- See Also
-
Links
- “ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”, Azerbayev et al 2023
- “MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding”, Wang et al 2023
- “Anime Crop Datasets: Faces, Figures, & Hands”, Gwern et al 2020
- “Goodbooks-10k: a New Dataset for Book Recommendations”, Z. 2017
-
“
goodbooks-10k: Ten Thousand Books, Six Million Ratings: Https://fastml.com/goodbooks-10k”, Z. 2017
- Link Bibliography
See Also
Links
“ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”, Azerbayev et al 2023
ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics
“MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding”, Wang et al 2023
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding
“Anime Crop Datasets: Faces, Figures, & Hands”, Gwern et al 2020
“Goodbooks-10k: a New Dataset for Book Recommendations”, Z. 2017
“goodbooks-10k: Ten Thousand Books, Six Million Ratings: Https://fastml.com/goodbooks-10k”, Z. 2017
goodbooks-10k: Ten thousand books, six million ratings: https://fastml.com/goodbooks-10k
Link Bibliography
-
https://arxiv.org/abs/2302.12433: “ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”, Zhangir Azerbayev, Bartosz Piotrowski, Hailey Schoelkopf, Edward W. Ayers, Dragomir Radev, Jeremy Avigad -
crop: “Anime Crop Datasets: Faces, Figures, & Hands”, Gwern, Arfafax, Shawn Presser, Anonymous, Danbooru Community -
https://fastml.com/goodbooks-10k-a-new-dataset-for-book-recommendations/: “Goodbooks-10k: a New Dataset for Book Recommendations”, Zygmunt Z.