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Update sentiment-analysis.md #9249

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6 changes: 3 additions & 3 deletions docs/machine-learning/tutorials/sentiment-analysis.md
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Expand Up @@ -222,7 +222,7 @@ Add the following code to the `Train` method:

At this point, you have a model of type <xref:Microsoft.ML.Runtime.Data.TransformerChain%601> that can be integrated into any of your existing or new .NET applications. Return the model at the end of the `Train` method.

[!code-csharp[ReturnModel](../../../samples/machine-learning/tutorials/SentimentAnalysis/Program.cs#11 "Return the model")]
[!code-csharp[ReturnModel](../../../samples/machine-learning/tutorials/SentimentAnalysis/Program.cs#10 "Return the model")]

## Evaluate the model

Expand Down Expand Up @@ -298,7 +298,7 @@ Console.WriteLine("The model is saved to {0}", _modelPath);
Create the `Predict` method, just after the `Evaluate` method, using the following code:

```csharp
public static void Predict(PredictionModel<SentimentData, SentimentPrediction> model)
private static void Predict(MLContext mlContext, ITransformer model)
{

}
Expand Down Expand Up @@ -326,7 +326,7 @@ Add a comment to test the trained model's prediction in the `Predict` method by

You can use that to predict the Toxic or Non Toxic sentiment of a single instance of the comment data. To get a prediction, use <xref:Microsoft.ML.Runtime.Data.PredictionFunction%602.Predict(%600)> on the data. Note that the input data is a string and the model includes the featurization. Your pipeline is in sync during training and prediction. You didn’t have to write preprocessing/featurization code specifically for predictions, and the same API takes care of both batch and one-time predictions.

[!code-csharp[Predict](../../../samples/machine-learning/tutorials/SentimentAnalysis/Program.cs#18 "Create a prediction of sentiment")]
[!code-csharp[Predict](../../../samples/machine-learning/tutorials/SentimentAnalysis/Program.cs#19 "Create a prediction of sentiment")]

### Model operationalization: prediction

Expand Down