Omkar2118/datasets huggingface dataset:
LMCQA
Full name: Legal Multiple Choice Question Answering

This dataset contains a set of multiple-choice questions related to various legal topics. The dataset contains 20 questions covering various aspects of legal knowledge, such as the workings of the European Commission, types of legal documents, procedures in the court system, legal definitions, and European Union+United Kingdom law, among others.
Randman

Versatile synthetic classification dataset based on precise input spike timings drawn from smooth random manifolds as previously described
PTR_NETS
Full name: Pointer Networks

These are the files containing the Convex Hull and Traveling Salesman Problem dataset present in the “Pointer Networks” paper:

Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. "Pointer networks." NIPS 2015

@inproceedings{vinyals2015pointer,
  title={Pointer networks},
  author={Vinyals, Oriol and Fortunato, Meire and Jaitly, Navdeep},
  booktitle={NIPS},
  pages={2692--2700},
  year={2015}
}

The following is an example from one of the lines from one of the files. It contains the 2d coordinates (in the format x_1 y_1 x_2 y_2 … x_N y_N) followed by the word “output” as a separator, and then the indices of the corresponding outputs (base 1) of the input points. In the example below, the convex hull was 1 2 3 4 5 1 (note that the last digit is repeated as it forms a tour), i.e., first point (0.99624295 0.92802603), followed by second point (0.24557767 0.80411435), etc.

0.99624295 0.92802603 0.24557767 0.80411435 0.43667577 0.33301639 0.36675234 0.71464094 0.80695481 0.14914953 output 1 2 3 5 1

Introducing paper: Pointer Networks
Faces Through Time

Faces Through Time (FTT) features 26,247 images of notable people from the 19th to 21st centuries, with roughly 1,900 images per decade on average. It is sourced from Wikimedia Commons, a crowdsourced and open-licensed collection of 50M images.

Introducing paper: What's in a Decade? Transforming Faces Through Time
Turkish Punctuation Restoration

we have prepared a dataset using publicly available TED Talks transcripts [27] and selected the Turkish corpus. The resulting Turkish punctuation restoration dataset currently consists of 146K sentences and 1.8M tokens. The ratio of the train, validation, and test splits are 0.8, 0.1, and 0.1, respectively. Data files contain two columns. The first column has the tokens separated by white space. The second column includes tags for each token.