:robot: Natural language processing with JavaScript

Related tags

Machine Learning nodejs nlp neural-network artificial-intelligence classification nlp-machine-learning classification-algorithm
Overview

classifier.js

Open Source Love version PRs Welcome CD

🤖 An library for natural language processing with JavaScript

Table of Contents

Instalation

npm i classifier.js
# or
yarn add classifier.js

Example of use

import { Classifier } from 'classifier.js'

const classifier = new Classifier({ percentualReturn: true })

classifier.learn('I like cats', ['animal'])
classifier.learn('Cats are cool', ['animal'])
classifier.learn('Dogs are noisy', ['animal'])
classifier.learn('I love animals', ['animal'])
classifier.learn('I like my horse', ['animal'])
classifier.learn('Chocolate is good', ['food'])
classifier.learn('I eat apple', ['food'])
classifier.learn('Juice is very good', ['food'])
classifier.learn('Brazilians eat rice and beans', ['food'])
classifier.learn('Bananas are good for health', ['food'])

classifier.classify('Apple juice is awesome')
// OUTPUT: { unknown: '20%', animal: '0%', food: '80%' }

Auto detection of numeric strings shape

import { Classifier } from 'classifier.js'

const classifier = new Classifier({ percentualReturn: true })

classifier.learn('000.000.000-11', ['cpf'])
classifier.learn('00.000.000/0001-00', ['cnpj'])
classifier.learn('00155-333', ['zipcode'])

classifier.classify('999.999.999-99')
// OUTPUT: { unknown: '0%', cpf: '100%', cnpj: '0%', zipcode: '0%' }
classifier.classify('99.999.999/9999-99')
// OUTPUT: { unknown: '0%', cpf: '0%', cnpj: '100%', zipcode: '0%' }
classifier.classify('99999-999')
// OUTPUT: { unknown: '0%', cpf: '0%', cnpj: '0%', zipcode: '100%' }

API

learn

Receive data with an array of related categories.

classifier.learn('Your sentence', ['your-category'])

classify

Classify a sentence based on received data.

classifier.classify('Sentence to classify')

resetKnowledge

Removes all that was learned.

classifier.resetKnowledge()

toJSON

Saves classifier data to a JSON file that can be imported later.

classifier.toJSON('myFolder/savedClassifier.json')
# Or simply
classifier.toJSON('savedClassifier.json')

fromJSON

Imports data from a JSON file.

classifier.fromJSON('myFolder/savedClassifier.json')
# Or simply
classifier.fromJSON('savedClassifier.json')

toYAML

Saves classifier data to an YAML file that can be imported later.

classifier.toJSON('myFolder/savedClassifier.json')
# Or simply
classifier.toJSON('savedClassifier.json')

fromYAML

Imports data from an YAML file.

classifier.fromJSON('myFolder/savedClassifier.yaml')
# Or simply
classifier.fromJSON('savedClassifier.yaml')
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Comments
  • Add storage options

    Add storage options

    🗒️ Context

    When you train the classifier, by default, all knowledge will be storage in memory, we are creating new saving option to not loose learned data after program execution.

    Options

    • [x] JSON
    • [x] YAML
    • [ ] Redis (Check need)
    enhancement 
    opened by NathanFirmo 1
  • RFC: text analisis

    RFC: text analisis

    📓 Context

    Google has a Natural Language API that can analize tokens and entiites inside a text. I think we could have something like this in classifier.js, even it was more simple... I would like to know your opinion about this :smile:

    Example of classification by Google API

    image

    RFC 
    opened by NathanFirmo 0
  • Add new classification algorithms

    Add new classification algorithms

    :spiral_notepad: Context

    Now the classifier has only one classification method. In the future, it would have more algorithms for diferents scenarios. It also would be able to automatically choose best algoritm for a type of dataset, considerating accuracy and computational complexity.

    enhancement 
    opened by NathanFirmo 0
  • Method calculate accuracy

    Method calculate accuracy

    🗒️ Context

    Add a method to calculate algorithm accuracy based on a datase.

    Example

    [
  {
    "sentence": "im feeling quite sad and sorry for myself but ill snap out of it soon",
    "category": "sadness"
  },
  {
    "sentence": "i feel like i am still looking at a blank canvas blank pieces of paper",
    "category": "sadness"
  },
  {
    "sentence": "i feel like a faithful servant",
    "category": "love"
  },
  {
    "sentence": "i am just feeling cranky and blue",
    "category": "anger"
  },
  {
    "sentence": "i can have for a treat or if i am feeling festive",
    "category": "joy"
  },
  {
    "sentence": "i start to feel more appreciative of what god has done for me",
    "category": "joy"
  },
]

    Behaviour

    The classifier will automatically split dataset into train and validation data and will calculate its own accuracy.

    enhancement 
    opened by NathanFirmo 0
Owner
Nathan Firmo
Just a software engineer student that loves Linux and NeoVim.
Nathan Firmo
GitHub
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