Scrub word embedding transform#2891

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Merged
wschin merged 4 commits into from
Mar 11, 2019
Merged

Scrub word embedding transform #2891

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12e165a
Scrub word embedding
wschin Mar 8, 2019
f99d131
Update EP
wschin Mar 8, 2019
3b292c1
Address a comment
wschin Mar 9, 2019
45869fb
Update EP
wschin Mar 11, 2019
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2 changes: 1 addition & 1 deletion docs/code/MlNetCookBook.md
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -782,7 +782,7 @@ var pipeline =
// NLP pipeline 4: word embeddings.
.Append(mlContext.Transforms.Text.TokenizeWords("TokenizedMessage", "NormalizedMessage"))
.Append(mlContext.Transforms.Text.ExtractWordEmbeddings("Embeddings", "TokenizedMessage",
WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D));
WordEmbeddingsExtractingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));

// Let's train our pipeline, and then apply it to the same data.
// Note that even on a small dataset of 70KB the pipeline above can take up to a minute to completely train.
Expand Down
6 changes: 3 additions & 3 deletions docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -61,8 +61,8 @@ public static void Example()

// Let's apply pretrained word embedding model GloVeTwitter25D.
// 25D means each word mapped into 25 dimensional space, basically each word represented by 25 float values.
var gloveWordEmbedding = ml.Transforms.Text.ExtractWordEmbeddings("GloveEmbeddings", "CleanWords",
WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D);
var gloveWordEmbedding = ml.Transforms.Text.ApplyWordEmbedding("GloveEmbeddings", "CleanWords",
WordEmbeddingEstimator.PretrainedModelKind.GloVeTwitter25D);

// We also have option to apply custom word embedding models.
// Let's first create one.
Expand All@@ -81,7 +81,7 @@ public static void Example()
file.WriteLine("best" + " " + string.Join(" ", 0f, 0f, 20f));
}
// Now let's add custom embedding on top of same words.
var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ExtractWordEmbeddings("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));
var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ApplyWordEmbedding("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));

// And do all required transformations.
var embeddingDataview = pipeline.Fit(wordsDataview).Transform(wordsDataview);
Expand Down
16 changes: 8 additions & 8 deletions src/Microsoft.ML.StaticPipe/WordEmbeddingsStaticExtensions.cs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,7 @@ public static class WordEmbeddingsStaticExtensions
/// <param name="input">Vector of tokenized text.</param>
/// <param name="modelKind">The pretrained word embedding model.</param>
/// <returns></returns>
public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
{
Contracts.CheckValue(input, nameof(input));
return new OutColumn(input, modelKind);
Expand All@@ -33,7 +33,7 @@ private sealed class OutColumn : Vector<float>
{
public PipelineColumn Input { get; }

public OutColumn(VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
public OutColumn(VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
: base(new Reconciler(modelKind), input)
{
Input = input;
Expand All@@ -48,10 +48,10 @@ public OutColumn(VarVector<string> input, string customModelFile = null)

private sealed class Reconciler : EstimatorReconciler
{
private readonly WordEmbeddingsExtractingEstimator.PretrainedModelKind? _modelKind;
private readonly WordEmbeddingEstimator.PretrainedModelKind? _modelKind;
private readonly string _customLookupTable;

public Reconciler(WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
public Reconciler(WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
{
_modelKind = modelKind;
_customLookupTable = null;
Expand All@@ -71,18 +71,18 @@ public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
{
Contracts.Assert(toOutput.Length == 1);

var cols = new WordEmbeddingsExtractingEstimator.ColumnOptions[toOutput.Length];
var cols = new WordEmbeddingEstimator.ColumnOptions[toOutput.Length];
for (int i = 0; i < toOutput.Length; ++i)
{
var outCol = (OutColumn)toOutput[i];
cols[i] = new WordEmbeddingsExtractingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
cols[i] = new WordEmbeddingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
}

bool customLookup = !string.IsNullOrWhiteSpace(_customLookupTable);
if (customLookup)
return new WordEmbeddingsExtractingEstimator(env, _customLookupTable, cols);
return new WordEmbeddingEstimator(env, _customLookupTable, cols);
else
return new WordEmbeddingsExtractingEstimator(env, _modelKind.Value, cols);
return new WordEmbeddingEstimator(env, _modelKind.Value, cols);
}
}
}
Expand Down
10 changes: 5 additions & 5 deletions src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -132,16 +132,16 @@ public static CommonOutputs.TransformOutput LightLda(IHostEnvironment env, Laten
}

[TlcModule.EntryPoint(Name = "Transforms.WordEmbeddings",
Desc = WordEmbeddingsExtractingTransformer.Summary,
UserName = WordEmbeddingsExtractingTransformer.UserName,
ShortName = WordEmbeddingsExtractingTransformer.ShortName)]
public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingsExtractingTransformer.Options input)
Desc = WordEmbeddingTransformer.Summary,
UserName = WordEmbeddingTransformer.UserName,
ShortName = WordEmbeddingTransformer.ShortName)]
public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingTransformer.Options input)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(input, nameof(input));

var h = EntryPointUtils.CheckArgsAndCreateHost(env, "WordEmbeddings", input);
var view = WordEmbeddingsExtractingTransformer.Create(h, input, input.Data);
var view = WordEmbeddingTransformer.Create(h, input, input.Data);
return new CommonOutputs.TransformOutput()
{
Model = new TransformModelImpl(h, view, input.Data),
Expand Down
22 changes: 11 additions & 11 deletions src/Microsoft.ML.Transforms/Text/TextCatalog.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -101,19 +101,19 @@ public static TextNormalizingEstimator NormalizeText(this TransformsCatalog.Text
/// <param name="catalog">The text-related transform's catalog.</param>
/// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
/// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
/// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
/// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
/// <example>
/// <format type="text/markdown">
/// <![CDATA[
/// [!code-csharp[FeaturizeText](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs)]
/// ]]>
/// </format>
/// </example>
public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
string outputColumnName,
string inputColumnName = null,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
=> new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
=> new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);

/// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
/// <param name="catalog">The text-related transform's catalog.</param>
Expand All@@ -127,16 +127,16 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
/// ]]>
/// </format>
/// </example>
public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
string outputColumnName,
string customModelFile,
string inputColumnName = null)
=> new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
=> new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
outputColumnName, customModelFile, inputColumnName ?? outputColumnName);

/// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
/// <param name="catalog">The text-related transform's catalog.</param>
/// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
/// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
/// <param name="columns">The array columns, and per-column configurations to extract embeedings from.</param>
/// <example>
/// <format type="text/markdown">
Expand All@@ -145,10 +145,10 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
/// ]]>
/// </format>
/// </example>
public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe,
params WordEmbeddingsExtractingEstimator.ColumnOptions[] columns)
=> new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);
public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding,
params WordEmbeddingEstimator.ColumnOptions[] columns)
=> new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);

/// <summary>
/// Tokenizes incoming text in <paramref name="inputColumnName"/>, using <paramref name="separators"/> as separators,
Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Scrub word embedding transform#2891

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 Merged

wschin
merged 4 commits into

from

Mar 11, 2019
 Merged


Scrub word embedding transform
#2891
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12e165a

Scrub word embedding


wschin Mar 8, 2019
f99d131

Update EP


wschin Mar 8, 2019
3b292c1

Address a comment


wschin Mar 9, 2019
45869fb

Update EP


wschin Mar 11, 2019

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2 changes: 1 addition & 1 deletion
docs/code/MlNetCookBook.md

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -782,7 +782,7 @@ var pipeline =
 // NLP pipeline 4: word embeddings.
 .Append(mlContext.Transforms.Text.TokenizeWords("TokenizedMessage", "NormalizedMessage"))
 .Append(mlContext.Transforms.Text.ExtractWordEmbeddings("Embeddings", "TokenizedMessage",
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D));
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));

// Let's train our pipeline, and then apply it to the same data.
// Note that even on a small dataset of 70KB the pipeline above can take up to a minute to completely train.
Expand Down

6 changes: 3 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -61,8 +61,8 @@ public static void Example()

 // Let's apply pretrained word embedding model GloVeTwitter25D.
 // 25D means each word mapped into 25 dimensional space, basically each word represented by 25 float values.
 var gloveWordEmbedding = ml.Transforms.Text.ExtractWordEmbeddings("GloveEmbeddings", "CleanWords",
WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D);
 var gloveWordEmbedding = ml.Transforms.Text.ApplyWordEmbedding("GloveEmbeddings", "CleanWords",
WordEmbeddingEstimator.PretrainedModelKind.GloVeTwitter25D);

 // We also have option to apply custom word embedding models.
 // Let's first create one.
Expand All@@ -81,7 +81,7 @@ public static void Example()
 file.WriteLine("best" + " " + string.Join(" ", 0f, 0f, 20f));
 }
 // Now let's add custom embedding on top of same words.
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ExtractWordEmbeddings("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ApplyWordEmbedding("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));

 // And do all required transformations.
 var embeddingDataview = pipeline.Fit(wordsDataview).Transform(wordsDataview);
Expand Down

16 changes: 8 additions & 8 deletions
src/Microsoft.ML.StaticPipe/WordEmbeddingsStaticExtensions.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,7 @@ public static class WordEmbeddingsStaticExtensions
 /// <param name="input">Vector of tokenized text.</param>
 /// <param name="modelKind">The pretrained word embedding model.</param>
 /// <returns></returns>
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 Contracts.CheckValue(input, nameof(input));
 return new OutColumn(input, modelKind);
Expand All@@ -33,7 +33,7 @@ private sealed class OutColumn : Vector<float>
 {
 public PipelineColumn Input { get; }

 public OutColumn(VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public OutColumn(VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 : base(new Reconciler(modelKind), input)
 {
 Input = input;
Expand All@@ -48,10 +48,10 @@ public OutColumn(VarVector<string> input, string customModelFile = null)

 private sealed class Reconciler : EstimatorReconciler
 {
 private readonly WordEmbeddingsExtractingEstimator.PretrainedModelKind? _modelKind;
 private readonly WordEmbeddingEstimator.PretrainedModelKind? _modelKind;
 private readonly string _customLookupTable;

 public Reconciler(WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public Reconciler(WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 _modelKind = modelKind;
 _customLookupTable = null;
Expand All@@ -71,18 +71,18 @@ public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
 {
 Contracts.Assert(toOutput.Length == 1);

 var cols = new WordEmbeddingsExtractingEstimator.ColumnOptions[toOutput.Length];
 var cols = new WordEmbeddingEstimator.ColumnOptions[toOutput.Length];
 for (int i = 0; i < toOutput.Length; ++i)
 {
 var outCol = (OutColumn)toOutput[i];
 cols[i] = new WordEmbeddingsExtractingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 cols[i] = new WordEmbeddingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 }

 bool customLookup = !string.IsNullOrWhiteSpace(_customLookupTable);
 if (customLookup)
 return new WordEmbeddingsExtractingEstimator(env, _customLookupTable, cols);
 return new WordEmbeddingEstimator(env, _customLookupTable, cols);
 else
 return new WordEmbeddingsExtractingEstimator(env, _modelKind.Value, cols);
 return new WordEmbeddingEstimator(env, _modelKind.Value, cols);
 }
 }
 }
Expand Down

10 changes: 5 additions & 5 deletions
src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -132,16 +132,16 @@ public static CommonOutputs.TransformOutput LightLda(IHostEnvironment env, Laten
 }

 [TlcModule.EntryPoint(Name = "Transforms.WordEmbeddings",
 Desc = WordEmbeddingsExtractingTransformer.Summary,
 UserName = WordEmbeddingsExtractingTransformer.UserName,
 ShortName = WordEmbeddingsExtractingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingsExtractingTransformer.Options input)
 Desc = WordEmbeddingTransformer.Summary,
 UserName = WordEmbeddingTransformer.UserName,
 ShortName = WordEmbeddingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingTransformer.Options input)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(input, nameof(input));

 var h = EntryPointUtils.CheckArgsAndCreateHost(env, "WordEmbeddings", input);
 var view = WordEmbeddingsExtractingTransformer.Create(h, input, input.Data);
 var view = WordEmbeddingTransformer.Create(h, input, input.Data);
 return new CommonOutputs.TransformOutput()
 {
 Model = new TransformModelImpl(h, view, input.Data),
Expand Down

22 changes: 11 additions & 11 deletions
src/Microsoft.ML.Transforms/Text/TextCatalog.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -101,19 +101,19 @@ public static TextNormalizingEstimator NormalizeText(this TransformsCatalog.Text
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
 /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <example>
 /// <format type="text/markdown">
 /// <![CDATA[
 /// [!code-csharp[FeaturizeText](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs)]
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string inputColumnName = null,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
Expand All@@ -127,16 +127,16 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string customModelFile,
 string inputColumnName = null)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 outputColumnName, customModelFile, inputColumnName ?? outputColumnName);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="columns">The array columns, and per-column configurations to extract embeedings from.</param>
 /// <example>
 /// <format type="text/markdown">
Expand All@@ -145,10 +145,10 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe,
 params WordEmbeddingsExtractingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding,
 params WordEmbeddingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);

 /// <summary>
 /// Tokenizes incoming text in <paramref name="inputColumnName"/>, using <paramref name="separators"/> as separators,
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Scrub word embedding transform#2891

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 Merged

wschin
merged 4 commits into

from

Mar 11, 2019
 Merged


Scrub word embedding transform
#2891
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12e165a

Scrub word embedding


wschin Mar 8, 2019
f99d131

Update EP


wschin Mar 8, 2019
3b292c1

Address a comment


wschin Mar 9, 2019
45869fb

Update EP


wschin Mar 11, 2019

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2 changes: 1 addition & 1 deletion
docs/code/MlNetCookBook.md

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -782,7 +782,7 @@ var pipeline =
 // NLP pipeline 4: word embeddings.
 .Append(mlContext.Transforms.Text.TokenizeWords("TokenizedMessage", "NormalizedMessage"))
 .Append(mlContext.Transforms.Text.ExtractWordEmbeddings("Embeddings", "TokenizedMessage",
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D));
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));

// Let's train our pipeline, and then apply it to the same data.
// Note that even on a small dataset of 70KB the pipeline above can take up to a minute to completely train.
Expand Down

6 changes: 3 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -61,8 +61,8 @@ public static void Example()

 // Let's apply pretrained word embedding model GloVeTwitter25D.
 // 25D means each word mapped into 25 dimensional space, basically each word represented by 25 float values.
 var gloveWordEmbedding = ml.Transforms.Text.ExtractWordEmbeddings("GloveEmbeddings", "CleanWords",
WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D);
 var gloveWordEmbedding = ml.Transforms.Text.ApplyWordEmbedding("GloveEmbeddings", "CleanWords",
WordEmbeddingEstimator.PretrainedModelKind.GloVeTwitter25D);

 // We also have option to apply custom word embedding models.
 // Let's first create one.
Expand All@@ -81,7 +81,7 @@ public static void Example()
 file.WriteLine("best" + " " + string.Join(" ", 0f, 0f, 20f));
 }
 // Now let's add custom embedding on top of same words.
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ExtractWordEmbeddings("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ApplyWordEmbedding("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));

 // And do all required transformations.
 var embeddingDataview = pipeline.Fit(wordsDataview).Transform(wordsDataview);
Expand Down

16 changes: 8 additions & 8 deletions
src/Microsoft.ML.StaticPipe/WordEmbeddingsStaticExtensions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,7 @@ public static class WordEmbeddingsStaticExtensions
 /// <param name="input">Vector of tokenized text.</param>
 /// <param name="modelKind">The pretrained word embedding model.</param>
 /// <returns></returns>
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 Contracts.CheckValue(input, nameof(input));
 return new OutColumn(input, modelKind);
Expand All@@ -33,7 +33,7 @@ private sealed class OutColumn : Vector<float>
 {
 public PipelineColumn Input { get; }

 public OutColumn(VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public OutColumn(VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 : base(new Reconciler(modelKind), input)
 {
 Input = input;
Expand All@@ -48,10 +48,10 @@ public OutColumn(VarVector<string> input, string customModelFile = null)

 private sealed class Reconciler : EstimatorReconciler
 {
 private readonly WordEmbeddingsExtractingEstimator.PretrainedModelKind? _modelKind;
 private readonly WordEmbeddingEstimator.PretrainedModelKind? _modelKind;
 private readonly string _customLookupTable;

 public Reconciler(WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public Reconciler(WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 _modelKind = modelKind;
 _customLookupTable = null;
Expand All@@ -71,18 +71,18 @@ public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
 {
 Contracts.Assert(toOutput.Length == 1);

 var cols = new WordEmbeddingsExtractingEstimator.ColumnOptions[toOutput.Length];
 var cols = new WordEmbeddingEstimator.ColumnOptions[toOutput.Length];
 for (int i = 0; i < toOutput.Length; ++i)
 {
 var outCol = (OutColumn)toOutput[i];
 cols[i] = new WordEmbeddingsExtractingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 cols[i] = new WordEmbeddingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 }

 bool customLookup = !string.IsNullOrWhiteSpace(_customLookupTable);
 if (customLookup)
 return new WordEmbeddingsExtractingEstimator(env, _customLookupTable, cols);
 return new WordEmbeddingEstimator(env, _customLookupTable, cols);
 else
 return new WordEmbeddingsExtractingEstimator(env, _modelKind.Value, cols);
 return new WordEmbeddingEstimator(env, _modelKind.Value, cols);
 }
 }
 }
Expand Down

10 changes: 5 additions & 5 deletions
src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -132,16 +132,16 @@ public static CommonOutputs.TransformOutput LightLda(IHostEnvironment env, Laten
 }

 [TlcModule.EntryPoint(Name = "Transforms.WordEmbeddings",
 Desc = WordEmbeddingsExtractingTransformer.Summary,
 UserName = WordEmbeddingsExtractingTransformer.UserName,
 ShortName = WordEmbeddingsExtractingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingsExtractingTransformer.Options input)
 Desc = WordEmbeddingTransformer.Summary,
 UserName = WordEmbeddingTransformer.UserName,
 ShortName = WordEmbeddingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingTransformer.Options input)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(input, nameof(input));

 var h = EntryPointUtils.CheckArgsAndCreateHost(env, "WordEmbeddings", input);
 var view = WordEmbeddingsExtractingTransformer.Create(h, input, input.Data);
 var view = WordEmbeddingTransformer.Create(h, input, input.Data);
 return new CommonOutputs.TransformOutput()
 {
 Model = new TransformModelImpl(h, view, input.Data),
Expand Down

22 changes: 11 additions & 11 deletions
src/Microsoft.ML.Transforms/Text/TextCatalog.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -101,19 +101,19 @@ public static TextNormalizingEstimator NormalizeText(this TransformsCatalog.Text
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
 /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <example>
 /// <format type="text/markdown">
 /// <![CDATA[
 /// [!code-csharp[FeaturizeText](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs)]
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string inputColumnName = null,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
Expand All@@ -127,16 +127,16 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string customModelFile,
 string inputColumnName = null)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 outputColumnName, customModelFile, inputColumnName ?? outputColumnName);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="columns">The array columns, and per-column configurations to extract embeedings from.</param>
 /// <example>
 /// <format type="text/markdown">
Expand All@@ -145,10 +145,10 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe,
 params WordEmbeddingsExtractingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding,
 params WordEmbeddingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);

 /// <summary>
 /// Tokenizes incoming text in <paramref name="inputColumnName"/>, using <paramref name="separators"/> as separators,
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Mar 11, 2019
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Scrub word embedding transform
#2891
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12e165a

Scrub word embedding


wschin Mar 8, 2019
f99d131

Update EP


wschin Mar 8, 2019
3b292c1

Address a comment


wschin Mar 9, 2019
45869fb

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2 changes: 1 addition & 1 deletion
docs/code/MlNetCookBook.md

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -782,7 +782,7 @@ var pipeline =
 // NLP pipeline 4: word embeddings.
 .Append(mlContext.Transforms.Text.TokenizeWords("TokenizedMessage", "NormalizedMessage"))
 .Append(mlContext.Transforms.Text.ExtractWordEmbeddings("Embeddings", "TokenizedMessage",
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D));
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));

// Let's train our pipeline, and then apply it to the same data.
// Note that even on a small dataset of 70KB the pipeline above can take up to a minute to completely train.
Expand Down

6 changes: 3 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -61,8 +61,8 @@ public static void Example()

 // Let's apply pretrained word embedding model GloVeTwitter25D.
 // 25D means each word mapped into 25 dimensional space, basically each word represented by 25 float values.
 var gloveWordEmbedding = ml.Transforms.Text.ExtractWordEmbeddings("GloveEmbeddings", "CleanWords",
WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D);
 var gloveWordEmbedding = ml.Transforms.Text.ApplyWordEmbedding("GloveEmbeddings", "CleanWords",
WordEmbeddingEstimator.PretrainedModelKind.GloVeTwitter25D);

 // We also have option to apply custom word embedding models.
 // Let's first create one.
Expand All@@ -81,7 +81,7 @@ public static void Example()
 file.WriteLine("best" + " " + string.Join(" ", 0f, 0f, 20f));
 }
 // Now let's add custom embedding on top of same words.
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ExtractWordEmbeddings("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ApplyWordEmbedding("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));

 // And do all required transformations.
 var embeddingDataview = pipeline.Fit(wordsDataview).Transform(wordsDataview);
Expand Down

16 changes: 8 additions & 8 deletions
src/Microsoft.ML.StaticPipe/WordEmbeddingsStaticExtensions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,7 @@ public static class WordEmbeddingsStaticExtensions
 /// <param name="input">Vector of tokenized text.</param>
 /// <param name="modelKind">The pretrained word embedding model.</param>
 /// <returns></returns>
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 Contracts.CheckValue(input, nameof(input));
 return new OutColumn(input, modelKind);
Expand All@@ -33,7 +33,7 @@ private sealed class OutColumn : Vector<float>
 {
 public PipelineColumn Input { get; }

 public OutColumn(VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public OutColumn(VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 : base(new Reconciler(modelKind), input)
 {
 Input = input;
Expand All@@ -48,10 +48,10 @@ public OutColumn(VarVector<string> input, string customModelFile = null)

 private sealed class Reconciler : EstimatorReconciler
 {
 private readonly WordEmbeddingsExtractingEstimator.PretrainedModelKind? _modelKind;
 private readonly WordEmbeddingEstimator.PretrainedModelKind? _modelKind;
 private readonly string _customLookupTable;

 public Reconciler(WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public Reconciler(WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 _modelKind = modelKind;
 _customLookupTable = null;
Expand All@@ -71,18 +71,18 @@ public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
 {
 Contracts.Assert(toOutput.Length == 1);

 var cols = new WordEmbeddingsExtractingEstimator.ColumnOptions[toOutput.Length];
 var cols = new WordEmbeddingEstimator.ColumnOptions[toOutput.Length];
 for (int i = 0; i < toOutput.Length; ++i)
 {
 var outCol = (OutColumn)toOutput[i];
 cols[i] = new WordEmbeddingsExtractingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 cols[i] = new WordEmbeddingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 }

 bool customLookup = !string.IsNullOrWhiteSpace(_customLookupTable);
 if (customLookup)
 return new WordEmbeddingsExtractingEstimator(env, _customLookupTable, cols);
 return new WordEmbeddingEstimator(env, _customLookupTable, cols);
 else
 return new WordEmbeddingsExtractingEstimator(env, _modelKind.Value, cols);
 return new WordEmbeddingEstimator(env, _modelKind.Value, cols);
 }
 }
 }
Expand Down

10 changes: 5 additions & 5 deletions
src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -132,16 +132,16 @@ public static CommonOutputs.TransformOutput LightLda(IHostEnvironment env, Laten
 }

 [TlcModule.EntryPoint(Name = "Transforms.WordEmbeddings",
 Desc = WordEmbeddingsExtractingTransformer.Summary,
 UserName = WordEmbeddingsExtractingTransformer.UserName,
 ShortName = WordEmbeddingsExtractingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingsExtractingTransformer.Options input)
 Desc = WordEmbeddingTransformer.Summary,
 UserName = WordEmbeddingTransformer.UserName,
 ShortName = WordEmbeddingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingTransformer.Options input)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(input, nameof(input));

 var h = EntryPointUtils.CheckArgsAndCreateHost(env, "WordEmbeddings", input);
 var view = WordEmbeddingsExtractingTransformer.Create(h, input, input.Data);
 var view = WordEmbeddingTransformer.Create(h, input, input.Data);
 return new CommonOutputs.TransformOutput()
 {
 Model = new TransformModelImpl(h, view, input.Data),
Expand Down

22 changes: 11 additions & 11 deletions
src/Microsoft.ML.Transforms/Text/TextCatalog.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -101,19 +101,19 @@ public static TextNormalizingEstimator NormalizeText(this TransformsCatalog.Text
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
 /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <example>
 /// <format type="text/markdown">
 /// <![CDATA[
 /// [!code-csharp[FeaturizeText](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs)]
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string inputColumnName = null,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
Expand All@@ -127,16 +127,16 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string customModelFile,
 string inputColumnName = null)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 outputColumnName, customModelFile, inputColumnName ?? outputColumnName);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="columns">The array columns, and per-column configurations to extract embeedings from.</param>
 /// <example>
 /// <format type="text/markdown">
Expand All@@ -145,10 +145,10 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe,
 params WordEmbeddingsExtractingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding,
 params WordEmbeddingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);

 /// <summary>
 /// Tokenizes incoming text in <paramref name="inputColumnName"/>, using <paramref name="separators"/> as separators,
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
Scrub word embedding transform#2891

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 Merged

wschin
merged 4 commits into

from

Mar 11, 2019
 Merged


Scrub word embedding transform
#2891
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12e165a

Scrub word embedding


wschin Mar 8, 2019
f99d131

Update EP


wschin Mar 8, 2019
3b292c1

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2 changes: 1 addition & 1 deletion
docs/code/MlNetCookBook.md

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Original file line numberDiff line numberDiff line change
Expand Up@@ -782,7 +782,7 @@ var pipeline =
 // NLP pipeline 4: word embeddings.
 .Append(mlContext.Transforms.Text.TokenizeWords("TokenizedMessage", "NormalizedMessage"))
 .Append(mlContext.Transforms.Text.ExtractWordEmbeddings("Embeddings", "TokenizedMessage",
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D));
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));

// Let's train our pipeline, and then apply it to the same data.
// Note that even on a small dataset of 70KB the pipeline above can take up to a minute to completely train.
Expand Down

6 changes: 3 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -61,8 +61,8 @@ public static void Example()

 // Let's apply pretrained word embedding model GloVeTwitter25D.
 // 25D means each word mapped into 25 dimensional space, basically each word represented by 25 float values.
 var gloveWordEmbedding = ml.Transforms.Text.ExtractWordEmbeddings("GloveEmbeddings", "CleanWords",
WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D);
 var gloveWordEmbedding = ml.Transforms.Text.ApplyWordEmbedding("GloveEmbeddings", "CleanWords",
WordEmbeddingEstimator.PretrainedModelKind.GloVeTwitter25D);

 // We also have option to apply custom word embedding models.
 // Let's first create one.
Expand All@@ -81,7 +81,7 @@ public static void Example()
 file.WriteLine("best" + " " + string.Join(" ", 0f, 0f, 20f));
 }
 // Now let's add custom embedding on top of same words.
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ExtractWordEmbeddings("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ApplyWordEmbedding("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));

 // And do all required transformations.
 var embeddingDataview = pipeline.Fit(wordsDataview).Transform(wordsDataview);
Expand Down

16 changes: 8 additions & 8 deletions
src/Microsoft.ML.StaticPipe/WordEmbeddingsStaticExtensions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,7 @@ public static class WordEmbeddingsStaticExtensions
 /// <param name="input">Vector of tokenized text.</param>
 /// <param name="modelKind">The pretrained word embedding model.</param>
 /// <returns></returns>
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 Contracts.CheckValue(input, nameof(input));
 return new OutColumn(input, modelKind);
Expand All@@ -33,7 +33,7 @@ private sealed class OutColumn : Vector<float>
 {
 public PipelineColumn Input { get; }

 public OutColumn(VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public OutColumn(VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 : base(new Reconciler(modelKind), input)
 {
 Input = input;
Expand All@@ -48,10 +48,10 @@ public OutColumn(VarVector<string> input, string customModelFile = null)

 private sealed class Reconciler : EstimatorReconciler
 {
 private readonly WordEmbeddingsExtractingEstimator.PretrainedModelKind? _modelKind;
 private readonly WordEmbeddingEstimator.PretrainedModelKind? _modelKind;
 private readonly string _customLookupTable;

 public Reconciler(WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public Reconciler(WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 _modelKind = modelKind;
 _customLookupTable = null;
Expand All@@ -71,18 +71,18 @@ public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
 {
 Contracts.Assert(toOutput.Length == 1);

 var cols = new WordEmbeddingsExtractingEstimator.ColumnOptions[toOutput.Length];
 var cols = new WordEmbeddingEstimator.ColumnOptions[toOutput.Length];
 for (int i = 0; i < toOutput.Length; ++i)
 {
 var outCol = (OutColumn)toOutput[i];
 cols[i] = new WordEmbeddingsExtractingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 cols[i] = new WordEmbeddingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 }

 bool customLookup = !string.IsNullOrWhiteSpace(_customLookupTable);
 if (customLookup)
 return new WordEmbeddingsExtractingEstimator(env, _customLookupTable, cols);
 return new WordEmbeddingEstimator(env, _customLookupTable, cols);
 else
 return new WordEmbeddingsExtractingEstimator(env, _modelKind.Value, cols);
 return new WordEmbeddingEstimator(env, _modelKind.Value, cols);
 }
 }
 }
Expand Down

10 changes: 5 additions & 5 deletions
src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -132,16 +132,16 @@ public static CommonOutputs.TransformOutput LightLda(IHostEnvironment env, Laten
 }

 [TlcModule.EntryPoint(Name = "Transforms.WordEmbeddings",
 Desc = WordEmbeddingsExtractingTransformer.Summary,
 UserName = WordEmbeddingsExtractingTransformer.UserName,
 ShortName = WordEmbeddingsExtractingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingsExtractingTransformer.Options input)
 Desc = WordEmbeddingTransformer.Summary,
 UserName = WordEmbeddingTransformer.UserName,
 ShortName = WordEmbeddingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingTransformer.Options input)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(input, nameof(input));

 var h = EntryPointUtils.CheckArgsAndCreateHost(env, "WordEmbeddings", input);
 var view = WordEmbeddingsExtractingTransformer.Create(h, input, input.Data);
 var view = WordEmbeddingTransformer.Create(h, input, input.Data);
 return new CommonOutputs.TransformOutput()
 {
 Model = new TransformModelImpl(h, view, input.Data),
Expand Down

22 changes: 11 additions & 11 deletions
src/Microsoft.ML.Transforms/Text/TextCatalog.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -101,19 +101,19 @@ public static TextNormalizingEstimator NormalizeText(this TransformsCatalog.Text
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
 /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <example>
 /// <format type="text/markdown">
 /// <![CDATA[
 /// [!code-csharp[FeaturizeText](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs)]
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string inputColumnName = null,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
Expand All@@ -127,16 +127,16 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string customModelFile,
 string inputColumnName = null)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 outputColumnName, customModelFile, inputColumnName ?? outputColumnName);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="columns">The array columns, and per-column configurations to extract embeedings from.</param>
 /// <example>
 /// <format type="text/markdown">
Expand All@@ -145,10 +145,10 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe,
 params WordEmbeddingsExtractingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding,
 params WordEmbeddingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);

 /// <summary>
 /// Tokenizes incoming text in <paramref name="inputColumnName"/>, using <paramref name="separators"/> as separators,
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Scrub word embedding transform#2891

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 Merged

wschin
merged 4 commits into

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Mar 11, 2019
 Merged


Scrub word embedding transform
#2891
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12e165a

Scrub word embedding


wschin Mar 8, 2019
f99d131

Update EP


wschin Mar 8, 2019
3b292c1

Address a comment


wschin Mar 9, 2019
45869fb

Update EP


wschin Mar 11, 2019

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2 changes: 1 addition & 1 deletion
docs/code/MlNetCookBook.md

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -782,7 +782,7 @@ var pipeline =
 // NLP pipeline 4: word embeddings.
 .Append(mlContext.Transforms.Text.TokenizeWords("TokenizedMessage", "NormalizedMessage"))
 .Append(mlContext.Transforms.Text.ExtractWordEmbeddings("Embeddings", "TokenizedMessage",
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D));
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));

// Let's train our pipeline, and then apply it to the same data.
// Note that even on a small dataset of 70KB the pipeline above can take up to a minute to completely train.
Expand Down

6 changes: 3 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -61,8 +61,8 @@ public static void Example()

 // Let's apply pretrained word embedding model GloVeTwitter25D.
 // 25D means each word mapped into 25 dimensional space, basically each word represented by 25 float values.
 var gloveWordEmbedding = ml.Transforms.Text.ExtractWordEmbeddings("GloveEmbeddings", "CleanWords",
WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D);
 var gloveWordEmbedding = ml.Transforms.Text.ApplyWordEmbedding("GloveEmbeddings", "CleanWords",
WordEmbeddingEstimator.PretrainedModelKind.GloVeTwitter25D);

 // We also have option to apply custom word embedding models.
 // Let's first create one.
Expand All@@ -81,7 +81,7 @@ public static void Example()
 file.WriteLine("best" + " " + string.Join(" ", 0f, 0f, 20f));
 }
 // Now let's add custom embedding on top of same words.
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ExtractWordEmbeddings("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ApplyWordEmbedding("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));

 // And do all required transformations.
 var embeddingDataview = pipeline.Fit(wordsDataview).Transform(wordsDataview);
Expand Down

16 changes: 8 additions & 8 deletions
src/Microsoft.ML.StaticPipe/WordEmbeddingsStaticExtensions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,7 @@ public static class WordEmbeddingsStaticExtensions
 /// <param name="input">Vector of tokenized text.</param>
 /// <param name="modelKind">The pretrained word embedding model.</param>
 /// <returns></returns>
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 Contracts.CheckValue(input, nameof(input));
 return new OutColumn(input, modelKind);
Expand All@@ -33,7 +33,7 @@ private sealed class OutColumn : Vector<float>
 {
 public PipelineColumn Input { get; }

 public OutColumn(VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public OutColumn(VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 : base(new Reconciler(modelKind), input)
 {
 Input = input;
Expand All@@ -48,10 +48,10 @@ public OutColumn(VarVector<string> input, string customModelFile = null)

 private sealed class Reconciler : EstimatorReconciler
 {
 private readonly WordEmbeddingsExtractingEstimator.PretrainedModelKind? _modelKind;
 private readonly WordEmbeddingEstimator.PretrainedModelKind? _modelKind;
 private readonly string _customLookupTable;

 public Reconciler(WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public Reconciler(WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 _modelKind = modelKind;
 _customLookupTable = null;
Expand All@@ -71,18 +71,18 @@ public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
 {
 Contracts.Assert(toOutput.Length == 1);

 var cols = new WordEmbeddingsExtractingEstimator.ColumnOptions[toOutput.Length];
 var cols = new WordEmbeddingEstimator.ColumnOptions[toOutput.Length];
 for (int i = 0; i < toOutput.Length; ++i)
 {
 var outCol = (OutColumn)toOutput[i];
 cols[i] = new WordEmbeddingsExtractingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 cols[i] = new WordEmbeddingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 }

 bool customLookup = !string.IsNullOrWhiteSpace(_customLookupTable);
 if (customLookup)
 return new WordEmbeddingsExtractingEstimator(env, _customLookupTable, cols);
 return new WordEmbeddingEstimator(env, _customLookupTable, cols);
 else
 return new WordEmbeddingsExtractingEstimator(env, _modelKind.Value, cols);
 return new WordEmbeddingEstimator(env, _modelKind.Value, cols);
 }
 }
 }
Expand Down

10 changes: 5 additions & 5 deletions
src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -132,16 +132,16 @@ public static CommonOutputs.TransformOutput LightLda(IHostEnvironment env, Laten
 }

 [TlcModule.EntryPoint(Name = "Transforms.WordEmbeddings",
 Desc = WordEmbeddingsExtractingTransformer.Summary,
 UserName = WordEmbeddingsExtractingTransformer.UserName,
 ShortName = WordEmbeddingsExtractingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingsExtractingTransformer.Options input)
 Desc = WordEmbeddingTransformer.Summary,
 UserName = WordEmbeddingTransformer.UserName,
 ShortName = WordEmbeddingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingTransformer.Options input)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(input, nameof(input));

 var h = EntryPointUtils.CheckArgsAndCreateHost(env, "WordEmbeddings", input);
 var view = WordEmbeddingsExtractingTransformer.Create(h, input, input.Data);
 var view = WordEmbeddingTransformer.Create(h, input, input.Data);
 return new CommonOutputs.TransformOutput()
 {
 Model = new TransformModelImpl(h, view, input.Data),
Expand Down

22 changes: 11 additions & 11 deletions
src/Microsoft.ML.Transforms/Text/TextCatalog.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -101,19 +101,19 @@ public static TextNormalizingEstimator NormalizeText(this TransformsCatalog.Text
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
 /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <example>
 /// <format type="text/markdown">
 /// <![CDATA[
 /// [!code-csharp[FeaturizeText](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs)]
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string inputColumnName = null,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
Expand All@@ -127,16 +127,16 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string customModelFile,
 string inputColumnName = null)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 outputColumnName, customModelFile, inputColumnName ?? outputColumnName);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="columns">The array columns, and per-column configurations to extract embeedings from.</param>
 /// <example>
 /// <format type="text/markdown">
Expand All@@ -145,10 +145,10 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe,
 params WordEmbeddingsExtractingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding,
 params WordEmbeddingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);

 /// <summary>
 /// Tokenizes incoming text in <paramref name="inputColumnName"/>, using <paramref name="separators"/> as separators,
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Scrub word embedding transform#2891

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 Merged

wschin
merged 4 commits into

from

Mar 11, 2019
 Merged


Scrub word embedding transform
#2891
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12e165a

Scrub word embedding


wschin Mar 8, 2019
f99d131

Update EP


wschin Mar 8, 2019
3b292c1

Address a comment


wschin Mar 9, 2019
45869fb

Update EP


wschin Mar 11, 2019

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2 changes: 1 addition & 1 deletion
docs/code/MlNetCookBook.md

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -782,7 +782,7 @@ var pipeline =
 // NLP pipeline 4: word embeddings.
 .Append(mlContext.Transforms.Text.TokenizeWords("TokenizedMessage", "NormalizedMessage"))
 .Append(mlContext.Transforms.Text.ExtractWordEmbeddings("Embeddings", "TokenizedMessage",
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D));
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));

// Let's train our pipeline, and then apply it to the same data.
// Note that even on a small dataset of 70KB the pipeline above can take up to a minute to completely train.
Expand Down

6 changes: 3 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -61,8 +61,8 @@ public static void Example()

 // Let's apply pretrained word embedding model GloVeTwitter25D.
 // 25D means each word mapped into 25 dimensional space, basically each word represented by 25 float values.
 var gloveWordEmbedding = ml.Transforms.Text.ExtractWordEmbeddings("GloveEmbeddings", "CleanWords",
WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D);
 var gloveWordEmbedding = ml.Transforms.Text.ApplyWordEmbedding("GloveEmbeddings", "CleanWords",
WordEmbeddingEstimator.PretrainedModelKind.GloVeTwitter25D);

 // We also have option to apply custom word embedding models.
 // Let's first create one.
Expand All@@ -81,7 +81,7 @@ public static void Example()
 file.WriteLine("best" + " " + string.Join(" ", 0f, 0f, 20f));
 }
 // Now let's add custom embedding on top of same words.
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ExtractWordEmbeddings("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ApplyWordEmbedding("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));

 // And do all required transformations.
 var embeddingDataview = pipeline.Fit(wordsDataview).Transform(wordsDataview);
Expand Down

16 changes: 8 additions & 8 deletions
src/Microsoft.ML.StaticPipe/WordEmbeddingsStaticExtensions.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,7 @@ public static class WordEmbeddingsStaticExtensions
 /// <param name="input">Vector of tokenized text.</param>
 /// <param name="modelKind">The pretrained word embedding model.</param>
 /// <returns></returns>
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 Contracts.CheckValue(input, nameof(input));
 return new OutColumn(input, modelKind);
Expand All@@ -33,7 +33,7 @@ private sealed class OutColumn : Vector<float>
 {
 public PipelineColumn Input { get; }

 public OutColumn(VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public OutColumn(VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 : base(new Reconciler(modelKind), input)
 {
 Input = input;
Expand All@@ -48,10 +48,10 @@ public OutColumn(VarVector<string> input, string customModelFile = null)

 private sealed class Reconciler : EstimatorReconciler
 {
 private readonly WordEmbeddingsExtractingEstimator.PretrainedModelKind? _modelKind;
 private readonly WordEmbeddingEstimator.PretrainedModelKind? _modelKind;
 private readonly string _customLookupTable;

 public Reconciler(WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public Reconciler(WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 _modelKind = modelKind;
 _customLookupTable = null;
Expand All@@ -71,18 +71,18 @@ public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
 {
 Contracts.Assert(toOutput.Length == 1);

 var cols = new WordEmbeddingsExtractingEstimator.ColumnOptions[toOutput.Length];
 var cols = new WordEmbeddingEstimator.ColumnOptions[toOutput.Length];
 for (int i = 0; i < toOutput.Length; ++i)
 {
 var outCol = (OutColumn)toOutput[i];
 cols[i] = new WordEmbeddingsExtractingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 cols[i] = new WordEmbeddingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 }

 bool customLookup = !string.IsNullOrWhiteSpace(_customLookupTable);
 if (customLookup)
 return new WordEmbeddingsExtractingEstimator(env, _customLookupTable, cols);
 return new WordEmbeddingEstimator(env, _customLookupTable, cols);
 else
 return new WordEmbeddingsExtractingEstimator(env, _modelKind.Value, cols);
 return new WordEmbeddingEstimator(env, _modelKind.Value, cols);
 }
 }
 }
Expand Down

10 changes: 5 additions & 5 deletions
src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -132,16 +132,16 @@ public static CommonOutputs.TransformOutput LightLda(IHostEnvironment env, Laten
 }

 [TlcModule.EntryPoint(Name = "Transforms.WordEmbeddings",
 Desc = WordEmbeddingsExtractingTransformer.Summary,
 UserName = WordEmbeddingsExtractingTransformer.UserName,
 ShortName = WordEmbeddingsExtractingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingsExtractingTransformer.Options input)
 Desc = WordEmbeddingTransformer.Summary,
 UserName = WordEmbeddingTransformer.UserName,
 ShortName = WordEmbeddingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingTransformer.Options input)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(input, nameof(input));

 var h = EntryPointUtils.CheckArgsAndCreateHost(env, "WordEmbeddings", input);
 var view = WordEmbeddingsExtractingTransformer.Create(h, input, input.Data);
 var view = WordEmbeddingTransformer.Create(h, input, input.Data);
 return new CommonOutputs.TransformOutput()
 {
 Model = new TransformModelImpl(h, view, input.Data),
Expand Down

22 changes: 11 additions & 11 deletions
src/Microsoft.ML.Transforms/Text/TextCatalog.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -101,19 +101,19 @@ public static TextNormalizingEstimator NormalizeText(this TransformsCatalog.Text
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
 /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <example>
 /// <format type="text/markdown">
 /// <![CDATA[
 /// [!code-csharp[FeaturizeText](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs)]
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string inputColumnName = null,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
Expand All@@ -127,16 +127,16 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string customModelFile,
 string inputColumnName = null)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 outputColumnName, customModelFile, inputColumnName ?? outputColumnName);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="columns">The array columns, and per-column configurations to extract embeedings from.</param>
 /// <example>
 /// <format type="text/markdown">
Expand All@@ -145,10 +145,10 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe,
 params WordEmbeddingsExtractingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding,
 params WordEmbeddingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);

 /// <summary>
 /// Tokenizes incoming text in <paramref name="inputColumnName"/>, using <paramref name="separators"/> as separators,
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Mar 11, 2019
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Scrub word embedding transform
#2891
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12e165a

Scrub word embedding


wschin Mar 8, 2019
f99d131

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wschin Mar 8, 2019
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wschin Mar 9, 2019
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2 changes: 1 addition & 1 deletion
docs/code/MlNetCookBook.md

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -782,7 +782,7 @@ var pipeline =
 // NLP pipeline 4: word embeddings.
 .Append(mlContext.Transforms.Text.TokenizeWords("TokenizedMessage", "NormalizedMessage"))
 .Append(mlContext.Transforms.Text.ExtractWordEmbeddings("Embeddings", "TokenizedMessage",
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D));
 WordEmbeddingsExtractingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));

// Let's train our pipeline, and then apply it to the same data.
// Note that even on a small dataset of 70KB the pipeline above can take up to a minute to completely train.
Expand Down

6 changes: 3 additions & 3 deletions
docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -61,8 +61,8 @@ public static void Example()

 // Let's apply pretrained word embedding model GloVeTwitter25D.
 // 25D means each word mapped into 25 dimensional space, basically each word represented by 25 float values.
 var gloveWordEmbedding = ml.Transforms.Text.ExtractWordEmbeddings("GloveEmbeddings", "CleanWords",
WordEmbeddingsExtractingEstimator.PretrainedModelKind.GloVeTwitter25D);
 var gloveWordEmbedding = ml.Transforms.Text.ApplyWordEmbedding("GloveEmbeddings", "CleanWords",
WordEmbeddingEstimator.PretrainedModelKind.GloVeTwitter25D);

 // We also have option to apply custom word embedding models.
 // Let's first create one.
Expand All@@ -81,7 +81,7 @@ public static void Example()
 file.WriteLine("best" + " " + string.Join(" ", 0f, 0f, 20f));
 }
 // Now let's add custom embedding on top of same words.
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ExtractWordEmbeddings("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));
 var pipeline = gloveWordEmbedding.Append(ml.Transforms.Text.ApplyWordEmbedding("CustomEmbeddings", @".\custommodel.txt", "CleanWords"));

 // And do all required transformations.
 var embeddingDataview = pipeline.Fit(wordsDataview).Transform(wordsDataview);
Expand Down

16 changes: 8 additions & 8 deletions
src/Microsoft.ML.StaticPipe/WordEmbeddingsStaticExtensions.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,7 @@ public static class WordEmbeddingsStaticExtensions
 /// <param name="input">Vector of tokenized text.</param>
 /// <param name="modelKind">The pretrained word embedding model.</param>
 /// <returns></returns>
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public static Vector<float> WordEmbeddings(this VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 Contracts.CheckValue(input, nameof(input));
 return new OutColumn(input, modelKind);
Expand All@@ -33,7 +33,7 @@ private sealed class OutColumn : Vector<float>
 {
 public PipelineColumn Input { get; }

 public OutColumn(VarVector<string> input, WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public OutColumn(VarVector<string> input, WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 : base(new Reconciler(modelKind), input)
 {
 Input = input;
Expand All@@ -48,10 +48,10 @@ public OutColumn(VarVector<string> input, string customModelFile = null)

 private sealed class Reconciler : EstimatorReconciler
 {
 private readonly WordEmbeddingsExtractingEstimator.PretrainedModelKind? _modelKind;
 private readonly WordEmbeddingEstimator.PretrainedModelKind? _modelKind;
 private readonly string _customLookupTable;

 public Reconciler(WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 public Reconciler(WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 {
 _modelKind = modelKind;
 _customLookupTable = null;
Expand All@@ -71,18 +71,18 @@ public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
 {
 Contracts.Assert(toOutput.Length == 1);

 var cols = new WordEmbeddingsExtractingEstimator.ColumnOptions[toOutput.Length];
 var cols = new WordEmbeddingEstimator.ColumnOptions[toOutput.Length];
 for (int i = 0; i < toOutput.Length; ++i)
 {
 var outCol = (OutColumn)toOutput[i];
 cols[i] = new WordEmbeddingsExtractingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 cols[i] = new WordEmbeddingEstimator.ColumnOptions(outputNames[outCol], inputNames[outCol.Input]);
 }

 bool customLookup = !string.IsNullOrWhiteSpace(_customLookupTable);
 if (customLookup)
 return new WordEmbeddingsExtractingEstimator(env, _customLookupTable, cols);
 return new WordEmbeddingEstimator(env, _customLookupTable, cols);
 else
 return new WordEmbeddingsExtractingEstimator(env, _modelKind.Value, cols);
 return new WordEmbeddingEstimator(env, _modelKind.Value, cols);
 }
 }
 }
Expand Down

10 changes: 5 additions & 5 deletions
src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -132,16 +132,16 @@ public static CommonOutputs.TransformOutput LightLda(IHostEnvironment env, Laten
 }

 [TlcModule.EntryPoint(Name = "Transforms.WordEmbeddings",
 Desc = WordEmbeddingsExtractingTransformer.Summary,
 UserName = WordEmbeddingsExtractingTransformer.UserName,
 ShortName = WordEmbeddingsExtractingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingsExtractingTransformer.Options input)
 Desc = WordEmbeddingTransformer.Summary,
 UserName = WordEmbeddingTransformer.UserName,
 ShortName = WordEmbeddingTransformer.ShortName)]
 public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingTransformer.Options input)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(input, nameof(input));

 var h = EntryPointUtils.CheckArgsAndCreateHost(env, "WordEmbeddings", input);
 var view = WordEmbeddingsExtractingTransformer.Create(h, input, input.Data);
 var view = WordEmbeddingTransformer.Create(h, input, input.Data);
 return new CommonOutputs.TransformOutput()
 {
 Model = new TransformModelImpl(h, view, input.Data),
Expand Down

22 changes: 11 additions & 11 deletions
src/Microsoft.ML.Transforms/Text/TextCatalog.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -101,19 +101,19 @@ public static TextNormalizingEstimator NormalizeText(this TransformsCatalog.Text
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
 /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <example>
 /// <format type="text/markdown">
 /// <![CDATA[
 /// [!code-csharp[FeaturizeText](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/WordEmbeddingTransform.cs)]
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string inputColumnName = null,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), outputColumnName, inputColumnName, modelKind);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
Expand All@@ -127,16 +127,16 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
 string outputColumnName,
 string customModelFile,
 string inputColumnName = null)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
 outputColumnName, customModelFile, inputColumnName ?? outputColumnName);

 /// <include file='doc.xml' path='doc/members/member[@name="WordEmbeddings"]/*' />
 /// <param name="catalog">The text-related transform's catalog.</param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingsExtractingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="modelKind">The embeddings <see cref="WordEmbeddingEstimator.PretrainedModelKind"/> to use. </param>
 /// <param name="columns">The array columns, and per-column configurations to extract embeedings from.</param>
 /// <example>
 /// <format type="text/markdown">
Expand All@@ -145,10 +145,10 @@ public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this Trans
 /// ]]>
 /// </format>
 /// </example>
 public static WordEmbeddingsExtractingEstimator ExtractWordEmbeddings(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingsExtractingEstimator.PretrainedModelKind modelKind = WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe,
 params WordEmbeddingsExtractingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingsExtractingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);
 public static WordEmbeddingEstimator ApplyWordEmbedding(this TransformsCatalog.TextTransforms catalog,
WordEmbeddingEstimator.PretrainedModelKind modelKind = WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding,
 params WordEmbeddingEstimator.ColumnOptions[] columns)
 => new WordEmbeddingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), modelKind, columns);

 /// <summary>
 /// Tokenizes incoming text in <paramref name="inputColumnName"/>, using <paramref name="separators"/> as separators,
Expand Down
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