Dive into the world of machine learning with the ClassifyText library, a minimalistic tool that allows you to classify text efficiently using n-grams and cosine similarity. Whether you are working in a browser or Node.js, this library helps you train a model with a plethora of text samples and swiftly predict appropriate labels for new samples. Let’s break down the installation and usage!

Installation

To get started, you need to install the library. This can be done easily via npm or yarn:

• Using npm: npm install ml-classify-text
• Using yarn: yarn add ml-classify-text

Getting Started

Now, let’s see how to make use of the ClassifyText library.

Importing the Library

Depending on your module system, you can import the library in the following ways:

• As an ES6 module: import Classifier from 'ml-classify-text'
• As a CommonJS module: const Classifier = require('ml-classify-text')

Setting Up a New Classifier Instance

To start using the Classifier, you need to create a new instance:

const classifier = new Classifier();

Training a Model

Having a collection of labeled text samples is essential for training your classifier. Here’s an analogy to make this clearer: think of your classifier as a student who learns from examples in a textbook. The positive examples are like good practices, and the negative examples show the student what to avoid. Let’s train our model:

const positive = ["This is great!", "Wow, I love it!", "It really is amazing"];
const negative = ["This is really bad!", "I hate it with a passion!", "Just terrible!"];

classifier.train(positive, positive);
classifier.train(negative, negative);

Getting a Prediction

Once trained, the classifier can now predict the labels for new text. It’s like our student taking a test after studying:

const predictions = classifier.predict("It sure is pretty great!");
if (predictions.length) {
    predictions.forEach((prediction) => {
        console.log(`${prediction.label} (${prediction.confidence})`);
    });
} else {
    console.log("No predictions returned");
}

Advanced Usage

You can also configure various options for your classifier, such as setting n-gram sizes. N-grams are sequences of words that allow you to analyze the text more effectively:

const classifier = new Classifier({ nGramMin: 2, nGramMax: 2 });
const tokens = classifier.tokenize("I really don't like it");
console.log(tokens);

Serializing a Model

After training, you might want to save your model for later use. Think of it as archiving a completed textbook for future reference:

const model = classifier.model;
console.log(model.serialize());

Documentation and Resources

For further reading and deeper insights, check out the comprehensive documentation:

• Classifier Documentation
• Model Documentation
• Vocabulary Documentation
• Prediction Documentation

Troubleshooting

If you encounter any issues while using the ClassifyText library, here are some tips to help you troubleshoot:

• Ensure that you have installed the library correctly via npm or yarn.
• Check your imported module for errors in syntax or file paths.
• Verify that your training data is well-structured and sample sizes are adequate for training.
• If predictions are consistently failing, revisit your training examples – more varied samples can improve results.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.