| |
| |
| @@ -0,0 +1,111 @@ |
| +"""Development-only fixed experiments, never a claim of independent holdout quality. |
| + |
| +All input paths are fixed repository synthetic corpora. Per-case payload evidence |
| +is intentionally synthetic. The three datasets are development data from Gate03d. |
| +""" |
| + |
| +import hashlib |
| +import json |
| +import resource |
| +import subprocess |
| +import sys |
| +import time |
| +from pathlib import Path |
| + |
| +from .atomic_output import write_candidate |
| +from .detectors import detect |
| +from .evaluate import score_cases |
| +from .investigate import Recorded |
| +from .recognizer import DEFAULT_PROMPTS, GLiNERRecognizer |
| +from .replacement import replace, resolve_overlaps, validate_entities |
| +from .terminology import DEFAULT_TERMS, ReligiousTerminology |
| + |
| + |
| +def run(cache, output): |
| + denied = [] |
| + |
| + def offline(event, args): |
| + if event in ("socket.connect", "socket.bind", "socket.getaddrinfo"): |
| + denied.append(event) |
| + raise PermissionError("qualification_network_disabled") |
| + |
| + sys.addaudithook(offline) |
| + start = time.perf_counter() |
| + model = GLiNERRecognizer(cache) |
| + load = (time.perf_counter() - start) * 1000 |
| + model.recognize("Synthetic warmup text.") |
| + root = Path(__file__).parent / "fixtures" |
| + paths = { |
| + "qualification": root / "qualification.json", |
| + "validation": root / "validation.json", |
| + "development03c": root / "holdout03c/cases.json", |
| + } |
| + suites = {n: json.loads(p.read_bytes()) for n, p in paths.items()} |
| + terminology = ReligiousTerminology() |
| + |
| + def hybrid(text): |
| + return detect(text) + terminology.detect(text) |
| + |
| + report = dict( |
| + schema_version="gate03d-development-comparison/v1", |
| + source_sha=subprocess.check_output(["git", "rev-parse", "HEAD"], text=True).strip(), |
| + model_revision=model.revision, |
| + corpus_sha256={n: hashlib.sha256(p.read_bytes()).hexdigest() for n, p in paths.items()}, |
| + terminology=list(DEFAULT_TERMS), |
| + loading_ms=load, |
| + experiments={}, |
| + automated_release_eligible=False, |
| + ) |
| + baseline = {} |
| + # Fixed before inference. No low-threshold sweep or fixture-specific patterns. |
| + for name, prompts, detector in ( |
| + ("control", DEFAULT_PROMPTS, detect), |
| + ( |
| + "semantic_status_prompt", |
| + DEFAULT_PROMPTS | {"RELIGION": "religion or religious non-affiliation"}, |
| + detect, |
| + ), |
| + ("hybrid_terminology", DEFAULT_PROMPTS, hybrid), |
| + ): |
| + model.prompts = dict(prompts) |
| + recorder = Recorded(model) |
| + result = dict(prompts=model.prompts, threshold=model.threshold, suites={}, changes=[]) |
| + for suite, cases in suites.items(): |
| + result["suites"][suite] = score_cases(recorder, cases, detector=detector) |
| + for case in cases: |
| + text = case["text"] |
| + raw = recorder.predictions[text] |
| + combined = resolve_overlaps(validate_entities(text, raw + detector(text))) |
| + output_text, _ = replace(text, combined) |
| + predicted = {(p.start, p.end, p.label) for p in combined} |
| + gold = {(p["start"], p["end"], p["label"]) for p in case["spans"]} |
| + item = dict( |
| + suite=suite, |
| + case_id=case["id"], |
| + false_positives=sorted(predicted - gold), |
| + false_negatives=sorted(gold - predicted), |
| + exact_match=output_text == case["expected"], |
| + predictions=[p.model_dump() for p in combined], |
| + ) |
| + key = (suite, case["id"]) |
| + if name == "control": |
| + baseline[key] = item |
| + if item["false_positives"] or item["false_negatives"]: |
| + result["changes"].append(item) |
| + elif item["predictions"] != baseline[key]["predictions"]: |
| + result["changes"].append(dict(before=baseline[key], after=item)) |
| + report["experiments"][name] = result |
| + report["peak_rss_mib"] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024 |
| + report["denied_network_calls"] = denied |
| + write_candidate(output, (json.dumps(report, ensure_ascii=False, indent=2) + "\n").encode()) |
| + |
| + |
| +if __name__ == "__main__": |
| + import argparse |
| + |
| + parser = argparse.ArgumentParser(description="Compare fixed Gate03d development alternatives") |
| + parser.add_argument("--cache", required=True) |
| + parser.add_argument("--output", required=True) |
| + args = parser.parse_args() |
| + run(args.cache, args.output) |
| + print("comparison_recorded_check_metrics_before_selection") |
| |
| |
| @@ -28,7 +28,7 @@ |
| return output |
| |
| |
| -def score_cases(recognizer, cases): |
| +def score_cases(recognizer, cases, *, detector=detect): |
| ner_counts, combined_counts, leaks, failures = Counter(), Counter(), Counter(), Counter() |
| literal_leaks = Counter() |
| partial_canaries, uncovered_canaries, coverage = Counter(), Counter(), Counter() |
| @@ -45,7 +45,7 @@ |
| try: |
| raw = validate_entities(text, recognizer.recognize(text)) |
| ner = {(s.start, s.end, s.label) for s in raw} |
| - combined = resolve_overlaps(validate_entities(text, raw + detect(text))) |
| + combined = resolve_overlaps(validate_entities(text, raw + detector(text))) |
| predicted = {(s.start, s.end, s.label) for s in combined} |
| actual, _ = replace(text, combined) |
| except SafeError as e: |
| |
| |
| @@ -0,0 +1,188 @@ |
| +"""Bounded literal protection of religious terminology, not personal attribution. |
| + |
| +A match says only that the source contains a protected religious/status term. |
| +Negations, quotations and general topics are intentionally protected too. No |
| +relationship between a term and a nearby person is inferred. This finite lexicon |
| +is neither a spelling corrector nor an exhaustive religion/affiliation detector. |
| +""" |
| + |
| +import unicodedata |
| + |
| +from ocr.schemas import SafeError |
| + |
| +from .normalization import dehyphenate |
| +from .schemas import Entity |
| + |
| +TERMINOLOGY_REVISION = "religious-terminology-v1" |
| +# Explicit EN/DE words and inflections, never fixture IDs, names or context templates. |
| +# Ambiguous adjectives (e.g. orthodox, evangelical) remain contextual model decisions. |
| +DEFAULT_TERMS = ( |
| + "religionslos", |
| + "konfessionslos", |
| + "konfessionsfrei", |
| + "ohne Konfession", |
| + "nicht religiös", |
| + "no religious affiliation", |
| + "no religion", |
| + "nonreligious", |
| + "non-religious", |
| + "atheist", |
| + "atheists", |
| + "atheism", |
| + "atheistisch", |
| + "Atheismus", |
| + "agnostic", |
| + "agnostics", |
| + "agnosticism", |
| + "agnostisch", |
| + "Agnostizismus", |
| + "Christentum", |
| + "Christianity", |
| + "Christen", |
| + "Christian", |
| + "Christians", |
| + "christlich", |
| + "christliche", |
| + "christlichen", |
| + "Katholizismus", |
| + "Catholicism", |
| + "katholisch", |
| + "katholische", |
| + "katholischen", |
| + "Catholic", |
| + "Catholics", |
| + "Protestantismus", |
| + "Protestantism", |
| + "Protestant", |
| + "Protestants", |
| + "evangelisch", |
| + "evangelische", |
| + "evangelischen", |
| + "Judentum", |
| + "Judaism", |
| + "jüdisch", |
| + "jüdische", |
| + "jüdischen", |
| + "Jewish", |
| + "Jude", |
| + "Jüdin", |
| + "Juden", |
| + "Jews", |
| + "Islam", |
| + "Muslim", |
| + "Muslims", |
| + "muslimisch", |
| + "muslimische", |
| + "muslimischen", |
| + "islamisch", |
| + "islamische", |
| + "islamischen", |
| + "Buddhismus", |
| + "Buddhism", |
| + "Buddhist", |
| + "Buddhists", |
| + "buddhistisch", |
| + "buddhistische", |
| + "buddhistischen", |
| + "Hinduismus", |
| + "Hinduism", |
| + "Hindu", |
| + "Hindus", |
| + "hinduistisch", |
| + "hinduistische", |
| + "hinduistischen", |
| + "Sikhismus", |
| + "Sikhism", |
| + "Sikh", |
| + "Sikhs", |
| + "sikhistisch", |
| + "Jainismus", |
| + "Jainism", |
| + "Jain", |
| + "Jains", |
| + "jainistisch", |
| + "Bahai", |
| + "Baháʼí Faith", |
| + "Bahá'í", |
| + "Baha'i", |
| + "Shinto", |
| + "Shintoismus", |
| + "Shintoism", |
| + "Taoismus", |
| + "Taoism", |
| + "Daoismus", |
| + "Daoism", |
| + "Zoroastrismus", |
| + "Zoroastrianism", |
| + "Orthodox Christianity", |
| + "Orthodoxie", |
| +) |
| + |
| + |
| +def _word(c): |
| + # Combining marks and connector punctuation belong to a word too. This avoids |
| + # matching a bare term inside a decomposed or underscore-connected identifier. |
| + return c.isalnum() or c == "_" or unicodedata.category(c).startswith("M") |
| + |
| + |
| +class ReligiousTerminology: |
| + """Operator-configurable literal terms; no user supplied regular expressions. |
| + |
| + At most 256 terms, 80 code points each, 2M input code points and 100k matches. |
| + Case folding and OCR line dehyphenation retain exact original source offsets. |
| + Configuration is trusted operator policy, not per-request model output. |
| + """ |
| + |
| + def __init__(self, terms=DEFAULT_TERMS): |
| + if not isinstance(terms, (tuple, list)) or not 1 <= len(terms) <= 256: |
| + raise SafeError("invalid_terminology_configuration") |
| + if any( |
| + not isinstance(t, str) |
| + or not 1 <= len(t) <= 80 |
| + or t != t.strip() |
| + or not all(c.isalpha() or c in " -'ʼáí" for c in t) |
| + for t in terms |
| + ): |
| + raise SafeError("invalid_terminology_configuration") |
| + self.terms = tuple(sorted({t.casefold() for t in terms}, key=lambda t: (-len(t), t))) |
| + |
| + def detect(self, text): |
| + if not isinstance(text, str) or len(text) > 2_000_000: |
| + raise SafeError("anonymization_text_limit") |
| + normalized, source_positions = dehyphenate(text) |
| + folded_parts, folded_positions = [], [] |
| + for i, char in enumerate(normalized): |
| + part = char.casefold() |
| + folded_parts.append(part) |
| + folded_positions.extend([i] * len(part)) |
| + folded = "".join(folded_parts) |
| + found = {} |
| + for term in self.terms: |
| + offset = 0 |
| + while (left := folded.find(term, offset)) >= 0: |
| + right = left + len(term) |
| + offset = right |
| + if (left and _word(folded[left - 1])) or ( |
| + right < len(folded) and _word(folded[right]) |
| + ): |
| + continue |
| + # Never accept part of an expanding case fold (e.g. one s in ß). |
| + if (left and folded_positions[left - 1] == folded_positions[left]) or ( |
| + right < len(folded_positions) |
| + and folded_positions[right - 1] == folded_positions[right] |
| + ): |
| + continue |
| + start = source_positions[folded_positions[left]] |
| + end = source_positions[folded_positions[right - 1]] + 1 |
| + found[(start, end)] = Entity( |
| + start=start, |
| + end=end, |
| + label="RELIGION", |
| + text=text[start:end], |
| + source="deterministic", |
| + model_revision=TERMINOLOGY_REVISION, |
| + normalization="identity" if normalized == text else "dehyphenation-v1", |
| + ) |
| + if len(found) > 100_000: |
| + raise SafeError("invalid_entity_spans") |
| + return list(found.values()) |
|
|
