Omkar2118/datasets huggingface dataset:
MiniWob++

MiniWob++ is a suite of web-browser based tasks introduced in Liu et al. (2018) (an extension of the earlier MiniWob task suite (Shi et al., 2017)). Tasks range from simple button clicking to complex form-filling, for example, to book a flight when given particular instructions (Fig. 1a).
Programmatic rewards are available for each task, permitting standard reinforcement learning techniques.

(Source: Section 2.1 of https://arxiv.org/pdf/2202.08137.pdf)
SAGC-A68
Full name: A space access graph dataset for the classification of spaces and space elements in apartment buildings

The analysis of building models for usable area, building safety, and energy efficiency requires accurate classification data of spaces and space elements. To reduce input model preparation effort and errors, automated classification of spaces and space elements is desirable. Although existing space function classifiers use space adjacency or connectivity graphs as input, the application of Graph Deep Learning (GDL) to space layout element classification has not been extensively researched due to the lack of suitable datasets. To bridge this gap, we introduce a dataset named SAGC-A68, which comprises access graphs automatically generated from 68 digital 3D models of space layouts of apartment buildings designed or built between 1952 and 2019 in 13 countries. Each access graph contains nodes representing spaces and space elements and edges representing the connection between them. Nodes are uniquely identified and characterized by 16 features including “Position X”, “Position Y”, “Position Z”, “Width”, “Height”, “Depth”, “Area”, “Volume”, “Is_internal”, “Door_opening_quantity”, “Window_quantity”, “Max_door_width”,” Encloses_ws”, “Is_contained_in_ws”, ”bounding_box”, and “Label” (28 identified labels are shown in bold type in Table 1). Edges are identified by a unique ID and characterized by three features, including “Z_angle”, “Delta_z”, and “Length”. In total, the dataset comprises 4871 nodes and 4566 edges, including disconnected nodes representing shafts. It is suitable for developing GDL models for space element and space function classification in Building information modeling (BIM) authoring systems.

Introducing paper: SAGC-A68: a space access graph dataset for the classification of spaces and space elements in apartment buildings
ConvSumX

**ConvSumX** is a cross-lingual conversation summarization benchmark, through a new annotation schema that explicitly considers source input context. ConvSumX consists of 2 sub-tasks under different real-world scenarios, with each covering 3 language directions.

Introducing paper: Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation
BeaverTails

**BeaverTails** is a dataset aimed at fostering research on safety alignment in large language models (LLMs). This dataset uniquely separates annotations of helpfulness and harmlessness for question-answering pairs, thus offering distinct perspectives on these crucial attributes. In total, the authors have compiled safety meta-labels for 30,207 question-answer (QA) pairs and gathered 30,144 pairs of expert comparison data for both
IU X-Ray

IU X-ray (Demner-Fushman et al., 2016) is a set of chest X-ray images paired with their corresponding diagnostic reports. The dataset contains 7,470 pairs of images and reports. A simplified version of *IU X-Ray* through **CPIR-MR** prompting was presented at AAAI 2025: [AAAI Article Link](https://ojs.aaai.org/index.php/AAAI/article/view/35281). Dataset Link: [SMR IU X-Ray](https://paperswithcode.com/dataset/smr-iu-x-ray).

Introducing paper: A Survey on Biomedical Image Captioning
Peir Gross

Peir Gross (Jing et al., 2018) was collected with descriptions in the Gross sub-collection from PEIR digital library, resulting in 7.442 image-caption pairs from 21 different sub-categories. Each caption contains only one sentence.

Introducing paper: A Survey on Biomedical Image Captioning