TextCatalog.TokenizeIntoCharactersAsKeys Method

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Package:

Microsoft.ML v3.0.1

Create a TokenizingByCharactersEstimator, which tokenizes by splitting text into sequences of characters using a sliding window.

public static Microsoft.ML.Transforms.Text.TokenizingByCharactersEstimator TokenizeIntoCharactersAsKeys (this Microsoft.ML.TransformsCatalog.TextTransforms catalog, string outputColumnName, string inputColumnName = default, bool useMarkerCharacters = true);

static member TokenizeIntoCharactersAsKeys : Microsoft.ML.TransformsCatalog.TextTransforms * string * string * bool -> Microsoft.ML.Transforms.Text.TokenizingByCharactersEstimator

<Extension()>
Public Function TokenizeIntoCharactersAsKeys (catalog As TransformsCatalog.TextTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional useMarkerCharacters As Boolean = true) As TokenizingByCharactersEstimator

Parameters

catalog

TransformsCatalog.TextTransforms

The text-related transform's catalog.

outputColumnName

String

Name of the column resulting from the transformation of inputColumnName. This column's data type will be a variable-sized vector of keys.

inputColumnName

String

Name of the column to transform. If set to null, the value of the outputColumnName will be used as source. This estimator operates over text data type.

useMarkerCharacters

Boolean

To be able to distinguish the tokens, for example for debugging purposes, you can choose to prepend a marker character, 0x02, to the beginning, and append another marker character, 0x03, to the end of the output vector of characters.

Returns

TokenizingByCharactersEstimator

Examples

using System;
using System.Collections.Generic;
using Microsoft.ML;

namespace Samples.Dynamic
{
    public static class TokenizeIntoCharactersAsKeys
    {
        public static void Example()
        {
            // Create a new ML context, for ML.NET operations. It can be used for
            // exception tracking and logging, as well as the source of randomness.
            var mlContext = new MLContext();

            // Create an empty list as the dataset. The
            // 'TokenizeIntoCharactersAsKeys' does not require training data as
            // the estimator ('TokenizingByCharactersEstimator') created by
            // 'TokenizeIntoCharactersAsKeys' API is not a trainable estimator.
            // The empty list is only needed to pass input schema to the pipeline.
            var emptySamples = new List<TextData>();

            // Convert sample list to an empty IDataView.
            var emptyDataView = mlContext.Data.LoadFromEnumerable(emptySamples);

            // A pipeline for converting text into vector of characters.
            // The 'TokenizeIntoCharactersAsKeys' produces result as key type.
            // 'MapKeyToValue' is need to map keys back to their original values.
            var textPipeline = mlContext.Transforms.Text
                .TokenizeIntoCharactersAsKeys("CharTokens", "Text",
                    useMarkerCharacters: false)
                .Append(mlContext.Transforms.Conversion.MapKeyToValue(
                    "CharTokens"));

            // Fit to data.
            var textTransformer = textPipeline.Fit(emptyDataView);

            // Create the prediction engine to get the character vector from the
            // input text/string.
            var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
                TransformedTextData>(textTransformer);

            // Call the prediction API to convert the text into characters.
            var data = new TextData()
            {
                Text = "ML.NET's " +
                "TokenizeIntoCharactersAsKeys API splits text/string into " +
                "characters."
            };

            var prediction = predictionEngine.Predict(data);

            // Print the length of the character vector.
            Console.WriteLine($"Number of tokens: {prediction.CharTokens.Length}");

            // Print the character vector.
            Console.WriteLine("\nCharacter Tokens: " + string.Join(",", prediction
                .CharTokens));

            //  Expected output:
            //   Number of tokens: 77
            //   Character Tokens: M,L,.,N,E,T,',s,<?>,T,o,k,e,n,i,z,e,I,n,t,o,C,h,a,r,a,c,t,e,r,s,A,s,K,e,y,s,<?>,A,P,I,<?>,
            //                     s,p,l,i,t,s,<?>,t,e,x,t,/,s,t,r,i,n,g,<?>,i,n,t,o,<?>,c,h,a,r,a,c,t,e,r,s,.
            //
            // <?>: is a unicode control character used instead of spaces ('\u2400').
        }

        private class TextData
        {
            public string Text { get; set; }
        }

        private class TransformedTextData : TextData
        {
            public string[] CharTokens { get; set; }
        }
    }
}