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Merge branch 'main' into 6182_load_csv_duplicate_columns
2 parents ecb9114 + 39235a7 commit 2c72e8c

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28 files changed

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‎build/libomp.rb‎

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@@ -15,7 +15,6 @@ class Libomp < Formula
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end
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depends_on "cmake" => :build
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depends_on macos: :yosemite
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def install
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system "cmake", ".", *std_cmake_args

‎src/Microsoft.Data.Analysis/DataFrame.IO.cs‎

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@@ -459,14 +459,13 @@ private static DataFrame ReadCsvLinesIntoDataFrame(WrappedStreamReaderOrStringRe
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if (addIndexColumn)
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{
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PrimitiveDataFrameColumn<int> indexColumn = new PrimitiveDataFrameColumn<int>("IndexColumn", columns[0].Length);
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for (int i = 0; i < columns[0].Length; i++)
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Int64DataFrameColumn indexColumn = new Int64DataFrameColumn("IndexColumn", columns[0].Length);
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for (long i = 0; i < columns[0].Length; i++)
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{
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indexColumn[i] = i;
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}
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columns.Insert(0, indexColumn);
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ret.Columns.Insert(0, indexColumn);
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}
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}
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return ret;

‎src/Microsoft.ML.AutoML/CodeGen/estimator-schema.json‎

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"ForecastBySsa",
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"TextClassifcation",
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"SentenceSimilarity",
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"ObjectDetection"
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"ObjectDetection",
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"QuestionAnswering"
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]
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},
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"nugetDependencies": {

‎src/Microsoft.ML.AutoML/CodeGen/question_answering_search_space.json‎

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{
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"$schema": "./search-space-schema.json#",
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"name": "question_answering_option",
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"search_space": [
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{
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"name": "ContextColumnName",
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"type": "string",
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"default": "Context"
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},
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{
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"name": "QuestionColumnName",
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"type": "string",
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"default": "Question"
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},
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{
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"name": "TrainingAnswerColumnName",
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"type": "string",
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"default": "TrainingAnswer"
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},
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{
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"name": "AnswerIndexStartColumnName",
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"type": "string",
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"default": "AnswerStart"
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},
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{
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"name": "ScoreColumnName",
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"type": "string",
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"default": "Score"
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},
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{
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"name": "predictedAnswerColumnName",
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"type": "string",
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"default": "Answer"
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},
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{
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"name": "BatchSize",
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"type": "integer",
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"default": 4
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},
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{
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"name": "MaxEpochs",
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"type": "integer",
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"default": 10
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},
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{
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"name": "TopKAnswers",
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"type": "integer",
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"default": 3
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},
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{
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"name": "Architecture",
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"type": "bertArchitecture",
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"default": "BertArchitecture.Roberta"
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}
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]
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}

‎src/Microsoft.ML.AutoML/CodeGen/search-space-schema.json‎

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"BertArchitecture.Roberta"
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]
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},
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"dataKind": {
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"type": "string",
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"enum": [
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"DataKind.Int16",
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"DataKind.SByte",
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"DataKind.Byte",
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"DataKind.UInt16",
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"DataKind.Int32",
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"DataKind.UInt32",
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"DataKind.Int64",
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"DataKind.UInt64",
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"DataKind.Single",
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"DataKind.Double",
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"DataKind.String",
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"DataKind.Boolean",
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"DataKind.TimeSpan",
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"DataKind.DateTime",
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"DataKind.DateTimeOffset"
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]
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},
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"bertArchitectureArray": {
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"type": "array",
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"items": {
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"dnn_featurizer_image_option",
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"text_classification_option",
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"sentence_similarity_option",
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"object_detection_option"
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"object_detection_option",
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"question_answering_option"
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]
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},
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"option_name": {
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"Steps",
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"MaxEpoch",
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"InitLearningRate",
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"WeightDecay"
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"WeightDecay",
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"ContextColumnName",
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"QuestionColumnName",
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"TrainingAnswerColumnName",
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"AnswerIndexStartColumnName",
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"predictedAnswerColumnName",
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"TopKAnswers",
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"TargetType"
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]
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},
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"option_type": {
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"anchor",
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"dnnModelFactory",
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"bertArchitecture",
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"imageClassificationArchType"
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"imageClassificationArchType",
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"dataKind"
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]
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}
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},

‎src/Microsoft.ML.AutoML/CodeGen/trainer-estimators.json‎

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"usingStatements": [ "Microsoft.ML", "Microsoft.ML.Trainers", "Microsoft.ML.TorchSharp" ],
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"searchOption": "object_detection_option"
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},
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{
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"functionName": "QuestionAnswering",
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"estimatorTypes": [ "MultiClassification" ],
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"nugetDependencies": [ "Microsoft.ML", "Microsoft.ML.TorchSharp" ],
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"usingStatements": [ "Microsoft.ML", "Microsoft.ML.Trainers", "Microsoft.ML.TorchSharp" ],
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"searchOption": "question_answering_option"
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},
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{
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"functionName": "ForecastBySsa",
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"estimatorTypes": [ "Forecasting" ],

‎src/Microsoft.ML.AutoML/CodeGen/type_converter_search_space.json‎

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{
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"name": "InputColumnNames",
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"type": "strings"
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},
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{
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"name": "TargetType",
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"type": "dataKind",
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"default": "DataKind.Single"
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}
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]
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}

‎src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj‎

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<AdditionalFiles Include="CodeGen\code_gen_flag.json" />
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<AdditionalFiles Include="CodeGen\*-estimators.json" />
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</ItemGroup>
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<ItemGroup>
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<EmbeddedResource Include="Tuner\Portfolios.json">
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<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>

‎src/Microsoft.ML.AutoML/SweepableEstimator/Estimators/QuestionAnswering.cs‎

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// Licensed to the .NET Foundation under one or more agreements.
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// The .NET Foundation licenses this file to you under the MIT license.
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// See the LICENSE file in the project root for more information.
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using Microsoft.ML.TorchSharp;
5+
using Microsoft.ML.TorchSharp.NasBert;
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using Microsoft.ML.TorchSharp.Roberta;
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8+
namespace Microsoft.ML.AutoML.CodeGen
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{
10+
internal partial class QuestionAnsweringMulti
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{
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public override IEstimator<ITransformer> BuildFromOption(MLContext context, QuestionAnsweringOption param)
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{
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return context.MulticlassClassification.Trainers.QuestionAnswer(
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contextColumnName: param.ContextColumnName,
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questionColumnName: param.QuestionColumnName,
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trainingAnswerColumnName: param.TrainingAnswerColumnName,
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answerIndexColumnName: param.AnswerIndexStartColumnName,
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predictedAnswerColumnName: param.PredictedAnswerColumnName,
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scoreColumnName: param.ScoreColumnName,
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batchSize: param.BatchSize,
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maxEpochs: param.MaxEpochs,
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topK: param.TopKAnswers,
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architecture: BertArchitecture.Roberta);
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}
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}
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}

‎src/Microsoft.ML.AutoML/SweepableEstimator/Estimators/TypeConvert.cs‎

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public override IEstimator<ITransformer> BuildFromOption(MLContext context, ConvertTypeOption param)
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{
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var inputOutputPairs = AutoMlUtils.CreateInputOutputColumnPairsFromStrings(param.InputColumnNames, param.OutputColumnNames);
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return context.Transforms.Conversion.ConvertType(inputOutputPairs);
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return context.Transforms.Conversion.ConvertType(inputOutputPairs, param.TargetType);
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}
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}
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}

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