ML.Net stuck on pretrained model Fit() method

there is some code example which works with Pretrained model (link to whole example page https://learn.microsoft.com/en-us/dotnet/api/microsoft.ml.textcatalog.applywordembedding?view=ml-dotnet):

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
.FastTextWikipedia300D));
// 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; }
}
}

}

So, in my case if I use FastTextWikipedia300D OR Glove200D OR Glove 100D pretrained models there is a stuck process which not ends even after 10 mins while run there:

> var textTransformer = textPipeline.Fit(emptyDataView);

I tried use this resolution: https://stackoverflow.com/a/54561423/5168936
But adding doesn't have any effect

> AppendCacheCheckpoint(mlContext)

are there any ways to understand why this happened? or I use it wrong; I will be happy for any idea. thank you!

Microsoft.ML package version in Nuget is: 2.0.1

I'm an idiot
to resolve this "issue" you should download word embeddings to your "..AppData\Local\mlnet-resources\WordVectors" folder;
as example adding downloaded file: glove.6B.300d.txt to that folder succeed the debug completely and Fit() works correctly without stuck process;
so, I close this question with my own answer