Datasets:
world_id int64 0 40k | params dict | prior listlengths 8 8 | state listlengths 0 12 | state_text stringlengths 86 176 | y_true int64 0 7 | posterior listlengths 8 8 | questions listlengths 3 3 |
|---|---|---|---|---|---|---|---|
0 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.0712,
"prior_alpha": 0.7
} | [
0.089,
0.177,
0.08,
0.064,
0.217,
0.126,
0.223,
0.024
] | [] | prior: c0=0.089 c1=0.177 c2=0.080 c3=0.064 c4=0.217 c5=0.126 c6=0.223 c7=0.024 | (no evidence) | 1 | [
0.089,
0.177,
0.08,
0.064,
0.217,
0.126,
0.223,
0.024
] | [
{
"type": "choice",
"options": [
{
"tok": "c1",
"class": 1
},
{
"tok": "c7",
"class": 7
},
{
"tok": "c5",
"class": 5
},
{
"tok": "c4",
"class": 4
},
{
"tok": "c2",
"class": 2... |
1 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.7454,
"prior_alpha": 0.7
} | [
0.012,
0.224,
0.003,
0.018,
0.099,
0.138,
0.5,
0.007
] | [
{
"f": 7,
"v": 4
},
{
"f": 8,
"v": 5
},
{
"f": 2,
"v": 4
},
{
"f": 1,
"v": 3
},
{
"f": 4,
"v": 4
},
{
"f": 5,
"v": 2
},
{
"f": 9,
"v": 4
}
] | prior: c0=0.012 c1=0.224 c2=0.003 c3=0.018 c4=0.099 c5=0.138 c6=0.500 c7=0.007 | f7=v4 | f8=v5 | f2=v4 | f1=v3 | f4=v4 | f5=v2 | f9=v4 | 5 | [
0.0000978596,
0.3214078985,
0.000380501,
0.0087464381,
0.0129035528,
0.0957420978,
0.5581497936,
0.0025718586
] | [
{
"type": "choice",
"options": [
{
"tok": "c0",
"class": 0
},
{
"tok": "c1",
"class": 1
},
{
"tok": "c6",
"class": 6
},
{
"tok": "c5",
"class": 5
},
{
"tok": "c7",
"class": 7... |
2 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.4704,
"prior_alpha": 0.7
} | [
0.002,
0.271,
0.013,
0.013,
0.197,
0.083,
0.107,
0.315
] | [
{
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},
{
"f": 0,
"v": 5
},
{
"f": 3,
"v": 2
},
{
"f": 10,
"v": 1
},
{
"f": 8,
"v": 4
},
{
"f": 11,
"v": 4
},
{
"f": 6,
"v": 3
}
] | prior: c0=0.002 c1=0.271 c2=0.013 c3=0.013 c4=0.197 c5=0.083 c6=0.107 c7=0.315 | f9=v5 | f0=v5 | f3=v2 | f10=v1 | f8=v4 | f11=v4 | f6=v3 | 3 | [
0.0000092888,
0.0000618809,
0.0022057351,
0.6588179374,
0.0001837193,
0.2831253722,
0.0474736734,
0.008122393
] | [
{
"type": "choice",
"options": [
{
"tok": "c3",
"class": 3
},
{
"tok": "c6",
"class": 6
},
{
"tok": "OUT",
"class": null
}
],
"target": [
0.6588179374,
0.0474736734,
0.29370838920000003
],
"... |
3 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.4711,
"prior_alpha": 0.7
} | [
0.189,
0.188,
0.077,
0.25,
0.17,
0.006,
0.092,
0.028
] | [
{
"f": 2,
"v": 5
},
{
"f": 9,
"v": 3
},
{
"f": 4,
"v": 4
},
{
"f": 0,
"v": 5
},
{
"f": 7,
"v": 1
}
] | prior: c0=0.189 c1=0.188 c2=0.077 c3=0.250 c4=0.170 c5=0.006 c6=0.092 c7=0.028 | f2=v5 | f9=v3 | f4=v4 | f0=v5 | f7=v1 | 3 | [
0.1822317937,
0.3870847737,
0.0070848019,
0.3186813035,
0.0533503722,
0.0000607668,
0.001957612,
0.0495485763
] | [
{
"type": "choice",
"options": [
{
"tok": "c3",
"class": 3
},
{
"tok": "c4",
"class": 4
},
{
"tok": "c5",
"class": 5
},
{
"tok": "c2",
"class": 2
},
{
"tok": "c1",
"class": 1... |
4 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.6602,
"prior_alpha": 0.7
} | [
0.195,
0.224,
0.106,
0.008,
0.025,
0.099,
0.022,
0.32
] | [
{
"f": 5,
"v": 2
},
{
"f": 11,
"v": 4
},
{
"f": 1,
"v": 5
},
{
"f": 2,
"v": 0
},
{
"f": 7,
"v": 4
},
{
"f": 10,
"v": 3
},
{
"f": 0,
"v": 5
}
] | prior: c0=0.195 c1=0.224 c2=0.106 c3=0.008 c4=0.025 c5=0.099 c6=0.022 c7=0.320 | f5=v2 | f11=v4 | f1=v5 | f2=v0 | f7=v4 | f10=v3 | f0=v5 | 5 | [
0.0005911964,
0.0000129809,
0.1072103075,
0.0080061333,
2.47e-7,
0.8449951168,
0.0047830948,
0.0344009233
] | [
{
"type": "choice",
"options": [
{
"tok": "c5",
"class": 5
},
{
"tok": "c3",
"class": 3
},
{
"tok": "c0",
"class": 0
},
{
"tok": "OUT",
"class": null
}
],
"target": [
0.8449951168000... |
5 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.6678,
"prior_alpha": 0.7
} | [
0.064,
0.146,
0.188,
0.006,
0.004,
0.053,
0.096,
0.443
] | [
{
"f": 9,
"v": 1
},
{
"f": 5,
"v": 1
},
{
"f": 8,
"v": 3
},
{
"f": 11,
"v": 3
},
{
"f": 0,
"v": 1
},
{
"f": 7,
"v": 3
},
{
"f": 2,
"v": 5
},
{
"f": 3,
"v": 5
},
{
"f": 6,
"v": 4
}
] | prior: c0=0.064 c1=0.146 c2=0.188 c3=0.006 c4=0.004 c5=0.053 c6=0.096 c7=0.443 | f9=v1 | f5=v1 | f8=v3 | f11=v3 | f0=v1 | f7=v3 | f2=v5 | f3=v5 | f6=v4 | 7 | [
0.0000571335,
0.0303458871,
0.0000368855,
4.94e-8,
0.0000192734,
0.0000071014,
0.0000263103,
0.9695073595
] | [
{
"type": "choice",
"options": [
{
"tok": "c7",
"class": 7
},
{
"tok": "c2",
"class": 2
},
{
"tok": "c4",
"class": 4
},
{
"tok": "OUT",
"class": null
}
],
"target": [
0.9695073595,
... |
6 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.6663,
"prior_alpha": 0.7
} | [
0.15,
0.102,
0.022,
0.045,
0.4,
0.094,
0.15,
0.036
] | [
{
"f": 7,
"v": 4
},
{
"f": 2,
"v": 2
},
{
"f": 5,
"v": 1
},
{
"f": 1,
"v": 0
},
{
"f": 10,
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},
{
"f": 0,
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},
{
"f": 4,
"v": 2
},
{
"f": 8,
"v": 1
},
{
"f": 9,
"v": 2
}
] | prior: c0=0.150 c1=0.102 c2=0.022 c3=0.045 c4=0.400 c5=0.094 c6=0.150 c7=0.036 | f7=v4 | f2=v2 | f5=v1 | f1=v0 | f10=v1 | f0=v3 | f4=v2 | f8=v1 | f9=v2 | 4 | [
0.1504795389,
0.0517365617,
0.0030705268,
0.0392807856,
0.6873521414,
0.0283914043,
0.0000204116,
0.0396686296
] | [
{
"type": "choice",
"options": [
{
"tok": "c0",
"class": 0
},
{
"tok": "c4",
"class": 4
},
{
"tok": "c2",
"class": 2
},
{
"tok": "c6",
"class": 6
},
{
"tok": "c7",
"class": 7... |
7 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.2199,
"prior_alpha": 0.7
} | [
0,
0.203,
0.079,
0.127,
0.076,
0.209,
0.272,
0.033
] | [
{
"f": 0,
"v": 5
},
{
"f": 4,
"v": 4
}
] | prior: c0=0.000 c1=0.203 c2=0.079 c3=0.127 c4=0.076 c5=0.209 c6=0.272 c7=0.033 | f0=v5 | f4=v4 | 6 | [
7.962182967e-301,
0.2082598108,
0.0732329393,
0.1274586676,
0.0679217027,
0.179659609,
0.3081461861,
0.0353210844
] | [
{
"type": "choice",
"options": [
{
"tok": "c7",
"class": 7
},
{
"tok": "c1",
"class": 1
},
{
"tok": "c4",
"class": 4
},
{
"tok": "c3",
"class": 3
},
{
"tok": "c6",
"class": 6... |
8 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.1939,
"prior_alpha": 0.7
} | [
0.103,
0.359,
0.003,
0.134,
0.12,
0.009,
0.267,
0.005
] | [] | prior: c0=0.103 c1=0.359 c2=0.003 c3=0.134 c4=0.120 c5=0.009 c6=0.267 c7=0.005 | (no evidence) | 0 | [
0.1027944112,
0.3592814371,
0.002994012,
0.1337325349,
0.119760479,
0.0089820359,
0.2674650699,
0.00499002
] | [
{
"type": "choice",
"options": [
{
"tok": "c7",
"class": 7
},
{
"tok": "c2",
"class": 2
},
{
"tok": "OUT",
"class": null
}
],
"target": [
0.00499002,
0.0029940120000000003,
0.9920159681
],
"... |
9 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.6302,
"prior_alpha": 0.7
} | [
0.105,
0.123,
0.026,
0.26,
0.228,
0.03,
0.049,
0.179
] | [
{
"f": 2,
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},
{
"f": 1,
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},
{
"f": 0,
"v": 3
},
{
"f": 10,
"v": 2
},
{
"f": 11,
"v": 2
},
{
"f": 9,
"v": 1
},
{
"f": 6,
"v": 5
},
{
"f": 4,
"v": 1
}
] | prior: c0=0.105 c1=0.123 c2=0.026 c3=0.260 c4=0.228 c5=0.030 c6=0.049 c7=0.179 | f2=v2 | f1=v1 | f0=v3 | f10=v2 | f11=v2 | f9=v1 | f6=v5 | f4=v1 | 3 | [
0.4687974531,
0.0624562224,
4.589e-7,
0.1686846817,
0.2943841213,
1e-10,
9.365e-7,
0.005676126
] | [
{
"type": "choice",
"options": [
{
"tok": "c2",
"class": 2
},
{
"tok": "c7",
"class": 7
},
{
"tok": "c6",
"class": 6
},
{
"tok": "c5",
"class": 5
},
{
"tok": "OUT",
"class": ... |
10 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.3629,
"prior_alpha": 0.7
} | [
0.681,
0.023,
0.009,
0.026,
0.002,
0.126,
0.078,
0.056
] | [
{
"f": 8,
"v": 5
},
{
"f": 3,
"v": 4
},
{
"f": 7,
"v": 0
}
] | prior: c0=0.681 c1=0.023 c2=0.009 c3=0.026 c4=0.002 c5=0.126 c6=0.078 c7=0.056 | f8=v5 | f3=v4 | f7=v0 | 0 | [
0.891259973,
0.0219660754,
0.0022102879,
0.0125250042,
0.0004263322,
0.0001918076,
0.035211674,
0.0362088457
] | [
{
"type": "choice",
"options": [
{
"tok": "c2",
"class": 2
},
{
"tok": "c1",
"class": 1
},
{
"tok": "c7",
"class": 7
},
{
"tok": "c4",
"class": 4
},
{
"tok": "c3",
"class": 3... |
11 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.2553,
"prior_alpha": 0.7
} | [
0.099,
0.135,
0.01,
0.116,
0.009,
0.158,
0.125,
0.349
] | [
{
"f": 5,
"v": 1
},
{
"f": 1,
"v": 4
},
{
"f": 0,
"v": 3
},
{
"f": 11,
"v": 3
},
{
"f": 4,
"v": 4
}
] | prior: c0=0.099 c1=0.135 c2=0.010 c3=0.116 c4=0.009 c5=0.158 c6=0.125 c7=0.349 | f5=v1 | f1=v4 | f0=v3 | f11=v3 | f4=v4 | 7 | [
0.0011692963,
0.2376613803,
0.0109077491,
0.0052878071,
0.0003422543,
0.0093777317,
0.0020433161,
0.7332104652
] | [
{
"type": "choice",
"options": [
{
"tok": "c7",
"class": 7
},
{
"tok": "c2",
"class": 2
},
{
"tok": "c5",
"class": 5
},
{
"tok": "c4",
"class": 4
},
{
"tok": "c6",
"class": 6... |
12 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.7178,
"prior_alpha": 0.7
} | [
0.047,
0.006,
0.204,
0.147,
0.056,
0.255,
0.018,
0.266
] | [
{
"f": 0,
"v": 3
},
{
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"v": 5
},
{
"f": 6,
"v": 4
},
{
"f": 2,
"v": 0
},
{
"f": 5,
"v": 5
},
{
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"v": 4
},
{
"f": 9,
"v": 3
},
{
"f": 7,
"v": 3
},
{
"f": 11,
"v": 4
},
{
"f": 1,
"v": 5
... | prior: c0=0.047 c1=0.006 c2=0.204 c3=0.147 c4=0.056 c5=0.255 c6=0.018 c7=0.266 | f0=v3 | f4=v5 | f6=v4 | f2=v0 | f5=v5 | f10=v4 | f9=v3 | f7=v3 | f11=v4 | f1=v5 | f8=v5 | 5 | [
0.0000324366,
8.02e-8,
0.0300387089,
0.0415765573,
2.51e-8,
0.9240795411,
0.0014786842,
0.0027939666
] | [
{
"type": "choice",
"options": [
{
"tok": "c2",
"class": 2
},
{
"tok": "c7",
"class": 7
},
{
"tok": "c1",
"class": 1
},
{
"tok": "c4",
"class": 4
},
{
"tok": "c6",
"class": 6... |
13 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.6107,
"prior_alpha": 0.7
} | [
0.03,
0.119,
0.053,
0.507,
0.005,
0.015,
0.114,
0.157
] | [
{
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},
{
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},
{
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"v": 0
},
{
"f": 9,
"v": 2
},
{
"f": 5,
"v": 4
},
{
"f": 11,
"v": 2
},
{
"f": 0,
"v": 5
}
] | prior: c0=0.030 c1=0.119 c2=0.053 c3=0.507 c4=0.005 c5=0.015 c6=0.114 c7=0.157 | f8=v1 | f6=v5 | f2=v0 | f9=v2 | f5=v4 | f11=v2 | f0=v5 | 3 | [
0.0325680988,
0.0105468529,
0.0000143575,
0.939062444,
0.0001240383,
2e-10,
0.006800269,
0.0108839393
] | [
{
"type": "choice",
"options": [
{
"tok": "c0",
"class": 0
},
{
"tok": "c6",
"class": 6
},
{
"tok": "c3",
"class": 3
},
{
"tok": "c7",
"class": 7
},
{
"tok": "c1",
"class": 1... |
14 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.3787,
"prior_alpha": 0.7
} | [
0.112,
0.033,
0.369,
0.088,
0.184,
0.03,
0.085,
0.098
] | [
{
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},
{
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},
{
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"v": 4
},
{
"f": 4,
"v": 0
},
{
"f": 11,
"v": 0
},
{
"f": 2,
"v": 3
}
] | prior: c0=0.112 c1=0.033 c2=0.369 c3=0.088 c4=0.184 c5=0.030 c6=0.085 c7=0.098 | f9=v0 | f5=v5 | f10=v4 | f4=v0 | f11=v0 | f2=v3 | 6 | [
0.0000273004,
0.028739285,
0.3550010853,
0.0083066909,
0.3028374152,
0.000003664,
0.3027441547,
0.0023404044
] | [
{
"type": "choice",
"options": [
{
"tok": "c3",
"class": 3
},
{
"tok": "c5",
"class": 5
},
{
"tok": "c2",
"class": 2
},
{
"tok": "OUT",
"class": null
}
],
"target": [
0.0083066909,
... |
15 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.6231,
"prior_alpha": 0.7
} | [
0,
0.036,
0.027,
0.128,
0.018,
0.369,
0.172,
0.251
] | [
{
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"v": 3
},
{
"f": 11,
"v": 4
},
{
"f": 3,
"v": 0
},
{
"f": 1,
"v": 5
},
{
"f": 9,
"v": 5
},
{
"f": 0,
"v": 5
},
{
"f": 10,
"v": 4
}
] | prior: c0=0.000 c1=0.036 c2=0.027 c3=0.128 c4=0.018 c5=0.369 c6=0.172 c7=0.251 | f8=v3 | f11=v4 | f3=v0 | f1=v5 | f9=v5 | f0=v5 | f10=v4 | 5 | [
1.096246498e-302,
3e-10,
0.0000619612,
0.1151340492,
0.0000254633,
0.8490484768,
0.0278014925,
0.0079285567
] | [
{
"type": "choice",
"options": [
{
"tok": "c6",
"class": 6
},
{
"tok": "c2",
"class": 2
},
{
"tok": "c0",
"class": 0
},
{
"tok": "c4",
"class": 4
},
{
"tok": "c3",
"class": 3... |
16 | {
"K": 8,
"F": 12,
"V": 6,
"rho": 0.5423,
"prior_alpha": 0.7
} | [
0.067,
0.172,
0.112,
0.01,
0.374,
0.077,
0.047,
0.141
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24 | {
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25 | {
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{
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26 | {
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27 | {
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{
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{
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29 | {
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{
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{
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{
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{
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{
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{
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{
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{
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33 | {
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{
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50 | {
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0.0002047036,
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{
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{
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{
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{
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54 | {
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55 | {
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{
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{
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57 | {
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} | [
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{
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{
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58 | {
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{
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{
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59 | {
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{
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{
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60 | {
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{
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{
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61 | {
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{
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{
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62 | {
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{
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... |
63 | {
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"rho": 0.5399,
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{
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{
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64 | {
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{
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{
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{
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"class": 7... |
65 | {
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{
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{
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66 | {
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{
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{
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{
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{
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] | prior: c0=0.103 c1=0.365 c2=0.002 c3=0.008 c4=0.111 c5=0.301 c6=0.063 c7=0.048 | f3=v4 | 1 | [
0.1579963286,
0.6852480431,
0.0004961397,
0.0059064739,
0.0473993777,
0.000636036,
0.0377799543,
0.0645376468
] | [
{
"type": "choice",
"options": [
{
"tok": "c2",
"class": 2
},
{
"tok": "c6",
"class": 6
},
{
"tok": "c0",
"class": 0
},
{
"tok": "OUT",
"class": null
}
],
"target": [
0.0004961397000... |
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certo — synthetic decision dataset
Synthetic decisions with a known, exact answer distribution, for training and evaluating calibrated decision models. Each example is generated from a conditional naive-Bayes evidence world, so the posterior over the answer is computed in closed form — you can grade a model against the true probabilities (posterior fidelity), not just accuracy.
Part of certo · project page: https://altslate-labs.github.io/certo/
Schema (JSONL, one example per line)
{
"world_id": 0,
"params": { "K": 8, "F": 12, "V": 6, "rho": 0.5, "prior_alpha": 0.7 },
"prior": [0.20, 0.05, ...], // per-example base rates (in the state)
"state": [ {"f": 3, "v": 7}, ... ], // revealed evidence (feature, value)
"state_text": "prior: c0=0.20 ... | f3=v7 | ...", // serialized input
"y_true": 5, // sampled true class (eval only)
"posterior": [0.02, ..., 0.66, ...], // EXACT P(class | evidence) — the answer key
"questions": [
{ "type": "choice", "options": [...], "target": [...] }, // named classes + OUT (= complement)
{ "type": "binary", "subset": [...], "target": 0.83 }, // P(y in subset)
{ "type": "score", "class_to_level": [...], "target": [...], "scalar": 3.9 }
]
}
posterioris the exact Bayesian answer given the shownprior+state— the target to grade against.- Each
questionis a marginalization of that posterior (choice with anOUT/none option, binary predicate, ordinal score). train.jsonl(40k, seed 0) ·val.jsonl(5k, seed 1).
Intended use
Train with soft (distributional) targets to get calibrated probabilities; grade with KL/TV to
posterior. See the certo repo for the generator
(datagen.py, reproducible + self-verified), models, and the training recipe.
Note: this is a synthetic dataset for studying and building calibrated decision models — not real-world data. License: MIT.
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