CoolFace
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sbi

mackelab /benchmarking_sbi_runs Benchmarking SBI Runs This dataset contains the raw, per-run results underlying the manuscript "Benchmarking Simulation-Based Inference" (Lueckmann, Boelts, Greenberg, Goncalves & Macke, AISTATS 2021). It is a direct migration of the Git LFS data from mackelab/benchmarking_sbi_runs on GitHub. For compiled, ready-to-use dataframes built from these raw results (and the code that produced them), see the companion repository:… See the full description on the dataset page: https://huggingface.co/datasets/mackelab/benchmarking_sbi_runs.100K<n<1M0 likes21k downloads2mo agoHugging FaceAstroCompress /SBI-16-3D SBI-16-3D Dataset SBI-16-3D is a dataset which is part of the AstroCompress project. It contains data assembled from the James Webb Space Telescope (JWST). Note that the underlying data is released under CC-4.0. Usage You first need to install the datasets and astropy packages: pip install datasets astropy There are two datasets: tiny and full, each with train and test splits. The tiny dataset has 2 4D images in the train and 1 in the test. The full dataset contains all… See the full description on the dataset page: https://huggingface.co/datasets/AstroCompress/SBI-16-3D.0 likes3.3k downloads10mo agoHugging Facesbintuitions /JMTEB JMTEB: Japanese Massive Text Embedding Benchmark JMTEB is a benchmark for evaluating Japanese text embedding models. It consists of 5 tasks, currently involving 28 datasets in total. You can find the update history here. TL;DR from datasets import load_dataset dataset = load_dataset("sbintuitions/JMTEB", name="<dataset_name>", split="<split>") JMTEB_DATASET_NAMES = ( 'livedoor_news', 'mewsc16_ja', 'sib200_japanese_clustering'… See the full description on the dataset page: https://huggingface.co/datasets/sbintuitions/JMTEB.tabulartext-classification10M<n<100M19 likes2.3k downloads6mo agoHugging Facesbintuitions /JamC-QA Dataset Card for JamC-QA English/Japanese Dataset Summary This benchmark evaluates knowledge specific to Japan through multiple-choice questions. It covers eight categories: culture, custom, regional_identity, geography, history, government, law, and healthcare. Achieving high performance requires broad and detailed understanding of Japan across these categories. Leaderboard Evaluation Metric In our evaluation, the LLM outputs the… See the full description on the dataset page: https://huggingface.co/datasets/sbintuitions/JamC-QA.tabularquestion-answering1K<n<10K6 likes1.8k downloads4mo agoHugging Faceaurelio-amerio /SBI-benchmarkstimeseries1M<n<10M1 likes1.6k downloads2mo agoHugging Facesbi-dev /sbibmtabular10M<n<100M0 likes1.5k downloads6d agoHugging Face