CPV Classifier

In this work we compare different approaches to Common Procurement Vocabulary (CPV) codes classification, using data extracted from the Spanish Treasury.

Resources

All training, testing datasets and code are available in Zenodo. Check them out!

Pre-processing

In order to preprocess the data, follow the steps below:

Download the atom datasets into a folder (in our case licitacionesPerfilesContratanteCompleto3_2019)
Run atom2csvBS.py, which will save the results in docs.csv
Run data2tt.py using docs.csv. As a result, you will obtain train.csv and test.csv, which are the datasets used in the notebooks

Notebooks

The notebooks below contain the different approaches compared:

Test for classic approaches: https://www.kaggle.com/marianavasloro/cpvtwodigitsml

Test for the MKaan approach: https://www.kaggle.com/code/marianavasloro/mkaan

Training and evaluation of fine-tuned RoBERTa: https://www.kaggle.com/code/marianavasloro/fine-tuned-roberta-for-spanish-cpv-codes

Datasets

Full: https://www.kaggle.com/datasets/marianavasloro/dataset

10%: https://www.kaggle.com/datasets/marianavasloro/dataset10

How to cite

If you use these datasets or our notebooks, please cite this repository and our 2022 SEPLN paper (to appear):

@software{maria_navas_loro_2022_6554604,
  author       = {María Navas Loro and
                  Daniel Garijo and
                  Oscar Corcho},
  title        = {Multi-label Text Classification for Public Procurement in Spanish},
  month        = may,
  year         = 2022,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.6554604},
  url          = {https://doi.org/10.5281/zenodo.6554604}
}

Acknowledgements

This work has been supported by NextProcurement European Action and the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with Universidad Politécnica de Madrid in the line Support for R&D projects for Beatriz Galindo researchers, in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).

We would like to thank Jennifer Tabita for her contributions to the initial set of notebooks, and the AI4Gov master students for their validation of the approach.

Source of the data: Ministerio de Hacienda.