diff --git a/NoSoliciting.Trainer/NoSoliciting.Trainer.csproj b/NoSoliciting.Trainer/NoSoliciting.Trainer.csproj
index ce5e5a2..755076e 100755
--- a/NoSoliciting.Trainer/NoSoliciting.Trainer.csproj
+++ b/NoSoliciting.Trainer/NoSoliciting.Trainer.csproj
@@ -10,8 +10,11 @@
-
+
+
+
+
diff --git a/NoSoliciting.Trainer/Program.cs b/NoSoliciting.Trainer/Program.cs
index ed0980f..3210a03 100644
--- a/NoSoliciting.Trainer/Program.cs
+++ b/NoSoliciting.Trainer/Program.cs
@@ -10,6 +10,7 @@ using CsvHelper;
using CsvHelper.Configuration;
using Microsoft.ML;
using Microsoft.ML.Data;
+using Microsoft.ML.TorchSharp;
using Microsoft.ML.Transforms.Text;
using MimeKit;
using Newtonsoft.Json;
@@ -211,12 +212,12 @@ namespace NoSoliciting.Trainer {
.Append(ctx.Transforms.CustomMapping(compute.GetMapping(), "Compute"))
.Append(ctx.Transforms.CustomMapping(normalise.GetMapping(), "Normalise"))
.Append(ctx.Transforms.Text.NormalizeText("MsgNormal", nameof(Data.Normalise.Normalised.NormalisedMessage), keepPunctuations: false, keepNumbers: false))
- .Append(ctx.Transforms.Text.TokenizeIntoWords("MsgTokens", "MsgNormal"))
- .Append(ctx.Transforms.Text.RemoveDefaultStopWords("MsgNoDefStop", "MsgTokens"))
- .Append(ctx.Transforms.Text.RemoveStopWords("MsgNoStop", "MsgNoDefStop", StopWords))
- .Append(ctx.Transforms.Conversion.MapValueToKey("MsgKey", "MsgNoStop"))
- .Append(ctx.Transforms.Text.ProduceNgrams("MsgNgrams", "MsgKey", weighting: NgramExtractingEstimator.WeightingCriteria.Tf))
- .Append(ctx.Transforms.NormalizeLpNorm("FeaturisedMessage", "MsgNgrams"))
+ // .Append(ctx.Transforms.Text.TokenizeIntoWords("MsgTokens", "MsgNormal"))
+ // .Append(ctx.Transforms.Text.RemoveDefaultStopWords("MsgNoDefStop", "MsgTokens"))
+ // .Append(ctx.Transforms.Text.RemoveStopWords("MsgNoStop", "MsgNoDefStop", StopWords))
+ // .Append(ctx.Transforms.Conversion.MapValueToKey("MsgKey", "MsgNoStop"))
+ // .Append(ctx.Transforms.Text.ProduceNgrams("MsgNgrams", "MsgKey", weighting: NgramExtractingEstimator.WeightingCriteria.Tf))
+ // .Append(ctx.Transforms.NormalizeLpNorm("FeaturisedMessage", "MsgNgrams"))
.Append(ctx.Transforms.Conversion.ConvertType("CPartyFinder", nameof(Data.Computed.PartyFinder)))
.Append(ctx.Transforms.Conversion.ConvertType("CShout", nameof(Data.Computed.Shout)))
.Append(ctx.Transforms.Conversion.ConvertType("CTrade", nameof(Data.Computed.ContainsTradeWords)))
@@ -224,8 +225,9 @@ namespace NoSoliciting.Trainer {
.Append(ctx.Transforms.Conversion.ConvertType("HasWard", nameof(Data.Computed.ContainsWard)))
.Append(ctx.Transforms.Conversion.ConvertType("HasPlot", nameof(Data.Computed.ContainsPlot)))
.Append(ctx.Transforms.Conversion.ConvertType("HasNumbers", nameof(Data.Computed.ContainsHousingNumbers)))
- .Append(ctx.Transforms.Concatenate("Features", "FeaturisedMessage", "CPartyFinder", "CShout", "CTrade", "HasWard", "HasPlot", "HasNumbers", "CSketch"))
- .Append(ctx.MulticlassClassification.Trainers.SdcaMaximumEntropy(exampleWeightColumnName: "Weight"))
+ // .Append(ctx.Transforms.Concatenate("Features", "FeaturisedMessage", "CPartyFinder", "CShout", "CTrade", "HasWard", "HasPlot", "HasNumbers", "CSketch"))
+ // .Append(ctx.MulticlassClassification.Trainers.SdcaMaximumEntropy(exampleWeightColumnName: "Weight"))
+ .Append(ctx.MulticlassClassification.Trainers.TextClassification(sentence1ColumnName: "MsgNormal"))
.Append(ctx.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
var train = mode switch {