TextClassificationTrainer Class
Definition
Important
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The IEstimator<TTransformer> for training a Deep Neural Network (DNN) to classify text.
public class TextClassificationTrainer : Microsoft.ML.TorchSharp.NasBert.NasBertTrainer<uint,long>type TextClassificationTrainer = class
inherit NasBertTrainer<uint32, int64>Public Class TextClassificationTrainer
Inherits NasBertTrainer(Of UInteger, Long)- Inheritance
-
TextClassificationTrainer
- Inheritance
Remarks
To create this trainer, use TextClassification.
Input and output columns
The input label column data must be key type and the sentence columns must be of type TextDataViewType.
This trainer outputs the following columns:
| Output column name | Column type | Description |
|---|---|---|
PredictedLabel |
key type | The predicted label's index. If its value is i, the actual label would be the i-th category in the key-valued input label type. |
Score |
Vector ofSingle | The scores of all classes. Higher value means higher probability to fall into the associated class. If the i-th element has the largest value, the predicted label index would be i. Note that i is a zero-based index. |
Trainer characteristics
| Characteristic | Value |
|---|---|
| Machine learning task | Multiclass classification |
| Is normalization required? | No |
| Is caching required? | No |
| Required NuGet in addition to Microsoft.ML | Microsoft.ML.TorchSharp and libtorch-cpu or libtorch-cuda-11.3 or any of the OS specific variants. |
| Exportable to ONNX | No |
Training algorithm details
Trains a Deep Neural Network (DNN) by leveraging an existing, pretrained NAS-BERT roBERTa model for the purpose of classifying text.
Methods
| Fit(IDataView) | (Inherited from NasBertTrainer<TLabelCol,TTargetsCol>) |
| GetOutputSchema(SchemaShape) | (Inherited from NasBertTrainer<TLabelCol,TTargetsCol>) |
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. |