Summary and Setup

Welcome

This lesson teaches the fundamentals of Natural Language Processing (NLP) in Python. It will equip you with the foundational skills and knowledge needed to carry over text-based research projects. The lesson is designed with researchers in the Humanities and Social Sciences in mind, but is also applicable to other fields of research.

On the first day we will dive in to text preprocessing and word embeddings while epxloring semantic shifts in various words over multiple decades. The second day begins with an introduction to transformers, and we will work on classification and named entity recognition with the BERT model. In the afternoon, we willl cover large language language models, and you will learn how to build your own agents.

Checklist

Prerequisites

Before joining this course, participants should have:

  • Basic Python programming skills

Software Setup

Discussion

Installing Python

Python is a popular language for scientific computing, and a frequent choice for machine learning as well. To install Python, follow the Beginner’s Guide or head straight to the download page. Note: We will use Python 3.12 for this workshop.

Please set up your Python environment at least a day in advance of the workshop. If you encounter problems with the installation procedure, ask your workshop organizers via e-mail for assistance so you are ready to go as soon as the workshop begins.

Installing the required packages

Pip is the package management system built into Python. Pip should be available in your system once you installed Python successfully. Please note that installing the packages can take some time, in particular on Windows.

Open a terminal (Mac/Linux) or Command Prompt (Windows) and run the following commands.

  1. Create a virtual environment called nlp_workshop:
python3 -m venv nlp_workshop
py -m venv nlp_workshop
  1. Activate the newly created virtual environment:
source nlp_workshop/bin/activate
nlp_workshop\Scripts\activate

Remember that you need to activate your environment every time you restart your terminal!

  1. Install the required packages:
python3 -m pip install -r requirements.txt
py -m pip install -r requirements.txt

Jupyter Lab

We will teach using Python in Jupyter Lab, a programming environment that runs in a web browser. Jupyter Lab is compatible with Firefox, Chrome, Safari and Chromium-based browsers. Note that Internet Explorer and Edge are not supported. See the Jupyter Lab documentation for an up-to-date list of supported browsers.

To start Jupyter Lab, open a terminal (Mac/Linux) or Command Prompt (Windows) and type the command:

jupyter lab

Ollama

We will use Ollama to run large language models. The installer (available for Linux/Windows/Mac OS) can be downloaded here:

https://ollama.com/download

Run the installer and follow the instructions on screen.

Next, download the model that we will be using from a terminal (Mac/Linux) or Command Prompt (Windows) by typing the command:

ollama pull llama3.2:1b

NOTE: if the previous command fails, you can pull the model manually inside the Ollama graphic interface. Open Ollama and on the right side of the “Send a message” prompt you will see a dropdown. Type there “llama3.2:1b” and then send any message so Ollama can download (pull) the model for you.

Data Sets

Datasets and example files are placed in the episodes/data/ directory.

You can manually download the zip file inside that directory by clicking on data.zip and then using the down arrow button (“download raw file”) that is on the upper right corner of the screen, below the word “History”.

Note: The text data files in data.zip should be placed in a directory called data/ in the root of the directory where your jupyter notebooks will reside.

Word2Vec & SpaCy English Models

To download all models required for the workshop with one simple platform-independent command:

invoke init-models

If this command fails for whatever reason, try the following manual approach. Download the Word2Vec model first:

python3 -m gensim.downloader --download word2vec-google-news-300 
py -m gensim.downloader --download word2vec-google-news-300

Then, download the trained pipelines for English from Spacy:

python3 -m spacy download en_core_web_sm
py -m spacy download en_core_web_sm