DeepStyle

DeepStyle provides pretrained models aiming to project text in a stylometric space. The base project consists in a new method of representation learning and a definition of writing style based on distributional properties. This repository contains datasets, pretrained models and other ressources that were used to train and test models.

Datasets and pretrained model

To get the datasets (i.e. the R-set and 22 main U-sets), please send me a private message or create a new issue in the repository.

The DeepStyle model (pretrained DBert-ft) is available at available at https://drive.google.com/file/d/1Y9TMjj04fVhNuJnhzaM4Wmn2CQfZ8r8U/view?usp=share_link.

Installation

git clone https://github.com/hayj/deepstyle
cd deepstyle
python setup.py install

Dependencies (tensorflow and transformers) will not be automatically installed since we leave the possibility for users to install newer versions. DeepStyle was tested on tensorflow-gpu 2.0 and transformers 2.4.1.

Usage of the DBert-ft model

1. Download the pretrained model available at https://drive.google.com/file/d/1Y9TMjj04fVhNuJnhzaM4Wmn2CQfZ8r8U/view?usp=share_link (both the config and weights).
2. Use the DeepStyle class in order to embed documents:

from deepstyle.model import DeepStyle
# Give the folder of the model:
m = DeepStyle("/path/to/the/folder/containing/both/files")
# Sample document:
doc = "Welcome to day two of cold, nasty, wet weather. Ick. Rain is so bad by itself... But when you mix it with a hella cold temperature and nasty wind... Not so much fun anymore."
# Embed a document:
print(m.embed(doc)) # Return a np.ndarray [-0.6553829, 0.3634828, ..., 1.2970213, 0.1685428]
# Get the pretrained model and use its methods (e.g. to get attentions):
m.model # See https://huggingface.co/transformers/model_doc/distilbert.html#tfdistilbertforsequenceclassification

Experiments

The folder experiments contains main experiments of the project. Some parts of the code are notebooks and need to be adapted to your python environment. For long runs, notebooks are converted into python files (e.g. for the DBert-ft training).

"No locks available" issue

In case you get this error, you can set the HDF5_USE_FILE_LOCKING env var, for instance, at the beginning of your script:

import os
os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'

Tested on

tensorflow-gpu==2.0.0
transformers==2.4.1
h5py==2.10.0

With python 3.6 and 3.7, CUDA 10.0 and CUDNN 7.

Command line demo

Here a command line demo of DeepStyle on Ubuntu 20:

cd ~/tmp
mkdir dbert-ft
cd dbert-ft
wget http://212.129.44.40/DeepStyle/dbert-ft/config.json
wget http://212.129.44.40/DeepStyle/dbert-ft/tf_model.h5
cd ../
conda create -n dbertft-env -y python=3.7 anaconda
conda activate dbertft-env
git clone https://github.com/hayj/deepstyle ; cd deepstyle ; pip uninstall deepstyle -y ; python setup.py install ; cd ../ ; rm -rf deepstyle
pip install --ignore-installed --upgrade tensorflow==2.0.0
pip install --ignore-installed --upgrade transformers==2.4.1
pip install --ignore-installed --upgrade h5py==2.10.0
ipython -c "from deepstyle.model import DeepStyle ; m = DeepStyle('dbert-ft') ; m.embed('Hello World')"

Citation

Link to the publication

Julien Hay, Bich-Liên Doan, Fabrice Popineau, et Ouassim Ait Elhara. Representation learning of writing style. In Proceedings of the 6th Workshop on Noisy User-generated Text (W-NUT 2020), November 2020.

Bibtex format:

@inproceedings{hay-2020-deepstyle,
    title = "Representation learning of writing style",
    author = "Hay, Julien and
      Doan, Bich-Li\^{e}n and
      Popineau, Fabrice and
      Ait Elhara, Ouassim",
    booktitle = "Proceedings of the 6th Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020"
}