JasonWang
/
fastText
réplica de https://github.com/facebookresearch/fastText


			
				
					
						
						
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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));
            }
        }

        import {FastText, addOnPostRun} from "./fasttext.js";

        addOnPostRun(() => {
            let ft = new FastText();

            const url = "lid.176.ftz";
            ft.loadModel(url).then(model => {
                /* isQuant */
                console.log(model.isQuant());

                /* getDimension */
                console.log(model.getDimension());

                /* getWordVector */
                let v = model.getWordVector("Hello");
                console.log(v);

                /* getSentenceVector */
                let v1 = model.getSentenceVector("Hello");
                console.log(v1);
                let v2 = model.getSentenceVector("Hello this is a sentence");
                console.log(v2);

                /* getNearestNeighbors */
                printVector(model.getNearestNeighbors("Hello", 10));

                /* getAnalogies */
                printVector(model.getAnalogies("paris", "france", "london", 10));

                /* getWordId */
                console.log(model.getWordId("Hello"));

                /* getSubwords */
                let subWordInformation = model.getSubwords("désinstitutionnalisation");
                printVector(subWordInformation[0]);

                /* getInputVector */
                console.log(model.getInputVector(832));
            });
        });

    </script>
</body>

</html>