feature, so subsequent builds can be faster. The console will
   return to the prompt when it's complete.

2. Run the image as a container.

   In a terminal, run the following command.

   ```console
   $ docker run -it basic-nlp 03_text_classification.py
   ```

   The following is a break down of the command:

   - `docker run`: This is the primary command used to run a new container from
     a Docker image.
   - `-it`: This is a combination of two options:
     - `-i` or `--interactive`: This keeps the standard input (STDIN) open even
       if not attached. It lets the container remain running in the
       foreground and be interactive.
     - `-t` or `--tty`: This allocates a pseudo-TTY, essentially simulating a
       terminal, like a command prompt or a shell. It's what lets you
       interact with the application inside the container.
   - `basic-nlp`: This specifies the name of the Docker image to use for
     creating the container. In this case, it's the image named `basic-nlp` that
     you created with the `docker build` command.
   - `03_text_classification.py`: This is the script you want to run inside the
     Docker container. It gets passed to the `entrypoint.sh` script, which runs
     it when the container starts.

   For more details, see the [docker run CLI reference](/reference/cli/docker/container/run/).

   > [!NOTE]
   >
   > For Windows users, you may get an error when running the container. Verify
   > that the line endings in the `entrypoint.sh` are `LF` (`\n`) and not `CRLF` (`\r\n`),
   > then rebuild the image. For more details, see [Avoid unexpected syntax errors, use Unix style line endings for files in containers](/desktop/troubleshoot-and-support/troubleshoot/topics/#Unexpected-syntax-errors-use-Unix-style-line endings-for-files-in-containers).

   You will see the following in your console after the container starts.

   ```console
   Enter the text for classification (type 'exit' to end):
   ```

3. Test the application.

   Enter some text to get the text classification.

   ```console
   Enter the text for classification (type 'exit' to end): I love containers!
   Accuracy: 1.00

   VADER Classification Report:
                 precision    recall  f1-score   support

              0       1.00      1.00      1.00         1
              1       1.00      1.00      1.00         1

       accuracy                           1.00         2
      macro avg       1.00      1.00      1.00         2
   weighted avg       1.00      1.00      1.00         2

   Test Text (Positive): 'I love containers!'
   Predicted Sentiment: Positive
   ```

## Summary

In this guide, you learned how to build and run a text classification
application. You learned how to build the application using Python with
scikit-learn and NLTK. Then you learned how to set up the environment and run
the application using Docker.

Related information:

- [Docker CLI reference](/reference/cli/docker/)
- [Dockerfile reference](/reference/dockerfile/)
- [Natural Language Toolkit](https://www.nltk.org/)
- [Python documentation](https://docs.python.org/3/)
- [scikit-learn](https://scikit-learn.org/)

## Next steps

Explore more [natural language processing guides](./_index.md).