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Sorry, it's no longer available on Hugging Face. Please reach out to those who have already downloaded it. If you have a copy, please refrain from re-uploading it to Hugging Face. The people here don't deserve it. See also: https://twitter.com/RealJosephus/status/1779913520529707387 GuanacoDataset News: We're heading towards multimodal VQA, with blip2-flan-t5-xxl Alignment to Guannaco 7B LLM. Still under construction: GuanacoVQA weight & GuanacoVQA Dataset Notice: Effective… See the full description on the dataset page: https://huggingface.co/datasets/JosephusCheung/GuanacoDataset.

Sci-Base: The Largest AI-Ready Scientific Foundation Dataset 🌌 The Sciverse Data Foundation Sciverse is a comprehensive, multi-layered scientific data foundation designed to provide the ultimate data infrastructure for the AI for Science (AI4S) community. As scientific research becomes increasingly data-driven, Sciverse supplies the essential, high-quality data resources required to build robust scientific knowledge systems and accelerate research. Sciverse… See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/Sci-Base.

For more details, please refer to the 𝐓𝐞𝐱𝐓𝐞𝐥𝐥𝐞𝐫 GitHub repository. IMPORTANT NOTE!!! The handwritten subset of this dataset was collected entirely from existing open source work, which includes all test sets. If you want to use this subset for your experimental ablation, please filter it yourself based on the latex label of the test set

DartLab Data Structured company data from DART & EDGAR disclosure filings DART 전자공시 + EDGAR 공시 데이터 — 한국 2,700사 / 미국 970사 What is this? Pre-collected Parquet files from DartLab — a Python library that turns DART (Korea) and EDGAR (US) disclosure filings into one structured company map. 한국 DART 전자공시 시스템과 미국 SEC EDGAR에서 수집한 기업 공시 데이터입니다. This dataset is the data layer behind DartLab. When you run dartlab.Company("005930"), the library automatically downloads the… See the full description on the dataset page: https://huggingface.co/datasets/eddmpython/dartlab-data.

Dataset Card for CIFAR-100 Dataset Summary The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses. There are two labels per image - fine label (actual class) and coarse label (superclass). Supported Tasks and Leaderboards image-classification: The… See the full description on the dataset page: https://huggingface.co/datasets/uoft-cs/cifar100.

Model Card for HEST-1k What is HEST-1k? A collection of 1,276 spatial transcriptomic profiles, each linked and aligned to a Whole Slide Image (with pixel size < 1.15 µm/px) and metadata. HEST-1k was assembled from 180 public and internal cohorts encompassing: 26 organs 2 species (Homo Sapiens and Mus Musculus) 398 cancer samples from 25 cancer types. HEST-1k processing enabled the identification of >1.5 million expression/morphology pairs and >76 million nuclei… See the full description on the dataset page: https://huggingface.co/datasets/MahmoodLab/hest.

DartLab Data Structured company data from DART & EDGAR disclosure filings DART 전자공시 + EDGAR 공시 데이터 — 한국 2,700사 / 미국 970사 What is this? Pre-collected Parquet files from DartLab — a Python library that turns DART (Korea) and EDGAR (US) disclosure filings into one structured company map. 한국 DART 전자공시 시스템과 미국 SEC EDGAR에서 수집한 기업 공시 데이터입니다. This dataset is the data layer behind DartLab. When you run dartlab.Company("005930"), the library automatically downloads the… See the full description on the dataset page: https://huggingface.co/datasets/DWKPartners/trading-data.

Dataset Card for Fish-Visual Trait Analysis (Fish-Vista) Note that the '</Use this dataset>' option will only load the CSV files. To download the entire dataset, including all processed images and segmentation annotations, refer to Instructions for downloading dataset and images. See Example Code to Use the Segmentation Dataset Figure 1. A schematic representation of the different tasks in Fish-Vista Dataset. Instructions for downloading dataset… See the full description on the dataset page: https://huggingface.co/datasets/saffatgazi/fish-vista.

Dataset Card for "openwebtext" Dataset Summary An open-source replication of the WebText dataset from OpenAI, that was used to train GPT-2. This distribution was created by Aaron Gokaslan and Vanya Cohen of Brown University. Supported Tasks and Leaderboards More Information Needed Languages More Information Needed Dataset Structure Data Instances plain_text Size of downloaded dataset files: 13.51 GB Size of the… See the full description on the dataset page: https://huggingface.co/datasets/Skylion007/openwebtext.

DartLab Data Structured company data from DART & EDGAR disclosure filings DART 전자공시 + EDGAR 공시 데이터 — 한국 2,700사 / 미국 970사 What is this? Pre-collected Parquet files from DartLab — a Python library that turns DART (Korea) and EDGAR (US) disclosure filings into one structured company map. 한국 DART 전자공시 시스템과 미국 SEC EDGAR에서 수집한 기업 공시 데이터입니다. This dataset is the data layer behind DartLab. When you run dartlab.Company("005930"), the library automatically downloads the… See the full description on the dataset page: https://huggingface.co/datasets/DWKPartners/dartlab-data.

DartLab Data Structured company data from DART & EDGAR disclosure filings DART 전자공시 + EDGAR 공시 데이터 — 한국 2,700사 / 미국 970사 What is this? Pre-collected Parquet files from DartLab — a Python library that turns DART (Korea) and EDGAR (US) disclosure filings into one structured company map. 한국 DART 전자공시 시스템과 미국 SEC EDGAR에서 수집한 기업 공시 데이터입니다. This dataset is the data layer behind DartLab. When you run dartlab.Company("005930"), the library automatically downloads the… See the full description on the dataset page: https://huggingface.co/datasets/Snowfall0601/dartlab-data.

This is a set of wildcards for danbooru tags. Artist：Prompts for random artist styles, covering approximately 0.6M different artists.Please select the appropriate version of the collection, ranging from 128 to 5000, based on the model's capabilities.The full version is not recommended for use as it includes too many artists with only one image on danbooru or other websites. Almost no model can generate a style that corresponds to these artists . Characters："Characters" is a set of wildcards… See the full description on the dataset page: https://huggingface.co/datasets/X779/Danbooruwildcards.

This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model. It contains 21M passages from wikipedia along with their DPR embeddings. The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages.