nl_tokens: Use the model trained so far to classify a new sample .
pl_tokens: function ( sentence , explain ) { // sentence = "['start'] " + sentence + " ['end']" var minWordsToSplit = 2 ; var words = sentence . split ( / / ) ; // var words = tokenizer.tokenize(sentence); if ( this . segmentSplitStrategy && words . length >= minWordsToSplit ) { var accumulatedClasses = { } ; var explanations = [ ] ; this . segmentSplitStrategy ( words , accumulatedClasses , explain , explanations ) ; var <mask> = Object . keys ( accumulatedClasses ) ; return ( explain > 0 ? { classes : classes , explanation : explanations } : classes ) ; } else { classification = this . bestClassOfSegment ( sentence , explain ) // classification = this.classifySegment(sentence, explain); //HERER // console.log(sentence) // console.log(classification) // process.exit(0) // process.exit(0) return ( explain > 0 ? { classes : classification [ 0 ] , // FEATURES explanation : [ [ classification [ 0 ] , sentence , [ 0 , sentence . length - 1 ] , classification [ 2 ] ] ] // explanation: [[classification[0], sentence, [0,sentence.length-1]]] } : classification [ 0 ] ) ; // return {classes: classification[0], // explanation: [[classification[0], sentence, [0,sentence.length-1]]]} // return {classes: classification.classes[0]} } }