datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
allpanel-live-feedlygo-public-witness-feed
LYGO Public Witness — public RESOURCE overlay
Snapshot of public HTTPS GET sources (USGS, EONET, ISS, NWS, GDACS, NOAA SWPC, Open-Meteo, OpenSky sample, launches, CoinGecko, Celestrak, lattice mirrors).
Every point is RESOURCE
Failed sources stay named SHADOW (no invented coordinates)
Not private intelligence. Not a live Star Chart write.
Live globe: https://chatagent.ca/witness/
Unified-FeedbackCollections of pairwise feedback datasets.
openai/summarize_from_feedback
openai/webgpt_comparisons
Dahoas/instruct-synthetic-prompt-responses
Anthropic/hh-rlhf
lmsys/chatbot_arena_conversations
openbmb/UltraFeedback
argilla/ultrafeedback-binarized-preferences-cleaned
berkeley-nest/Nectar
Codes to reproduce the dataset: jdf-prog/UnifiedFeedback
Dataset formats
{
"id": "...",
"conv_A": [
{
"role": "user",
"content": "...",
},
{
"role": "assistant"… See the full description on the dataset page: https://huggingface.co/datasets/llm-blender/Unified-Feedback.dynamic-feed
Dynamic Feed — Live AI Data
Fresh, structured data that language models don't have on their own (it changes
faster than training cutoffs). Updated daily from authoritative sources.
Live API & MCP: https://dynamicfeed.ai · remote MCP: https://dynamicfeed.ai/mcp
What's inside
File
Contents
models.json
Current pricing & capabilities for ~250 AI models (USD per 1M tokens, context windows, vision/tool support).
mcp_servers.json
A registry of MCP servers… See the full description on the dataset page: https://huggingface.co/datasets/dynamicfeed/dynamic-feed.Code-Feedback OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
[🏠Homepage]
|
[🛠️Code]
Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/Code-Feedback.btc_briefing_feed
BTC daily briefing feed
This dataset retains one JSON file per published UTC day. Read each file's
feed_version; historical files keep the contract under which they were
published.
The latest snapshot uses btc_briefing/2 and is built from the origo ClickHouse
tables: 15m/1d OHLCV bars, measured volume-at-price in integer satoshis split
by taker side, and per-minute series, exact daily percentiles and 8h session
aggregates of the depth200 book. Its time fields are UTC epoch… See the full description on the dataset page: https://huggingface.co/datasets/vaquum/btc_briefing_feed.summarize_from_feedbackSummarize from Feedback contains the human feedback data released by the "Learning to summarize from human feedback" paper.vision-feedback-mix-binarized
Dataset Card for Vision-Feedback-Mix-Binarized
Introduction
This dataset aims to provide large-scale vision feedback data.
It is a combination of the following high-quality vision feedback datasets:
zhiqings/LLaVA-Human-Preference-10K: 9,422 samples
MMInstruction/VLFeedback: 80,258 samples
YiyangAiLab/POVID_preference_data_for_VLLMs: 17,184 samples
openbmb/RLHF-V-Dataset: 5,733 samples
openbmb/RLAIF-V-Dataset: 83,132 samples
We also offer a cleaned version in… See the full description on the dataset page: https://huggingface.co/datasets/Rendra86318/vision-feedback-mix-binarized.text-2-image-Rich-Human-Feedback
Building upon Google's research Rich Human Feedback for Text-to-Image Generation we have collected over 1.5 million responses from 152'684 individual humans using Rapidata via the Python API. Collection took roughly 5 days.
If you get value from this dataset and would like to see more in the future, please consider liking it.
Overview
We asked humans to evaluate AI-generated images in style, coherence and prompt alignment. For images that contained flaws, participants were… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-image-Rich-Human-Feedback.Multimodal_Fish_Feeding_Intensitysummarize_from_feedback_smallfeedback_data_training
Repair replay update — September 15, 2026
The split still contains 161,030 weighted rows, with the same category counts:
Category
Rows
Share
Distinct examples before → after
One-shot
79,970
49.66%
35,197 → 35,197
Regular repairs
60,931
37.84%
40,530 → 48,726
Rollout-derived deep repairs
20,129
12.50%
436 → 1,825
This adds 9,585 distinct checked repair examples while preserving every legacy distinct row and every one-shot row's multiplicity. The new examples… See the full description on the dataset page: https://huggingface.co/datasets/formalmathatepfl/feedback_data_training.Code-Feedback
Dataset Card for CodeFeedback
This is a formatted version of m-a-p/Code-Feedback to store the conversations in the same format as the OpenAI SDK.
Feedback-Collection
Dataset Card
Dataset Summary
The Feedback Collection is a dataset designed to induce fine-grained evaluation capabilities into language models.\
Recently, proprietary LLMs (e.g., GPT-4) have been used to evaluate long-form responses. In our experiments, we found that open-source LMs are not capable of evaluating long-form responses, showing low correlation with both human evaluators and GPT-4.\
In our paper, we found that by (1) fine-tuning feedback generated by GPT-4… See the full description on the dataset page: https://huggingface.co/datasets/prometheus-eval/Feedback-Collection.cosmo-1B-claude_3h_feedbackSWE-bench__style-3__fs-oracle_large_tokenlength
Dataset Card for "SWE-bench__style-3__fs-oracle"
More Information needed
vietnamese_students_feedbackStudents’ feedback is a vital resource for the interdisciplinary research involving the combining of two different
research fields between sentiment analysis and education.
Vietnamese Students’ Feedback Corpus (UIT-VSFC) is the resource consists of over 16,000 sentences which are
human-annotated with two different tasks: sentiment-based and topic-based classifications.
To assess the quality of our corpus, we measure the annotator agreements and classification evaluation on the
UIT-VSFC corpus. As a result, we obtained the inter-annotator agreement of sentiments and topics with more than over
91% and 71% respectively. In addition, we built the baseline model with the Maximum Entropy classifier and achieved
approximately 88% of the sentiment F1-score and over 84% of the topic F1-score.summarize_from_feedback_tldr_3_filteredThis is the query dataset taken directly from https://github.com/openai/summarize-from-feedback/tree/700967448d10004279f138666442bf1497d0e705#reddit-tldr-dataset
SWE-bench__style-3__fs-oracle
Dataset Card for "SWE-bench__style-3__fs-oracle"
More Information needed
llm-human-feedback-collector-chat-interface-dpowb-feedbacks
Dataset Card for Wildberries products
Dataset Summary
The dataset contains product reviews from the Russian marketplace Wildberries, collected by mining about The dataset was collected by bruteforcing possible product identifiers (about 230 million) and querying all available feedbacks for them. The data are stored in zstd-archives containing jsonl-files. The 'nmId' in the dataset usually corresponds to the valid product article on the site, but sometimes reviews are… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/wb-feedbacks.text-2-image-Rich-Human-Feedback-32k
Building upon Google's research Rich Human Feedback for Text-to-Image Generation, and the
smaller, previous version of this dataset, we have collected over 3.7 million responses from 307'415 individual humans for the open-image-preference-v1 dataset using Rapidata via the Python API. Collection took less than 2 weeks.
If you get value from this dataset and would like to see more in the future, please consider liking it ♥️
Overview
We asked humans to evaluate AI-generated… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-image-Rich-Human-Feedback-32k.Fish_Feeding_Intensity_Datasetreflection_eval_prompt1fast-feedback-benchmark
fast-feedback: offline benchmark data for cheap scaling-recipe ranking
Curated snapshot (2026-08-11) of public training-curve datasets used to build an
offline ground-truth benchmark for evaluating cheap feedback mechanisms that rank
LLM scaling recipes / architectures without training full ladder families.
A feedback mechanism is a budgeted procedure ("train these small rungs to N tokens,
inspect the curves, output a ranking"). This data lets such procedures be evaluated
by… See the full description on the dataset page: https://huggingface.co/datasets/jhyuckkim/fast-feedback-benchmark.omnyx-inventory-feedomy_f3m_motor_feedback_vla_probe_rock_real_left_probe_rightThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "omy_f3m_motor_feedback",
"total_episodes": 20,
"total_frames": 10481,
"total_tasks": 1,
"total_videos": 40,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:20"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/jyLee0111/omy_f3m_motor_feedback_vla_probe_rock_real_left_probe_right.mano_feedbackVayuChat_Feedbackruncam-feed-camera-egocentric-rgb-imu
RunCam Feed Camera - Egocentric RGB + IMU Sample Dataset
A small sample dataset captured with the RunCam Feed Camera for egocentric video and synchronized motion-sensor workflows.
Capture Device
Video: H.265 MP4, 1920x1080, 60 fps for V01-V06
Nominal video bitrate: 18 Mbps
Horizontal field of view: 126 degrees
Device weight: approximately 26 g
IMU: ICM-42607, 6-axis
IMU sampling rate: 800 Hz for the recordings in this sample
Firmware reported in the GCSV files:… See the full description on the dataset page: https://huggingface.co/datasets/RunCam/runcam-feed-camera-egocentric-rgb-imu.