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.

Scritto dal modello di indicizzazione a partire dal testo della issue.