# TransformsCatalog.TextTransforms Class

## Definition

Class used by MLContext to create instances of text data transform components.

public sealed class TransformsCatalog.TextTransforms
type TransformsCatalog.TextTransforms = class
Public NotInheritable Class TransformsCatalog.TextTransforms
Inheritance
TransformsCatalog.TextTransforms

## Extension Methods

 Create an WordEmbeddingEstimator, which is a text featurizer that converts a vector of text into a numerical vector using pre-trained embeddings models. Create an WordEmbeddingEstimator, which is a text featurizer that converts vectors of text into numerical vectors using pre-trained embeddings models. Create a TextFeaturizingEstimator, which transforms a text column into featurized float array that represents normalized counts of n-grams and char-grams. Create a TextFeaturizingEstimator, which transforms a text column into a featurized vector of Single that represents normalized counts of n-grams and char-grams. Create a LatentDirichletAllocationEstimator, which uses LightLDA to transform text (represented as a vector of floats) into a vector of Single indicating the similarity of the text with each topic identified. Creates a TextNormalizingEstimator, which normalizes incoming text in inputColumnName by optionally changing case, removing diacritical marks, punctuation marks, numbers, and outputs new text as outputColumnName. Create a NgramHashingEstimator, which copies the data from the column specified in inputColumnName to a new column: outputColumnName and produces a vector of counts of hashed n-grams. Create a NgramHashingEstimator, which takes the data from the multiple columns specified in inputColumnNames to a new column: outputColumnName and produces a vector of counts of hashed n-grams. Create a WordHashBagEstimator, which maps the column specified in inputColumnName to a vector of counts of hashed n-grams in a new column named outputColumnName. Create a WordHashBagEstimator, which maps the multiple columns specified in inputColumnNames to a vector of counts of hashed n-grams in a new column named outputColumnName. Creates a NgramExtractingEstimator which produces a vector of counts of n-grams (sequences of consecutive words) encountered in the input text. Create a WordHashBagEstimator, which maps the column specified in inputColumnName to a vector of n-gram counts in a new column named outputColumnName. Create a WordHashBagEstimator, which maps the multiple columns specified in inputColumnNames to a vector of n-gram counts in a new column named outputColumnName. Create a CustomStopWordsRemovingEstimator, which copies the data from the column specified in inputColumnName to a new column: outputColumnName and removes predifined set of text specific for language from it. Create a CustomStopWordsRemovingEstimator, which copies the data from the column specified in inputColumnName to a new column: outputColumnName and removes text specified in stopwords from it. Create a TokenizingByCharactersEstimator, which tokenizes by splitting text into sequences of characters using a sliding window. Create a WordTokenizingEstimator, which tokenizes input text using separators as separators.