Verify word embedding model downloader

Internal user reported a stall during the .Fit() of the word embedding transform.

On first use of the word embedding transform, it downloads the word embedding model from the CDN.

To test:
1. Clear any copies of the fastText300D word embedding file from local machine
Check local folder, and ~/.local/share/mlnet-resources/WordVectors/ for a file named `wiki.en.vec`
2. Create example code using the FastTextWikipedia300D (6.6GB) in the word embedding transform
3. Time how long it takes to download (or fail)

Example code:
```C#
var featurizeTextOptions = new TextFeaturizingEstimator.Options()
{
// Produce cleaned tokens for input to the word embedding transform
OutputTokensColumnName = "OutputTokens",

// Text cleaning (not shown is stop word removal)
KeepDiacritics = true, // Non-default
KeepPunctuations = false,
KeepNumbers = false, // Non-default
CaseMode = TextNormalizingEstimator.CaseMode.Lower,

// Row-wise normalization (see: NormalizeLpNorm)
Norm = TextFeaturizingEstimator.NormFunction.L2,

// Use ML.NET's built-in stop word remover (non-default)
StopWordsRemoverOptions = new StopWordsRemovingEstimator.Options() { Language = TextFeaturizingEstimator.Language.English },

// ngram options
WordFeatureExtractor = new WordBagEstimator.Options()
{
NgramLength = 2,
UseAllLengths = true, // Produce both unigrams and bigrams
Weighting = NgramExtractingEstimator.WeightingCriteria.Tf, // Can also use TF-IDF or IDF
},

// chargram options
CharFeatureExtractor = new WordBagEstimator.Options()
{
NgramLength = 3,
UseAllLengths = false, // Produce only tri-chargrams and not single/double characters
Weighting = NgramExtractingEstimator.WeightingCriteria.Tf, // Can also use TF-IDF or IDF
},
};

// Featurization pipeline
var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label", "Label") // Needed for multi-class to convert string labels to the Key type

// Create ngrams, and cleaned tokens for the word embedding
.Append(mlContext.Transforms.Text.FeaturizeText("FeaturesText", featurizeTextOptions, new[] { "InputText" })) // Use above options object

// Word embedding transform reads in the cleaned tokens from the text featurizer
.Append(mlContext.Transforms.Text.ApplyWordEmbedding("FeaturesWordEmbedding",
"OutputTokens", WordEmbeddingEstimator.PretrainedModelKind.FastTextWikipedia300D))

// Feature vector is the concatenation of the ngrams from the text transform, and the word embeddings
.Append(mlContext.Transforms.Concatenate("Features", new[] { "FeaturesText", "FeaturesWordEmbedding" }))

// Enable if numeric features are also included. Normalization is generally unneeded if only using the output from FeaturizeText as it's row-wise normalized w/ a L2-norm; word embeddings are also well behaved.
//.Append(mlContext.Transforms.NormalizeMinMax("Features", "Features"))

// Cache the featurized dataset in memory for added speed
.AppendCacheCheckpoint(mlContext);

// Trainer
var trainer = mlContext.MulticlassClassification.Trainers.OneVersusAll(mlContext.BinaryClassification.Trainers.AveragedPerceptron(labelColumnName: "Label", numberOfIterations: 10, featureColumnName: "Features"), labelColumnName: "Label")
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel", "PredictedLabel"));

var trainingPipeline = pipeline.Append(trainer);
```

The code here shows a full example of the `FeaturizeText` for use with the `ApplyWordEmbedding`. Specifically, it creates the tokens for the `ApplyWordEmbedding` by removing numbers, keeping diacritics, and lowercases to match how the fastText model was created. The text cleaning reduces the out-of-vocabulary (OOV) issue in the word embedding. For any specific dataset, these options can be tested.

Side note:
We should make a sample of `FeaturizeText` with `ApplyWordEmbedding`. I wrote the above since I couldn't locate one to link-to in this issue.

Additional user report: https://github.com/dotnet/machinelearning/issues/5450#issuecomment-714930905