wink-embeddings-sg-100d

100-dimensional English word embeddings for wink-nlp

This pre-trained 100-dimensional English word embedding set is specifically optimized for winkNLP. This package (~110MB download, ~310MB installed) includes embeddings for over 350K English words. Boost accuracy in semantic similarity, text classification, and more – even in the browser.

Getting Started

$3

It requires Node.js version 16.0.0 or above and winkNLP version 2.1.0 or above.

$3

The model must be installed along with the wink-nlp and the wink-eng-lite-web-model:

``sh

`Install wink-nlp`


npm install wink-nlp --save
Install wink-eng-lite-web-model

npm install wink-eng-lite-web-model --save
Install wink-embeddings-sg-100d

npm install wink-embeddings-sg-100d --save


$3

We start by requiring the wink-nlp package, the wink-eng-lite-web-model and the wink-embeddings-sg-100d. Then we instantiate wink-nlp using the language model and the embeddings:

`javascript // Load wink-nlp package. const winkNLP = require( 'wink-nlp' ); // Load english language model. const model = require( 'wink-eng-lite-web-model' ); // Load word embeddings. const vectors = require( 'wink-embeddings-sg-100d' ); // Load similarity utility. const similarity = require( 'wink-nlp/utilities/similarity.js' );

// Use only tokenization and sentence boundary detection pipe. const nlp = winkNLP( model, [ 'sbd' ], vectors ); // Obtain "its" helper to extract item properties. const its = nlp.its; // Obtain "as" reducer helper to reduce a collection. const as = nlp.as; // The following text contains 4-sentences, where the first // two and the last two have high similarity. const text =The cat rested on the carpet. The kitten slept on the rug.
The table was in the drawing room. The desk was in the study room.; // This will hold the array of vectors for each sentence. const v = []; // Run the nlp pipe. const doc = nlp.readDoc( text ); // Compute each sentence's embedding and fill in "v[i]". // Only use words and ignore stop words. doc .sentences() .each( ( s, k ) => { v[ k ] = s .tokens() .filter( (t) => (t.out(its.type) === 'word' && !t.out(its.stopWordFlag))) .out(its.value, as.vector); }) // Compute & print similarity for each unique pair. for ( let i = 0; i < v.length; i += 1 ) { for ( let j = i; j < v.length; j += 1 ) { if ( i !== j ) console.log( doc.sentences().itemAt( i ).out(), ' & ', doc.sentences().itemAt( j ).out(), +similarity.vector.cosine( v[ i ], v[ j ] ).toFixed( 2 ) ); } }``
The output of the above example is visually illustrated below:

Need Help?

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About winkJS

WinkJS is a family of open source packages for Natural Language Processing, Machine Learning, and Statistical Analysis in NodeJS. The code is thoroughly documented for easy human comprehension and has a test coverage of ~100% for reliability to build production grade solutions.

Copyright & License

It is licensed under the terms of the MIT License.

wink-embeddings-sg-100d

100-dimensional English word embeddings for wink-nlp

Install wink-nlp

npm install wink-nlp --save

Install wink-eng-lite-web-model

npm install wink-eng-lite-web-model --save

Install wink-embeddings-sg-100d

npm install wink-embeddings-sg-100d --save

$3

We start by requiring the wink-nlp package, the wink-eng-lite-web-model and the wink-embeddings-sg-100d. Then we instantiate wink-nlp using the language model and the embeddings:

javascript
// Load wink-nlp package.
const winkNLP = require( 'wink-nlp' );
// Load english language model.
const model = require( 'wink-eng-lite-web-model' );
// Load word embeddings.
const vectors = require( 'wink-embeddings-sg-100d' );
// Load similarity utility.
const similarity = require( 'wink-nlp/utilities/similarity.js' );

// Use only tokenization and sentence boundary detection pipe.
const nlp = winkNLP( model, [ 'sbd' ], vectors );
// Obtain "its" helper to extract item properties.
const its = nlp.its;
// Obtain "as" reducer helper to reduce a collection.
const as = nlp.as;
// The following text contains 4-sentences, where the first
// two and the last two have high similarity.
const text =

The cat rested on the carpet. The kitten slept on the rug.
The table was in the drawing room. The desk was in the study room.

;
// This will hold the array of vectors for each sentence.
const v = [];
// Run the nlp pipe.
const doc = nlp.readDoc( text );
// Compute each sentence's embedding and fill in "v[i]".
// Only use words and ignore stop words.
doc
    .sentences()
    .each( ( s, k ) => {
      v[ k ] = s
        .tokens()
        .filter( (t) => (t.out(its.type) === 'word' && !t.out(its.stopWordFlag)))
        .out(its.value, as.vector);
    })
// Compute & print similarity for each unique pair.
for ( let i = 0; i < v.length; i += 1 ) {
    for ( let j = i; j < v.length; j += 1 ) {
        if ( i !== j )
          console.log(
            doc.sentences().itemAt( i ).out(), ' & ',
            doc.sentences().itemAt( j ).out(),
            +similarity.vector.cosine( v[ i ], v[ j ] ).toFixed( 2 )
          );
    }
}

``
The output of the above example is visually illustrated below: