1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
|
rankprofile[].name "default"
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.W_1"
rankprofile[].fef.property[].value "tensor(hidden[9],out[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_0"
rankprofile[].fef.property[].value "tensor(hidden[9],x[9])"
rankprofile[].fef.property[].name "vespa.type.query.b_out"
rankprofile[].fef.property[].value "tensor(out[1])"
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.W_0"
rankprofile[].fef.property[].value "tensor(hidden[9],x[9])"
rankprofile[].fef.property[].name "vespa.type.query.b_0"
rankprofile[].fef.property[].value "tensor(hidden[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.b_1"
rankprofile[].fef.property[].value "tensor(out[9])"
rankprofile[].fef.property[].name "vespa.type.query.W_out"
rankprofile[].fef.property[].value "tensor(out[9])"
rankprofile[].fef.property[].name "vespa.type.query.b_out"
rankprofile[].fef.property[].value "tensor(out[1])"
rankprofile[].name "neuralNetworkProfile"
rankprofile[].fef.property[].name "rankingExpression(freshnessRank).rankingScript"
rankprofile[].fef.property[].value "nativeRank + freshness(createdAt)"
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(log10_1p@af9a8c53ba738798).rankingScript"
rankprofile[].fef.property[].value "log10(rankingExpression(aVoteCountToUse) + 1)"
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(log10_1p@6ad21b437fe95dd9).rankingScript"
rankprofile[].fef.property[].value "log10(rankingExpression(dvCountToUse) + 1)"
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(log10_1p@c05478688f81fe20).rankingScript"
rankprofile[].fef.property[].value "log10(rankingExpression(uniqueRCountToUse) + 1)"
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(log10_1p@53f0a2c000e82f4).rankingScript"
rankprofile[].fef.property[].value "log10(rankingExpression(uvCountToUse) + 1)"
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(log10_1p@d7da61ad34902e89).rankingScript"
rankprofile[].fef.property[].value "log10(rankingExpression(totalvote) + 1)"
rankprofile[].fef.property[].name "rankingExpression(hsScoreToUse).rankingScript"
rankprofile[].fef.property[].value "attribute(hsScore)"
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(tdToUse).rankingScript"
rankprofile[].fef.property[].value "pow(2,0 - ((rankingExpression(rankedAt) - rankingExpression(createdAtToUse)) / query(decay)))"
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(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@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 "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(totalPR).rankingScript"
rankprofile[].fef.property[].value "rankingExpression(uniqueRCountToUse) + query(voteToRRatio) * (rankingExpression(uvCountToUse) - rankingExpression(dvCountToUse)) - rankingExpression(aVoteCountToUse)"
rankprofile[].fef.property[].name "rankingExpression(nCScoreToUse).rankingScript"
rankprofile[].fef.property[].value "if (rankingExpression(totalPR) > 0, log10(rankingExpression(totalPR)), 0)"
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(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(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(get_model_weights).rankingScript"
rankprofile[].fef.property[].value "if (query(field) == 0, constant(field), query(field))"
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])"
|
