TextCatalog.TokenizeIntoWords 메서드

정의

네임스페이스:

Microsoft.ML

어셈블리:

Microsoft.ML.Transforms.dll

패키지:

Microsoft.ML v2.0.0

Create a WordTokenizingEstimator, which tokenizes input text using separators as separators.

public static Microsoft.ML.Transforms.Text.WordTokenizingEstimator TokenizeIntoWords (this Microsoft.ML.TransformsCatalog.TextTransforms catalog, string outputColumnName, string inputColumnName = default, char[] separators = default);

static member TokenizeIntoWords : Microsoft.ML.TransformsCatalog.TextTransforms * string * string * char[] -> Microsoft.ML.Transforms.Text.WordTokenizingEstimator

<Extension()>
Public Function TokenizeIntoWords (catalog As TransformsCatalog.TextTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional separators As Char() = Nothing) As WordTokenizingEstimator

매개 변수

catalog

TransformsCatalog.TextTransforms

텍스트 관련 변환의 카탈로그입니다.

outputColumnName

String

의 변환에서 생성된 열의 inputColumnName이름입니다. 이 열의 데이터 형식은 텍스트의 가변 크기 벡터입니다.

inputColumnName

String

변환할 열의 이름입니다. 이 값으로 null설정하면 값이 outputColumnName 원본으로 사용됩니다. 이 추정기는 텍스트 데이터 형식의 텍스트 및 벡터 스칼라에서 작동합니다.

separators

Char[]

사용할 구분 기호입니다(기본적으로 공백 문자 사용).

반환

WordTokenizingEstimator

예제

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

namespace Samples.Dynamic
{
    public static class TokenizeIntoWords
    {
        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 'TokenizeIntoWords' does
            // not require training data as the estimator
            // ('WordTokenizingEstimator') created by 'TokenizeIntoWords' 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 words.
            // The following call to 'TokenizeIntoWords' tokenizes text/string into
            // words using space as a separator. Space is also a default value for
            // the 'separators' argument if it is not specified.
            var textPipeline = mlContext.Transforms.Text.TokenizeIntoWords("Words",
                "Text", separators: new[] { ' ' });

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

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

            // Call the prediction API to convert the text into words.
            var data = new TextData()
            {
                Text = "ML.NET's TokenizeIntoWords API " +
                "splits text/string into words using the list of characters " +
                "provided as separators."
            };

            var prediction = predictionEngine.Predict(data);

            // Print the length of the word vector.
            Console.WriteLine($"Number of words: {prediction.Words.Length}");

            // Print the word vector.
            Console.WriteLine($"\nWords: {string.Join(",", prediction.Words)}");

            //  Expected output:
            //   Number of words: 15
            //   Words: ML.NET's,TokenizeIntoWords,API,splits,text/string,into,words,using,the,list,of,characters,provided,as,separators.
        }

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

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