Skip to content

Remove redundant parenthesis from the "Analyze sentiment using the ML.NET CLI" documentation. #40651

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
May 1, 2024
Merged

Remove redundant parenthesis from the "Analyze sentiment using the ML.NET CLI" documentation. #40651

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
1e27768
Update sentiment-analysis-cli.md
EngincanV Apr 28, 2024
19dacd4
Update sentiment-analysis-cli.md
EngincanV May 1, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions docs/machine-learning/tutorials/sentiment-analysis-cli.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -164,7 +164,7 @@ Those enumerated assets are explained in the following steps of the tutorial.
}
```

- The first lines of code create a *single sample data*, in this case based on the first row of your dataset to be used for the prediction. You can also create your own 'hard-coded' data by updating the code:
- The first lines of code create a *single sample data*. In this case, the single sample data is based on the first row of the dataset to be used for the prediction. You can also create your own 'hard-coded' data by updating the code:

```csharp
ModelInput sampleData = new ModelInput()
Expand All @@ -178,9 +178,9 @@ Those enumerated assets are explained in the following steps of the tutorial.

1. Run the project, either using the original sample data loaded from the first row of the dataset or by providing your own custom hard-coded sample data. You should get a prediction comparable to:

![ML.NET CLI run the app from Visual Studio](./media/mlnet-cli/mlnet-cli-console-app.png))
![ML.NET CLI run the app from Visual Studio](./media/mlnet-cli/mlnet-cli-console-app.png)

1. Try changing the hard-coded sample data to other sentences with different sentiment and see how the model predicts positive or negative sentiment.
Try changing the hard-coded sample data to other sentences with different sentiments and see how the model predicts positive or negative sentiment.

## Infuse your end-user applications with ML model predictions

Expand Down