TextCatalog.ApplyWordEmbedding 方法

參考

定義

命名空間:: Microsoft.ML

組件:: Microsoft.ML.Transforms.dll

套件:: Microsoft.ML v3.0.1

套件:: Microsoft.ML v1.0.0

套件:: Microsoft.ML v1.1.0

套件:: Microsoft.ML v1.2.0

套件:: Microsoft.ML v1.3.1

套件:: Microsoft.ML v1.4.0

套件:: Microsoft.ML v1.5.5

套件:: Microsoft.ML v1.6.0

套件:: Microsoft.ML v1.7.0

套件:: Microsoft.ML v2.0.0

重要

部分資訊涉及發行前產品，在發行之前可能會有大幅修改。 Microsoft 對此處提供的資訊，不做任何明確或隱含的瑕疵擔保。

多載

ApplyWordEmbedding(TransformsCatalog+TextTransforms, String, String, WordEmbeddingEstimator+PretrainedModelKind)	建立 WordEmbeddingEstimator ，這是文字特徵化工具，使用預先定型的內嵌模型，將文字向量轉換成數值向量。
ApplyWordEmbedding(TransformsCatalog+TextTransforms, String, String, String)	建立 WordEmbeddingEstimator ，這是文字特徵化工具，它會使用預先定型的內嵌模型，將文字向量轉換成數值向量。

ApplyWordEmbedding(TransformsCatalog+TextTransforms, String, String, WordEmbeddingEstimator+PretrainedModelKind)

建立 WordEmbeddingEstimator ，這是文字特徵化工具，使用預先定型的內嵌模型，將文字向量轉換成數值向量。

public static Microsoft.ML.Transforms.Text.WordEmbeddingEstimator ApplyWordEmbedding (this Microsoft.ML.TransformsCatalog.TextTransforms catalog, string outputColumnName, string inputColumnName = default, Microsoft.ML.Transforms.Text.WordEmbeddingEstimator.PretrainedModelKind modelKind = Microsoft.ML.Transforms.Text.WordEmbeddingEstimator+PretrainedModelKind.SentimentSpecificWordEmbedding);

static member ApplyWordEmbedding : Microsoft.ML.TransformsCatalog.TextTransforms * string * string * Microsoft.ML.Transforms.Text.WordEmbeddingEstimator.PretrainedModelKind -> Microsoft.ML.Transforms.Text.WordEmbeddingEstimator

<Extension()>
Public Function ApplyWordEmbedding (catalog As TransformsCatalog.TextTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional modelKind As WordEmbeddingEstimator.PretrainedModelKind = Microsoft.ML.Transforms.Text.WordEmbeddingEstimator+PretrainedModelKind.SentimentSpecificWordEmbedding) As WordEmbeddingEstimator

參數

catalog: TransformsCatalog.TextTransforms

與文字相關的轉換目錄。

outputColumnName: String

轉換所產生的 inputColumnName 資料行名稱。此資料行的資料類型將是的 Single 向量。

inputColumnName: String

要轉換的資料行名稱。如果設定為 null ，則會將的值 outputColumnName 當做來源使用。此估算器會透過文字資料類型的已知大小向量運作。

modelKind: WordEmbeddingEstimator.PretrainedModelKind

要使用的內 WordEmbeddingEstimator.PretrainedModelKind 嵌。

傳回

WordEmbeddingEstimator

範例

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

namespace Samples.Dynamic
{
    public static class ApplyWordEmbedding
    {
        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 'ApplyWordEmbedding' does
            // not require training data as the estimator ('WordEmbeddingEstimator')
            // created by 'ApplyWordEmbedding' 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 a 150-dimension embedding vector
            // using pretrained 'SentimentSpecificWordEmbedding' model. The
            // 'ApplyWordEmbedding' computes the minimum, average and maximum values
            // for each token's embedding vector. Tokens in 
            // 'SentimentSpecificWordEmbedding' model are represented as
            // 50 -dimension vector. Therefore, the output is of 150-dimension [min,
            // avg, max].
            //
            // The 'ApplyWordEmbedding' API requires vector of text as input.
            // The pipeline first normalizes and tokenizes text then applies word
            // embedding transformation.
            var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
                .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
                    "Text"))
                .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
                    "Tokens", WordEmbeddingEstimator.PretrainedModelKind
                    .SentimentSpecificWordEmbedding));

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

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

            // Call the prediction API to convert the text into embedding vector.
            var data = new TextData()
            {
                Text = "This is a great product. I would " +
                "like to buy it again."
            };
            var prediction = predictionEngine.Predict(data);

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

            // Print the embedding vector.
            Console.Write("Features: ");
            foreach (var f in prediction.Features)
                Console.Write($"{f:F4} ");

            //  Expected output:
            //   Number of Features: 150
            //   Features: -1.2489 0.2384 -1.3034 -0.9135 -3.4978 -0.1784 -1.3823 -0.3863 -2.5262 -0.8950 ...
        }

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

        private class TransformedTextData : TextData
        {
            public float[] Features { get; set; }
        }
    }
}

適用於

ApplyWordEmbedding(TransformsCatalog+TextTransforms, String, String, String)

建立 WordEmbeddingEstimator ，這是文字特徵化工具，它會使用預先定型的內嵌模型，將文字向量轉換成數值向量。

public static Microsoft.ML.Transforms.Text.WordEmbeddingEstimator ApplyWordEmbedding (this Microsoft.ML.TransformsCatalog.TextTransforms catalog, string outputColumnName, string customModelFile, string inputColumnName = default);

static member ApplyWordEmbedding : Microsoft.ML.TransformsCatalog.TextTransforms * string * string * string -> Microsoft.ML.Transforms.Text.WordEmbeddingEstimator

<Extension()>
Public Function ApplyWordEmbedding (catalog As TransformsCatalog.TextTransforms, outputColumnName As String, customModelFile As String, Optional inputColumnName As String = Nothing) As WordEmbeddingEstimator

參數

catalog: TransformsCatalog.TextTransforms