TextCatalog.TokenizeIntoCharactersAsKeys Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Paket:

Microsoft.ML v5.0.0-preview.1.25125.4

Quelle:

TextCatalog.cs

Erstellen Sie einen TokenizingByCharactersEstimator, der tokenisiert wird, indem Sie Text in Zeichenabfolgen unter Verwendung eines Gleitfensters aufteilen.

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

Parameter

catalog

TransformsCatalog.TextTransforms

Der Katalog der textbezogenen Transformation.

outputColumnName

String

Name der Spalte, die sich aus der Transformation von inputColumnName. Der Datentyp dieser Spalte ist ein Schlüsselvektor mit variabler Größe.

inputColumnName

String

Name der zu transformierenden Spalte. Bei Festlegung auf null, wird der Wert des Werts outputColumnName als Quelle verwendet. Dieser Schätzer wird über den Textdatentyp betrieben.

useMarkerCharacters

Boolean

Um die Token zu unterscheiden, z. B. für Debuggingzwecke, können Sie festlegen, dass ein Markierungszeichen, 0x02an den Anfang und an das Ende des Ausgabevektors von Zeichen angefügt 0x03wird.

Gibt zurück

TokenizingByCharactersEstimator

Beispiele

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; }
        }
    }
}