A script that trains a natural language processing, machine learning model in Swift

by iOS Example

??‍? Training a NLP Model in Swift

A script that trains a natural language processing, machine learning model in Swift.

? Technologies:

  • CreateML
  • An open source dataset I found online, that has a set of first-person sentences, labelled with the emotion

? Overview

Parsing Options

I believe these are only for if your data is formatted as a CSV file. The MLDataTable also works with JSON by default.

var parsingOptions = MLDataTable.ParsingOptions()

parsingOptions.containsHeader = true // the first line of my csv was: text,label
parsingOptions.delimiter = "," // my columns were separated by commas, but could be semi-colons, etc
parsingOptions.lineTerminator = "\n" // my rows were separated by new lines

var data = try MLDataTable(contentsOf: csv, options: parsingOptions) // using the parsing options

? Fixing incorrect data types

The MLTextClassifier is a multiclass classifier, and uses String for the label, rather than Int. My csv used had numbers for the labels, and these were automatically interpreted as Int. There may be a way to specify the type explicitly, but I created a new column and replaced my old one with it:

// using names to identify the columns
let labelColumnName = "label"
let stringLabelColumnName = "stringLabel"
let textColumnName = "text"

// setting the map of values I want each number to represent
let labelsDictionary: [Int : String] = [
    0: "sadness",
    1: "joy",
    2: "love",
    3: "anger",
    4: "fear"
]

// go through each entry in the label column, and create a new column that uses the string values
let stringLabelColumn = data[labelColumnName].map { label -> String in
    return labelsDictionary[label] ?? "unknown"
}

// remove the old column, and add the new one to our MLDataTable
data.removeColumn(named: labelColumnName)
data.addColumn(stringLabelColumn, named: stringLabelColumnName)

? Evaluating our model

In my code I print the training accuracy, validation accuracy and evaluation accuracy. This was so I could see how my model performs, and with my seed I got the values:

Training Accuracy:   99.67622571692877
Validation Accuracy: 86.00252206809584
Evaluation Accuracy: 88.04321139209428

so about 88% accuracy when I evaluated with an unseen test dataset (made with MLDataTable.randomSplit().

✏️ Using the model

I then saved my model to disk using MLTextClassifier.write(). This model can now be used in other Swift projects.

GitHub

View Github