Machine learning toolkit for natural language processing. Written for LxMLS - Lisbon Machine Learning Summer School
- Scientific Python and Mathematical background
- Linear Classifiers (Gradient Descent)
- Feed-forward models in deep learning (Backpropagation)
- Sequence models in deep learning
- Attention Models (Transformers)
Bear in mind that the main purpose of the toolkit is educational. You may resort to other toolboxes if you are looking for efficient implementations of the algorithms described.
- Use the student branch not this one 🚨!
Download the code. If you are used to git just clone the student branch. For example from the command line in do
git clone [email protected]:LxMLS/lxmls-toolkit.git lxmls-toolkit-student
If you do not have a pyhon installation, install Anaconda. Go to
https://www.anaconda.com/download/
and follow the instructions for installation using Python 3. After setting up the anaconda:
use your favorite git tool to create a clone of this repository
navigate to the folder where the repository resides
install anaconda (see instruction)
conda create --name lxmls_new
conda activate lxmls_new
conda install pip
pip install --editable .
If you prefer pip to Anaconda you can install the toolkit in a way that does not interfere with your existing installation.
For this you can use a virtual environment as follows
virtualenv venv
source venv/bin/activate (on Windows: .\venv\Scripts\activate)
pip install pip setuptools --upgrade
pip install --editable .
This will install the toolkit in a way that is modifiable. Remember to run scripts from the root directory lxmls-toolkit-student
If you want to also virtualize you Python version (e.g. you are stuck with Python2 on your system), have a look at pyenv.
- Run from the project root directory. If an importing error occurs, try first adding the current path to the
PYTHONPATHenvironment variable, e.g.:export PYTHONPATH=.