datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Edge-Agent-Reasoning-WebSearch-260K.Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/BlueIsGreen/Edge-Agent-Reasoning-WebSearch-260K.Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/DEMIRUNC/Edge-Agent-Reasoning-WebSearch-260K.Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/Torenn/Edge-Agent-Reasoning-WebSearch-260K.edge-agent-reasoning-websearch-260k
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/ppenner/edge-agent-reasoning-websearch-260k.Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/JACKYS999/Edge-Agent-Reasoning-WebSearch-260K.Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/kanepi-1977/Agent-Reasoning-WebSearch-260K.Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/svryn/Edge-Agent-Reasoning-WebSearch-260K.Sentiment-Reasoning
Sentiment Reasoning for Healthcare
ACL 2025 Industry Track (Oral)
Khai-Nguyen Nguyen*, Khai Le-Duc*, Bach Phan Tat, Duy Le, Long Vo-Dang, Truong-Son Hy
*Equal contribution
Please press ⭐ button and/or cite papers if you feel helpful.
Sentiment Reasoning pipeline
Paper: Sentiment Reasoning for Healthcare
Code: https://github.com/leduckhai/Sentiment-Reasoning
Abstract:Transparency in AI healthcare decision-making is crucial. By incorporating rationales to explain reason… See the full description on the dataset page: https://huggingface.co/datasets/leduckhai/Sentiment-Reasoning.echobench-reasoning-backfill-83
EchoBench 1000
Canonical dataset file: echobench_1000_static880_shift120_reasoning_prosody_gold.jsonl.
1,000 samples total: 880 static + 120 emotion-shift.
Static styles: 220 normal, 220 implicit, 220 very high intense, 220 satire/self-mockery.
Every sample includes a valid relative audio_path, prosody labels, and reasoning_annotation.
Static samples include binary polarity; shift samples are evaluated with their emotion-transition labels.
Audio files are under audio/ and… See the full description on the dataset page: https://huggingface.co/datasets/ddwang2000/echobench-reasoning-backfill-83.audio-reasoning-qa-post-public
audio-reasoning-qa-post-public
Question-answering and multi-task audio reasoning annotations across 15 public audio QA datasets. Spans general audio QA (Clotho-AQA, HeySQuAD), music reasoning (MU-LLaMA, MusicBench, LLARK-MTAT, Music-AVQA), speech-grounded QA (LibriSQA, GigaSpeech), and NVIDIA-aggregator skill subsets (TemporalQA, CountingQA, AudioSet-Speech-QA, GigaSpeech-Long-QA). Closes a substantial slice of the public audio-reasoning SFT gap (compare to NVIDIA AudioSkills-XL… See the full description on the dataset page: https://huggingface.co/datasets/vhands/audio-reasoning-qa-post-public.