Omkar2118/datasets huggingface dataset:
EgoISM-HOI

EgoISM-HOI is a new multimodal dataset composed of synthetic and real images of egocentric human-objects interactions in an industrial environment with rich annotations of hands and objects. EgoISM-HOI contains a total of 39,304 RGB images, 23,356 depth maps and instance segmentation masks, 59,860 hand annotations, 237,985 object instances across 19 object categories and 35,416 egocentric human-object interactions.

Introducing paper: Exploiting Multimodal Synthetic Data for Egocentric Human-Object Interaction Detection in an Industrial Scenario
LGP

Generated for further pre-training pre-trained models like BERT, RoBERTa, ALBERT, DeBERTa, etc.. in order to get stronger logical reasoning ability.

Introducing paper: IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
GUE
Full name: Genome Understanding Evaluation

A collection of $28$ datasets across $7$ tasks constructed for genome language model evaluation. Contains seven tasks: promoter prediction. core promoter prediction, splice site prediction, covid variant classification, epigenetic marks prediction, and  transcription factor binding sites prediction on human and mouse.

Introducing paper: DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome
WDC Block
Full name: WDC Block: A Blocking Benchmark

WDC Block is a benchmark for comparing the performance of blocking methods that are used as part of entity resolution pipelines.

Entity resolution aims to identify records in two datasets (A and B) that describe the same real-world entity. Since comparing all record pairs between two datasets can be computationally expensive, entity resolution is approached in two steps, blocking and matching. Blocking applies a computationally cheap method to remove non-matching record pairs and produces a smaller set of candidate record pairs reducing the workload of the matcher. During matching a more expensive pair-wise matcher produces a final set of matching record pairs. 

Existing benchmark datasets for blocking and matching are rather small with respect to the Cartesian product AxB for comparing all records and the vocabulary size. If blockers are evaluated only on these small datasets, effects resulting from a high number of records or from a large vocabulary size (large number of unique tokens that need to be indexed) may be missed. The Web Data Commons Block (WDC-Block) is a new blocking benchmark that provides much larger datasets and thus requires blockers that address these scalability challenges. WDC Block features a maximal Cartesian product of 200 billion pairs of product offers which were extracted form 3,259 e-shops.  Additionally, we provide three development sets with different sizes (~1K pairs, ~5K pairs & ~20K pairs) to experiment with different amounts of training data for the blockers.

Introducing paper: SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines