pyeurovoc 1.0.1

Python API for multilingual legal document classification with EuroVoc descriptors using BERT models.

EuroVoc-BERT

PyEuroVoc is a tool for legal document classification with EuroVoc descriptors. It supports 22 languages: Bulgarian (bg), Czech (cs), Danish (da), German (de), Greek (el), English (en), Spanish (es), Estonian (et), Finnish (fi), French (fr), Hungarian (hu), Italian (it), Lithuanian (lt), Latvian (lv), Maltese (mt), Dutch (nl), Polish (pl), Portuguese (pt), Romanian (ro), Slovak (sk), Slovenian (sl), Sweedish (sv).

The tool uses BERT at its core. The list of BERT variant for each language can be found here. The performance of each model is outlined in our paper.

Installation

Make sure you have Python3 installed and a suitable package for PyTorch. Then, install pyeurovoc with pip:

pip install pyeurovoc

Usage

Import the EuroVocBERT class from pyeurovoc. Instantiate the class with the desired langauge (default is "en") and then simply pass a document text to the model.

from pyeurovoc import EuroVocBERT

model = EuroVocBERT(lang="en")
prediction = model("Commission Decision on a modification of the system of aid applied in Italy in respect of shipbuilding")

The prediction of the model is a dictionary that contains the predicted ID descriptors as keys together with their confidence score as values.

{'155': 0.9995473027229309, '230': 0.9377984404563904, '889': 0.9193254113197327, '1519': 0.714003324508667, '5020': 0.5, '5541': 0.5}

The number of most probable labels returned by the model is controlled by the num_labels parameter (default is 6).

prediction = model("Commission Decision on a modification of the system of aid applied in Italy in respect of shipbuilding", num_labels=4)

Which outputs:

{'155': 0.9995473027229309, '230': 0.9377984404563904, '889': 0.9193254113197327, '1519': 0.714003324508667}

Training your own models

Download Dataset

Firstly, you need to download the datasets. Use the download_datasets.sh script in data to do that.

./download_datasets.sh

Preprocess

Once the datasets has finished downloading, you need to preprocess them using the preprocess.py script. It takes as input the model per language configuration file and the path to the dataset.

python preprocess.py --config [model_config] --data_path [dataset_path]

Train

Training is done using the train.py script. It will automatically load the preprocessed files created by the previous step, and will save the best model for each split at the path given by the -save_path argument. To view the full list of available arguments, run python train.py --help.

python train.py --config [model_config] --data_path [dataset_path] 
                --epochs [n_epochs] --batch_size [batch_size] 
                --max_grad_norm [max_grad_norm]
                --device [device]
                --save_path [model_save_path]
                --logging_step [logging_step]
                --verbose [verbose]

Evaluate

To evaluate the performance of each model on a split, run the evaluate.py script. As in the case of training, it provides several arguments that can be visualized with python evaluate.py --help.

python evaluate.py --config [model_config] --mt_labels [mt_labels_path] --data_path [dataset_path]
                   --models_path [models_ckpt_path] 
                   --batch_size [batch_size]
                   --device [device]
                   --output_path [results_output_path]
                   --loggin_step [logging_step]
                   --verbose [verbose]

Credits

Coming soon...