datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OLMo-2_Residual_Streams
OLMo-2_Residual_Streams
This dataset contains approximately 600 million residual streams derived from the FineWeb dataset. The residual streams were extracted using the allenai/OLMo-2-1124-7B-Instruct model and are stored in .parquet format.
Dataset Description
Usage
The dataset should work just fine with load_dataset:
>>> from datasets import load_dataset
# after loading the data, cast to bf16 using torch.view()
>>> dataset =… See the full description on the dataset page: https://huggingface.co/datasets/open-concept-steering/OLMo-2_Residual_Streams.conceptual-captions-cc12m-llavanext
Dataset Card for conceptual-captions-cc12m-llavanext
Dataset Summary
This is a data of 21,930,344 synthetic captions for 10,965,172 images from conceptual_12m. In the interest of reproducibility, an archive found here on Huggingface was used (cc12m-wds). The captions were produced using llama3-llava-next-8b inferenced in float16, followed by cleanup and shortening with Meta-Llama-3-8B.
Languages
The captions are in English.
Data Instances
An… See the full description on the dataset page: https://huggingface.co/datasets/CaptionEmporium/conceptual-captions-cc12m-llavanext.photo-concept-bucket
Photo Concept Bucket
The purpose of this dataset was to distribute a high quality, free-to-use dataset containing samples that require no attribution and have an open license.
All of the images were captioned in a cluster containing:
38x 3090 24G
6x 4090 24G
8x A5000 24G
2x A100 80G
A couple volunteers running a 3090 or 4090.
The model was running in fp8 precision using 🤗Transformers and 🤗Accelerate for easy multi-GPU captioning.
The captioning was spread across 10 different… See the full description on the dataset page: https://huggingface.co/datasets/bghira/photo-concept-bucket.friend-bench
Can a model — or a human — tell how two people are related from a 20-second clip of how they interact?
🌐 Built on Seamless Interaction
FriendBench is a suite of benchmarks for social perception from thin-slice dyadic
interaction — inferring facts about two people's relationship from a brief clip of how they
interact, built on the Seamless Interaction
dataset. Each released set is a config of this repository.
🎧 Multi-modal — text, audio, and video for every clip
🎯 Objective label —… See the full description on the dataset page: https://huggingface.co/datasets/fluid-concepts/friend-bench.conceptual_captions_3m_zh_tiny_0
Dataset Card for "conceptual_captions_3m_zh_tiny_0"
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neuronpedia-sae-concepts
Neuronpedia SAE Concepts
Complete extraction of all individual concepts from every Sparse Autoencoder (SAE) released on Neuronpedia, plus all public features from Anthropic's Towards Monosemanticity (2023) and Scaling Monosemanticity (2024) papers.
Quick Start
from datasets import load_dataset
# Full Neuronpedia dataset (77M rows, streaming recommended)
ds = load_dataset("hbe/neuronpedia-sae-concepts", split="train", streaming=True)
# Unique concepts with essential… See the full description on the dataset page: https://huggingface.co/datasets/hbe/neuronpedia-sae-concepts.multimodal-peer-collaboration-samples
Multimodal Peer Collaboration Samples - Embodied Map Task with Two Camera Angles
Two non-experts collaborate to build working circuits under asymmetric information: the instructor has the manual, the student has the components, and synchronized audio and dual-camera video capture how shared understanding emerges.
▶ Watch the interactions · See Expert Instruction samples · Discuss the full collection
Sister collection: Expert Instruction, a teacher and a student in… See the full description on the dataset page: https://huggingface.co/datasets/fluid-concepts/multimodal-peer-collaboration-samples.multimodal-expert-instruction-samples
Multimodal Expert Instruction Samples - Musical Instrument Lessons with Channel-separated Audio and Video
A music teacher and a student work through two one-on-one lessons: both voices and both instruments on separate tracks, the student on camera, with the lesson plans, the instructions given to each side and both sides' post-lesson ratings alongside.
▶ Watch the lessons · See Peer Collaboration samples · Discuss the full collection
Sister collection: Peer Collaboration… See the full description on the dataset page: https://huggingface.co/datasets/fluid-concepts/multimodal-expert-instruction-samples.conceptual_captions_3m_zh_tiny_5
Dataset Card for "conceptual_captions_3m_zh_tiny_5"
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conceptual_captions_3m_zh_tiny_2
Dataset Card for "conceptual_captions_3m_zh_tiny_2"
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conceptual_captions_3m_zh_tiny_4
Dataset Card for "conceptual_captions_3m_zh_tiny_4"
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conceptual_captions_3m_zh_tiny_1
Dataset Card for "conceptual_captions_3m_zh_tiny_1"
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ConceptARC_Rule_Annotations
ConceptARC-style rule annotations
This release bundles grid-style tasks with natural-language rules written by people or proposed by models, human ratings of how well those rules match the task, and automatic checks of whether each model’s output grid is correct. The is_correct and err fields capture only that grid check, not whether a human endorsed the rule wording. Each row is one attempt on one test case.
On the Hugging Face Hub, the dataset card YAML exposes two subsets (see… See the full description on the dataset page: https://huggingface.co/datasets/AIHumanAbstraction/ConceptARC_Rule_Annotations.conceptual_captions_3m_zh_tiny_3
Dataset Card for "conceptual_captions_3m_zh_tiny_3"
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concept_coverage_laion_6m
📦 Freeze-Align Dataset
The Freeze-Align Dataset (concept_coverage_laion_6m) is a curated collection of high-quality image-text pairs designed to facilitate efficient multimodal alignment using frozen unimodal encoders. This dataset supports the research presented in our CVPR 2025 paper, "Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment", enabling models to achieve CLIP-level performance with significantly reduced computational resources.
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/mayug/concept_coverage_laion_6m.concept-data-lift-pmmdeepscaler-teacher-sft-vllm-official-40k-clean-v4-conceptual
deepscaler-teacher-sft-vllm-official-40k-clean-v4-conceptual
Filtered version of ThunderstormXXL/deepscaler-teacher-sft-vllm-official-40k.
Filtering
reward filter enabled: False
minimum official reward: 1.0
scoring errors rejected: False
maximum text tokens: 32768
maximum response chars: 200000
near-duplicate SimHash hamming threshold: 4
required <think>...</think> and final boxed answer after reasoning
exact text/problem/response dedupe and near problem dedupe… See the full description on the dataset page: https://huggingface.co/datasets/ThunderstormXXL/deepscaler-teacher-sft-vllm-official-40k-clean-v4-conceptual.concept-injection-results
Concept Injection Results
Experimental data and analysis logs from the research "Replicating Introspection on Injected Content in Open-Source Language Models"
Overview
This project enables researchers to inject concept vectors directly into a model's hidden layers during inference, allowing investigation of whether language models can detect and report on artificially induced "thoughts."
This dataset contains the raw responses and processed evaluations produced… See the full description on the dataset page: https://huggingface.co/datasets/vazirani/concept-injection-results.analogy_concept_questions
Analogy Concept Questions
This dataset was generated using YourBench (v0.6.0), an open-source framework for generating domain-specific benchmarks from document collections.
Pipeline Steps
ingestion: Read raw source documents, convert them to normalized markdown and save for downstream steps
summarization: Perform hierarchical summarization: chunk-level LLM summaries followed by combine-stage reduction
chunking: Split texts into token-based single-hop and multi-hop… See the full description on the dataset page: https://huggingface.co/datasets/msaramhassan/analogy_concept_questions.concept-cot-corpus-mini
Concept-Labeled CoT Corpus (Mini)
496 Qwen3-8B chain-of-thought rollouts on prompts from 9 safety, bias, and fairness datasets. Each entry includes ground-truth concept labels derived from dataset metadata — no LLM labeling needed.
Source Datasets
Source
Count
Labels
WildGuard (allenai/wildguardmix)
145
harm subcategory, is_harmful
LMSYS neutral (lmsys/lmsys-chat-1m)
86
safe baseline
BeaverTails (PKU-Alignment/BeaverTails)
59
14 multi-label harm categories… See the full description on the dataset page: https://huggingface.co/datasets/ceselder/concept-cot-corpus-mini.Related_Concepts_with_relevant_relasasia-dataverse-rice-pre-a-concept-for-crop-health-syndrome-model
RICE-PRE - a concept for crop health syndrome model
Source: Harvard Dataverse — doi:10.7910/DVN/VZP5CG
Publisher: IRRI Dataverse
Authors: Sparks, Adam
Files in dataset: 5
RICE-PRE, a concept for crop health syndrome model Adam Sparks and Serge Savary Background Plant disease management recommendations are a central premise of botanic epidemiology. Because many rice growers and their extension support systems are increasingly unable to accurately diagnose crop health… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-dataverse-rice-pre-a-concept-for-crop-health-syndrome-model.sali_tagsafrica-arising-rice-pre-a-concept-for-crop-health-syndrome-model
RICE-PRE - a concept for crop health syndrome model | Africa (Harvard Dataverse / AfricaRISING) | Africa (Electric Sheep Africa metadata inventory)
Size category: n<1K - Formats: parquet - Sector: agriculture_food - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-arising-rice-pre-a-concept-for-crop-health-syndrome-model.spam_concept_awarefiltered_conceptOA_Domain_Conceptsconceptual_captions_3m_zh_tiny_6
Dataset Card for "conceptual_captions_3m_zh_tiny_6"
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template-test-results-conceptual-reframing-v4-fixvisual_concepts