Omkar2118/datasets huggingface dataset:
RGBE-SEG

To perform universal event stream segmentation, we collected a large-scale RGB-Event dataset for event-centric segmentation, from current available pixel-level aligned datasets ([VisEvent](https://sites.google.com/view/viseventtrack/), [COESOT](https://github.com/Event-AHU/COESOT)), namely RGBE-SEG. The RGBE-SEG included 65,957 image-event pairs, 64,957 for training and 1,000 for testing. The test set contained 38,760 masks, and we artificially divided it into easy, medium, and hard subsets based on the complexity of scenarios. All ground truth masks were generated by images and the well-trained SAM.

Introducing paper: Segment Any Events via Weighted Adaptation of Pivotal Tokens
PolymerAbstracts

A dataset of 750 polymer abstracts annotated with the entity types: POLYMER, POLYMER_CLASS, PROPERTY_VALUE, PROPERTY_NAME, MONOMER, ORGANIC_MATERIAL, INORGANIC_MATERIAL, and MATERIAL_AMOUNT.  This data set was annotated for training a named entity recognition model for these entity types.

Introducing paper: A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processing
CC3D

The CC3D dataset [1] of 3D CAD models was collected from a free online service for sharing CAD designs [2]. In total, the collected dataset contains 50k+ models, unrestricted to any category, with varying complexity from simple to highly detailed designs. These CAD models are converted to meshes, and each mesh was virtually scanned using a proprietary 3D scanning pipeline developed by Artec3D [3]. The typical size of the resulting scans is in the order of 100K points and faces, while the meshes converted from CAD models are usually more than an order of magnitude lighter. The availability of CAD-3D scan pairings, the high-resolution of meshes, and the variability of the models make the CC3D dataset stand out among other alternatives. 

[1] Pvdeconv: Point-voxel deconvolution for autoencoding cad construction in 3d, Cherenkova, Kseniya, Djamila Aouada, and Gleb Gusev, 2020 IEEE International Conference on Image Processing (ICIP).

[2] “3dcontentcentral,” https://www.3dcontentcentral.com.

[3] “Artec3d,” https://www.artec3d.com/.

Introducing paper: PvDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D
Character-LLM Data

This is the training datasets for Character-LLM, which contains nine characters experience data used to train Character-LLMs.
RepoBench

RepoBench is a benchmark designed for evaluating repository-level code auto-completion systems, focusing on more complex, real-world programming scenarios involving multiple files. It comprises three tasks: RepoBench-R (Retrieval), measuring the system's ability to retrieve relevant code snippets; RepoBench-C (Code Completion), assessing the prediction of the next line of code with both in-file and cross-file context; and RepoBench-P (Pipeline), evaluating complex tasks requiring both retrieval and prediction. RepoBench aims to provide a comprehensive performance comparison to foster continuous improvement in auto-completion systems.

Introducing paper: RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems