TextCatalog.ApplyWordEmbedding 메서드
정의
중요
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오버로드
| ApplyWordEmbedding(TransformsCatalog+TextTransforms, String, String, WordEmbeddingEstimator+PretrainedModelKind) |
WordEmbeddingEstimator미리 학습된 embeddings 모델을 사용하여 텍스트 벡터를 숫자 벡터로 변환하는 텍스트 기능 변환기입니다. |
| ApplyWordEmbedding(TransformsCatalog+TextTransforms, String, String, String) |
WordEmbeddingEstimator미리 학습된 embeddings 모델을 사용하여 텍스트 벡터를 숫자 벡터로 변환하는 텍스트 기능화기입니다. |
ApplyWordEmbedding(TransformsCatalog+TextTransforms, String, String, WordEmbeddingEstimator+PretrainedModelKind)
WordEmbeddingEstimator미리 학습된 embeddings 모델을 사용하여 텍스트 벡터를 숫자 벡터로 변환하는 텍스트 기능 변환기입니다.
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 WordEmbeddingEstimator매개 변수
- catalog
- TransformsCatalog.TextTransforms
텍스트 관련 변환의 카탈로그입니다.
- inputColumnName
- String
변환할 열의 이름입니다. 이 값으로 null설정하면 값이 outputColumnName 원본으로 사용됩니다.
이 추정기는 텍스트 데이터 형식의 알려진 크기 벡터에서 작동합니다.
사용할 포함입니다 WordEmbeddingEstimator.PretrainedModelKind .
반환
예제
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; }
}
}
}
적용 대상
ApplyWordEmbedding(TransformsCatalog+TextTransforms, String, String, String)
WordEmbeddingEstimator미리 학습된 embeddings 모델을 사용하여 텍스트 벡터를 숫자 벡터로 변환하는 텍스트 기능화기입니다.
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 WordEmbeddingEstimator매개 변수
- catalog
- TransformsCatalog.TextTransforms
텍스트 관련 변환의 카탈로그입니다.
- customModelFile
- String
사용할 미리 학습된 embeddings 모델의 경로입니다.
- inputColumnName
- String
변환할 열의 이름입니다. 이 값으로 null설정하면 값이 outputColumnName 원본으로 사용됩니다.
이 추정기는 텍스트 데이터 형식의 알려진 크기 벡터에서 작동합니다.
반환
예제
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; }
}
}
}