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See [nodejs demo](https://github.com/asterics/predictionary/blob/master/demo/node-demo/app.mjs) which can be run with:\n\n`node --experimental-modules demo/node-demo/app.mjs`\n\n## Basic usage\nMinimum working example for basic usage:\n\n```javascript\nimport Predictionary from 'predictionary' //only if installed via npm\n\nlet predictionary = Predictionary.instance();\npredictionary.addWords(['apple', 'apricot', 'banana']);\nlet suggestions = predictionary.predict('ap'); // == ['apple', 'apricot'];\n```\n\n## Demo\nSee working demo: [to live demo](https://asterics.github.io/predictionary/demo/)\n\n## API Documentation\nsee full API documentation: [to API documentation](https://asterics.github.io/predictionary/docs/Predictionary.html)\n\n## Acknowledgements\nThanks to Mark Davies, Professor of Linguistics at Brigham Young University (Utah), for the permission to use his n-gram sample data from https://www.ngrams.info/iweb.asp for training purposes of this library.\n\nWord frequency lists for demos are taken from the Centre for Translation Studies, University of Leeds, see http://corpus.leeds.ac.uk/list.html - thanks!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fasterics%2Fpredictionary","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fasterics%2Fpredictionary","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fasterics%2Fpredictionary/lists"}