{"version":3,"sources":["../src/index.ts","../src/types.ts","../src/decoder.ts","../src/model-loader.ts","../src/runtime/capability.ts","../src/runtime/ort-web-loader.ts"],"sourcesContent":["import { decodeBioTokenSpans } from \"./decoder\";\nimport { loadTokenClassificationPipeline } from \"./model-loader\";\nimport {\n OPENMED_SPAN_SCHEMA_VERSION,\n type DeidentifyOptions,\n type ExtractPiiOptions,\n type OpenMedDeidentifyResult,\n type OpenMedSpan,\n type SpanAction,\n} from \"./types\";\n\nexport {\n CANONICAL_LABELS,\n OPENMED_SPAN_SCHEMA_VERSION,\n POLICY_LABELS,\n SPAN_ACTIONS,\n} from \"./types\";\nexport type {\n CanonicalLabel,\n DecodedEntitySpan,\n DeidentifyOptions,\n ExtractPiiOptions,\n LoadModelOptions,\n LoadOnnxModelOptions,\n ModelLoader,\n OpenMedOnnxVariant,\n OpenMedDeidentifyResult,\n OpenMedSpan,\n PolicyLabel,\n SpanAction,\n TextHash,\n TokenClassificationCallOptions,\n TokenClassificationEntity,\n TokenClassificationOutput,\n TokenClassificationPipeline,\n TransformersRuntime,\n} from \"./types\";\nexport {\n decodeBioTokenSpans,\n flattenTokenClassificationOutput,\n normalizeLabel,\n policyLabelFor,\n refinePrivacyFilterSpan,\n trimSpanWhitespace,\n} from \"./decoder\";\nexport {\n isLocalModelReference,\n loadOnnxModel,\n loadTokenClassificationPipeline,\n} from \"./model-loader\";\nexport {\n detectOrtWebCapabilities,\n selectOrtWebBackend,\n} from \"./runtime/capability\";\nexport type {\n CapabilityDetectionOptions,\n OrtCapabilityGlobalScope,\n OrtWebBackend,\n OrtWebBackendChoice,\n OrtWebCapabilityProfile,\n} from \"./runtime/capability\";\nexport {\n assertOfflineAssetPath,\n clearOrtWebSessionCache,\n configureOrtWebRuntime,\n loadOrtWebSession,\n loadOrtWebTokenClassificationPipeline,\n normalizeOrtAssetPath,\n} from \"./runtime/ort-web-loader\";\nexport type {\n OrtExecutionProvider,\n OrtFeeds,\n OrtInferenceSession,\n OrtResults,\n OrtSessionCreateOptions,\n OrtTensorLike,\n OrtTokenClassificationDecodeContext,\n OrtWebLoadedSession,\n OrtWebLoaderOptions,\n OrtWebRuntime,\n OrtWebRuntimeProvider,\n OrtWebSessionCache,\n OrtWebTokenClassificationPipelineOptions,\n} from \"./runtime/ort-web-loader\";\n\nconst DEFAULT_MODEL_ID = \"OpenMed/privacy-filter-transformersjs\";\nconst DEFAULT_HASH_SECRET = \"openmedkit-web\";\n\nexport async function extractPii(\n text: string,\n options: ExtractPiiOptions = {},\n): Promise {\n const pipeline =\n options.pipeline ??\n (await (options.modelLoader ?? loadTokenClassificationPipeline)(\n options.model ?? DEFAULT_MODEL_ID,\n options.loaderOptions,\n ));\n const rawOutput = await pipeline(text, {\n aggregation_strategy: \"none\",\n ...(options.pipelineOptions ?? {}),\n });\n const decoded = decodeBioTokenSpans(text, rawOutput, {\n threshold: options.threshold ?? 0,\n });\n\n const spans: OpenMedSpan[] = [];\n for (const entity of decoded) {\n const surface = text.slice(entity.start, entity.end);\n spans.push({\n schema_version: OPENMED_SPAN_SCHEMA_VERSION,\n doc_id: options.docId ?? \"document\",\n start: entity.start,\n end: entity.end,\n text_hash: await hmacTextHash(\n surface,\n options.hashSecret ?? DEFAULT_HASH_SECRET,\n ),\n entity_type: entity.entity_type,\n canonical_label: entity.canonical_label,\n policy_label: entity.policy_label,\n regulatory_tags: [...(options.regulatoryTags ?? [])],\n score: entity.score,\n detector: options.detector ?? \"transformersjs\",\n evidence: {\n token_count: entity.token_count,\n },\n action: \"keep\",\n replacement: null,\n reversible_id: null,\n section: options.section ?? null,\n metadata: {\n ...(options.model ? { model: options.model } : {}),\n ...(options.metadata ?? {}),\n },\n });\n }\n return spans;\n}\n\nexport async function deidentify(\n text: string,\n options: DeidentifyOptions = {},\n): Promise {\n const spans = await extractPii(text, options);\n const redactionSpans = spans.map((span) => {\n const replacement = options.replacement?.(span) ?? `[${span.canonical_label}]`;\n return {\n ...span,\n action: \"redact\" as SpanAction,\n replacement,\n };\n });\n return {\n text,\n deidentifiedText: spansToRedactedText(text, redactionSpans),\n spans: redactionSpans,\n };\n}\n\nexport function spansToRedactedText(text: string, spans: OpenMedSpan[]): string {\n let redacted = text;\n for (const span of [...spans].sort((left, right) => right.start - left.start)) {\n redacted =\n redacted.slice(0, span.start) +\n (span.replacement ?? `[${span.canonical_label}]`) +\n redacted.slice(span.end);\n }\n return redacted;\n}\n\nexport async function hmacTextHash(\n surface: string | Uint8Array,\n secret: string | Uint8Array,\n): Promise<`hmac-sha256:${string}`> {\n const payload = toBytes(surface);\n const key = toBytes(secret);\n const subtle = globalThis.crypto?.subtle;\n if (subtle) {\n const cryptoKey = await subtle.importKey(\n \"raw\",\n toArrayBuffer(key),\n { name: \"HMAC\", hash: \"SHA-256\" },\n false,\n [\"sign\"],\n );\n const signature = await subtle.sign(\"HMAC\", cryptoKey, toArrayBuffer(payload));\n return `hmac-sha256:${toHex(new Uint8Array(signature))}`;\n }\n\n const { createHmac } = await import(\"node:crypto\");\n const digest = createHmac(\"sha256\", key).update(payload).digest(\"hex\");\n return `hmac-sha256:${digest}`;\n}\n\nfunction toBytes(value: string | Uint8Array): Uint8Array {\n return typeof value === \"string\" ? new TextEncoder().encode(value) : value;\n}\n\nfunction toArrayBuffer(bytes: Uint8Array): ArrayBuffer {\n const copy = new Uint8Array(bytes.byteLength);\n copy.set(bytes);\n return copy.buffer;\n}\n\nfunction toHex(bytes: Uint8Array): string {\n return Array.from(bytes, (value) => value.toString(16).padStart(2, \"0\")).join(\n \"\",\n );\n}\n","export const OPENMED_SPAN_SCHEMA_VERSION = 1 as const;\n\nexport const CANONICAL_LABELS = [\n \"ACCOUNT_NUMBER\",\n \"AGE\",\n \"AMOUNT\",\n \"API_KEY\",\n \"BIC\",\n \"BITCOIN_ADDRESS\",\n \"BUILDING_NUMBER\",\n \"CREDIT_CARD\",\n \"CREDIT_CARD_ISSUER\",\n \"CURRENCY\",\n \"CVV\",\n \"DATE\",\n \"DATE_OF_BIRTH\",\n \"EMAIL\",\n \"ETHEREUM_ADDRESS\",\n \"EYE_COLOR\",\n \"FIRST_NAME\",\n \"GENDER\",\n \"GPS_COORDINATES\",\n \"HEIGHT\",\n \"IBAN\",\n \"ID_NUM\",\n \"IMEI\",\n \"IP_ADDRESS\",\n \"JOB_DEPARTMENT\",\n \"JOB_TITLE\",\n \"LAST_NAME\",\n \"LITECOIN_ADDRESS\",\n \"LOCATION\",\n \"MAC_ADDRESS\",\n \"MASKED_NUMBER\",\n \"MIDDLE_NAME\",\n \"OCCUPATION\",\n \"ORDINAL_DIRECTION\",\n \"ORGANIZATION\",\n \"OTHER\",\n \"PASSWORD\",\n \"PERSON\",\n \"PHONE\",\n \"PIN\",\n \"PREFIX\",\n \"SSN\",\n \"STREET_ADDRESS\",\n \"TIME\",\n \"URL\",\n \"USER_AGENT\",\n \"USERNAME\",\n \"VEHICLE_REGISTRATION\",\n \"VIN\",\n \"ZIPCODE\"\n] as const;\n\nexport type CanonicalLabel = (typeof CANONICAL_LABELS)[number];\n\nexport const POLICY_LABELS = [\n \"DIRECT_IDENTIFIER\",\n \"QUASI_IDENTIFIER\",\n \"CLINICAL_CONCEPT\"\n] as const;\n\nexport type PolicyLabel = (typeof POLICY_LABELS)[number];\n\nexport const SPAN_ACTIONS = [\n \"keep\",\n \"redact\",\n \"replace\",\n \"mask\",\n \"hash\",\n \"format_preserve\"\n] as const;\n\nexport type SpanAction = (typeof SPAN_ACTIONS)[number];\n\nexport type TextHash = `hmac-sha256:${string}`;\n\nexport interface OpenMedSpan {\n schema_version: typeof OPENMED_SPAN_SCHEMA_VERSION;\n doc_id: string;\n start: number;\n end: number;\n text_hash: TextHash;\n entity_type: string;\n canonical_label: CanonicalLabel;\n policy_label: PolicyLabel;\n regulatory_tags: string[];\n score: number | null;\n detector: string | null;\n evidence: Record;\n action: SpanAction;\n replacement: string | null;\n reversible_id: string | null;\n section: string | null;\n metadata: Record;\n}\n\nexport interface TokenClassificationEntity {\n entity?: string;\n entity_group?: string;\n score?: number;\n word?: string;\n start: number;\n end: number;\n index?: number;\n}\n\nexport type TokenClassificationOutput =\n | TokenClassificationEntity[]\n | TokenClassificationEntity[][];\n\nexport interface TokenClassificationCallOptions {\n aggregation_strategy?: \"none\" | \"simple\" | \"first\" | \"average\" | \"max\";\n ignore_labels?: string[];\n [key: string]: unknown;\n}\n\nexport type TokenClassificationPipeline = (\n text: string,\n options?: TokenClassificationCallOptions,\n) => TokenClassificationOutput | Promise;\n\nexport interface DecodedEntitySpan {\n entity_type: string;\n canonical_label: CanonicalLabel;\n policy_label: PolicyLabel;\n start: number;\n end: number;\n score: number | null;\n token_count: number;\n}\n\nexport interface ExtractPiiOptions {\n pipeline?: TokenClassificationPipeline;\n model?: string;\n modelLoader?: ModelLoader;\n loaderOptions?: LoadModelOptions;\n threshold?: number;\n docId?: string;\n hashSecret?: string | Uint8Array;\n detector?: string | null;\n lang?: string;\n section?: string | null;\n regulatoryTags?: string[];\n metadata?: Record;\n pipelineOptions?: TokenClassificationCallOptions;\n}\n\nexport interface DeidentifyOptions extends ExtractPiiOptions {\n replacement?: (span: OpenMedSpan) => string;\n}\n\nexport interface OpenMedDeidentifyResult {\n text: string;\n deidentifiedText: string;\n spans: OpenMedSpan[];\n}\n\nexport interface LoadModelOptions {\n localFilesOnly?: boolean;\n allowRemoteModels?: boolean;\n revision?: string;\n quantized?: boolean;\n dtype?: string;\n device?: string;\n runtime?: TransformersRuntime | (() => Promise);\n pipelineOptions?: Record;\n}\n\nexport type OpenMedOnnxVariant = \"int8\" | \"fp32\" | \"fp16\";\n\nexport interface LoadOnnxModelOptions extends LoadModelOptions {\n variant?: OpenMedOnnxVariant;\n}\n\nexport interface TransformersRuntime {\n pipeline: (\n task: \"token-classification\",\n model: string,\n options?: Record,\n ) => Promise | TokenClassificationPipeline;\n env?: {\n allowRemoteModels?: boolean;\n allowLocalModels?: boolean;\n [key: string]: unknown;\n };\n}\n\nexport type ModelLoader = (\n model: string,\n options?: LoadModelOptions,\n) => Promise;\n","import {\n CANONICAL_LABELS,\n type CanonicalLabel,\n type DecodedEntitySpan,\n type PolicyLabel,\n type TokenClassificationEntity,\n type TokenClassificationOutput,\n} from \"./types\";\n\nconst CANONICAL_LABEL_SET = new Set(CANONICAL_LABELS);\n\nconst POLICY_LABEL_BY_CANONICAL: Record = {\n ACCOUNT_NUMBER: \"DIRECT_IDENTIFIER\",\n AGE: \"QUASI_IDENTIFIER\",\n AMOUNT: \"QUASI_IDENTIFIER\",\n API_KEY: \"DIRECT_IDENTIFIER\",\n BIC: \"DIRECT_IDENTIFIER\",\n BITCOIN_ADDRESS: \"DIRECT_IDENTIFIER\",\n BUILDING_NUMBER: \"DIRECT_IDENTIFIER\",\n CREDIT_CARD: \"DIRECT_IDENTIFIER\",\n CREDIT_CARD_ISSUER: \"QUASI_IDENTIFIER\",\n CURRENCY: \"QUASI_IDENTIFIER\",\n CVV: \"DIRECT_IDENTIFIER\",\n DATE: \"QUASI_IDENTIFIER\",\n DATE_OF_BIRTH: \"DIRECT_IDENTIFIER\",\n EMAIL: \"DIRECT_IDENTIFIER\",\n ETHEREUM_ADDRESS: \"DIRECT_IDENTIFIER\",\n EYE_COLOR: \"QUASI_IDENTIFIER\",\n FIRST_NAME: \"DIRECT_IDENTIFIER\",\n GENDER: \"QUASI_IDENTIFIER\",\n GPS_COORDINATES: \"DIRECT_IDENTIFIER\",\n HEIGHT: \"QUASI_IDENTIFIER\",\n IBAN: \"DIRECT_IDENTIFIER\",\n ID_NUM: \"DIRECT_IDENTIFIER\",\n IMEI: \"DIRECT_IDENTIFIER\",\n IP_ADDRESS: \"DIRECT_IDENTIFIER\",\n JOB_DEPARTMENT: \"QUASI_IDENTIFIER\",\n JOB_TITLE: \"QUASI_IDENTIFIER\",\n LAST_NAME: \"DIRECT_IDENTIFIER\",\n LITECOIN_ADDRESS: \"DIRECT_IDENTIFIER\",\n LOCATION: \"QUASI_IDENTIFIER\",\n MAC_ADDRESS: \"DIRECT_IDENTIFIER\",\n MASKED_NUMBER: \"DIRECT_IDENTIFIER\",\n MIDDLE_NAME: \"DIRECT_IDENTIFIER\",\n OCCUPATION: \"QUASI_IDENTIFIER\",\n ORDINAL_DIRECTION: \"QUASI_IDENTIFIER\",\n ORGANIZATION: \"QUASI_IDENTIFIER\",\n OTHER: \"CLINICAL_CONCEPT\",\n PASSWORD: \"DIRECT_IDENTIFIER\",\n PERSON: \"DIRECT_IDENTIFIER\",\n PHONE: \"DIRECT_IDENTIFIER\",\n PIN: \"DIRECT_IDENTIFIER\",\n PREFIX: \"DIRECT_IDENTIFIER\",\n SSN: \"DIRECT_IDENTIFIER\",\n STREET_ADDRESS: \"DIRECT_IDENTIFIER\",\n TIME: \"QUASI_IDENTIFIER\",\n URL: \"DIRECT_IDENTIFIER\",\n USER_AGENT: \"DIRECT_IDENTIFIER\",\n USERNAME: \"DIRECT_IDENTIFIER\",\n VEHICLE_REGISTRATION: \"DIRECT_IDENTIFIER\",\n VIN: \"DIRECT_IDENTIFIER\",\n ZIPCODE: \"QUASI_IDENTIFIER\",\n};\n\nconst ALIAS_MAP: Record = {\n accountname: \"ACCOUNT_NUMBER\",\n accountnumber: \"ACCOUNT_NUMBER\",\n address: \"STREET_ADDRESS\",\n aadhaar: \"ID_NUM\",\n age: \"AGE\",\n amount: \"AMOUNT\",\n apikey: \"API_KEY\",\n bankaccount: \"ACCOUNT_NUMBER\",\n bic: \"BIC\",\n birthdate: \"DATE_OF_BIRTH\",\n bitcoinaddress: \"BITCOIN_ADDRESS\",\n buildingnumber: \"BUILDING_NUMBER\",\n city: \"LOCATION\",\n cnpj: \"ID_NUM\",\n codicefiscale: \"ID_NUM\",\n company: \"ORGANIZATION\",\n country: \"LOCATION\",\n county: \"LOCATION\",\n cpf: \"ID_NUM\",\n creditcard: \"CREDIT_CARD\",\n creditcardissuer: \"CREDIT_CARD_ISSUER\",\n creditcardnumber: \"CREDIT_CARD\",\n creditdebitcard: \"CREDIT_CARD\",\n currency: \"CURRENCY\",\n currencycode: \"CURRENCY\",\n currencyname: \"CURRENCY\",\n currencysymbol: \"CURRENCY\",\n cvv: \"CVV\",\n date: \"DATE\",\n dateofbirth: \"DATE_OF_BIRTH\",\n department: \"JOB_DEPARTMENT\",\n dni: \"ID_NUM\",\n dob: \"DATE_OF_BIRTH\",\n doctor: \"PERSON\",\n email: \"EMAIL\",\n emailaddress: \"EMAIL\",\n employer: \"ORGANIZATION\",\n ethereumaddress: \"ETHEREUM_ADDRESS\",\n eyecolor: \"EYE_COLOR\",\n familyname: \"LAST_NAME\",\n fax: \"PHONE\",\n firstname: \"FIRST_NAME\",\n fullname: \"PERSON\",\n gender: \"GENDER\",\n givenname: \"FIRST_NAME\",\n gps: \"GPS_COORDINATES\",\n gpscoordinates: \"GPS_COORDINATES\",\n height: \"HEIGHT\",\n iban: \"IBAN\",\n id: \"ID_NUM\",\n identifier: \"ID_NUM\",\n idnum: \"ID_NUM\",\n imei: \"IMEI\",\n ip: \"IP_ADDRESS\",\n ipaddress: \"IP_ADDRESS\",\n jobdepartment: \"JOB_DEPARTMENT\",\n jobtitle: \"JOB_TITLE\",\n lastname: \"LAST_NAME\",\n licenseplate: \"VEHICLE_REGISTRATION\",\n litecoinaddress: \"LITECOIN_ADDRESS\",\n location: \"LOCATION\",\n macaddress: \"MAC_ADDRESS\",\n maskednumber: \"MASKED_NUMBER\",\n medicalrecordnumber: \"ID_NUM\",\n middlename: \"MIDDLE_NAME\",\n mrn: \"ID_NUM\",\n name: \"PERSON\",\n nationalid: \"ID_NUM\",\n nhs: \"ID_NUM\",\n nhsnumber: \"ID_NUM\",\n nie: \"ID_NUM\",\n npi: \"ID_NUM\",\n occupation: \"OCCUPATION\",\n ordinaldirection: \"ORDINAL_DIRECTION\",\n organization: \"ORGANIZATION\",\n password: \"PASSWORD\",\n patient: \"PERSON\",\n person: \"PERSON\",\n personalurl: \"URL\",\n phone: \"PHONE\",\n phonenumber: \"PHONE\",\n pin: \"PIN\",\n place: \"LOCATION\",\n postalcode: \"ZIPCODE\",\n postcode: \"ZIPCODE\",\n prefix: \"PREFIX\",\n profession: \"OCCUPATION\",\n region: \"LOCATION\",\n secondaryaddress: \"STREET_ADDRESS\",\n sex: \"GENDER\",\n socialsecuritynumber: \"SSN\",\n ssn: \"SSN\",\n state: \"LOCATION\",\n steuerid: \"ID_NUM\",\n street: \"STREET_ADDRESS\",\n streetaddress: \"STREET_ADDRESS\",\n surname: \"LAST_NAME\",\n swift: \"BIC\",\n telephone: \"PHONE\",\n teudatzehut: \"ID_NUM\",\n time: \"TIME\",\n title: \"PREFIX\",\n tz: \"ID_NUM\",\n url: \"URL\",\n urlpersonal: \"URL\",\n useragent: \"USER_AGENT\",\n userhandle: \"USERNAME\",\n username: \"USERNAME\",\n vin: \"VIN\",\n vrm: \"VEHICLE_REGISTRATION\",\n website: \"URL\",\n zip: \"ZIPCODE\",\n zipcode: \"ZIPCODE\",\n};\n\ntype BoundaryTag = \"B\" | \"I\" | \"E\" | \"S\" | null;\n\ninterface PreparedToken {\n label: string;\n boundary: BoundaryTag;\n baseLabel: string;\n canonicalLabel: CanonicalLabel;\n policyLabel: PolicyLabel;\n start: number;\n end: number;\n score: number | null;\n index: number;\n}\n\ninterface ActiveSpan {\n baseLabel: string;\n canonicalLabel: CanonicalLabel;\n policyLabel: PolicyLabel;\n start: number;\n end: number;\n scores: number[];\n tokenCount: number;\n}\n\nexport function normalizeLabel(label: string): CanonicalLabel {\n if (!label) {\n return \"OTHER\";\n }\n const key = labelKey(label);\n if (!key) {\n return \"OTHER\";\n }\n const alias = ALIAS_MAP[key];\n if (alias) {\n return alias;\n }\n const upper = label\n .toUpperCase()\n .replace(/[-\\s]+/g, \"_\")\n .replace(/[^A-Z0-9_]/g, \"\");\n return CANONICAL_LABEL_SET.has(upper) ? (upper as CanonicalLabel) : \"OTHER\";\n}\n\nexport function policyLabelFor(label: string): PolicyLabel {\n return POLICY_LABEL_BY_CANONICAL[normalizeLabel(label)];\n}\n\nexport function decodeBioTokenSpans(\n text: string,\n output: TokenClassificationOutput,\n options: { threshold?: number } = {},\n): DecodedEntitySpan[] {\n const threshold = options.threshold ?? 0;\n const tokens = flattenTokenClassificationOutput(output)\n .map((token, fallbackIndex) => prepareToken(token, fallbackIndex))\n .filter((token): token is PreparedToken => token !== null)\n .sort((left, right) => left.index - right.index || left.start - right.start);\n\n const spans: DecodedEntitySpan[] = [];\n let current: ActiveSpan | null = null;\n let previousIndex: number | null = null;\n\n for (const token of tokens) {\n if (previousIndex !== null && token.index !== previousIndex + 1) {\n if (current !== null) {\n pushDecodedSpan(spans, current, text, threshold);\n current = null;\n }\n }\n\n if (token.boundary === null) {\n if (current !== null) {\n pushDecodedSpan(spans, current, text, threshold);\n current = null;\n }\n previousIndex = token.index;\n continue;\n }\n\n if (token.boundary === \"S\") {\n if (current !== null) {\n pushDecodedSpan(spans, current, text, threshold);\n }\n pushDecodedSpan(spans, activeSpanFromToken(token), text, threshold);\n current = null;\n } else if (token.boundary === \"B\") {\n if (current !== null) {\n pushDecodedSpan(spans, current, text, threshold);\n }\n current = activeSpanFromToken(token);\n } else if (\n token.boundary === \"I\" &&\n current !== null &&\n current.baseLabel === token.baseLabel\n ) {\n appendTokenToActiveSpan(current, token);\n } else if (\n token.boundary === \"E\" &&\n current !== null &&\n current.baseLabel === token.baseLabel\n ) {\n appendTokenToActiveSpan(current, token);\n pushDecodedSpan(spans, current, text, threshold);\n current = null;\n } else {\n if (current !== null) {\n pushDecodedSpan(spans, current, text, threshold);\n }\n current = activeSpanFromToken(token);\n if (token.boundary === \"E\") {\n pushDecodedSpan(spans, current, text, threshold);\n current = null;\n }\n }\n\n previousIndex = token.index;\n }\n\n if (current !== null) {\n pushDecodedSpan(spans, current, text, threshold);\n }\n return mergeAdjacentSpans(spans, text);\n}\n\nexport function flattenTokenClassificationOutput(\n output: TokenClassificationOutput,\n): TokenClassificationEntity[] {\n if (output.length === 0) {\n return [];\n }\n const first = output[0];\n if (Array.isArray(first)) {\n return (output as TokenClassificationEntity[][]).flat();\n }\n return output as TokenClassificationEntity[];\n}\n\nexport function trimSpanWhitespace(\n start: number,\n end: number,\n text: string,\n): { start: number; end: number } {\n let nextStart = Math.max(0, Math.min(start, text.length));\n let nextEnd = Math.max(nextStart, Math.min(end, text.length));\n while (nextStart < nextEnd && /\\s/.test(text[nextStart] ?? \"\")) {\n nextStart += 1;\n }\n while (nextEnd > nextStart && /\\s/.test(text[nextEnd - 1] ?? \"\")) {\n nextEnd -= 1;\n }\n return { start: nextStart, end: nextEnd };\n}\n\nexport function refinePrivacyFilterSpan(\n label: string,\n start: number,\n end: number,\n text: string,\n): { start: number; end: number } {\n const trimmed = trimSpanWhitespace(start, end, text);\n const spanText = text.slice(trimmed.start, trimmed.end);\n const normalized = label.toLowerCase();\n const structuredPatterns: Array<[string, RegExp]> = [\n [\"email\", /\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,}\\b/],\n [\"url\", /\\b(?:https?:\\/\\/|www\\.)[^\\s,;)\\]]+/],\n [\"phone\", /(?:\\+?1[\\s.-]?)?(?:\\(?\\d{3}\\)?[\\s.-]?)\\d{3}[\\s.-]?\\d{4}/],\n ];\n\n for (const [hint, pattern] of structuredPatterns) {\n if (!normalized.includes(hint)) {\n continue;\n }\n const match = pattern.exec(spanText);\n if (match?.index !== undefined) {\n return {\n start: trimmed.start + match.index,\n end: trimmed.start + match.index + match[0].length,\n };\n }\n }\n\n let nextEnd = trimmed.end;\n const lowerSpan = spanText.toLowerCase();\n for (const suffix of [\" and\", \" or\"]) {\n if (lowerSpan.endsWith(suffix)) {\n nextEnd -= suffix.length;\n break;\n }\n }\n return trimSpanWhitespace(trimmed.start, nextEnd, text);\n}\n\nfunction prepareToken(\n token: TokenClassificationEntity,\n fallbackIndex: number,\n): PreparedToken | null {\n const label = token.entity_group ?? token.entity ?? \"\";\n const start = Number(token.start);\n const end = Number(token.end);\n if (!Number.isFinite(start) || !Number.isFinite(end) || end <= start) {\n return null;\n }\n const parsed = parseBoundaryLabel(label);\n const canonicalLabel = normalizeLabel(parsed.baseLabel);\n return {\n label,\n boundary: parsed.boundary,\n baseLabel: parsed.baseLabel,\n canonicalLabel,\n policyLabel: POLICY_LABEL_BY_CANONICAL[canonicalLabel],\n start,\n end,\n score: token.score === undefined ? null : Number(token.score),\n index: token.index ?? fallbackIndex,\n };\n}\n\nfunction activeSpanFromToken(token: PreparedToken): ActiveSpan {\n return {\n baseLabel: token.baseLabel,\n canonicalLabel: token.canonicalLabel,\n policyLabel: token.policyLabel,\n start: token.start,\n end: token.end,\n scores: token.score === null ? [] : [token.score],\n tokenCount: 1,\n };\n}\n\nfunction appendTokenToActiveSpan(span: ActiveSpan, token: PreparedToken): void {\n span.end = token.end;\n if (token.score !== null) {\n span.scores.push(token.score);\n }\n span.tokenCount += 1;\n}\n\nfunction pushDecodedSpan(\n spans: DecodedEntitySpan[],\n span: ActiveSpan,\n text: string,\n threshold: number,\n): void {\n const refined = refinePrivacyFilterSpan(span.baseLabel, span.start, span.end, text);\n if (refined.end <= refined.start) {\n return;\n }\n const score =\n span.scores.length > 0\n ? span.scores.reduce((total, value) => total + value, 0) /\n span.scores.length\n : null;\n if (score !== null && score < threshold) {\n return;\n }\n spans.push({\n entity_type: span.baseLabel,\n canonical_label: span.canonicalLabel,\n policy_label: span.policyLabel,\n start: refined.start,\n end: refined.end,\n score,\n token_count: span.tokenCount,\n });\n}\n\nfunction parseBoundaryLabel(label: string): {\n boundary: BoundaryTag;\n baseLabel: string;\n} {\n if (label === \"O\") {\n return { boundary: null, baseLabel: \"O\" };\n }\n if (/^[BIES]-/.test(label)) {\n return {\n boundary: label[0] as Exclude,\n baseLabel: label.slice(2),\n };\n }\n return { boundary: \"B\", baseLabel: label };\n}\n\nfunction labelKey(label: string): string {\n const stripped = label.trim().replace(/^[BIES]-/, \"\");\n return stripped.toLowerCase().replace(/[^a-z0-9]/g, \"\");\n}\n\nfunction mergeAdjacentSpans(\n spans: DecodedEntitySpan[],\n text: string,\n): DecodedEntitySpan[] {\n const merged: DecodedEntitySpan[] = [];\n for (const span of spans) {\n const previous = merged.at(-1);\n if (\n previous &&\n previous.entity_type === span.entity_type &&\n previous.end <= span.start &&\n text.slice(previous.end, span.start).trim() === \"\"\n ) {\n previous.end = span.end;\n previous.token_count += span.token_count;\n if (previous.score !== null && span.score !== null) {\n previous.score =\n (previous.score + span.score) / 2;\n } else {\n previous.score = previous.score ?? span.score;\n }\n continue;\n }\n merged.push({ ...span });\n }\n return merged;\n}\n","import type {\n LoadModelOptions,\n LoadOnnxModelOptions,\n TokenClassificationPipeline,\n TransformersRuntime,\n} from \"./types\";\n\nconst TRANSFORMERS_JS_MODULE = \"@huggingface/transformers\";\nconst ONNX_MODEL_FILENAMES = {\n int8: \"model_int8\",\n fp32: \"model\",\n fp16: \"model_fp16\",\n} as const;\n\nexport async function loadOnnxModel(\n model: string,\n options: LoadOnnxModelOptions = {},\n): Promise {\n const { variant = \"int8\", pipelineOptions, ...loaderOptions } = options;\n return loadTokenClassificationPipeline(model, {\n ...loaderOptions,\n quantized: false,\n pipelineOptions: {\n subfolder: \"\",\n model_file_name: ONNX_MODEL_FILENAMES[variant],\n ...pipelineOptions,\n },\n });\n}\n\nexport async function loadTokenClassificationPipeline(\n model: string,\n options: LoadModelOptions = {},\n): Promise {\n const runtime = await resolveRuntime(options.runtime);\n const localReference = isLocalModelReference(model);\n const localFilesOnly = options.localFilesOnly ?? localReference;\n const allowRemoteModels = options.allowRemoteModels ?? !localFilesOnly;\n const previousAllowRemote = runtime.env?.allowRemoteModels;\n const previousAllowLocal = runtime.env?.allowLocalModels;\n\n if (runtime.env) {\n runtime.env.allowLocalModels = true;\n runtime.env.allowRemoteModels = allowRemoteModels;\n }\n\n try {\n const pipelineOptions: Record = {\n ...(options.revision === undefined ? {} : { revision: options.revision }),\n ...(options.quantized === undefined ? {} : { quantized: options.quantized }),\n ...(options.dtype === undefined ? {} : { dtype: options.dtype }),\n ...(options.device === undefined ? {} : { device: options.device }),\n ...(options.pipelineOptions ?? {}),\n local_files_only: localFilesOnly,\n localFilesOnly,\n };\n return await runtime.pipeline(\n \"token-classification\",\n model,\n pipelineOptions,\n );\n } finally {\n if (runtime.env) {\n if (previousAllowRemote === undefined) {\n delete runtime.env.allowRemoteModels;\n } else {\n runtime.env.allowRemoteModels = previousAllowRemote;\n }\n if (previousAllowLocal === undefined) {\n delete runtime.env.allowLocalModels;\n } else {\n runtime.env.allowLocalModels = previousAllowLocal;\n }\n }\n }\n}\n\nexport function isLocalModelReference(model: string): boolean {\n if (model.startsWith(\"file://\")) {\n return true;\n }\n if (\n model.startsWith(\"/\") ||\n model.startsWith(\"./\") ||\n model.startsWith(\"../\") ||\n model.startsWith(\"~\")\n ) {\n return true;\n }\n return /^[A-Za-z]:[\\\\/]/.test(model);\n}\n\nasync function resolveRuntime(\n runtime?: LoadModelOptions[\"runtime\"],\n): Promise {\n if (typeof runtime === \"function\") {\n return runtime();\n }\n if (runtime) {\n return runtime;\n }\n const moduleName = TRANSFORMERS_JS_MODULE;\n try {\n return (await import(moduleName)) as TransformersRuntime;\n } catch (error) {\n throw new Error(\n \"Install @huggingface/transformers or pass a token-classification pipeline.\",\n { cause: error },\n );\n }\n}\n","export type OrtWebBackend = \"webgpu\" | \"wasm-simd-threads\" | \"wasm-basic\";\n\nexport interface OrtWebCapabilityProfile {\n webgpu: boolean;\n wasm: boolean;\n wasmSimd: boolean;\n sharedArrayBuffer: boolean;\n crossOriginIsolated: boolean;\n hardwareConcurrency: number;\n}\n\nexport interface OrtWebBackendChoice {\n backend: OrtWebBackend;\n executionProviders: (\"webgpu\" | \"wasm\")[];\n wasm: {\n simd: boolean;\n threads: boolean;\n numThreads: number;\n };\n reason: string;\n}\n\nexport interface OrtCapabilityGlobalScope {\n WebAssembly?: Pick;\n SharedArrayBuffer?: typeof SharedArrayBuffer;\n crossOriginIsolated?: boolean;\n navigator?: {\n gpu?: unknown;\n hardwareConcurrency?: number;\n };\n}\n\nexport interface CapabilityDetectionOptions {\n globalScope?: OrtCapabilityGlobalScope;\n overrides?: Partial;\n}\n\nconst MAX_WASM_THREADS = 4;\nconst MIN_THREADED_WASM_CONCURRENCY = 2;\n\nconst WASM_SIMD_PROBE = new Uint8Array([\n 0x00, 0x61, 0x73, 0x6d, 0x01, 0x00, 0x00, 0x00, 0x01, 0x05, 0x01, 0x60,\n 0x00, 0x01, 0x7b, 0x03, 0x02, 0x01, 0x00, 0x0a, 0x0a, 0x01, 0x08, 0x00,\n 0x41, 0x00, 0xfd, 0x0f, 0xfd, 0x62, 0x0b,\n]);\n\nexport function detectOrtWebCapabilities(\n options: CapabilityDetectionOptions = {},\n): OrtWebCapabilityProfile {\n const scope = options.globalScope ?? globalThis;\n const navigatorLike = scope.navigator as\n | { gpu?: unknown; hardwareConcurrency?: number }\n | undefined;\n const wasm = scope.WebAssembly;\n const hardwareConcurrency = Math.max(\n 1,\n Math.floor(navigatorLike?.hardwareConcurrency ?? 1),\n );\n const detected: OrtWebCapabilityProfile = {\n webgpu: navigatorLike?.gpu !== undefined,\n wasm: wasm !== undefined,\n wasmSimd: detectWasmSimd(wasm),\n sharedArrayBuffer: scope.SharedArrayBuffer !== undefined,\n crossOriginIsolated: scope.crossOriginIsolated === true,\n hardwareConcurrency,\n };\n\n return {\n ...detected,\n ...(options.overrides ?? {}),\n };\n}\n\nexport function selectOrtWebBackend(\n profile: OrtWebCapabilityProfile,\n): OrtWebBackendChoice {\n const wasmThreadsAvailable =\n profile.wasm &&\n profile.wasmSimd &&\n profile.sharedArrayBuffer &&\n profile.crossOriginIsolated &&\n profile.hardwareConcurrency >= MIN_THREADED_WASM_CONCURRENCY;\n const numThreads = wasmThreadsAvailable\n ? Math.min(MAX_WASM_THREADS, profile.hardwareConcurrency)\n : 1;\n\n if (profile.webgpu) {\n return {\n backend: \"webgpu\",\n executionProviders: profile.wasm ? [\"webgpu\", \"wasm\"] : [\"webgpu\"],\n wasm: {\n simd: profile.wasmSimd,\n threads: wasmThreadsAvailable,\n numThreads,\n },\n reason: \"navigator.gpu is available\",\n };\n }\n\n if (!profile.wasm) {\n throw new Error(\"ONNX Runtime Web requires WebAssembly when WebGPU is absent.\");\n }\n\n if (wasmThreadsAvailable) {\n return {\n backend: \"wasm-simd-threads\",\n executionProviders: [\"wasm\"],\n wasm: {\n simd: true,\n threads: true,\n numThreads,\n },\n reason:\n \"WebAssembly SIMD, SharedArrayBuffer, and cross-origin isolation are available\",\n };\n }\n\n return {\n backend: \"wasm-basic\",\n executionProviders: [\"wasm\"],\n wasm: {\n simd: false,\n threads: false,\n numThreads: 1,\n },\n reason:\n \"WebGPU or threaded wasm prerequisites are unavailable; using single-threaded wasm\",\n };\n}\n\nfunction detectWasmSimd(\n wasm: Pick | undefined,\n): boolean {\n if (wasm === undefined) {\n return false;\n }\n try {\n return wasm.validate(WASM_SIMD_PROBE);\n } catch {\n return false;\n }\n}\n","import type {\n TokenClassificationCallOptions,\n TokenClassificationOutput,\n TokenClassificationPipeline,\n} from \"../types\";\nimport {\n detectOrtWebCapabilities,\n selectOrtWebBackend,\n type CapabilityDetectionOptions,\n type OrtWebBackendChoice,\n type OrtWebCapabilityProfile,\n} from \"./capability\";\n\nconst ONNXRUNTIME_WEB_MODULE = \"onnxruntime-web\";\n\nexport type OrtExecutionProvider = \"webgpu\" | \"wasm\";\n\nexport type OrtTensorLike = {\n data?: unknown;\n dims?: readonly number[];\n type?: string;\n [key: string]: unknown;\n};\n\nexport type OrtFeeds = Record;\nexport type OrtResults = Record;\n\nexport interface OrtInferenceSession {\n run(\n feeds: OrtFeeds,\n options?: Record,\n ): Promise | OrtResults;\n}\n\nexport interface OrtSessionCreateOptions {\n executionProviders?: OrtExecutionProvider[];\n graphOptimizationLevel?: \"disabled\" | \"basic\" | \"extended\" | \"all\" | string;\n [key: string]: unknown;\n}\n\nexport interface OrtWebRuntime {\n env?: {\n wasm?: {\n wasmPaths?: string;\n simd?: boolean;\n numThreads?: number;\n proxy?: boolean;\n [key: string]: unknown;\n };\n [key: string]: unknown;\n };\n InferenceSession: {\n create(\n modelPath: string,\n options?: OrtSessionCreateOptions,\n ): Promise | OrtInferenceSession;\n };\n}\n\nexport type OrtWebRuntimeProvider =\n | OrtWebRuntime\n | (() => Promise | OrtWebRuntime);\n\nexport interface OrtWebLoaderOptions {\n modelPath: string;\n assetPath: string;\n runtime?: OrtWebRuntimeProvider;\n capabilities?: Partial;\n globalScope?: CapabilityDetectionOptions[\"globalScope\"];\n sessionOptions?: OrtSessionCreateOptions;\n cache?: OrtWebSessionCache;\n}\n\nexport interface OrtWebLoadedSession {\n session: OrtInferenceSession;\n backend: OrtWebBackendChoice;\n modelPath: string;\n assetPath: string;\n cacheKey: string;\n}\n\nexport type OrtWebSessionCache = Map>;\n\nexport interface OrtWebTokenClassificationPipelineOptions\n extends OrtWebLoaderOptions {\n tokenize: (text: string) => OrtFeeds | Promise;\n decode: (context: OrtTokenClassificationDecodeContext) =>\n | TokenClassificationOutput\n | Promise;\n runOptions?: Record;\n}\n\nexport interface OrtTokenClassificationDecodeContext {\n text: string;\n inputs: OrtFeeds;\n outputs: OrtResults;\n session: OrtInferenceSession;\n backend: OrtWebBackendChoice;\n callOptions?: TokenClassificationCallOptions;\n}\n\nexport const DEFAULT_ORT_WEB_SESSION_CACHE: OrtWebSessionCache = new Map();\n\nexport function assertOfflineAssetPath(path: string, label = \"asset path\"): string {\n const trimmed = path.trim();\n if (trimmed.length === 0) {\n throw new Error(`ONNX Runtime Web ${label} must not be empty.`);\n }\n if (trimmed.startsWith(\"//\")) {\n throw new Error(`ONNX Runtime Web ${label} must be local/offline, not remote.`);\n }\n if (/^[A-Za-z]:[\\\\/]/.test(trimmed)) {\n return trimmed;\n }\n if (/^[A-Za-z][A-Za-z0-9+.-]*:/.test(trimmed)) {\n if (!trimmed.startsWith(\"file://\")) {\n throw new Error(\n `ONNX Runtime Web ${label} must be local/offline, not remote.`,\n );\n }\n }\n return trimmed;\n}\n\nexport function normalizeOrtAssetPath(path: string): string {\n const localPath = assertOfflineAssetPath(path, \"wasm asset path\");\n if (localPath.endsWith(\"/\") || localPath.endsWith(\"\\\\\")) {\n return localPath;\n }\n return `${localPath}/`;\n}\n\nexport async function loadOrtWebSession(\n options: OrtWebLoaderOptions,\n): Promise {\n const modelPath = assertOfflineAssetPath(options.modelPath, \"model path\");\n const assetPath = normalizeOrtAssetPath(options.assetPath);\n const capabilities = resolveCapabilities(options);\n const backend = selectOrtWebBackend(capabilities);\n const cache = options.cache ?? 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