Based on a government mandate, the Swiss National Science Foundation (SNSF) supports scientific research in all academic disciplines. It is the leading organisation for the promotion of scientific research in Switzerland. On the Data Portal, the SNSF publishes data on the evaluated projects and the persons involved in order to provide transparency and facilitate the analysis of funding activities.

In this space, the SNSF Data Team provides fine-tuned models for classifying grant peer review texts along 12 categories relevant to the evaluation criteria specified by the SNSF. In particular, the models are based on the allenai/specter2_base model and fine-tuned for a binary classification task on a sentence level. For more details, see the the following preprint:

A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports

by Gabriel Okasa, Alberto de León, Michaela Strinzel, Anne Jorstad, Katrin Milzow, Matthias Egger, and Stefan Müller, available on arXiv: https://arxiv.org/abs/2411.16662 . The fine-tuning codes are open-sourced on GitHub: https://github.com/snsf-data/ml-peer-review-analysis .

The model cards provide further details on the models, the fine-tuning procedure and evaluation metrics as well as minimal examples for usage of the models.