Language models

WinkNLP comes with pre-trained language models with gzipped sizes starting from ~890MB (expanded sizes less than 3MB) onwards:

wink-eng-lite-model

Size ~890KB gzipped.
Usage Node.js server side.
Pipeline Sentence Boundary Detection (sbd), Negation Handling (negation), Sentiment Analysis (sentiment), Named Entity Recognition (ner), Part-of-speech tagging (pos), Custom Entity Recognition (cer).
Model specific helpers

its.pospart-of-speech tag.

its.lemma — pos specific lemma.

its.stem — based on Porter Stemmer version 2.

its.negationFlag — true if a token is negated.

its.stopWordFlag — true if a token is a stop word.

its.type, its.detail, its.span — for .entities.out() and .customEntities.out() methods.

its.readabilityStats — includes Flesch Reading Ease Score (fres), list complex words and their count, reading time in mins & seconds, sentiment score and more.

Word Vectors NA
License MIT

wink-eng-lite-web-model

Size ~1MB gzipped.
Usage Broswer side; can also be used on server side with Node.js.
Pipeline Sentence Boundary Detection (sbd), Negation Handling (negation), Sentiment Analysis (sentiment), Named Entity Recognition (ner), Part-of-speech tagging (pos), Custom Entity Recognition (cer).
Model specific helpers

its.pospart-of-speech tag.

its.lemma — pos specific lemma.

its.stem — based on Porter Stemmer version 2.

its.negationFlag — true if a token is negated.

its.stopWordFlag — true if a token is a stop word.

its.type, its.detail, its.span — for .entities.out() and .customEntities.out() methods.

its.readabilityStats — includes Flesch Reading Ease Score (fres), list complex words and their count, reading time in mins & seconds, sentiment score and more.

Word Vectors NA
License MIT

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