ML.NET NER - Mismatched state_dict sizes: expected 60, but found 126 entries.

Hello,
I'm testing the NER capabilities of ML.NET and on training I'm getting following error:
Error: Mismatched state_dict sizes: expected 60, but found 126 entries.

---
**System Information:**
- OS & Version: Windows 10
- ML.NET Version: ML.NET v4.0.0
- .NET Version: .NET 8.0

**Description of the bug**
on `var transformer = estimator.Fit(dataView);` -> **Mismatched state_dict sizes: expected 60, but found 126 entries**

```
Mismatched state_dict sizes: expected 60, but found 126 entries.
in TorchSharp.torch.nn.Module.load(BinaryReader reader, Boolean strict, IList`1 skip, Dictionary`2 loadedParameters)
in TorchSharp.torch.nn.Module.load(String location, Boolean strict, IList`1 skip, Dictionary`2 loadedParameters)
in Microsoft.ML.TorchSharp.NasBert.NasBertTrainer`2.NasBertTrainerBase.CreateModule(IChannel ch, IDataView input)
in Microsoft.ML.TorchSharp.TorchSharpBaseTrainer`2.TrainerBase..ctor(TorchSharpBaseTrainer`2 parent, IChannel ch, IDataView input, String modelUrl)
in Microsoft.ML.TorchSharp.NasBert.NasBertTrainer`2.NasBertTrainerBase..ctor(TorchSharpBaseTrainer`2 parent, IChannel ch, IDataView input, String modelUrl)
in Microsoft.ML.TorchSharp.NasBert.NerTrainer.Trainer..ctor(TorchSharpBaseTrainer`2 parent, IChannel ch, IDataView input)
in Microsoft.ML.TorchSharp.NasBert.NerTrainer.CreateTrainer(TorchSharpBaseTrainer`2 parent, IChannel ch, IDataView input)
in Microsoft.ML.TorchSharp.TorchSharpBaseTrainer`2.Fit(IDataView input)
in Microsoft.ML.Data.EstimatorChain`1.Fit(IDataView input)
in Program.Main(String[] args) in C:\Users\pierc\source\repos\ML_NER_TEST\Program.cs: riga 64
```
**Sample Projects**

```
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.TorchSharp;

namespace ML_NER_TEST
{
public class Program
{
public static void Main(string[] args)
{
try
{
var context = new MLContext()
{
FallbackToCpu = true,
GpuDeviceId = 0
};

var labels = context.Data.LoadFromEnumerable(
[
new Label { Key = "PERSON" }, // People, including fictional.
new Label { Key = "NORP" }, // Nationalities or religious or political groups.
new Label { Key = "FAC" }, // Buildings, airports, highways, bridges, etc.
new Label { Key = "ORG" }, // Companies, agencies, institutions, etc.
new Label { Key = "GPE" }, // Countries, cities, states.
new Label { Key = "LOC" }, // Non-GPE locations, mountain ranges, bodies of water.
new Label { Key = "PRODUCT" }, // Objects, vehicles, foods, etc. (Not services.)
new Label { Key = "EVENT" }, // Named hurricanes, battles, wars, sports events, etc.
new Label { Key = "WORK_OF_ART" }, // Titles of books, songs, etc.
new Label { Key = "LAW" }, // Named documents made into laws.
new Label { Key = "LANGUAGE" }, // Any named language.
new Label { Key = "DATE" }, // Absolute or relative dates or periods.
new Label { Key = "TIME" }, // Times smaller than a day.
new Label { Key = "PERCENT" }, // Percentage, including "%".
new Label { Key = "MONEY" }, // Monetary values, including unit.
new Label { Key = "QUANTITY" }, // Measurements, as of weight or distance.
new Label { Key = "ORDINAL" }, // "first", "second", etc.
new Label { Key = "CARDINAL" }, // Numerals that do not fall under another type.
new Label { Key = "OBJECT" }, // An Object, Entity might be a Spoon, or a Soccer Ball. Needs Sub Categories.
]);

var dataView = context.Data.LoadFromEnumerable(
new List([
new InputTrainingData()
{
// Testing longer than 512 words.
Sentence = "Alice and Bob live in the USA",
Label = ["PERSON", "0", "PERSON", "0", "0", "0", "COUNTRY"]
},
new InputTrainingData()
{
Sentence = "Frank and Alice traveled along the California coast.",
Label = ["PERSON", "0", "PERSON", "0", "0", "0", "COUNTRY", "0"]
},
]));

var chain = new EstimatorChain();

var estimator = chain.Append(context.Transforms.Conversion.MapValueToKey("Label", keyData: labels))
.Append(context.MulticlassClassification.Trainers.NamedEntityRecognition(outputColumnName: "Predictions"))
.Append(context.Transforms.Conversion.MapKeyToValue("Predictions"));

Console.WriteLine("Training the model...");

var transformer = estimator.Fit(dataView);

Console.WriteLine("Model trained!");

var transformerSchema = transformer.GetOutputSchema(dataView.Schema);

string sentence = "Alice and Bob live in the USA";
var engine = context.Model.CreatePredictionEngine(transformer);

Console.WriteLine("Predicting...");

Output predictions = engine.Predict(new Input { Sentence = sentence });

Console.WriteLine($"Predictions: {sentence} - {string.Join(", ", predictions.Predictions)}");

transformer.Dispose();
Console.WriteLine("Success!");
Console.ReadLine();
}
catch (Exception ex)
{
Console.WriteLine($"Error: {ex.Message}");
Console.ReadLine();
}
}
private class Input
{
public string Sentence;
public string[] Label;
}
private class Output
{
public string[] Predictions;
}
public class Label
{
public string Key { get; set; }
}
private class InputTrainingData
{
public string Sentence;
public string[] Label;
}
}
}
```

**Additional context**

```

Exe
net8.0
enable
disable

```