TextCatalog.TokenizeIntoCharactersAsKeys Méthode

Définition

Espace de noms:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Paquet:

Microsoft.ML v3.0.1

Créez un TokenizingByCharactersEstimator, qui tokenise en fractionnant le texte en séquences de caractères à l’aide d’une fenêtre glissante.

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

Paramètres

catalog

TransformsCatalog.TextTransforms

Catalogue de transformation liée au texte.

outputColumnName

String

Nom de la colonne résultant de la transformation de inputColumnName. Le type de données de cette colonne sera un vecteur de clés de taille variable.

inputColumnName

String

Nom de la colonne à transformer. Si la valeur est définie null, la valeur du outputColumnName fichier sera utilisée comme source. Cet estimateur fonctionne sur le type de données texte.

useMarkerCharacters

Boolean

Pour pouvoir distinguer les jetons, par exemple à des fins de débogage, vous pouvez choisir d’ajouter un caractère de marqueur, 0x02au début et d’ajouter un autre caractère de marqueur, 0x03à la fin du vecteur de sortie des caractères.

Retours

TokenizingByCharactersEstimator

Exemples

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; }
        }
    }
}