pandora

A (language-independent) Tagger-Lemmatizer for Latin & the Vernacular

The tagging technology behind Pandora is described in the following papers:

• Kestemont, M., De Pauw, G., Van Nie, R. & Daelemans, W., ‘Lemmatisation for Variation-Rich Languages Using Deep Learning’. Forthcoming in: DSH – Digital Scholarship in the Humanities. https://academic.oup.com/dsh/article/doi/10.1093/llc/fqw034/2669790/Lemmatization-for-variation-rich-languages-using
• Kestemont, M. & J. de Gussem, ‘Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learning’, Journal of Data Mining & Digital Humanities (2017), pp. 17. Special Issue on Computer-Aided Processing of Intertextuality in Ancient Languages, ed. M. Buechler and L. Mellerin https://jdmdh.episciences.org/3835/pdf.

Install

For now, installation needs to be done by pulling the repository and installing the required libraries yourself. Currently, Pandora relies to either Keras (+TensorFlow) or Pytorch as backends. In order to run Pandora with the Pytorch backend, you should go to pytorch.org and follow the installation instructions.

Environment free

Note : if you have CUDA installed, you should do pip install -r requirements-gpu.txt instead

git clone https://github.com/hipster-philology/pandora.git
cd pandora
pip install -r requirements.txt

Virtualenv

For CUDA-Ready machines owner:

git clone https://github.com/hipster-philology/pandora.git
cd pandora
virtualenv env
source env/bin/activate
pip install -r requirements-gpu.txt

For the others:

git clone https://github.com/hipster-philology/pandora.git
cd pandora
virtualenv env
source env/bin/activate
pip install -r requirements.txt

Scripts

Note : with Virtualenv install, do not forget to do source env/bin/activate.

main.py

train.py allows you to train your own models :

python train.py --help
python train.py config.txt --dev /path/to/dev/resources --train /path/to/train/resources --test /path/to/test/resources
python train.py config.txt --dev /path/to/dev/resources --train /path/to/train/resources --test /path/to/test/resources --nb_epochs 1
python train.py path/to/model/config.txt --load --dev /path/to/dev/resources --train /path/to/train/resources --test /path/to/test/resources

unseen.py

tagger.py allows you to annotate a string or folder

python tagger.py --help
python tagger.py path/to/model/dir --string --input "Cur in theatrum, Cato severe, venisti?"
python tagger.py path/to/model/dir --input /path/to/dir/to/annotate/ --output /path/to/output/dir/
python tagger.py path/to/model/dir --tokenized_input --input /path/to/dir/to/annotate/ --output /path/to/output/dir/

Note that we do not officially support the Theano backend for keras (anymore), because the Theano development will halt after the 1.0 release (https://groups.google.com/forum/#!topic/theano-users/7Poq8BZutbY).

Examples

The repository includes sample configurations (see config_example folder), and is shipped with a small test data-set of Old French epic texts from the Geste corpus (https://github.com/Jean-Baptiste-Camps/Geste).

To launch training on this corpus, do

python3 train.py config_geste.txt --train data/geste/train --dev data/geste/dev --test data/geste/test