Omkar2118/datasets huggingface dataset:
HarMeme

HarMeme is a benchmark dataset for hateful meme classification containing 3, 544 memes related to COVID-19 collected from the Internet

Introducing paper: Detecting Harmful Memes and Their Targets
TARA

TARA is a dataset for tool-augmented reward modeling, which includes comprehensive comparison data of human preferences and detailed tool invocation processes.

TARA comprises a total of 13,604 training datasets and 1,469 test sets, each consisting of a question, a positive answer, and a negative answer. TARA incorporates a diverse set of seven tools that span across various domains, encompassing mathematical operations, code-related inquiries, closed-ended and open-ended question answering, knowledge-based queries, and time-sensitive information requests.

Refer to [github](https://github.com/ernie-research/Tool-Augmented-Reward-Model?tab=readme-ov-file#-dataset) for details.

Introducing paper: Tool-Augmented Reward Modeling
ConCon Dataset
Full name: Continually Confounded Dataset

ConCon: Continually Confounded Dataset is a confounded visual dataset for continual learning. 

Files contained:

case_disjoint, case_strict and unconfounded consist of splits for training (train), validation (val), testing (test). 

Each split within case_disjoint and case_strict contains 3 tasks (t0, t1, t2) whereas there is 1 task (t0) within unconfounded.

Each task contains folders for postive(1) and negative(0) images.

Introducing paper: Where is the Truth? The Risk of Getting Confounded in a Continual World
HierText

HierText is the first dataset featuring hierarchical annotations of text in natural scenes and documents. The dataset contains 11639 images selected from the [Open Images dataset](https://storage.googleapis.com/openimages/web/index.html), providing high quality word (~1.2M), line, and paragraph level annotations. Text lines are defined as connected sequences of words that are aligned in spatial proximity and are logically connected. Text lines that belong to the same semantic topic and are geometrically coherent form paragraphs. Images in HierText are rich in text, with average of more than 100 words per image.
P2S
Full name: Points2Surf

We introduced this dataset in Points2Surf, a method that turns point clouds into meshes.

It consists of objects from the [_ABC Dataset_](https://paperswithcode.com/dataset/abc-dataset-1), a collection of _Famous_ meshes and objects from [_Thingi10k_](https://paperswithcode.com/dataset/thingi10k).
These are mostly single objects per file, sometimes a couple of disconnected objects. Objects from the _ABC Dataset_ are CAD-models, the others are mostly statues with organic structures.

We created realistic point clouds using a simulated time-of-flight sensor from [_BlenSor_](https://www.blensor.org/). The point clouds have typical artifacts like noise and scan shadows.

Finally, we created training data consisting of randomly sampled query points with their ground-truth signed distance. The query points are 50% uniformly distributed in the unit cube and 50% near the surface with some random offset.

The training set consists of 4950 _ABC_ objects with varying number of scans and noise strength. 
The validation sets are the same as the test set. 
The _ABC_ test sets contain 100 objects, _Famous_ 22 and _Thingi10k_ 100. The test set variants are as follows:
(1) _ABC_ var (like training set), no noise, strong noise; 
(2) _Famous_ no noise, medium noise, strong noise, sparse, dense scans;
(3) _Thingi10k_ no noise, medium noise, strong noise, sparse, dense scans

Introducing paper: Points2Surf: Learning Implicit Surfaces from Point Cloud Patches