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import { AutoModel, AutoTokenizer } from 'https://cdn.jsdelivr.net/npm/@huggingface/[email protected]';
let tokenizer, model;
let isModelLoaded = false;
async function loadModel() {
try {
tokenizer = await AutoTokenizer.from_pretrained('ai4privacy/llama-ai4privacy-english-anonymiser-openpii');
model = await AutoModel.from_pretrained('ai4privacy/llama-ai4privacy-english-anonymiser-openpii', { dtype: "q8" });
isModelLoaded = true;
} catch (err) {
console.error("Error loading model:", err);
isModelLoaded = false;
throw err;
}
}
async function processText(text, threshold = 0.3) {
if (!isModelLoaded) {
throw new Error('Model not loaded');
}
const inputs = await tokenizer(text);
const inputTokens = inputs.input_ids.data;
const tokenStrings = Array.from(inputTokens).map(id =>
tokenizer.decode([id], { skip_special_tokens: false })
);
const { logits } = await model(inputs);
const logitsData = Array.from(logits.data);
const numTokens = tokenStrings.length;
const numClasses = 3;
const logitsPerToken = [];
for (let i = 0; i < numTokens; i++) {
logitsPerToken.push(logitsData.slice(i * numClasses, (i + 1) * numClasses));
}
function softmax(logits) {
const expLogits = logits.map(Math.exp);
const sumExp = expLogits.reduce((a, b) => a + b, 0);
return expLogits.map(exp => exp / sumExp);
}
const tokenPredictions = tokenStrings.map((token, i) => {
const probs = softmax(logitsPerToken[i]);
const maxSensitive = Math.max(probs[0], probs[1]);
return {
token: token,
start: i,
end: i + 1,
probabilities: {
"B-PRIVATE": probs[0],
"I-PRIVATE": probs[1],
"O": probs[2]
},
maxSensitiveScore: maxSensitive
};
});
const aggregated = aggregatePrivacyTokens(tokenPredictions, threshold);
const { maskedText, replacements } = maskText(tokenPredictions, aggregated);
return { maskedText, replacements };
}
function aggregatePrivacyTokens(tokenPredictions, threshold) {
const aggregated = [];
let i = 0;
const n = tokenPredictions.length;
while (i < n) {
const currentToken = tokenPredictions[i];
if (['[CLS]', '[SEP]'].includes(currentToken.token)) {
i++;
continue;
}
const startsWithSpace = currentToken.token.startsWith(' ');
const isFirstWord = aggregated.length === 0 && i === 0;
if (startsWithSpace || isFirstWord) {
const group = {
tokens: [currentToken],
indices: [i],
scores: [currentToken.maxSensitiveScore],
startsWithSpace: startsWithSpace
};
i++;
while (i < n &&
!tokenPredictions[i].token.startsWith(' ') &&
!['[CLS]', '[SEP]'].includes(tokenPredictions[i].token)) {
group.tokens.push(tokenPredictions[i]);
group.indices.push(i);
group.scores.push(tokenPredictions[i].maxSensitiveScore);
i++;
}
if (Math.max(...group.scores) >= threshold) {
aggregated.push(group);
}
} else {
i++;
}
}
return aggregated;
}
function maskText(tokenPredictions, aggregatedGroups) {
const maskedTokens = [];
const replacements = [];
const maskedIndices = new Set();
let redactedCounter = 1;
aggregatedGroups.forEach(group => {
group.indices.forEach(idx => maskedIndices.add(idx));
});
tokenPredictions.forEach((token, idx) => {
if (['[CLS]', '[SEP]'].includes(token.token)) return;
if (maskedIndices.has(idx)) {
const group = aggregatedGroups.find(g => g.indices[0] === idx);
if (group) {
const originalTokens = group.tokens.map(t => t.token);
const originalText = originalTokens
.map((token, i) => (i === 0 && group.startsWithSpace ? token.trimStart() : token))
.join('');
const placeholder = `[PII_${redactedCounter}]`;
replacements.push({
original: originalText,
placeholder: placeholder,
activation: Math.max(...group.scores)
});
redactedCounter++;
const maskWithSpace = group.startsWithSpace ? ` ${placeholder}` : placeholder;
maskedTokens.push(maskWithSpace);
}
} else {
maskedTokens.push(token.token);
}
});
// First join the tokens, then split into lines.
const joinedText = maskedTokens.join('');
// For each line, collapse only spaces and tabs.
const processedLines = joinedText.split('\n').map(line => line.replace(/[ \t]+/g, ' ').trim());
const maskedText = processedLines.join('\n').trim();
return { maskedText, replacements };
}
export { loadModel, processText, isModelLoaded }; |