd9).rankingScript" -rankprofile[].fef.property[].value "log10(rankingExpression(dvCountToUse) + 1)" -rankprofile[].fef.property[].name "rankingExpression(log10_1p@c05478688f81fe20).rankingScript" -rankprofile[].fef.property[].value "log10(rankingExpression(uniqueRCountToUse) + 1)" -rankprofile[].fef.property[].name "rankingExpression(log10_1p@53f0a2c000e82f4).rankingScript" -rankprofile[].fef.property[].value "log10(rankingExpression(uvCountToUse) + 1)" -rankprofile[].fef.property[].name "rankingExpression(log10_1p@d7da61ad34902e89).rankingScript" -rankprofile[].fef.property[].value "log10(rankingExpression(totalvote) + 1)" -rankprofile[].fef.property[].name "rankingExpression(nn_input).rankingScript" -rankprofile[].fef.property[].value "concat(rankingExpression(log10_1p@af9a8c53ba738798), concat(rankingExpression(log10_1p@6ad21b437fe95dd9), concat(rankingExpression(log10_1p@c05478688f81fe20), concat(rankingExpression(log10_1p@53f0a2c000e82f4), concat(rankingExpression(phat), concat(rankingExpression(log10_1p@d7da61ad34902e89), concat(rankingExpression(hsScoreToUse), concat(rankingExpression(tdToUse), rankingExpression(tScoreToUse), x), x), x), x), x), x), x), x)" -rankprofile[].fef.property[].name "rankingExpression(nn_input).type" -rankprofile[].fef.property[].value "tensor(x[9])" -rankprofile[].fef.property[].name "rankingExpression(get_model_weights).rankingScript" -rankprofile[].fef.property[].value "if (query(field) == 0, constant(field), query(field))" -rankprofile[].fef.property[].name "rankingExpression(get_model_weights@1f2b4afc2c45fbee).rankingScript" -rankprofile[].fef.property[].value "if (query(W_0) == 0, constant(W_0), query(W_0))" -rankprofile[].fef.property[].name "rankingExpression(get_model_weights@e752cecc7900ff3e).rankingScript" -rankprofile[].fef.property[].value "if (query(b_0) == 0, constant(b_0), query(b_0))" -rankprofile[].fef.property[].name "rankingExpression(layer_0).rankingScript" -rankprofile[].fef.property[].value "elu(join(reduce(join(rankingExpression(nn_input), rankingExpression(get_model_weights@1f2b4afc2c45fbee), f(a,b)(a * b)), sum, x), rankingExpression(get_model_weights@e752cecc7900ff3e), f(a,b)(a + b)))" -rankprofile[].fef.property[].name "rankingExpression(layer_0).type" -rankprofile[].fef.property[].value "tensor(hidden[9])" -rankprofile[].fef.property[].name "rankingExpression(get_model_weights@eac265fa16b752cf).rankingScript" -rankprofile[].fef.property[].value "if (query(W_1) == 0, constant(W_1), query(W_1))" -rankprofile[].fef.property[].name "rankingExpression(get_model_weights@b953c19adb7d2154).rankingScript" -rankprofile[].fef.property[].value "if (query(b_1) == 0, constant(b_1), query(b_1))" -rankprofile[].fef.property[].name "rankingExpression(layer_1).rankingScript" -rankprofile[].fef.property[].value "elu(join(reduce(join(rankingExpression(layer_0), rankingExpression(get_model_weights@eac265fa16b752cf), f(a,b)(a * b)), sum, hidden), rankingExpression(get_model_weights@b953c19adb7d2154), f(a,b)(a + b)))" -rankprofile[].fef.property[].name "rankingExpression(layer_1).type" -rankprofile[].fef.property[].value "tensor(out[9])" -rankprofile[].fef.property[].name "rankingExpression(get_model_weights@418462473aa32b7d).rankingScript" -rankprofile[].fef.property[].value "if (query(W_out) == 0, constant(W_out), query(W_out))" -rankprofile[].fef.property[].name "rankingExpression(get_model_weights@23f46853cab72961).rankingScript" -rankprofile[].fef.property[].value "if (query(b_out) == 0, constant(b_out), query(b_out))" -rankprofile[].fef.property[].name "rankingExpression(layer_out).rankingScript" -rankprofile[].fef.property[].value "reduce(join(reduce(join(rankingExpression(layer_1), rankingExpression(get_model_weights@418462473aa32b7d), f(a,b)(a * b)), sum, out), rankingExpression(get_model_weights@23f46853cab72961), f(a,b)(a + b)), sum)" -rankprofile[].fef.property[].name "vespa.rank.firstphase" -rankprofile[].fef.property[].value "rankingExpression(freshnessRank)" -rankprofile[].fef.property[].name "vespa.rank.secondphase" -rankprofile[].fef.property[].value "rankingExpression(layer_out)" -rankprofile[].fef.property[].name "vespa.hitcollector.heapsize" -rankprofile[].fef.property[].value "2000" -rankprofile[].fef.property[].name "vespa.type.query.b_out" -rankprofile[].fef.property[].value "tensor(out[1])" -rankprofile[].fef.property[].name "vespa.type.query.W_out" -rankprofile[].fef.property[].value "tensor(out[9])" -rankprofile[].fef.property[].name "vespa.type.query.b_0" -rankprofile[].fef.property[].value "tensor(hidden[9])" -rankprofile[].fef.property[].name "vespa.type.query.b_1" -rankprofile[].fef.property[].value "tensor(out[9])" -rankprofile[].fef.property[].name "vespa.type.query.W_1" -rankprofile[].fef.property[].value "tensor(hidden[9],out[9])" -rankprofile[].fef.property[].name "vespa.type.query.W_0" -rankprofile[].fef.property[].value "tensor(hidden[9],x[9])" |