Omkar2118/datasets huggingface dataset:
WikiWeb2M
Full name: Wikipedia Webpage 2M

**Wikipedia Webpage 2M (WikiWeb2M)** is a multimodal open source dataset consisting of over 2 million English Wikipedia articles. It is created by rescraping the ∼2M English articles in WIT. Each webpage sample includes the page URL and title, section titles, text, and indices, images and their captions.

Source: [WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset](https://arxiv.org/pdf/2305.05432v1.pdf)

Image Source: [WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset](https://arxiv.org/pdf/2305.05432v1.pdf)

Introducing paper: WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset
ParsVQA-Caps

Despite recent advances in vision-and-language tasks, most progress is still focused on resource-rich languages such as English. Furthermore, widespread vision-and-language datasets directly adopt images representative of American or European cultures resulting in bias. Hence we introduce ParsVQA-Caps, the first benchmark in Persian for Visual Question Answering and Image Captioning tasks. We utilize two ways to collect datasets for each task, human-based and template-based for VQA and human-based and web-based for image captioning. The image captioning dataset consists of over 7.5k
images and about 9k captions. The VQA dataset consists of almost 11k images and 28.5k question and answer pairs with short and long answers usable for both classification and generation VQA.

source: [ParsVQA-Caps: A Benchmark for Visual Question Answering and Image Captioning in Persian](https://www.winlp.org/wp-content/uploads/2022/11/68_Paper.pdf)

Introducing paper: ParsVQA-Caps: A Benchmark for Visual Question Answering and Image Captioning in Persian
CoScript

**CoScript** is a constrained language planning dataset, which consists of 55,000 scripts.

Source: [Distilling Script Knowledge from Large Language Models for Constrained Language Planning](https://arxiv.org/pdf/2305.05252v1.pdf)

Image Source: [Distilling Script Knowledge from Large Language Models for Constrained Language Planning](https://arxiv.org/pdf/2305.05252v1.pdf)

Introducing paper: Distilling Script Knowledge from Large Language Models for Constrained Language Planning
SecurityEval

Automated source code generation is currently a popular machine learning-based task. It can be helpful for software developers to write functionally correct code from a given context. However, just like human developers, a code generation model can produce vulnerable code, which the developers can mistakenly use. For this reason, evaluating the security of a code generation model is a must. In this paper, we describe SecurityEval, an evaluation dataset to fulfill this purpose. It contains 130 samples for 75 vulnerability types, which are mapped to the Common Weakness Enumeration (CWE). We also demonstrate using our dataset to evaluate one open-source (i.e., InCoder) and one closed-source code generation model (i.e., GitHub Copilot).

Introducing paper: SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques
QDAT Quran Recitation

QDAT data set contains 1500 WAV files along with sound files stored on Excel CSV file format. The sound file contains links to the WAV files attached with other features: Age, Gender, and the correctness of the recitation of the three recitation rules and the final goal shows the correctness of the whole reading.