Rename NameEntity to NamedEntity (#6917)

src/Microsoft.ML.TorchSharp/NasBert/BertTaskType.cs

@@ -4,6 +4,7 @@
 
 using System;
 using System.Collections.Generic;
+using System.ComponentModel;
 using System.Text;
 
 namespace Microsoft.ML.TorchSharp.NasBert
@@ -17,7 +18,10 @@ namespace Microsoft.ML.TorchSharp.NasBert
         MaskedLM = 1,
         TextClassification = 2,
         SentenceRegression = 3,
-        NameEntityRecognition = 4,
+        NamedEntityRecognition = 4,
+        [Obsolete("Please use NamedEntityRecognition instead", false)]
+        [EditorBrowsable(EditorBrowsableState.Never)]
+        NameEntityRecognition = NamedEntityRecognition,
         QuestionAnswering = 5
     }
 }

src/Microsoft.ML.TorchSharp/NasBert/NasBertTrainer.cs

@@ -204,7 +204,7 @@ namespace Microsoft.ML.TorchSharp.NasBert
                 EnglishRoberta tokenizerModel = Tokenizer.RobertaModel();
 
                 NasBertModel model;
-                if (Parent.BertOptions.TaskType == BertTaskType.NameEntityRecognition)
+                if (Parent.BertOptions.TaskType == BertTaskType.NamedEntityRecognition)
                     model = new NerModel(Parent.BertOptions, tokenizerModel.PadIndex, tokenizerModel.SymbolsCount, Parent.Option.NumberOfClasses);
                 else
                     model = new ModelForPrediction(Parent.BertOptions, tokenizerModel.PadIndex, tokenizerModel.SymbolsCount, Parent.Option.NumberOfClasses);
@@ -268,7 +268,7 @@ namespace Microsoft.ML.TorchSharp.NasBert
             private protected override void RunModelAndBackPropagate(ref List<Tensor> inputTensors, ref Tensor targetsTensor)
             {
                 Tensor logits = default;
-                if (Parent.BertOptions.TaskType == BertTaskType.NameEntityRecognition)
+                if (Parent.BertOptions.TaskType == BertTaskType.NamedEntityRecognition)
                 {
                     int[,] lengthArray = new int[inputTensors.Count, 1];
                     for (int i = 0; i < inputTensors.Count; i++)
@@ -293,7 +293,7 @@ namespace Microsoft.ML.TorchSharp.NasBert
                 torch.Tensor loss;
                 if (Parent.BertOptions.TaskType == BertTaskType.TextClassification)
                     loss = torch.nn.CrossEntropyLoss(reduction: Parent.BertOptions.Reduction).forward(logits, targetsTensor);
-                else if (Parent.BertOptions.TaskType == BertTaskType.NameEntityRecognition)
+                else if (Parent.BertOptions.TaskType == BertTaskType.NamedEntityRecognition)
                 {
                     targetsTensor = targetsTensor.@long().view(-1);
                     logits = logits.view(-1, logits.size(-1));
@@ -338,7 +338,7 @@ namespace Microsoft.ML.TorchSharp.NasBert
                 outColumns[Option.ScoreColumnName] = new SchemaShape.Column(Option.ScoreColumnName, SchemaShape.Column.VectorKind.Vector,
                     NumberDataViewType.Single, false, new SchemaShape(AnnotationUtils.AnnotationsForMulticlassScoreColumn(labelCol)));
             }
-            else if (BertOptions.TaskType == BertTaskType.NameEntityRecognition)
+            else if (BertOptions.TaskType == BertTaskType.NamedEntityRecognition)
             {
                 var metadata = new List<SchemaShape.Column>();
                 metadata.Add(new SchemaShape.Column(AnnotationUtils.Kinds.KeyValues, SchemaShape.Column.VectorKind.Vector,
@@ -387,7 +387,7 @@ namespace Microsoft.ML.TorchSharp.NasBert
                             TextDataViewType.Instance.ToString(), sentenceCol2.GetTypeString());
                 }
             }
-            else if (BertOptions.TaskType == BertTaskType.NameEntityRecognition)
+            else if (BertOptions.TaskType == BertTaskType.NamedEntityRecognition)
             {
                 if (labelCol.ItemType != NumberDataViewType.UInt32)
                     throw Host.ExceptSchemaMismatch(nameof(inputSchema), "label", Option.LabelColumnName,
@@ -535,7 +535,7 @@ namespace Microsoft.ML.TorchSharp.NasBert
                     info[1] = new DataViewSchema.DetachedColumn(Parent.Options.ScoreColumnName, new VectorDataViewType(NumberDataViewType.Single, Parent.Options.NumberOfClasses), meta.ToAnnotations());
                     return info;
                 }
-                else if (Parent.BertOptions.TaskType == BertTaskType.NameEntityRecognition)
+                else if (Parent.BertOptions.TaskType == BertTaskType.NamedEntityRecognition)
                 {
                     var info = new DataViewSchema.DetachedColumn[1];
                     var keyType = Parent.LabelColumn.Annotations.Schema.GetColumnOrNull(AnnotationUtils.Kinds.KeyValues)?.Type as VectorDataViewType;

src/Microsoft.ML.TorchSharp/NasBert/NerTrainer.cs

@@ -35,7 +35,7 @@ namespace Microsoft.ML.TorchSharp.NasBert
     /// </summary>
     /// <remarks>
     /// <format type="text/markdown"><![CDATA[
-    /// To create this trainer, use [NER](xref:Microsoft.ML.TorchSharpCatalog.NameEntityRecognition(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,System.String,System.String,System.String,Int32,Int32,Int32,Microsoft.ML.TorchSharp.NasBert.BertArchitecture,Microsoft.ML.IDataView)).
+    /// To create this trainer, use [NER](xref:Microsoft.ML.TorchSharpCatalog.NamedEntityRecognition(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,System.String,System.String,System.String,Int32,Int32,Int32,Microsoft.ML.TorchSharp.NasBert.BertArchitecture,Microsoft.ML.IDataView)).
     ///
     /// ### Input and Output Columns
     /// The input label column data must be a Vector of [string](xref:Microsoft.ML.Data.TextDataViewType) type and the sentence columns must be of type<xref:Microsoft.ML.Data.TextDataViewType>.
@@ -54,7 +54,7 @@ namespace Microsoft.ML.TorchSharp.NasBert
     /// | 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 name entity recognition.
+    /// Trains a Deep Neural Network(DNN) by leveraging an existing pre-trained NAS-BERT roBERTa model for the purpose of named entity recognition.
     /// ]]>
     /// </format>
     /// </remarks>
@@ -93,7 +93,7 @@ namespace Microsoft.ML.TorchSharp.NasBert
                 BatchSize = batchSize,
                 MaxEpoch = maxEpochs,
                 ValidationSet = validationSet,
-                TaskType = BertTaskType.NameEntityRecognition
+                TaskType = BertTaskType.NamedEntityRecognition
             })
         {
         }
@@ -295,7 +295,7 @@ namespace Microsoft.ML.TorchSharp.NasBert
 
             options.Sentence1ColumnName = ctx.LoadString();
             options.Sentence2ColumnName = ctx.LoadStringOrNull();
-            options.TaskType = BertTaskType.NameEntityRecognition;
+            options.TaskType = BertTaskType.NamedEntityRecognition;
 
             BinarySaver saver = new BinarySaver(env, new BinarySaver.Arguments());
             DataViewType type;

src/Microsoft.ML.TorchSharp/TorchSharpCatalog.cs

@@ -4,6 +4,7 @@
 
 using System;
 using System.Collections.Generic;
+using System.ComponentModel;
 using System.Text;
 using Microsoft.ML.Data;
 using Microsoft.ML.TorchSharp.AutoFormerV2;
@@ -161,7 +162,45 @@ namespace Microsoft.ML.TorchSharp
         }
 
         /// <summary>
-        /// Fine tune a NAS-BERT model for Name Entity Recognition. The limit for any sentence is 512 tokens. Each word typically
+        /// Obsolete: please use the <see cref="NamedEntityRecognition(MulticlassClassificationCatalog.MulticlassClassificationTrainers, string, string, string, int, int, BertArchitecture, IDataView)"/> method instead
+        /// </summary>
+        /// <param name="catalog">The transform's catalog.</param>
+        /// <param name="labelColumnName">Name of the label column. Column should be a key type.</param>
+        /// <param name="outputColumnName">Name of the output column. It will be a key type. It is the predicted label.</param>
+        /// <param name="sentence1ColumnName">Name of the column for the first sentence.</param>
+        /// <param name="batchSize">Number of rows in the batch.</param>
+        /// <param name="maxEpochs">Maximum number of times to loop through your training set.</param>
+        /// <param name="architecture">Architecture for the model. Defaults to Roberta.</param>
+        /// <param name="validationSet">The validation set used while training to improve model quality.</param>
+        /// <returns></returns>
+        [Obsolete("Please use NamedEntityRecognition method instead", false)]
+        [EditorBrowsable(EditorBrowsableState.Never)]
+        public static NerTrainer NameEntityRecognition(
+            this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
+            string labelColumnName = DefaultColumnNames.Label,
+            string outputColumnName = DefaultColumnNames.PredictedLabel,
+            string sentence1ColumnName = "Sentence",
+            int batchSize = 32,
+            int maxEpochs = 10,
+            BertArchitecture architecture = BertArchitecture.Roberta,
+            IDataView validationSet = null)
+            => NamedEntityRecognition(catalog, labelColumnName, outputColumnName, sentence1ColumnName, batchSize, maxEpochs, architecture, validationSet);
+
+        /// <summary>
+        /// Obsolete: please use the <see cref="NamedEntityRecognition(MulticlassClassificationCatalog.MulticlassClassificationTrainers, NerTrainer.NerOptions)"/> method instead
+        /// </summary>
+        /// <param name="catalog">The transform's catalog.</param>
+        /// <param name="options">The full set of advanced options.</param>
+        /// <returns></returns>
+        [Obsolete("Please use NamedEntityRecognition method instead", false)]
+        [EditorBrowsable(EditorBrowsableState.Never)]
+        public static NerTrainer NameEntityRecognition(
+            this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
+            NerTrainer.NerOptions options)
+            => NamedEntityRecognition(catalog, options);
+
+        /// <summary>
+        /// 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.
         /// </summary>
@@ -174,7 +213,7 @@ namespace Microsoft.ML.TorchSharp
         /// <param name="architecture">Architecture for the model. Defaults to Roberta.</param>
         /// <param name="validationSet">The validation set used while training to improve model quality.</param>
         /// <returns></returns>
-        public static NerTrainer NameEntityRecognition(
+        public static NerTrainer NamedEntityRecognition(
             this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
             string labelColumnName = DefaultColumnNames.Label,
             string outputColumnName = DefaultColumnNames.PredictedLabel,
@@ -186,12 +225,12 @@ namespace Microsoft.ML.TorchSharp
             => new NerTrainer(CatalogUtils.GetEnvironment(catalog), labelColumnName, outputColumnName, sentence1ColumnName, batchSize, maxEpochs, validationSet, architecture);
 
         /// <summary>
-        /// Fine tune a Name Entity Recognition model.
+        /// Fine tune a Named Entity Recognition model.
         /// </summary>
         /// <param name="catalog">The transform's catalog.</param>
         /// <param name="options">The full set of advanced options.</param>
         /// <returns></returns>
-        public static NerTrainer NameEntityRecognition(
+        public static NerTrainer NamedEntityRecognition(
             this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
             NerTrainer.NerOptions options)
             => new NerTrainer(CatalogUtils.GetEnvironment(catalog), options);

test/Microsoft.ML.Tests/NerTests.cs

@@ -54,7 +54,7 @@ namespace Microsoft.ML.Tests
                 }));
             var chain = new EstimatorChain<ITransformer>();
             var estimator = chain.Append(ML.Transforms.Conversion.MapValueToKey("Label", keyData: labels))
-               .Append(ML.MulticlassClassification.Trainers.NameEntityRecognition(outputColumnName: "outputColumn"))
+               .Append(ML.MulticlassClassification.Trainers.NamedEntityRecognition(outputColumnName: "outputColumn"))
                .Append(ML.Transforms.Conversion.MapKeyToValue("outputColumn"));
 
             var estimatorSchema = estimator.GetOutputSchema(SchemaShape.Create(dataView.Schema));