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README.md
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@@ -112,4 +112,27 @@ Our model achieves the following intrinsic evaluation results:
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[6]: https://www.cs.rit.edu/~dprl/ARQMath/arqmath-resources.html
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[9]: https://github.com/huggingface/transformers/issues/16936
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[10]: https://github.com/huggingface/transformers/pull/17119
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[11]: https://huggingface.co/witiko/mathberta/resolve/main/learning-curves.png
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[6]: https://www.cs.rit.edu/~dprl/ARQMath/arqmath-resources.html
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[9]: https://github.com/huggingface/transformers/issues/16936
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[10]: https://github.com/huggingface/transformers/pull/17119
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[11]: https://huggingface.co/witiko/mathberta/resolve/main/learning-curves.png
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## Citing
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### Text
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Vít Novotný and Michal Štefánik. “Combining Sparse and Dense Information
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Retrieval. Soft Vector Space Model and MathBERTa at ARQMath-3”.
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In: *Proceedings of the Working Notes of CLEF 2022*. To Appear.
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CEUR-WS, 2022.
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### Bib(La)TeX
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``` bib
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@inproceedings{novotny2022combining,
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booktitle = {Proceedings of the Working Notes of {CLEF} 2022},
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title = {Combining Sparse and Dense Information Retrieval},
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subtitle = {Soft Vector Space Model and MathBERTa at ARQMath-3},
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author = {Novotný, Vít and Štefánik, Michal},
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publisher = {{CEUR-WS}},
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year = {2022},
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note = {To Appear},
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}
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```
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