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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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