usage
Datasets
All datasets matching “usage”agent-usage
Agent Usage on the Hugging Face Hub
Coding agents are real users of the Hugging Face Hub. Claude Code, Codex, Cursor, and a growing list of harnesses are searching for models, building and pushing datasets, training models on Jobs, spinning up Spaces — tens of millions of requests so far (hf CLI for agents). Now there's public data on which ones.
Requests made through the huggingface_hub library (including the hf CLI) carry an agent/<name> User-Agent token identifying the… See the full description on the dataset page: https://huggingface.co/datasets/huggingface/agent-usage.Skill-UsageFrappe-mobile-app-usageDataset Description: Frappe Processed Dataset
The Frappe dataset has been processed to refine the quality of user-item interactions by removing entries where either users or items had fewer than 5 interactions. This pruning resulted in a significant reduction in the dataset size:
Number of Users: 651 (a reduction of 31.97% from the original dataset)
Number of Items: 1127 (a reduction of 72.39%)
Total Number of Interactions: 84,373 (a reduction of 12.30%)
Columns Overview:
The dataset… See the full description on the dataset page: https://huggingface.co/datasets/abadesalex/Frappe-mobile-app-usage.llm-benchmark-usage
LLM Benchmark Usage (2023–2026)
Which evaluation benchmarks 39 AI labs use to evaluate their models, and how that's
changed over time — hand-built from 62 papers, technical reports, system cards, model
cards, and blog posts, covering 128 models from 2023-07 to 2026-07.
from datasets import load_dataset
models = load_dataset("SaylorTwift/llm-benchmark-usage", "models")["models"]
models
One row per model, fully self-contained. 128 rows.
column
type… See the full description on the dataset page: https://huggingface.co/datasets/SaylorTwift/llm-benchmark-usage.ai_recruiting_agent_usageusage-sensitivity-probe
usage-sensitivity-probe
Pair-distilled simulation of usage-dependent mention validity, mined from
rafmacalaba/data-use-mentions + Luna tier verdicts
(training/build_usage_sensitivity_sim.py, seed 0).
Every row contains a contrastive surface string — a mention judged BOTH as
a data source (tier1/tier2) in some contexts and as invalid
(tier3_nonmention/junk: promissory, logframe, container, bibliography, ...)
in others. Gold labels ONLY the data-source instances; activity… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/usage-sensitivity-probe.