datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
cifar10
Dataset Card for CIFAR-10
Dataset Summary
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain… See the full description on the dataset page: https://huggingface.co/datasets/uoft-cs/cifar10.cifar100
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.lambada
Dataset Card for LAMBADA
Dataset Summary
The LAMBADA evaluates the capabilities of computational models
for text understanding by means of a word prediction task.
LAMBADA is a collection of narrative passages sharing the characteristic
that human subjects are able to guess their last word if
they are exposed to the whole passage, but not if they
only see the last sentence preceding the target word.
To succeed on LAMBADA, computational models cannot
simply rely on local… See the full description on the dataset page: https://huggingface.co/datasets/cimec/lambada.civil_comments
Dataset Card for "civil_comments"
Dataset Summary
The comments in this dataset come from an archive of the Civil Comments
platform, a commenting plugin for independent news sites. These public comments
were created from 2015 - 2017 and appeared on approximately 50 English-language
news sites across the world. When Civil Comments shut down in 2017, they chose
to make the public comments available in a lasting open archive to enable future
research. The original data… See the full description on the dataset page: https://huggingface.co/datasets/google/civil_comments.dmi-aarhus-predictions
DMI Aarhus Predictions
Prediction and frontend contract dataset for the Aarhus weather pipeline. Maintained by Ciroc0.
Primary files
File
Purpose
Produced by
predictions_latest.parquet
Current future + verified prediction store
dmi-collector
frontend_snapshot.json
Primary integration contract for the Vercel frontend
dmi-collector
Compatibility files
File
Status
Notes
predictions.parquet
Legacy
Still read by compatibility… See the full description on the dataset page: https://huggingface.co/datasets/Ciroc0/dmi-aarhus-predictions.TextPecker-1.5M
TextPecker-1.5M: A Dataset for Training and evaluating TextPecker
This repository contains the TextPecker-1.5M dataset, a new benchmark proposed in the paper "TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering".
Code and Project Page
The official implementation and project details for the TextPecker and TextPecker-1.5M dataset can be found on the GitHub repository:
https://github.com/CIawevy/TextPecker
Sample Usage
You… See the full description on the dataset page: https://huggingface.co/datasets/CIawevy/TextPecker-1.5M.cissp-llmbench
CISSP-LLMBench
cifar100dmi-aarhus-weather-data
DMI Aarhus Weather Data
Training data and model artifact dataset for the Aarhus weather pipeline. Maintained by Ciroc0.
Primary files
File
Purpose
Produced by
training_matrix.parquet
Current source of truth for training rows and causal observation context
dmi-collector
model_registry.json
Active bucket registry per target
dmi-ml-trainer
model_meta.json
Training timestamp, sample count and training window
dmi-ml-trainer
temperature_models.pkl… See the full description on the dataset page: https://huggingface.co/datasets/Ciroc0/dmi-aarhus-weather-data.cifar-10-cciaa-annual-reports
CIAA Annual Reports — Nepali transcripts, ruled tables and chart data
Machine-readable transcripts of the annual reports of Nepal's Commission for the
Investigation of Abuse of Authority (अख्तियार दुरुपयोग अनुसन्धान आयोग, CIAA) —
all 35 it has published to date. The 1st to 35th reports, fiscal years
BS 2047/48 – 2081/82 (AD 1990–2025).
The CIAA publishes these as PDFs whose text layer is, for several years, legacy
pre-Unicode Devanagari that ordinary extractors turn into… See the full description on the dataset page: https://huggingface.co/datasets/damo-da/ciaa-annual-reports.glue-ci
Dataset Card for GLUE
Dataset Summary
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
Supported Tasks and Leaderboards
The leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:
ax
A manually-curated evaluation dataset for fine-grained analysis of system… See the full description on the dataset page: https://huggingface.co/datasets/evaluate/glue-ci.cif-dataset
Cracks in the Foundation
A civil-infrastructure visual inspection dataset for instance segmentation with 6 defect/condition categories:
Algae · Crack · Net-Crack · Crack with Precipitation · Rust · Spalling
Each sample is either a full-resolution inspection image or a 1024×1024 tile derived from one.
Tiled samples carry extra fields (tile_row, tile_col, file_name_original, …) that are None for full-resolution samples.
Splits
Each split is its own parquet shard and… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/cif-dataset.cifar10cinic10
Dataset Card for CINIC-10
CINIC-10 has a total of 270,000 images equally split amongst three subsets: train, validate, and test. This means that CINIC-10 has 4.5 times as many samples than CIFAR-10.
Dataset Details
In each subset (90,000 images), there are ten classes (identical to CIFAR-10 classes). There are 9000 images per class per subset. Using the suggested data split (an equal three-way split), CINIC-10 has 1.8 times as many training samples as in CIFAR-10.… See the full description on the dataset page: https://huggingface.co/datasets/flwrlabs/cinic10.mmlu-cs
Czech MMLU
This is a Czech translation of the original MMLU dataset, created using the WMT 21 En-X model.
The 'auxiliary_train' subset is not included.
The translation was completed for use within the Czech-Bench evaluation framework.
The script used for translation can be reviewed here.
Citation
Original dataset:
@article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and… See the full description on the dataset page: https://huggingface.co/datasets/CIIRC-NLP/mmlu-cs.cityscapescic-ids-2017
CIC-IDS-2017 Dataset
This repository contains the CIC-IDS-2017 dataset with the original PCAPs and the CSVs converted to Parquet format for easier use.
Dataset Structure
Configurations
machine_learning: Contains the flow-based features used for ML training (Converted from MachineLearningCVE CSVs).
traffic_labels: Contains the labelled flows (Converted from TrafficLabelling CSVs). Timestamps have been normalized to UTC.
Raw Data
The pcap/ folder… See the full description on the dataset page: https://huggingface.co/datasets/bvsam/cic-ids-2017.asta-summary-citation-counts
Dataset Summary
This dataset tracks which scientific papers are most often cited by Asta, an agentic research platform that uses retrieval-augmented generation (RAG) to answer scientific questions. Each record is a paper cited by Asta's Summarize Literature tool, ranked by the number of times the system cited that paper. Across more than 113,000 user queries, we track 4M citations to over 2M distinct papers. By making this data public, we aim to create a transparent, trackable… See the full description on the dataset page: https://huggingface.co/datasets/allenai/asta-summary-citation-counts.speculators-ci-datasets
speculator-tutorial
Raw vs. on-policy regenerated conversation data for training speculative-decoding
drafters (EAGLE-3 / DFlash / DSpark style), with the original source data kept alongside
so you can see exactly what regeneration changes and why it matters.
Prompts come from UltraChat-200k. The verifier / teacher model is Qwen/Qwen3-8B.
Why regenerate at all?
A speculative-decoding drafter is trained to predict what the verifier would say next.
If you train it… See the full description on the dataset page: https://huggingface.co/datasets/inference-optimization/speculators-ci-datasets.cifar100_2_to_100_constant_size_dataset
Dataset Card for "cifar100_2_to_100_constant_size_dataset"
More Information needed
semasia-cifar100
Latents for cifar100 (timm)
This repository hosts precomputed latent representations (embeddings) extracted from timm image-classification backbones on cifar100, released as part of SEMASIA — a large-scale resource for studying semantic communication, cross-model latent space alignment, and explainability.
Each config corresponds to a single model;
only that model's Parquet files are read on load_dataset.
Usage
Load with datasets and… See the full description on the dataset page: https://huggingface.co/datasets/spaicom-lab/semasia-cifar100.mmsu-ci-2000circa
Dataset Card for CIRCA
Dataset Summary
The Circa (meaning ‘approximately’) dataset aims to help machine learning systems to solve the problem of interpreting indirect answers to polar questions.
The dataset contains pairs of yes/no questions and indirect answers, together with annotations for the interpretation of the answer. The data is collected in 10 different social conversational situations (eg. food preferences of a friend).
The following are the situational… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/circa.mmmu-ci-50TTA-Cityscapes-Cduckdb_ci_testsCIRCLE-40K
Highlights
40,000 high-quality video-based spatial reasoning training samples
Built primarily from five large-scale real-world indoor 3D scene datasets (ScanNet, ScanNet++, S3DIS, ARKitScenes, Aria Digital Twin), plus a small ProcTHOR simulated subset
Covers diverse spatial skills: geometric perception, spatial relations, counting, and temporal / appearance-order reasoning over video
Filtered with rejection sampling using Qwen3-VL-8B-Instruct to reduce ambiguous or low-quality… See the full description on the dataset page: https://huggingface.co/datasets/hyin-ustc/CIRCLE-40K.CIFAKE-image-datasetEOC-Bench
EOC-Bench : Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?
🔍 Overview
we introduce EOC-Bench, an innovative benchmark designed to systematically evaluate object-centric embodied cognition in dynamic egocentric scenarios.
Specially, EOC-Bench features 3,277 meticulously annotated QA pairs categorized into three temporal categories: Past, Present, and Future, covering 11 fine-grained evaluation dimensions and 3 visual object referencing… See the full description on the dataset page: https://huggingface.co/datasets/CircleRadon/EOC-Bench.