NerTrainer Class
Definition
Important
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The IEstimator<TTransformer> for training a Deep Neural Network(DNN) to classify text.
public class NerTrainer : Microsoft.ML.TorchSharp.NasBert.NasBertTrainer<Microsoft.ML.Data.VBuffer<uint>,Microsoft.ML.Data.VBuffer<long>>type NerTrainer = class
inherit NasBertTrainer<VBuffer<uint32>, VBuffer<int64>>Public Class NerTrainer
Inherits NasBertTrainer(Of VBuffer(Of UInteger), VBuffer(Of Long))- Inheritance
Remarks
To create this trainer, use NER.
Input and Output Columns
The input label column data must be a Vector of string type and the sentence columns must be of typeTextDataViewType.
This trainer outputs the following columns:
| Output Column Name | Column Type | Description |
|---|---|---|
PredictedLabel |
Vector of 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. |
| -- | -- | |
| 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 pre-trained NAS-BERT roBERTa model for the purpose of named entity recognition.
Methods
| 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. |