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--- |
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title: README |
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emoji: 📊 |
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colorTo: red |
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sdk: static |
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pinned: false |
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short_description: Organization card providing details on the fine-tuned models |
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--- |
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Based on a government mandate, the Swiss National Science Foundation (SNSF) supports scientific research in all academic disciplines. |
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It is the leading organisation for the promotion of scientific research in Switzerland. |
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On the [Data Portal](https://data.snf.ch/), the SNSF publishes data on the evaluated projects and the persons involved in order to provide transparency and facilitate the analysis of funding activities. |
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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. |
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In particular, the models are based on the `allenai/specter2_base` model and fine-tuned for a binary classification task on a sentence level. |
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For more details, see the the following preprint: |
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**A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports** |
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by [Gabriel Okasa](https://orcid.org/0000-0002-3573-7227), |
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[Alberto de León](https://orcid.org/0009-0002-0401-2618), |
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[Michaela Strinzel](https://orcid.org/0000-0003-3181-0623), |
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[Anne Jorstad](https://orcid.org/0000-0002-6438-1979), |
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[Katrin Milzow](https://orcid.org/0009-0002-8959-2534), |
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[Matthias Egger](https://orcid.org/0000-0001-7462-5132), and |
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[Stefan Müller](https://orcid.org/0000-0002-6315-4125), available on arXiv: https://arxiv.org/abs/2411.16662 . |
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The fine-tuning codes are open-sourced on GitHub: https://github.com/snsf-data/ml-peer-review-analysis . |
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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. |
