Machine learning toolkit for natural language processing. Written for Lisbon Machine Learning Summer School (lxmls.it.pt). This covers
- Scientific Python and Mathematical background
- Linear Classifiers
- Sequence Models
- Structured Prediction
- Syntax and Parsing
- Feed-forward models in deep learning
- Sequence models in deep learning
Machine learning toolkit for natural language processing. Written for LxMLS - Lisbon Machine Learning Summer School
If you are not familiar with Git, just download the zip available in the Clone or Download button. Important: Use the student version. It should be the one in the page displaying this README. Then unzip and enter the main folder. This will be your working folder
cd lxmls-toolkit-student
If you feel comfortable with Git you may instead clone the repo and checkout the student branch
git clone https://github.com/LxMLS/lxmls-toolkit.git
cd lxmls-toolkit/
git checkout student
If you are new to Python, the simplest method is to use Anacondato handle your packages, just go to
https://www.anaconda.com/download/
and follow the instructions. If you prefer pip, install the toolkit modules in a virtual environment. It is safer to update pip and setuptools first
virtualenv venv
source venv/bin/activate
pip install pip setuptools --upgrade
pip install -r requirements.txt
This will not interfere with your existing installation. In both cases, you will need to get a pip or conda command for your platform for pytorch from
http://pytorch.org/ and apply them.
Finally call
python setup.py develop
to instal the toolkit in a way that is modifiable.
Bear in mind that the main purpose of the toolkit is educative. You may resort to other toolboxes if you are looking for efficient implementations of the algorithms described.
Some day will require you to complete code from previous days. If you have not completed the exercises you can allways use the solve.py command as for example
python solve.py sequence_models
Important: This will delete your code on the correspondig file!. Save it before. To undo solving (this wont return your code) do
python solve.py --undo sequence_models