3,148 machine learning datasets
3,148 dataset results
The Sixth Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing Challenge for Clinical Records focused on the temporal relations in clinical narratives. The organizers provided the research community with a corpus of discharge summaries annotated with temporal information, to be used for the development and evaluation of temporal reasoning systems. 18 teams from around the world participated in the challenge. During the workshop, participating teams presented comprehensive reviews and analysis of their systems, and outlined future research directions suggested by the challenge contributions.
We take advantage of the ground truth of NLVR images, design CFGs to generate stories, and use spatial reasoning rules to ask and answer spatial reasoning questions. This automatically generated data is called SpaRTQA. https://aclanthology.org/2021.naacl-main.364/
TEMPOral reasoning in video and language (TEMPO) is a dataset that consists of two parts: a dataset with real videos and template sentences (TEMPO - Template Language) which allows for controlled studies on temporal language, and a human language dataset which consists of temporal sentences annotated by humans (TEMPO - Human Language).
The ability to recognize analogies is fundamental to human cognition. Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
EgoProceL is a large-scale dataset for procedure learning. It consists of 62 hours of egocentric videos recorded by 130 subjects performing 16 tasks for procedure learning. EgoProceL contains videos and key-step annotations for multiple tasks from CMU-MMAC, EGTEA Gaze+, and individual tasks like toy-bike assembly, tent assembly, PC assembly, and PC disassembly. EgoProceL overcomes the limitations of third-person videos. As, using third-person videos makes the manipulated object small in appearance and often occluded by the actor, leading to significant errors. In contrast, we observe that videos obtained from first-person (egocentric) wearable cameras provide an unobstructed and clear view of the action.
Moral Foundations Reddit Corpus (MFRC) is a collection of 16,123 Reddit comments that have been curated from 12 distinct subreddits, hand-annotated by at least three trained annotators for 8 categories of moral sentiment (i.e., Care, Proportionality, Equality, Purity, Authority, Loyalty, Thin Morality, Implicit/Explicit Morality) based on the updated Moral Foundations Theory (MFT) framework.
Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes, which in most cases combines an image with a concept or catchphrase. The challenge of dealing with memes is that they are region-specific and their meaning is often obscured in humour or sarcasm. To facilitate the computational modelling of trolling in the memes for Indian languages, we created a meme dataset for Tamil (TamilMemes). We annotated and released the dataset containing suspected trolls and not-troll memes. In this paper, we use the a image classification to address the difficulties involved in the classification of troll memes with the existing methods. We found that the identification of a troll meme with such an image classifier is not feasible which has been corroborated with precision,
SciRepEval is a comprehensive benchmark for training and evaluating scientific document representations. It includes 25 challenging and realistic tasks, 11 of which are new, across four formats: classification, regression, ranking and search.
ClueWeb22 is the newest iteration of the ClueWeb line of datasets, provides 10 billion web pages affiliated with rich information. Its design was influenced by the need for a high quality, large scale web corpus to support a range of academic and industry research, for example, in information systems, retrieval-augmented AI systems, and model pretraining. Compared with earlier CLUEWeb corpora, the ClUEWeb22 corpus is larger, more varied, of higher-quality, and aligned with the document distributions in commercial web search. Besides raw HTML, the dataset includes rich information about the web pages provided by industry-standard document understanding systems, including the visual representation of pages rendered by a web browser, parsed HTML structure information from a neural network parser, and pre-processed cleaned document text.
RadQA is a radiology question answering dataset with 3074 questions posed against radiology reports and annotated with their corresponding answer spans (resulting in a total of 6148 question-answer evidence pairs) by physicians. The questions are manually created using the clinical referral section of the reports that take into account the actual information needs of ordering physicians and eliminate bias from seeing the answer context (and, further, organically create unanswerable questions). The answer spans are marked within the Findings and Impressions sections of a report. The dataset aims to satisfy the complex clinical requirements by including complete (yet concise) answer phrases (which are not just entities) that can span multiple lines.
The dataset contains single-shot videos taken from moving cameras in underwater environments. The first shard of a new Marine Video Kit dataset is presented to serve for video retrieval and other computer vision challenges. In addition to basic meta-data statistics, we present several insights based on low-level features as well as semantic annotations of selected keyframes. 1379 videos with a length from 2 s to 4.95 min, with the mean and median duration of each video is 29.9 s, and 25.4 s, respectively. We capture data from 11 different regions and countries during the time from 2011 to 2022.
We propose a test to measure the multitask accuracy of large Chinese language models. We constructed a large-scale, multi-task test consisting of single and multiple-choice questions from various branches of knowledge. The test encompasses the fields of medicine, law, psychology, and education, with medicine divided into 15 sub-tasks and education into 8 sub-tasks. The questions in the dataset were manually collected by professionals from freely available online resources, including university medical examinations, national unified legal professional qualification examinations, psychological counselor exams, graduate entrance examinations for psychology majors, and the Chinese National College Entrance Examination. In total, we collected 11,900 questions, which we divided into a few-shot development set and a test set. The few-shot development set contains 5 questions per topic, amounting to 55 questions in total. The test set comprises 11,845 questions.
WebCPM is a Chinese LFQA dataset. It contains 5,500 high-quality question-answer pairs, together with 14,315 supporting facts and 121,330 web search actions.
Bactrian-X is a comprehensive multilingual parallel dataset of 3.4 million instruction-response pairs across 52 languages. The instructions were obtained from alpaca-52k, and dolly-15k, and tranlated into 52 languages (52 languages x 67k instances = 3.4M instances).
FinRED is a relation extraction dataset curated from financial news and earning call transcripts containing relations from the finance domain. FinRED has been created by mapping Wikidata triplets using distance supervision method.
Paper | Github | Dataset| Model
This is the first general Underwater Image Instance Segmentation (UIIS) dataset containing 4,628 images for 7 categories with pixel-level annotations for underwater instance segmentation task
KaMed is a knowledge-aware medical dialogue dataset, which contains over 60,000 medical dialogue sessions with 5,682 entities (such as Asthma and Atropine).
Speech encompasses a wealth of information, including but not limited to content, paralinguistic, and environmental information. This comprehensive nature of speech significantly impacts communication and is crucial for human-computer interaction. Chat-Oriented Large Language Models (LLMs), known for their general-purpose assistance capabilities, have evolved to handle multi-modal inputs, including speech. Although these models can be adept at recognizing and analyzing speech, they often fall short of generating appropriate responses. We argue that this is due to the lack of principles on task definition and model development, which requires open-source datasets and metrics suitable for model evaluation. To bridge the gap, we present SD-Eval, a benchmark dataset aimed at multidimensional evaluation of spoken dialogue understanding and generation. SD-Eval focuses on paralinguistic and environmental information and includes 7,303 utterances, amounting to 8.76 hours of speech data. The da
The iWildCam2020-WILDS dataset is a variant of the iWildCam 2020 dataset. iWildCam2020-WILDS is a benchmark dataset designed to test OOD generalization for the task of species classification. The label space consists of 182 species. Each domain corresponds to a different location of the camera trap. The training and test images belong to disjoint sets of locations in the OOD setting.