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A neural parsing pipeline for segmentation, morphological tagging, dependency parsing and lemmatization with pre-trained models for more than 50 languages. Top ranker in the CoNLL-18 Shared Task.



For Ubuntu-based systems, the pre-requisities are

sudo apt install build-essential python3-dev python3-virtualenv python3-tk

Download the code

Clone the parser as well as all of its submodules as follows:

git clone
cd Turku-neural-parser-pipeline
git checkout orig-parser-pre-2021
git submodule update --init --recursive

Setup Python environment

We highly recommend that you make a virtual environment for the parser and install the wheel package:

python3 -m venv venv-parser-neural
source venv-parser-neural/bin/activate
pip3 install wheel

Then you need to install the necessary libraries (note: remember to remove tensorflow or pytorch from the requirements if you need to install them separately due to particular limitations of your machine):

pip3 install -r requirements-gpu.txt


pip3 install -r requirements-cpu.txt

After downloading a model, you should be able to run the parser.

Tensorflow and pytorch

In case default versions of Tensorflow or Pytorch do not match your CUDA installation, it makes sense to install those packages separately.

You can install an older or newer version of tensorflow by specifying the version number when running pip (parser is tested to work at least with 1.5.0 – 1.12.2):

pip3 install tensorflow-gpu==1.12.2

In case pytorch would not install correctly through pip, you may need to install PyTorch by selecting the appropriate options from For a typical GPU install you would select something like “Linux - pip - 3.5 - CUDA 9.1” matching the version of your python and CUDA. If you run on CPU and have no CUDA, then select None.

  1. Run the commands which gives
  2. Run yet pip3 install torchtext when (1) is ready and you’re done

Download the models

All models are available here and you can use the following utility script to fetch the model you need:

python3 fi_tdt

Test the model

Now you can test the model