Add calibrated thresholds (IdeaLens-1M calibration split)
Browse files- thresholds.json +258 -0
thresholds.json
ADDED
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@@ -0,0 +1,258 @@
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| 1 |
+
{
|
| 2 |
+
"model": "rishanthrajendhran/IdeaLens-LogisticClassifier",
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| 3 |
+
"input": "outline",
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| 4 |
+
"flag_rule": "flag the document as AI when P(human) < cut",
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| 5 |
+
"calibration": {
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| 6 |
+
"data": "IdeaLens-1M, calibration split (human documents only)",
|
| 7 |
+
"scores_from": "the training run's stored predictions on the calibration split",
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| 8 |
+
"n_humans": 80000,
|
| 9 |
+
"n_per_format": {
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| 10 |
+
"Academic Writing": 10000,
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| 11 |
+
"Creative Writing": 10000,
|
| 12 |
+
"Knowledge Article": 10000,
|
| 13 |
+
"News Article": 10000,
|
| 14 |
+
"Nonfiction Writing": 10000,
|
| 15 |
+
"Personal About Page": 10000,
|
| 16 |
+
"Personal Blog": 10000,
|
| 17 |
+
"User Reviews": 10000
|
| 18 |
+
},
|
| 19 |
+
"n_per_topic": {
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| 20 |
+
"Art & Design": 3338,
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| 21 |
+
"Crime & Law": 3931,
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| 22 |
+
"Education & Jobs": 3974,
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| 23 |
+
"Entertainment": 4692,
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| 24 |
+
"Fashion & Beauty": 2999,
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| 25 |
+
"Finance & Business": 4855,
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| 26 |
+
"Food & Dining": 2696,
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| 27 |
+
"Games": 4257,
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| 28 |
+
"Hardware": 2253,
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| 29 |
+
"Health": 5069,
|
| 30 |
+
"History": 2554,
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| 31 |
+
"Home & Hobbies": 4192,
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| 32 |
+
"Industrial": 2299,
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| 33 |
+
"Literature": 3571,
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| 34 |
+
"Politics": 3728,
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| 35 |
+
"Religion": 3617,
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| 36 |
+
"Science & Tech.": 4154,
|
| 37 |
+
"Social Life": 3126,
|
| 38 |
+
"Software": 2257,
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| 39 |
+
"Software Dev.": 2468,
|
| 40 |
+
"Sports & Fitness": 4224,
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| 41 |
+
"Transportation": 2810,
|
| 42 |
+
"Travel": 2936
|
| 43 |
+
}
|
| 44 |
+
},
|
| 45 |
+
"method": {
|
| 46 |
+
"global": "the target-FPR quantile of P(human) over all calibration humans",
|
| 47 |
+
"per_group": "the group's own quantile, shrunk toward the global cut with weight n / (n + 2500)",
|
| 48 |
+
"min_group_humans": 200,
|
| 49 |
+
"estimability": "a cut at FPR q is given only when q * n >= 25 calibration humans",
|
| 50 |
+
"missing_cut": "no cut for a group means none could be estimated; do not substitute the global cut"
|
| 51 |
+
},
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| 52 |
+
"fpr_targets": [
|
| 53 |
+
0.001,
|
| 54 |
+
0.005,
|
| 55 |
+
0.01,
|
| 56 |
+
0.02,
|
| 57 |
+
0.05,
|
| 58 |
+
0.1,
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| 59 |
+
0.2
|
| 60 |
+
],
|
| 61 |
+
"global": {
|
| 62 |
+
"0.001": 0.006381482638648472,
|
| 63 |
+
"0.005": 0.02867227201083185,
|
| 64 |
+
"0.01": 0.059536946194257215,
|
| 65 |
+
"0.02": 0.11625680052414059,
|
| 66 |
+
"0.05": 0.26407016543721995,
|
| 67 |
+
"0.1": 0.4527246278095192,
|
| 68 |
+
"0.2": 0.6914557194237397
|
| 69 |
+
},
|
| 70 |
+
"per_format": {
|
| 71 |
+
"0.005": {
|
| 72 |
+
"Academic Writing": 0.04351781290630925,
|
| 73 |
+
"Creative Writing": 0.06842656490325572,
|
| 74 |
+
"Knowledge Article": 0.013549577866957773,
|
| 75 |
+
"News Article": 0.037116423168727354,
|
| 76 |
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"Nonfiction Writing": 0.012762849974220107,
|
| 77 |
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"Personal About Page": 0.07863581954726177,
|
| 78 |
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"Personal Blog": 0.046289725369901086,
|
| 79 |
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"User Reviews": 0.04360359362805784
|
| 80 |
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},
|
| 81 |
+
"0.01": {
|
| 82 |
+
"Academic Writing": 0.07635705935657296,
|
| 83 |
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"Creative Writing": 0.10501832944669903,
|
| 84 |
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"Knowledge Article": 0.027691557082781336,
|
| 85 |
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"News Article": 0.07071243140554262,
|
| 86 |
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"Nonfiction Writing": 0.02639998383650473,
|
| 87 |
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"Personal About Page": 0.12256619670774015,
|
| 88 |
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"Personal Blog": 0.08518712118511351,
|
| 89 |
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"User Reviews": 0.06876486919167552
|
| 90 |
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},
|
| 91 |
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"0.02": {
|
| 92 |
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"Academic Writing": 0.13087090157725204,
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| 93 |
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"Knowledge Article": 0.06444587818531136,
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"News Article": 0.12888065915747376,
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|
| 100 |
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},
|
| 101 |
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"0.05": {
|
| 102 |
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"Academic Writing": 0.2871091883467899,
|
| 103 |
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"Creative Writing": 0.34134221029738165,
|
| 104 |
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"Knowledge Article": 0.16816069858792998,
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| 105 |
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"News Article": 0.27790715836408686,
|
| 106 |
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"Nonfiction Writing": 0.19307199601744005,
|
| 107 |
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"Personal About Page": 0.3461023636669453,
|
| 108 |
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|
| 109 |
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|
| 110 |
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},
|
| 111 |
+
"0.1": {
|
| 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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|
| 118 |
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|
| 119 |
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"User Reviews": 0.4108623709803477
|
| 120 |
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},
|
| 121 |
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"0.2": {
|
| 122 |
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"Academic Writing": 0.7040847618922953,
|
| 123 |
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"Creative Writing": 0.7555976322117836,
|
| 124 |
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"Knowledge Article": 0.6076391823221505,
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| 125 |
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"News Article": 0.6928231697944629,
|
| 126 |
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"Nonfiction Writing": 0.6612782871532749,
|
| 127 |
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"Personal About Page": 0.7080410462869612,
|
| 128 |
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"Personal Blog": 0.7464235601260868,
|
| 129 |
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"User Reviews": 0.641015771150212
|
| 130 |
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}
|
| 131 |
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},
|
| 132 |
+
"per_topic": {
|
| 133 |
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"0.005": {
|
| 134 |
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"Health": 0.022651713994766107
|
| 135 |
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},
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| 136 |
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"0.01": {
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| 137 |
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"Education & Jobs": 0.06362716182631294,
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"History": 0.08692817077890863,
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| 155 |
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| 156 |
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"0.02": {
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| 158 |
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},
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"0.2": {
|
| 233 |
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| 234 |
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| 239 |
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| 240 |
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| 242 |
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"Health": 0.6552730154253925,
|
| 243 |
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"History": 0.7675307823675691,
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| 244 |
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| 245 |
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| 249 |
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| 250 |
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| 251 |
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"Software": 0.6338074889944196,
|
| 252 |
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"Software Dev.": 0.6665265239523223,
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| 253 |
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|
| 254 |
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| 255 |
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"Travel": 0.6900682456678775
|
| 256 |
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}
|
| 257 |
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}
|
| 258 |
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}
|