datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gametime-outputs
Gametime Outputs
Model outputs (stereo full-duplex mix of user prompt + model response) for the Gametime benchmark.
Load
from datasets import load_dataset
ds = load_dataset("gametime-benchmark/gametime-outputs", "moshi", split="basic")
ex = next(iter(ds))
wav = ex["audio"]["array"] # numpy float array, shape=(n, 2) stereo
sr = ex["audio"]["sampling_rate"] # int (24000 for most, 48000 for gpt-realtime)
print(ex["id"], sr, wav.shape, ex["dataset"])… See the full description on the dataset page: https://huggingface.co/datasets/gametime-benchmark/gametime-outputs.asr-benchmark-outputs
SaarAI ASR Benchmark Outputs
Raw per-utterance model outputs (transcription manifests) produced by the gsma-asr-bench runners on SaarAI/asr-leaderboard-datasets.
files: 508
utterances: 4390208
languages: 7
models: 47
Layout
data/<language_name>/<split>__<dataset_config>__<model_slug>.jsonl
index.jsonl # one record per file (language, split, model, rows, sha256, ...)
index.csv
Directories categorise by language name; the file name begins with the split name… See the full description on the dataset page: https://huggingface.co/datasets/SaarAI/asr-benchmark-outputs.business-model-kg-benchmark-outputs
Business Model KG Benchmark and Outputs
This repository contains the public evaluation package for Business Model KG:
gold benchmark triples and final generated graph outputs.
Business Model KG turns SEC 10-K business descriptions into standardized
business-model knowledge graphs. This package is meant for inspecting and
reproducing the evaluation side of the project without needing the full training
workspace or runtime model assets.
Layout
benchmarks/
dev/clean/… See the full description on the dataset page: https://huggingface.co/datasets/WindyITS/business-model-kg-benchmark-outputs.