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<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1.0, maximum-scale=1.0, user-scalable=no">
</head>
<body>
<script type="module">
const printVector = function(predictions, limit) {
limit = limit || Infinity;
for (let i=0; i<predictions.size() && i<limit; i++){
let prediction = predictions.get(i);
console.log(predictions.get(i));
}
}
const trainCallback = (progress, loss, wst, lr, eta) => {
console.log([progress, loss, wst, lr, eta]);
};
import {FastText, addOnPostRun} from "./fasttext.js";
addOnPostRun(() => {
let ft = new FastText();
ft.trainSupervised("cooking.train", {
'lr':1.0,
'epoch':10,
'loss':'hs',
'wordNgrams':2,
'dim':50,
'bucket':200000
}, trainCallback).then(model => {
console.log('Trained.');
printVector(model.predict("Which baking dish is best to bake a banana bread ?", 5, 0.0));
/* getInputMatrix */
let inputMatrix = model.getInputMatrix();
console.log(inputMatrix.cols());
console.log(inputMatrix.rows());
console.log(inputMatrix.at(1, 2));
/* getOutputMatrix */
let outputMatrix = model.getOutputMatrix();
console.log(outputMatrix.cols());
console.log(outputMatrix.rows());
console.log(outputMatrix.at(1, 2));
/* getWords */
let wordsInformation = model.getWords();
printVector(wordsInformation[0], 30); // words
printVector(wordsInformation[1], 30); // frequencies
/* getLabels */
let labelsInformation = model.getLabels();
printVector(labelsInformation[0], 30); // labels
printVector(labelsInformation[1], 30); // frequencies
});
});
</script>
</body>
</html>
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