TextCatalog.ApplyWordEmbedding Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Transforms.dll

Paket:

Microsoft.ML v3.0.1

Overload

ApplyWordEmbedding(TransformsCatalog+TextTransforms, String, String, WordEmbeddingEstimator+PretrainedModelKind)	WordEmbeddingEstimatorBuat , yang merupakan featurizer teks yang mengonversi vektor teks menjadi vektor numerik menggunakan model penyematan yang telah dilatih sebelumnya.
ApplyWordEmbedding(TransformsCatalog+TextTransforms, String, String, String)	WordEmbeddingEstimatorBuat , yang merupakan featurizer teks yang mengonversi vektor teks menjadi vektor numerik menggunakan model penyematan yang telah dilatih sebelumnya.

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

WordEmbeddingEstimatorBuat , yang merupakan featurizer teks yang mengonversi vektor teks menjadi vektor numerik menggunakan model penyematan yang telah dilatih sebelumnya.

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

Parameter

catalog

TransformsCatalog.TextTransforms

Katalog transformasi terkait teks.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Jenis data kolom ini akan menjadi vektor .Single

inputColumnName

String

Nama kolom yang akan diubah. Jika diatur ke null, nilai outputColumnName akan digunakan sebagai sumber. Estimator ini beroperasi di atas vektor jenis data teks ukuran yang diketahui.

modelKind

WordEmbeddingEstimator.PretrainedModelKind

Penyematan yang WordEmbeddingEstimator.PretrainedModelKind akan digunakan.

Mengembalikan

WordEmbeddingEstimator

Contoh

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; }
        }
    }
}

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

WordEmbeddingEstimatorBuat , yang merupakan featurizer teks yang mengonversi vektor teks menjadi vektor numerik menggunakan model penyematan yang telah dilatih sebelumnya.

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

Parameter

catalog

TransformsCatalog.TextTransforms

Katalog transformasi terkait teks.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Jenis data kolom ini akan menjadi vektor .Single

customModelFile

String

Jalur model penyematan yang telah dilatih sebelumnya untuk digunakan.

inputColumnName

String

Nama kolom yang akan diubah. Jika diatur ke null, nilai outputColumnName akan digunakan sebagai sumber. Estimator ini beroperasi di atas vektor jenis data teks ukuran yang diketahui.

Mengembalikan

WordEmbeddingEstimator

Contoh

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

namespace Samples.Dynamic
{
    public static class ApplyCustomWordEmbedding
    {
        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);

            // Write a custom 3-dimensional word embedding model with 4 words.
            // Each line follows '<word> <float> <float> <float>' pattern.
            // Lines that do not confirm to the pattern are ignored.
            var pathToCustomModel = @".\custommodel.txt";
            using (StreamWriter file = new StreamWriter(pathToCustomModel, false))
            {
                file.WriteLine("great 1.0 2.0 3.0");
                file.WriteLine("product -1.0 -2.0 -3.0");
                file.WriteLine("like -1 100.0 -100");
                file.WriteLine("buy 0 0 20");
            }

            // A pipeline for converting text into a 9-dimension word embedding
            // vector using the custom word embedding model. The 
            // 'ApplyWordEmbedding' computes the minimum, average and maximum values
            // for each token's embedding vector. Tokens in 'custommodel.txt' model
            // are represented as 3-dimension vector. Therefore, the output is of
            // 9 -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",
                    pathToCustomModel, "Tokens"));

            // 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: 9
            //   Features: -1.0000 0.0000 -100.0000 0.0000 34.0000 -25.6667 1.0000 100.0000 20.0000
        }

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

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