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WP6

Download the code

To download the code as a local copy to run the code in your own machine, it is recommended to clone the repository using git.

  • To see the different releases codes, make sure you are viewing the releases branch. Click on the drop down list with the branch symbol at the top left and select this branch.
  • To navigate between different versions click on the drop down list again and select the tags tab. There you can access the available releases (for now pre-alpha prototype is the only one).
  • Then click on the green code button and copy the HTTPS link.
  • To download an specific release, open a terminal in your computer and run the following command:
your@user:~$ git clone --depth 1 --releases <tag_name> <copied_url>
  • The code will be downloaded to the terminal directory

Repository structure: Pre alpha and Alpha gaps

The repository contains code for the pre alpha and the alpha prototype. For the former, a task consisting of true/false sentence classification was implemented, and it can be found under the ´pre_alpha´ folder, along with example jupyter notebooks showing the training pipeline. For the latter, some research was made about hybrid quantum-classical algorithms and how quantum could fit in more complex NLP algorithm architectures. This lead to creating what XanaduAI calls Dressed Quantm Circuits. For this purpose, pretrained classical models are used as preprocessing layers in a transfer learning fashion. We wanted to implement Neural Networks able to transform pretrained word vectors into usable lower dimensional vectors acting as the parameters for the rotations of variational quantum circuits. A first iteration (WIP) of this approach can be found under the folder ´alpha´. A Juyter Notebook can be found where context dependant BERT vectors are retrieved for the words in a sentence, and a neural network reduce the dimensionality of the parameters to encode the sentence as a DisCoCat circuit.

Jupyter notebooks

An example of the pre-alpha implementation is found in my_lib/alpha/DressedQuantumCircuit directory, and more examples for the pre-alpha are located in ``my_lib/pre_alpha/notebooks` containing examples demonstrating how the quantum and classical NLP modules are used to solve NLP-related problems (currently – a sentence classification task).

Setup

To run the notebooks you must install some software dependencies and download a spacy language model. Due to some library incompatibilities, the quantum and classical NLP modules currently cannot share a common set of software dependencies and, as a result, need to be run in separete Python environments. The instructions below describe the steps to set up an environment for the quantum NLP module using the requirements.txt file in the project's root. The steps for setting up an environement for the classical NLP module are identical except for the fact that a different requirements file – ./my_lib/pre_alpha/notebooks/classical/requirements.txt – should be used.

An additional ./my_lib/alpha/DressedQuantumCircuit/requirements.txt needs to be installed to run the Notebook for the Alpha protoype demonstration.

Dependencies

We recommend using virtualenv or anaconda to create an isolated environment for the project's dependencies. Here are instructions for virtualenv.

# Install virtualenv using your distribution's package manager or pip, e.g.,
apt install virtualenv  # if using Ubuntu

# Create a new virtual environment called, e.g., venv.
# This will create a directory called venv in the project root which will
# contain all of the packages installed in the following steps. It can be safelly
# delted to get rid of the these packages.
virtualenv venv

# Activate the newly created environment
. ./venv/bin/activate

# Install the dependencies into the virtual environment
pip install -r requirements.txt
A note on the Python version

We've tested that the code works using Python 3.9. Earlier versions might not work on Linux due to incompatibility with myqlm.

Spacy language model

Some of the tools used in the quantum NLP module require a language model to be downloaded to the users system. To do it automatically, execute the following command into the virtual environment after the dependencies have been installed (see the Dependencies section above).

# Make sure the virtual environement is activated
. ./venv/bin/activate

# The language model will be stored in the venv directory
python -m spacy download en_core_web_lg

Running

To run the the notebooks, start jupyter lab and navigate to the notebooks in my_lib/pre_alpha/notebooks/

# Make sure the virtual environement is activated
. ./venv/bin/activate

# Start jupyter lab and follow the instructions in the console
# output to open jupyter lab in the browser
jupyter-lab

More information

Some more information about the notebooks is provided in my_lib/pre_alpha/notebooks/README.md

Generating the animal dataset

Manual dataset generation isn't necessary for running the Jupyter notebooks. However, if needed for some different purpose, the dataset can be generated using the following commands.

Run

# Choose a seed number, e.g, 1337, to deterministically randomize the sentence order
./my_lib/pre_alpha/src/DataPreparation/gen_animal_dataset.py --seed 1337 > outfile

to generate a tab-separated file containing lines of the form <sentence>\t<sentence_type>\t<truth_value> where <truth_value> is 1 if the sentence states a fact that is true and 0 otherwise, and <sentence_type> denotes the sentence type, e.g., NOUN-TVERB-NOUN.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 951821.