CustomStopWordsRemovingEstimator Class
Definition
Important
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public sealed class CustomStopWordsRemovingEstimator : Microsoft.ML.Data.TrivialEstimator<Microsoft.ML.Transforms.Text.CustomStopWordsRemovingTransformer>type CustomStopWordsRemovingEstimator = class
inherit TrivialEstimator<CustomStopWordsRemovingTransformer>Public NotInheritable Class CustomStopWordsRemovingEstimator
Inherits TrivialEstimator(Of CustomStopWordsRemovingTransformer)- Inheritance
Remarks
Estimator Characteristics
| Does this estimator need to look at the data to train its parameters? | No |
| Input column data type | Vector of Text |
| Output column data type | Vector of Text |
| Exportable to ONNX | Yes |
The resulting CustomStopWordsRemovingTransformer creates a new column, named as specified by the output column name parameter, and fills it with a vector of words containing all of the words in the input column except those given by the stopwords parameter. All text comparison made by casting provided words and words from input column to lower case using casing rules of invariant culture.
Check the See Also section for links to usage examples.
Methods
| Fit(IDataView) | (Inherited from TrivialEstimator<TTransformer>) |
| GetOutputSchema(SchemaShape) |
Returns the SchemaShape of the schema which will be produced by the transformer. Used for schema propagation and verification in a pipeline. |
Extension Methods
| AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment) |
Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes. |
| WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>) |
Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called. |