datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
LoopTool-23k
Overview
LoopTool is a fully automatic, model-aware iterative framework that tightly couples data generation and model training for tool-augmented LLM learning
The LoopTool-2w is released as part of Closing the Data–Training Loop for Robust LLM Tool Calls
The dataset comprises 23,040 tool-call samples, involving 20,813 APIs. In each sample, the instruction contains the corresponding set of available tools for that sample; the input corresponds to the dialogue history of the… See the full description on the dataset page: https://huggingface.co/datasets/zhangkangning/LoopTool-23k.hedgehog-loop-control-r4
hedgehog-loop-control-r4
Hedgehog — loop-control round 4 (termination/repetition fixes).
Contents
train.jsonl (2944 rows)
validation.jsonl (438 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Original content for the Hedgehog extraction model (Michael Anthony Falabella).
audio-loop
Audio Loop — Long-Audio Reasoning Benchmarks
Two evaluation sets for long-form audio reasoning, packaged as one dataset
with two configs:
Config
Task
Audio source
Items
aae_tts
Argument structure & contradiction
TTS-synthesized argumentative essays
20
iq2_qa
Multi-hop QA over debates
Recorded live debates
13
Load annotations
from datasets import load_dataset
aae = load_dataset("audioloop/audio-loop", "aae_tts", split="test")
iq2 =… See the full description on the dataset page: https://huggingface.co/datasets/audioloop/audio-loop.