README.md

---
title: Sentiment Analysis App
emoji: 🚀
colorFrom: green
colorTo: purple
sdk: streamlit
sdk_version: 1.17.0
app_file: app.py
pinned: false
---

# AI Project: Finetuning Language Models - Toxic Tweets

Hello! This is a project for CS-UY 4613: Artificial Intelligence. I'm providing a step-by-step instruction on finetuning language models for detecting toxic tweets.

# Milestone 1

This milestone includes setting up docker and creating a development environment on Windows 11.

## 1. Enable WSL2 feature

The Windows Subsystem for Linux (WSL) lets developers install a Linux distribution on Windows.

```
wsl --install 
```

Ubuntu is the default distribution installed and WSL2 is the default version. 
After creating linux username and password, Ubuntu can be seen in Windows Terminal now.
Details can be found [here](https://learn.microsoft.com/en-us/windows/wsl/install).

![](milestone1/wsl2.png)

## 2. Download and install the Linux kernel update package

The package needs to be downloaded before installing Docker Desktop. 
However, this error might occur:

`Error: wsl_update_x64.msi unable to run because "This update only applies to machines with the Windows Subsystem for Linux"`

Solution: Opened Windows features and enabled "Windows Subsystem for Linux". 
Successfully ran update [package](https://docs.microsoft.com/windows/wsl/wsl2-kernel).

![](milestone1/kernal_update_sol.png)

## 3. Download Docker Desktop

After downloading the [Docker App](https://www.docker.com/products/docker-desktop/), WSL2 based engine is automatically enabled. 
If not, follow [this link](https://docs.docker.com/desktop/windows/wsl/) for steps to turn on WSL2 backend. 
Open the app and input `docker version` in Terminal to check server running.

![](milestone1/docker_version.png)
Docker is ready to go.

## 4. Create project container and image

First we download the Ubuntu image from Docker’s library with:
```
docker pull ubuntu
```
We can check the available images with:
```
docker image ls
```
We can create a container named *AI_project* based on Ubuntu image with:
```
docker run -it --name=AI_project ubuntu
```
The `–it` options instruct the container to launch in interactive mode and enable a Terminal typing interface. 
After this, a shell is generated and we are directed to Linux Terminal within the container.
`root` represents the currently logged-in user with highest privileges, and `249cf37645b4` is the container ID.

![](milestone1/docker_create_container.png)

## 5. Hello World!

Now we can mess with the container by downloading python and pip needed for the project. 
First we update and upgrade packages by: (`apt` is Advanced Packaging Tool)
```
apt update && apt upgrade
```
Then we download python and pip with:
```
apt install python3 pip
```
We can confirm successful installation by checking the current version of python and pip.
Then create a script file of *hello_world.py* under `root` directory, and run the script.
You will see the following in VSCode and Terminal.

![](milestone1/vscode.png)
![](milestone1/hello_world.png)

## 6. Commit changes to a new image specifically for the project

After setting up the container we can commit changes to a specific project image with a tag of *milestone1* with:
```
docker commit [CONTAINER] [NEW_IMAGE]:[TAG]
```
Now if we check the available images there should be a new image for the project. If we list all containers we should be able to identify the one we were working on through container ID.

![](milestone1/commit_to_new_image.png)

The Docker Desktop app should match the image list we see on Terminal.

![](milestone1/app_image_list.png)