3,148 machine learning datasets
3,148 dataset results
WMT 2018 is a collection of datasets used in shared tasks of the Third Conference on Machine Translation. The conference builds on a series of twelve previous annual workshops and conferences on Statistical Machine Translation.
The Hallmarks of Cancer (*HOC) corpus consists of 1852 PubMed publication abstracts manually annotated by experts according to the Hallmarks of Cancer taxonomy. The taxonomy consists of 37 classes in a hierarchy. Zero or more class labels are assigned to each sentence in the corpus.
The SemEval-2018 hypernym discovery evaluation benchmark (Camacho-Collados et al. 2018) contains three domains (general, medical and music) and is also available in Italian and Spanish (not in this repository). For each domain a target corpus and vocabulary (i.e. hypernym search space) are provided. The dataset contains both concepts (e.g. dog) and entities (e.g. Manchester United) up to trigrams.
TextOCR is a dataset to benchmark text recognition on arbitrary shaped scene-text. TextOCR requires models to perform text-recognition on arbitrary shaped scene-text present on natural images. TextOCR provides ~1M high quality word annotations on TextVQA images allowing application of end-to-end reasoning on downstream tasks such as visual question answering or image captioning.
The Yelp Reviews Polarity dataset is obtained from the Yelp Dataset Challenge in 2015 (1,569,264 samples that have review text).
The Cambrian Vision-Centric Benchmark (CV-Bench) is designed to address the limitations of existing vision-centric benchmarks by providing a comprehensive evaluation framework for multimodal large language models (MLLMs). With 2,638 manually-inspected examples, CV-Bench significantly surpasses other vision-centric MLLM benchmarks, offering 3.5 times more examples than RealWorldQA and 8.8 times more than MMVP.
EBM-NLP annotates PICO (Participants, Interventions, Comparisons and Outcomes) spans in clinical trial abstracts. The corresponding PICO Extraction task aims to identify the spans in clinical trial abstracts that describe the respective PICO elements.
DialogRE is the first human-annotated dialogue-based relation extraction dataset, containing 1,788 dialogues originating from the complete transcripts of a famous American television situation comedy Friends. The are annotations for all occurrences of 36 possible relation types that exist between an argument pair in a dialogue. DialogRE is available in English and Chinese.
stocknet-dataset This repository releases a comprehensive dataset for stock movement prediction from tweets and historical stock prices. Please cite the following paper [bib] if you use this dataset,
PanLex translates words in thousands of languages. Its database is panlingual (emphasizes coverage of every language) and lexical (focuses on words, not sentences).
Fashion-Gen consists of 293,008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by professional stylists. Each item is photographed from a variety of angles.
The Open Table-and-Text Question Answering (OTT-QA) dataset contains open questions which require retrieving tables and text from the web to answer. This dataset is re-annotated from the previous HybridQA dataset. The dataset is collected by UCSB NLP group and issued under MIT license.
VisualMRC is a visual machine reading comprehension dataset that proposes a task: given a question and a document image, a model produces an abstractive answer.
For goal-oriented document-grounded dialogs, it often involves complex contexts for identifying the most relevant information, which requires better understanding of the inter-relations between conversations and documents. Meanwhile, many online user-oriented documents use both semi-structured and unstructured contents for guiding users to access information of different contexts. Thus, we create a new goal-oriented document-grounded dialogue dataset that captures more diverse scenarios derived from various document contents from multiple domains such ssa.gov and studentaid.gov. For data collection, we propose a novel pipeline approach for dialogue data construction, which has been adapted and evaluated for several domains.
Adversarial GLUE (AdvGLUE) is a new multi-task benchmark to quantitatively and thoroughly explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks. In particular, we systematically apply 14 textual adversarial attack methods to GLUE tasks to construct AdvGLUE, which is further validated by humans for reliable annotations.
MAD (Movie Audio Descriptions) is an automatically curated large-scale dataset for the task of natural language grounding in videos or natural language moment retrieval. MAD exploits available audio descriptions of mainstream movies. Such audio descriptions are redacted for visually impaired audiences and are therefore highly descriptive of the visual content being displayed. MAD contains over 384,000 natural language sentences grounded in over 1,200 hours of video, and provides a unique setup for video grounding as the visual stream is truly untrimmed with an average video duration of 110 minutes. 2 orders of magnitude longer than legacy datasets.
In this project, we introduce InfoSeek, a visual question answering dataset tailored for information-seeking questions that cannot be answered with only common sense knowledge. Using InfoSeek, we analyze various pre-trained visual question answering models and gain insights into their characteristics. Our findings reveal that state-of-the-art pre-trained multi-modal models (e.g., PaLI-X, BLIP2, etc.) face challenges in answering visual information-seeking questions, but fine-tuning on the InfoSeek dataset elicits models to use fine-grained knowledge that was learned during their pre-training.
The Open Entity dataset is a collection of about 6,000 sentences with fine-grained entity types annotations. The entity types are free-form noun phrases that describe appropriate types for the role the target entity plays in the sentence. Sentences were sampled from Gigaword, OntoNotes and web articles. On average each sentence has 5 labels.
A new dataset of goal-oriented dialogues that are grounded in the associated documents.
Spot-the-diff is a dataset consisting of 13,192 image pairs along with corresponding human provided text annotations stating the differences between the two images.