Omkar2118/datasets huggingface dataset:
SMR IU X-Ray
Full name: Simplified Medical Reports

This paper introduces **CPIR-MR** (Chained Prompting for Improved Readability of Medical Reports), a method designed to simplify complex chest X-ray reports for better patient understanding. The authors extend the IU X-Ray dataset with **Simplified Medical Reports (SMRs)** generated via chained prompting and propose a **multi-modal text decoder (MTD)** that integrates BLIP embeddings with classification outputs to generate **Simplified Medical Explanations (SMEs)**.<br><br>  
Key highlights:<br>  
- Uses **few-shot and Chain-of-Thought (CoT)** prompting for generating structured, readable outputs.<br>  
- Maintains medical accuracy while improving **readability and sentiment consistency**.<br>  
- Introduces **CPMK-E**, a chained prompting system for keyword extraction and evaluation using Gemini 1.5 Flash.<br>  
- Shows strong performance in text complexity reduction and **semantic similarity preservation**.<br><br>

Introducing paper: From Complexity to Clarity: Transforming Chest X-ray Reports with Chained Prompting (Student Abstract)
Segmentasi Simantic Angsa Datasets

Description:

<a href="https://gts.ai/dataset-download/segmentasi-simantic-angsa-datasets/" target="_blank">👉 Download the dataset here</a>

This dataset is designed for semantic segmentation in computer vision, specifically targeting the segmentation of swan objects. It provides labeled images for training models to distinguish between different regions within the images.

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Labels in the Dataset:

Background (Label “0”): Represents the areas of the image that do not contain any swan objects.

Swan (Label “1”): Represents the regions of the image that contain swan objects.

The dataset includes a variety of images featuring swans in different environments and poses, ensuring comprehensive coverage for effective model training. The high-quality annotations enable precise segmentation of swan objects from the background, making it a valuable resource for developing and evaluating semantic segmentation algorithms.

This dataset is sourced from Kaggle.
DynToM

As Large Language Models (LLMs) increasingly participate in human-AI interactions, evaluating their Theory of Mind (ToM) capabilities - particularly their ability to track dynamic mental states - becomes crucial. While existing benchmarks assess basic ToM abilities, they predominantly focus on static snapshots of mental states, overlooking the temporal evolution that characterizes real-world social interactions. We present **DynToM**, a novel benchmark specifically designed to evaluate LLMs' ability to understand and track the temporal progression of mental states across interconnected scenarios. Through a systematic four-step framework, we generate 1,100 social contexts encompassing 5,500 scenarios and 78,100 questions, each validated for realism and quality. Our comprehensive evaluation of ten state-of-the-art LLMs reveals that their average performance underperforms humans by 44.7\%, with performance degrading significantly when tracking and reasoning about the shift of mental states. This performance gap highlights fundamental limitations in current LLMs' ability to model the dynamic nature of human mental states.

Introducing paper: Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States
Doodle Dataset

Description:

<a href="https://gts.ai/dataset-download/doodle-dataset/" target="_blank">👉 Download the dataset here</a>

The Doodle Dataset Classifier Prepared Dataset comprises over 1 million images spanning 340 classes of doodles. Sourced from the Quick, Draw! dataset, it features grayscale images of hand-drawn sketches organized by class. Each image has been processed to facilitate machine learning tasks, ensuring a clean and easy-to-use version of the original dataset.

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Images: Grayscale images of doodles, each sized at 255×255 pixels.

Classes: 340 distinct categories of doodles, each stored in its own directory.

Total Images: 1,020,000 images, with each class containing precisely 3,000 images.

This refined dataset provides a manageable and structured subset of the original Quick, Draw! dataset by Google, which contains approximately 50 million images

This dataset is sourced from Kaggle.
CCI4.0

We introduce CCI4.0, a large-scale bilingual pre-training dataset engineered for superior data quality and diverse human-like reasoning trajectory. CCI4.0 occupies roughly $35$ TB of disk space and comprises two sub-datasets: CCI4.0-M2-Base and CCI4.0-M2-CoT. CCI4.0-M2-Base combines a $5.2$ TB carefully curated Chinese web corpus, a $22.5$ TB English subset from Nemotron-CC, and diverse sources from math, wiki, arxiv, and code. Although these data are mostly sourced from well-processed datasets, the quality standards of various domains are dynamic and require extensive expert experience and labor to process. So, we propose a novel pipeline justifying data quality mainly based on models through two-stage deduplication, multiclassifier quality scoring, and domain-aware fluency filtering. We extract $4.5$ billion pieces of CoT(Chain-of-Thought) templates, named CCI4.0-M2-CoT. Differing from the distillation of CoT from larger models, our proposed staged CoT extraction exemplifies diverse reasoning patterns and significantly decreases the possibility of hallucination. Empirical evaluations demonstrate that LLMs pre-trained in CCI4.0 benefit from cleaner, more reliable training signals, yielding consistent improvements in downstream tasks, especially in math and code reflection tasks. Our results underscore the critical role of rigorous data curation and human thinking templates in advancing LLM performance, shedding some light on automatically processing pretraining corpora.
IndoorLRS
Full name: Indoor Lidar-RGBD Scan Dataset

http://redwood-data.org/indoor_lidar_rgbd/