TorchSharpCatalog.NamedEntityRecognition Method
Definition
Important
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Overloads
| NamedEntityRecognition(MulticlassClassificationCatalog+MulticlassClassificationTrainers, NerTrainer+NerOptions) |
Fine tune a Named Entity Recognition model. |
| NamedEntityRecognition(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Int32, Int32, BertArchitecture, IDataView) |
Fine tune a NAS-BERT model for Named Entity Recognition. The limit for any sentence is 512 tokens. Each word typically will map to a single token, and we automatically add 2 specical tokens (a start token and a separator token) so in general this limit will be 510 words for all sentences. |
NamedEntityRecognition(MulticlassClassificationCatalog+MulticlassClassificationTrainers, NerTrainer+NerOptions)
- Source:
- TorchSharpCatalog.cs
- Source:
- TorchSharpCatalog.cs
- Source:
- TorchSharpCatalog.cs
Fine tune a Named Entity Recognition model.
public static Microsoft.ML.TorchSharp.NasBert.NerTrainer NamedEntityRecognition(this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, Microsoft.ML.TorchSharp.NasBert.NerTrainer.NerOptions options);static member NamedEntityRecognition : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * Microsoft.ML.TorchSharp.NasBert.NerTrainer.NerOptions -> Microsoft.ML.TorchSharp.NasBert.NerTrainer<Extension()>
Public Function NamedEntityRecognition (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, options As NerTrainer.NerOptions) As NerTrainerParameters
The transform's catalog.
- options
- NerTrainer.NerOptions
The full set of advanced options.
Returns
Applies to
NamedEntityRecognition(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Int32, Int32, BertArchitecture, IDataView)
- Source:
- TorchSharpCatalog.cs
- Source:
- TorchSharpCatalog.cs
- Source:
- TorchSharpCatalog.cs
Fine tune a NAS-BERT model for Named Entity Recognition. The limit for any sentence is 512 tokens. Each word typically will map to a single token, and we automatically add 2 specical tokens (a start token and a separator token) so in general this limit will be 510 words for all sentences.
public static Microsoft.ML.TorchSharp.NasBert.NerTrainer NamedEntityRecognition(this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, string labelColumnName = "Label", string outputColumnName = "PredictedLabel", string sentence1ColumnName = "Sentence", int batchSize = 32, int maxEpochs = 10, Microsoft.ML.TorchSharp.NasBert.BertArchitecture architecture = Microsoft.ML.TorchSharp.NasBert.BertArchitecture.Roberta, Microsoft.ML.IDataView validationSet = default);static member NamedEntityRecognition : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * string * string * string * int * int * Microsoft.ML.TorchSharp.NasBert.BertArchitecture * Microsoft.ML.IDataView -> Microsoft.ML.TorchSharp.NasBert.NerTrainer<Extension()>
Public Function NamedEntityRecognition (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, Optional labelColumnName As String = "Label", Optional outputColumnName As String = "PredictedLabel", Optional sentence1ColumnName As String = "Sentence", Optional batchSize As Integer = 32, Optional maxEpochs As Integer = 10, Optional architecture As BertArchitecture = Microsoft.ML.TorchSharp.NasBert.BertArchitecture.Roberta, Optional validationSet As IDataView = Nothing) As NerTrainerParameters
The transform's catalog.
- labelColumnName
- String
Name of the label column. Column should be a key type.
- outputColumnName
- String
Name of the output column. It will be a key type. It is the predicted label.
- sentence1ColumnName
- String
Name of the column for the first sentence.
- batchSize
- Int32
Number of rows in the batch.
- maxEpochs
- Int32
Maximum number of times to loop through your training set.
- architecture
- BertArchitecture
Architecture for the model. Defaults to Roberta.
- validationSet
- IDataView
The validation set used while training to improve model quality.