Omkar2118/datasets huggingface dataset:
LongWanjuan

A 160B bilingual long-text dataset with 3 categories: holistic, aggregated and chaotic long texts.

Introducing paper: LongWanjuan: Towards Systematic Measurement for Long Text Quality
SignboardText

This dataset contains images and annotations for scene text detection and recognition. It is made up of two parts: (1) 1,175 images manually labeled with a total of 59,588 text instances at the line and word levels; and (2) 929 signboard images collected from the VinText, Total-Text, and ICDAR15 datasets. Each text instance in the first part of our dataset has a quadrilateral bounding box and a ground truth character sequence associated with it. In the second part, images are selected if they contain signboards. This portion of the dataset comprises 20,261 text instances at word levels. This brings the total text instances in our final dataset up to 79,814. Following the ICDAR15 standard, we annotated each image with all of the text instances, polygons, and content that were present. Manual annotations were done on each and every image.
TCB-DS
Full name: Toxigenic Cyanobacteria Dataset

The TCB-DS dataset is a specialized collection of microscopic images focusing on the automatic recognition of cyanobacteria genera. This dataset was meticulously compiled to address the challenges associated with the varying image qualities due to differences in contrast, resolution, size, lighting, and the presence of noise in the original images. It includes 2,591 images with varying dimensions, ranging from a minimum of 11 × 41 pixels to a maximum of 5184 × 3456 pixels.

After using this dataset please cite: Automated identification of toxigenic cyanobacterial genera for water quality control purposes https://doi.org/10.1016/j.jenvman.2024.121274

Introducing paper: Automated identification of toxigenic cyanobacterial genera for water quality control purposes
SMAP
Full name: Soil Moisture Active Passive

Soil Moisture Active Passive (SMAP) dataset is a dataset of soil samples and telemetry information using the Mars rover by NASA. Originally published in https://arxiv.org/abs/1802.04431 and used for the unsupervised anomaly detection task in time series data. Later it was used in many popular anomaly detection methods and benchmarks that distribute it in their repositories e.g., https://github.com/OpsPAI/MTAD

Introducing paper: Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
COCO-Noisy
Full name: Microsoft Common Objects in Context with 20% of Noisy Correspondence and 1K test data

This dataset is based on MS COCO that have 20% of data randomly shuffled to simulate noisy correspondence.

Introducing paper: Learning with Noisy Correspondence for Cross-modal Matching
MUTE
Full name: Multimodal Bengali Hateful Memes Dataset

##MUTE

This is the first open-source Bengali Hateful Meme dataset, consisting of around 4200 memes annotated with two labels: hate and not hate.

Introducing paper: MUTE: A Multimodal Dataset for Detecting Hateful Memes
WSRD+

A version of the WSRD Dataset will be used as a benchmark for the NTIRE24 Challenge on Image Shadow Removal.
Xmaster6y/lczero-planning-tcec

TCEC games dataset.

Introducing paper: Contrastive Sparse Autoencoders for Interpreting Planning of Chess-Playing Agents
HeSum
Full name: Abstractive Text Summarization in Hebrew

While large language models (LLMs) excel in various natural language tasks in English, their performance in lower-resourced languages like Hebrew, especially for generative tasks such as abstractive summarization, remains unclear. The high morphological richness in Hebrew adds further challenges due to the ambiguity in sentence comprehension and the complexities in meaning construction. In this paper, we address this resource and evaluation gap by introducing HeSum, a novel benchmark specifically designed for abstractive text summarization in Modern Hebrew. HeSum consists of 10,000 article-summary pairs sourced from Hebrew news websites written by professionals. Linguistic analysis confirms HeSum's high abstractness and unique morphological challenges. We show that HeSum presents distinct difficulties for contemporary state-of-the-art LLMs, establishing it as a valuable testbed for generative language technology in Hebrew, and MRLs generative challenges in general.

code and data - https://github.com/OnlpLab/HeSum

Introducing paper: HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew