0
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| 1
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| 2
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| 3
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| 4
float64 -0.16
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| 5
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| 6
float64 -0.2
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| 7
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| 8
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| 9
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| 10
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| 11
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| 12
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| 13
float64 -0.16
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| 14
float64 -0.14
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| 15
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| 16
float64 -0.16
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| 17
float64 -0.15
0.16
| 18
float64 -0.16
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| 19
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| 20
float64 -0.15
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| 21
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| 22
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| 23
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| 24
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| 25
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| 26
float64 -0.16
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| 27
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| 28
float64 -0.17
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| 29
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| 30
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| 31
float64 -0.16
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| 32
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| 33
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| 34
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| 35
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| 36
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| 37
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| 38
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| 39
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| 40
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| 41
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| 42
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| 43
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| 44
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| 45
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| 46
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| 47
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| 48
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| 49
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| 50
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| 51
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| 52
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| 53
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| 54
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| 55
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| 56
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| 57
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| 58
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| 59
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| 60
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| 61
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| 62
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| 63
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| 64
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| 65
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| 66
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| 67
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| 68
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| 69
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| 70
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| 71
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| 72
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| 73
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| 74
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| 75
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| 76
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| 77
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| 78
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| 79
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| 80
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| 81
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| 82
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0.18
| 83
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0.17
| 84
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| 85
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| 86
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| 87
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| 88
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| 89
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| 90
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| 91
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| 92
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0.15
| 93
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0.16
| 94
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| 95
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| 96
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| 97
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| 98
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| 99
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| 100
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0.15
| 101
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0.16
| 102
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0.16
| 103
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0.17
| 104
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| 105
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| 106
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0.14
| 107
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0.18
| 108
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| 109
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| 110
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0.17
| 111
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0.16
| 112
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| 113
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| 114
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| 115
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| 116
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| 117
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0.18
| 118
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| 119
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| 120
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| 121
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0.18
| 122
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| 123
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0.16
| 124
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| 125
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| 126
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0.14
| 127
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0.16
| 128
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0.15
| 129
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| 130
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| 131
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0.15
| 132
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0.19
| 133
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| 134
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0.18
| 135
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0.16
| 136
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0.15
| 137
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| 138
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0.16
| 139
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0.15
| 140
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| 141
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0.18
| 142
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0.16
| 143
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0.15
| 144
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0.15
| 145
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0.15
| 146
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0.14
| 147
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0.16
| 148
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0.17
| 149
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0.16
| 150
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0.15
| 151
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0.16
| 152
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0.16
| 153
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0.13
| 154
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0.16
| 155
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0.16
| 156
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0.17
| 157
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0.16
| 158
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0.16
| 159
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0.15
| 160
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0.15
| 161
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0.16
| 162
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| 163
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0.17
| 164
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0.18
| 165
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0.16
| 166
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0.18
| 167
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0.17
| 168
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0.15
| 169
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0.17
| 170
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0.15
| 171
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0.16
| 172
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0.15
| 173
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0.16
| 174
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0.18
| 175
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0.17
| 176
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0.17
| 177
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0.15
| 178
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0.16
| 179
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0.16
| 180
float64 -0.18
0.26
| 181
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0.18
| 182
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0.14
| 183
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0.18
| 184
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0.16
| 185
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0.18
| 186
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0.15
| 187
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0.16
| 188
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0.17
| 189
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0.17
| 190
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0.17
| 191
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0.16
| 192
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0.17
| 193
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0.16
| 194
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0.14
| 195
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0.19
| 196
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0.17
| 197
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0.19
| 198
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0.16
| 199
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0.15
| 200
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0.16
| 201
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0.14
| 202
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0.18
| 203
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0.17
| 204
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| 205
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0.17
| 206
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0.16
| 207
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0.16
| 208
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0.17
| 209
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0.16
| 210
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0.19
| 211
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0.15
| 212
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| 213
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| 214
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| 215
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| 216
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| 217
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| 218
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0.16
| 219
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0.17
| 220
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0.18
| 221
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0.14
| 222
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0.18
| 223
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0.16
| 224
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0.14
| 225
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0.17
| 226
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0.15
| 227
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0.16
| 228
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0.15
| 229
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0.18
| 230
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0.17
| 231
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0.17
| 232
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0.16
| 233
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0.15
| 234
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0.16
| 235
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0.18
| 236
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0.16
| 237
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0.16
| 238
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0.17
| 239
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0.16
| 240
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0.15
| 241
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0.16
| 242
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0.16
| 243
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0.15
| 244
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0.15
| 245
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0.15
| 246
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0.15
| 247
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0.15
| 248
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0.15
| 249
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0.15
| 250
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0.17
| 251
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0.15
| 252
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0.15
| 253
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0.15
| 254
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0.16
| 255
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0.17
| 256
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0.15
| 257
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0.15
| 258
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0.14
| 259
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0.16
| 260
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0.16
| 261
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0.17
| 262
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0.15
| 263
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0.15
| 264
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0.16
| 265
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| 266
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0.16
| 267
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| 268
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0.18
| 269
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| 270
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0.16
| 271
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0.17
| 272
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0.14
| 273
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0.14
| 274
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0.16
| 275
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0.18
| 276
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0.19
| 277
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0.16
| 278
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0.17
| 279
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0.15
| 280
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0.16
| 281
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0.16
| 282
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0.19
| 283
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0.16
| 284
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0.17
| 285
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0.18
| 286
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0.14
| 287
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0.18
| 288
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0.16
| 289
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0.16
| 290
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0.15
| 291
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0.16
| 292
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0.14
| 293
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0.16
| 294
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0.18
| 295
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0.16
| 296
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0.14
| 297
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0.15
| 298
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0.14
| 299
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0.16
| 300
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0.15
| 301
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0.15
| 302
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0.17
| 303
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0.14
| 304
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0.18
| 305
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0.16
| 306
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0.15
| 307
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0.17
| 308
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0.24
| 309
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0.15
| 310
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0.17
| 311
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0.18
| 312
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0.17
| 313
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0.15
| 314
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0.15
| 315
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0.14
| 316
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0.15
| 317
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0.15
| 318
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0.16
| 319
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0.16
| 320
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0.16
| 321
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0.16
| 322
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0.19
| 323
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0.15
| 324
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0.17
| 325
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0.17
| 326
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0.17
| 327
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0.14
| 328
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0.17
| 329
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0.17
| 330
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0.16
| 331
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0.18
| 332
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0.14
| 333
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0.21
| 334
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0.18
| 335
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0.16
| 336
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0.14
| 337
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0.16
| 338
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0.17
| 339
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0.15
| 340
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0.15
| 341
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0.16
| 342
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0.15
| 343
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0.16
| 344
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0.15
| 345
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0.16
| 346
float64 -0.15
0.15
| 347
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0.16
| 348
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0.18
| 349
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0.16
| 350
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0.16
| 351
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0.16
| 352
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0.15
| 353
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0.14
| 354
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0.15
| 355
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0.17
| 356
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0.15
| 357
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0.15
| 358
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0.16
| 359
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0.19
| 360
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0.21
| 361
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0.16
| 362
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0.15
| 363
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0.15
| 364
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0.16
| 365
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0.16
| 366
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0.16
| 367
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0.14
| 368
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0.17
| 369
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0.15
| 370
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0.17
| 371
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0.15
| 372
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0.17
| 373
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0.15
| 374
float64 -0.14
0.17
| 375
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0.15
| 376
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0.15
| 377
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0.15
| 378
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0.15
| 379
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0.16
| 380
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0.16
| 381
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0.12
| 382
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0.16
| 383
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0.17
| 384
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0.17
| 385
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0.15
| 386
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0.14
| 387
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0.16
| 388
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0.15
| 389
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0.15
| 390
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0.15
| 391
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0.19
| 392
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0.15
| 393
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0.18
| 394
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0.16
| 395
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0.14
| 396
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0.15
| 397
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0.16
| 398
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0.16
| 399
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0.17
| 400
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0.16
| 401
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0.17
| 402
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0.15
| 403
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0.16
| 404
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0.14
| 405
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0.14
| 406
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0.14
| 407
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0.14
| 408
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0.2
| 409
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0.14
| 410
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0.15
| 411
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0.15
| 412
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0.17
| 413
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0.18
| 414
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0.19
| 415
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0.16
| 416
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0.17
| 417
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0.17
| 418
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0.15
| 419
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0.19
| 420
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0.17
| 421
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0.18
| 422
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0.15
| 423
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0.17
| 424
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0.16
| 425
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0.16
| 426
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0.15
| 427
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0.14
| 428
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0.14
| 429
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0.16
| 430
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0.18
| 431
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0.19
| 432
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0.2
| 433
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0.16
| 434
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0.17
| 435
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0.19
| 436
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0.16
| 437
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0.16
| 438
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0.17
| 439
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0.15
| 440
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0.14
| 441
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0.14
| 442
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0.18
| 443
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0.15
| 444
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0.18
| 445
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0.17
| 446
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0.15
| 447
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0.16
| 448
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0.15
| 449
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0.15
| 450
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0.16
| 451
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0.17
| 452
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0.16
| 453
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0.14
| 454
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0.17
| 455
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0.14
| 456
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0.16
| 457
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0.15
| 458
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0.15
| 459
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0.18
| 460
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0.18
| 461
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0.18
| 462
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0.18
| 463
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0.17
| 464
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0.17
| 465
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0.18
| 466
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0.16
| 467
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0.14
| 468
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0.16
| 469
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0.15
| 470
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0.15
| 471
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0.16
| 472
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0.15
| 473
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0.17
| 474
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0.17
| 475
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0.16
| 476
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0.16
| 477
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0.16
| 478
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0.17
| 479
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0.16
| 480
float64 -0.17
0.16
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-0.069909 | 0.066886 | -0.034235 | 0.005007 | 0.119173 | 0.034577 | -0.024038 | 0.007765 | 0.036091 | 0.061182 | -0.033773 | 0.020755 | 0.035043 | 0.066523 | 0.09657 | -0.062808 | 0.033774 | 0.013027 | -0.04277 | 0.003076 | -0.021437 | 0.033471 | 0.023864 | 0.024715 | -0.052823 | -0.07223 | 0.034131 | 0.036123 | 0.031915 | 0.022505 | -0.002109 | -0.015244 | -0.072577 | 0.031245 | -0.022724 | -0.017529 | -0.002803 | 0.058642 | 0.031842 | -0.002934 | 0.053162 | -0.019554 | 0.017336 | 0.03815 | -0.006353 | 0.001195 | 0.031688 | 0.028065 | -0.060179 | -0.026983 | 0.023774 | 0.020079 | 0.028689 | -0.021063 | -0.04375 | 0.077379 | 0.015617 | -0.007102 | 0.113707 | -0.026592 | -0.016082 | 0.039208 | -0.001893 | -0.031747 | -0.050761 | 0.063676 | 0.033082 | -0.007804 | -0.049859 | 0.000269 | -0.005753 | -0.012605 | -0.029732 | 0.072628 | 0.019979 | 0.026071 | -0.004791 | -0.003603 | -0.039192 | 0.041422 | 0.016014 | -0.042804 | -0.013009 | 0.049383 | 0.016009 | -0.01654 | -0.041876 | 0.012124 | 0.010514 | -0.030475 | -0.010381 | 0.004276 | 0.046567 | -0.103769 | -0.065793 | 0.054567 | 0.03473 | -0.051778 | -0.037354 | 0.025023 | -0.039684 | 0.002487 | 0.026014 | 0.036331 | 0.000913 | 0.019239 | 0.056396 | -0.04406 | 0.003493 | 0.004849 | 0.009903 | -0.024407 | 0.015439 | 0.010949 | -0.006956 | 0.115537 | -0.033457 | -0.034919 | -0.023905 | 0.011455 | 0.014088 | 0.034905 | -0.009185 | 0.063692 | 0.042261 | 0.001892 | 0.046558 | -0.01084 | 0.040971 | 0.023928 | 0.020606 | -0.023921 | -0.015076 | -0.006673 | -0.032252 | 0.013728 | -0.10951 | -0.004392 | 0.02961 | 0.014631 | 0.048171 | -0.011688 | -0.025675 | 0.052638 | -0.026523 | 0.016767 | 0.014422 | -0.015737 | -0.012111 | -0.049356 | 0.00868 | 0.027191 | -0.031682 | -0.031076 | -0.058641 | 0.003002 | -0.018088 | 0.03836 | -0.010094 | 0.036718 | -0.037648 | -0.023343 | -0.05824 | 0.041024 | -0.031451 | 0.040802 | 0.009085 | 0.015223 | -0.020974 | 0.013883 | 0.034003 | 0.020758 | 0.059948 | 0.002654 | 0.04446 | -0.018046 | -0.019817 | -0.013186 | 0.027842 | -0.016073 | -0.016536 | -0.006464 | -0.030745 | -0.007469 | 0.051954 | -0.010116 | -0.02468 | 0.025004 | 0.032212 | -0.072279 | -0.052869 | 0.006402 | -0.015757 | -0.004241 | -0.031962 | -0.023503 | -0.008266 | 0.007276 | -0.089707 | 0.019289 | -0.003527 | -0.087905 | -0.036835 | 0.032477 | 0.012644 | -0.04685 | -0.003672 | -0.010886 | -0.022439 | 0.028337 | 0.020287 | -0.04122 | -0.02156 | -0.002139 | 0.006417 | -0.013041 | 0.004523 | 0.018849 | 0.033728 | 0.015804 | -0.044898 | 0.067179 | -0.071152 | -0.009461 | 0.027275 | -0.012258 | -0.015513 | -0.02035 | -0.009896 | 0.001472 | -0.01311 | -0.018933 | 0.023514 | -0.016228 | -0.016915 | -0.044757 | -0.039983 | 0.038085 | 0.070193 | -0.018066 | 0.006142 | -0.047743 | -0.038608 | 0.002366 | -0.016961 | 0.010194 | 0.021512 | 0.003815 | -0.022848 | -0.04071 | 0.007886 | 0.004063 | -0.022049 | 0.012306 | -0.025674 | -0.027246 | 0.076263 | 0.051301 | 0.003058 | 0.035051 | -0.082672 | 0.054045 | -0.025197 | 0.002332 | 0.047996 | -0.011362 | 0.01504 | -0.006403 | -0.069059 | 0.021801 | -0.041282 | 0.003613 | -0.04171 | 0.010935 | 0.002442 | 0.052301 | 0.01377 | -0.035543 | 0.04599 | 0.041241 | -0.05023 | 0.010611 | 0.103492 | -0.031744 | -0.002001 | 0.05764 | -0.046911 | 0.013834 | 0.009382 | 0.020696 | 0.009001 | 0.025023 | 0.028853 | -0.01572 | 0.042883 | -0.010411 | 0.014482 | -0.021626 | 0.015238 | 0.015578 | 0.005792 | -0.049458 | -0.005062 | -0.013725 | -0.002698 | 0.058483 | -0.011209 | 0.040524 | -0.010118 | -0.004237 | 0.096886 | 0.060086 | -0.012549 | 0.061251 | 0.007033 | 0.047605 | -0.019086 | 0.029412 | -0.020987 | -0.000145 | -0.003358 | 0.035224 | 0.064143 | 0.033074 | 0.012786 | 0.03441 | 0.006546 | -0.023186 | 0.024331 | -0.04385 | -0.044622 | -0.015085 | -0.07771 | 0.080602 | 0.080812 | 0.068223 | 0.035515 | -0.031986 | -0.036092 | -0.00681 | 0.020212 | -0.007343 | 0.047261 | -0.00526 | -0.08279 | -0.040174 | -0.031582 | 0.013936 | -0.000612 | -0.037861 | -0.049218 | 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0.035315 | -0.040921 | 0.053996 | 0.047899 | -0.00112 | -0.010232 | 0.016707 | 0.018309 | 0.045232 | 0.039525 | 0.077917 | 0.006106 | 0.003755 | -0.045937 | 0.011967 | 0.044638 | -0.033451 | 0.027345 | -0.019386 | -0.021597 | -0.004476 | 0.022209 | -0.058508 | 0.027412 | -0.0336 | 0.040344 | 0.000858 | -0.002811 | -0.002941 | 0.024414 | 0.034562 | 0.028397 | -0.015114 | 0.028652 | -0.01519 | 0.036053 | -0.066627 | 0.018186 | 0.018386 | 0.029817 | -0.041436 | -0.005173 | -0.015523 | -0.032141 | -0.026694 | 0.034697 | -0.057693 | -0.036254 | -0.04172 | -0.044865 | -0.032452 | -0.010962 | -0.030415 | -0.005057 | -0.02955 | 0.037338 | -0.009376 | 0.031973 | 0.001194 | -0.028992 | 0.017546 | 0.039127 | 0.025708 | 0.02833 | -0.01398 | -0.055387 | -0.040147 | 0.025675 | 0.04004 | -0.108454 | 0.054233 | -0.027636 | 0.036869 | -0.008272 | -0.010899 | 0.029522 | -0.014647 | -0.02553 | 0.007093 | 0.001541 | -0.011597 | 0.009646 | 0.034903 | -0.061037 | -0.011159 | 0.002993 | -0.014727 | 0.060159 | -0.046549 | 0.004532 | -0.054723 | 0.037831 | -0.021955 | -0.023955 | 0.047121 | -0.002112 | 0.002983 | -0.01726 | -0.043739 | -0.064324 | -0.062911 | 0.024556 | -0.01194 | 0.011107 | -0.020486 | 0.017668 | 0.018894 | 0.056714 | -0.011152 | 0.049858 | -0.010903 | -0.009234 | -0.050538 | -0.048668 | 0.019312 | -0.040432 | -0.028688 | 0.001342 | 0.008694 | 0.003334 | 0.002562 | -0.054532 | 0.04645 | 0.029361 | -0.000555 | 0.004386 | 0.002458 | 0.025903 | -0.00199 | -0.030711 | 0.081299 | -0.002041 | 0.040735 | 0.073472 | -0.052268 | -0.02714 | -0.007163 | -0.014355 | -0.010548 | 0.019095 | 0.009097 | 0.009547 | 0.019493 | -0.012831 | 0.020267 | 0.083675 | -0.017536 | -0.028406 | 0.01251 | 0.042027 | -0.006774 | -0.011035 | 0.007067 | -0.018456 |
0.071791 | 0.0246 | -0.016303 | -0.03472 | 0.00784 | 0.038625 | -0.001077 | -0.038284 | 0.00599 | 0.038509 | -0.05039 | -0.020033 | 0.024961 | 0.037503 | 0.066564 | 0.000231 | 0.024991 | 0.012317 | -0.008349 | -0.038595 | -0.05243 | -0.01299 | 0.00765 | -0.001202 | -0.056757 | 0.000303 | 0.061819 | -0.017885 | -0.016117 | 0.041439 | 0.008281 | -0.016927 | -0.036015 | -0.027525 | 0.003025 | 0.032566 | 0.025416 | 0.004217 | -0.015094 | 0.012044 | 0.000849 | 0.02378 | -0.041276 | -0.02156 | -0.032775 | 0.016015 | 0.002356 | -0.026427 | -0.059906 | 0.03233 | 0.064061 | -0.008039 | 0.051017 | 0.030623 | -0.009714 | 0.080091 | -0.074436 | 0.009725 | -0.004965 | -0.057136 | 0.012886 | 0.019041 | 0.036023 | -0.014633 | -0.02258 | 0.011503 | -0.003787 | -0.000248 | 0.010302 | -0.012927 | -0.01586 | -0.047627 | 0.025006 | -0.003361 | 0.066117 | -0.024671 | 0.05015 | -0.048542 | 0.009924 | 0.036291 | 0.025025 | 0.04613 | 0.04856 | -0.032316 | 0.010263 | -0.022037 | -0.0094 | 0.065799 | 0.058939 | 0.001352 | -0.007354 | 0.016055 | -0.027912 | -0.016848 | 0.002341 | 0.003626 | -0.024692 | -0.011266 | -0.000713 | 0.044532 | 0.01815 | 0.026605 | 0.058103 | 0.036986 | -0.055885 | 0.000768 | -0.022511 | -0.016872 | -0.005665 | -0.023381 | -0.064467 | 0.032853 | -0.027991 | -0.017417 | -0.003804 | -0.034925 | -0.027012 | 0.005857 | 0.029596 | 0.053542 | 0.034013 | -0.058429 | -0.022414 | -0.078869 | 0.019088 | 0.007813 | 0.007176 | 0.000629 | -0.03798 | 0.015682 | 0.003066 | -0.001608 | -0.004179 | 0.001906 | -0.017699 | -0.01537 | -0.034785 | -0.008758 | 0.015887 | -0.017881 | -0.019912 | -0.020491 | -0.070877 | 0.018047 | -0.025679 | -0.001621 | -0.035503 | 0.021912 | 0.001018 | 0.013917 | -0.049238 | 0.025209 | 0.003195 | -0.017405 | 0.041292 | 0.043922 | -0.001361 | -0.080833 | 0.075712 | -0.039653 | -0.017938 | 0.036558 | -0.029063 | 0.031871 | 0.018491 | -0.064374 | -0.02211 | 0.019809 | -0.011183 | -0.019949 | -0.043225 | -0.034329 | -0.042476 | 0.053975 | 0.012249 | -0.014777 | 0.012812 | -0.000012 | 0.000268 | 0.025831 | 0.0184 | 0.014275 | 0.023719 | -0.052492 | -0.031817 | 0.001109 | 0.008465 | 0.024524 | 0.002303 | 0.095621 | 0.006072 | -0.011551 | 0.071323 | -0.065343 | 0.008556 | -0.040726 | -0.030919 | 0.004672 | 0.024659 | -0.017961 | 0.026696 | -0.068628 | -0.013429 | 0.014885 | 0.019956 | -0.038319 | 0.007596 | -0.071675 | 0.015858 | 0.011353 | 0.042181 | 0.062711 | 0.036563 | 0.00623 | 0.012298 | 0.017217 | 0.01515 | -0.001474 | -0.01525 | 0.047974 | 0.025635 | 0.011648 | -0.028716 | -0.021436 | 0.005092 | -0.007165 | -0.015532 | 0.02595 | 0.020125 | -0.022938 | -0.020308 | 0.084312 | -0.025165 | 0.002798 | 0.084946 | -0.015929 | -0.029403 | 0.029824 | -0.005487 | -0.024691 | 0.012229 | -0.015424 | 0.006473 | 0.037522 | 0.015668 | -0.030659 | 0.03097 | 0.002881 | -0.037299 | -0.007626 | -0.016374 | 0.020563 | -0.025966 | -0.012181 | -0.029769 | -0.00187 | 0.000262 | -0.049202 | 0.019365 | 0.027542 | -0.01166 | 0.005838 | -0.001859 | -0.028075 | 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End of preview. Expand
in Dataset Viewer.
Only for the reaseaching usage.
The original data from http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz.
We use nq_distilbert-base-v1
model encode all the data to the PyTorch Tensors. And normalize
the embeddings by using sentence_transformers.util.normalize_embeddings
.
How to use
See notebook Wikipedia Q&A Retrieval-Semantic Search
Installing the package
!pip install sentence-transformers==2.3.1
The converting process
# the whole process takes 1287.0s GPU P100
import os
import json
import gzip
from sentence_tranformers.util import http_get
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import normalize_embeddings
os.environ['DATASET_NAME']='simplewiki-2020-11-01.jsonl.gz'
os.environ['DATASET_URL']='http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz'
os.environ['MODEL_NAME']='multi-qa-MiniLM-L6-cos-v1'
os.environ['CROSS_CODE_NAME']='cross-encoder/ms-marco-MiniLM-L-6-v2'
http_get(os.getenv('DATASET_URL'), os.getenv('DATASET_NAME'))
passages=[]
with gzip.open(os.getenv('DATASET_NAME'), 'rt', encoding='utf-8') as fIn:
for line in fIn:
data=json.loads(line.strip())
# add all paragraphs
# passages.extend(data['paragraphs'])
# only add the first paragraph
# passages.append(data['paragraph'][0])
for paragraph in data['paragraphs']:
# We encode the passages as [title, text]
passages.append([data['title'], paragraph])
print('Passages:', len(passages))
bi_encoder=SentenceTransformer('nq-distilbert-base-v1')
bi_encoder.max_seq_length=256
bi_encoder.to('cuda')
corpus_embeddings=bi_encoder.encode(passages, convert_to_tensor=True, show_progress_bar=True).to('cuda')
corpus_embeddings=normalize_embeddings(corpus_embeddings)
len(corpus_embeddings)
import pandas as pd
embedding_data=pd.DataFrame(corpus_embeddings.cpu())
embedding_data.to_csv('simple_english_wikipedia_2020_11_01.csv', index=False)
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