No .zip file is shown after the model was built

**System Information (please complete the following information):**
- Model Builder or CLI Version: 16.18.2
- Visual Studio Version (if applicable): Visual Studio for Mac 17.6.11 (build 400)

**Describe the bug**
- On which page of the tutorial did you run into an issue (URL): https://dotnet.microsoft.com/en-us/learn/ml-dotnet/get-started-tutorial/generate-code
- Clear description of the problem: After successfully running this command < mlnet classification --dataset "yelp_labelled.txt" --label-col 1 --has-header false --name SentimentModel --train-time 60>, there is no SentimentModel.zip file.
Here is all I have:

[trungnguyen@Trungs-MacBook-Pro SentimentModel % ls
Program.cs SentimentModel.csproj SentimentModel.mbconfig SentimentModel.training.cs
SentimentModel.consumption.cs SentimentModel.evaluate.cs SentimentModel.mlnet]

**To Reproduce**
Steps to reproduce the behavior:
1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
4. See error

**Expected behavior**
A clear and concise description of what you expected to happen.

I should have following files instead:

Program.cs: This file contains code to run the model.
SentimentModel.consumption.cs: This file contains the model input and output classes and a Predict method that can be used for model consumption.
SentimentModel.mbconfig: This file is a JSON file that keeps track of the configurations and results from your training.
SentimentModel.training.cs: This file contains the training pipeline (data transforms, algorithm, and algorithm parameters) used to train the final model.
SentimentModel.zip: This file is the trained ML.NET model, which is a serialized zip file.

**Screenshots**

**Additional context**
Add any other context about the problem here.

Start by reproducing the `mlnet classification` command from the issue with `yelp_labelled.txt`, then inspect the generated SentimentModel files listed in the report. Compare the result with the generate-code tutorial's expected SentimentModel.zip output and determine where model creation or reporting diverges. Done means the command produces the trained zip file alongside the other generated files.

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