sensitivity-scanner-v6 / scanner_spec.json
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{
"model": "ft-v6",
"source": "experiments/finetune/out/small-ft-v6/best",
"labels": [
"medical",
"student_records",
"id_numbers",
"financial",
"credentials",
"legal_hr",
"none"
],
"label_descriptions": {
"medical": "Health, medical, or mental-health details about a specific person",
"student_records": "Grades, conduct, advising, accommodations, or other FERPA records about a named student",
"id_numbers": "Social Security, passport, driver's license, visa, or other ID numbers",
"financial": "Bank accounts, card numbers, salaries, income, or payroll for a specific person",
"credentials": "Real passwords, API keys, private keys, or login secrets",
"legal_hr": "Legal, HR, disciplinary, harassment, criminal, or immigration matters about a person",
"none": "None of these: general, public, aggregate, fictional, redacted, or placeholder content"
},
"prefix_ids": [
287,
128003,
10562,
128010,
1159,
294,
1516,
261,
1159,
261,
289,
2338,
271,
14901,
1045,
314,
266,
996,
604,
128010,
1234,
616,
74658,
294,
40198,
261,
3360,
261,
15500,
261,
11120,
261,
289,
340,
120623,
2227,
314,
266,
1784,
1234,
128010,
9764,
616,
63452,
294,
2491,
2590,
261,
9878,
261,
1986,
280,
268,
2989,
261,
6828,
261,
289,
340,
2819,
1687,
128010,
999,
294,
2122,
2681,
261,
1081,
1687,
261,
11661,
261,
1766,
261,
289,
11473,
270,
266,
996,
604,
128010,
10104,
294,
2858,
13405,
261,
5804,
5258,
261,
1094,
5258,
261,
289,
6607,
7595,
128010,
1334,
616,
9512,
294,
7121,
261,
6530,
261,
17343,
261,
8930,
261,
3030,
261,
289,
4854,
2973,
314,
266,
604,
128010,
3083,
294,
7422,
265,
378,
294,
1042,
261,
613,
261,
9401,
261,
12967,
261,
56991,
261,
289,
11736,
822,
287,
128007,
1159,
128007,
1234,
616,
74658,
128007,
9764,
616,
63452,
128007,
999,
128007,
10104,
128007,
1334,
616,
9512,
128007,
3083,
1263,
1263,
128002
],
"marker_positions": [
137,
139,
143,
147,
149,
151,
155
],
"none_index": 6,
"scoring": "multi-label: P(label) = sigmoid(logit). A block's score is 1 - P(none) = sigmoid(-logit_none).",
"threshold": 0.5,
"block_words": 100,
"text_encoding": "split the block with the word regex (URL | email | @handle | word(-word)* | any non-space), lower-case each piece, tokenize each piece alone without special tokens, append after prefix_ids",
"recommended_file": "onnx/model_fp16.onnx"
}