datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
semantic-repair-routing
semantic-repair-routing
The 84,819 supervised pairs that trained
SemanticRepair-270M:
a message somebody actually wrote, and the requests inside it restated
plainly, one per line.
It teaches one narrow thing. An embedding router compares a question with
the description of every capability it can reach. People do not write the
way capabilities are described — they hedge, they apologise, they ask two
things in one breath, they name what they do not want. This data pairs
the first… See the full description on the dataset page: https://huggingface.co/datasets/Gramscii-IT/semantic-repair-routing.meta-routing
MetaRouting Dataset
This dataset contains synthetic benchmark artifacts for the Research MetaRouting project, covering meta-decision policies for agentic workflows: when to answer directly, decompose, retrieve, execute code, delegate, verify, or recover from failures.
Source repository: https://github.com/anote-ai/Research-MetaRouting
Displayable Configs
The Hugging Face viewer reads normalized JSONL tables under viewer/:
dai2026_traces, dai2026_tasks… See the full description on the dataset page: https://huggingface.co/datasets/anote-ai/meta-routing.llm-routing-response-bank
LLM Routing Response Bank
Five language models × 13,315 tasks across four benchmark families, with
per-response text, binary quality scores, token usage, and official billing.
Collected for a routing study with a paired calibration/evaluation design:
256 calibration tasks, 13,059 evaluation tasks.
Contents
file
rows
note
tasks_cal.jsonl / tasks_eval.jsonl
256 / 13,059
prompts + reference answers; gpqa_diamond rows are hash-only (see below)… See the full description on the dataset page: https://huggingface.co/datasets/Lurume/llm-routing-response-bank.