datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
datasets-tests-compressiontests-raw-jsonldatatrove-testsDatasets used for datatrove testing.
Each split contains the same data:
dst = [
{"text": "hello"},
{"text": "world"},
{"text": "how"},
{"text": "are"},
{"text": "you"},
]
But based on the split name the data are sharded into n-bins
dataset-test-1IfEvalCode-testsetSpatialLM-Testset
SpatialLM Testset
Project page | Paper | Code
We provide a test set of 107 preprocessed point clouds and their corresponding GT layouts, point clouds are reconstructed from RGB videos using MASt3R-SLAM. SpatialLM-Testset is quite challenging compared to prior clean RGBD scan datasets due to the noises and occlusions in the point clouds reconstructed from monocular RGB videos.
Folder Structure
Outlines of the dataset files:… See the full description on the dataset page: https://huggingface.co/datasets/manycore-research/SpatialLM-Testset.backendbench_tests
TorchBench
The TorchBench suite of BackendBench is designed to mimic real-world use cases. It provides operators and inputs derived from 155 model traces found in TIMM (67), Hugging Face Transformers (45), and TorchBench (43). (These are also the models PyTorch developers use to validate performance.) You can view the origin of these traces by switching the subset in the dataset viewer to ops_traces_models and torchbench for the full dataset.
When running BackendBench, much of the… See the full description on the dataset page: https://huggingface.co/datasets/GPUMODE/backendbench_tests.vindr-cxr-testsetduckdb_ci_teststest_sample_1000THCHS-30-tests
THCHS-30 Test Set
THCHS-30 test split for Mandarin Chinese speech recognition benchmarking.
Dataset Info
Language: Mandarin Chinese (zh-CN)
Samples: 2,495
Speakers: 10
Sample Rate: 16 kHz
License: Apache 2.0
Usage
from datasets import load_dataset
# After uploading to HuggingFace
dataset = load_dataset("your-username/thchs30-test")
# Example
print(dataset['train'][0])
# {
# 'audio': {'array': [...], 'sampling_rate': 16000, 'path': 'audio/D11_750.wav'},
#… See the full description on the dataset page: https://huggingface.co/datasets/FluidInference/THCHS-30-tests.video-SALMONN_2_testset
video-SALMONN 2 Benchmark
Generate the caption corresponding to the video and the audio with video_salmonn2_test.json
Organize your results in the format like the following example:
[
{
"id": ["0.mp4"],
"pred": "Generated Caption"
}
]
Replace res_file in eval.py with your result file.
Run python3 eval.pytest-sync-commentsSpatialGen-Testset
SpatialGen Testset
This repository contains the test set for SPATIALGEN: Layout-guided 3D Indoor Scene Generation, a novel multi-view multi-modal diffusion model for generating realistic and semantically consistent 3D indoor scenes.
Project page | Paper | Code
We provide a test set of 48 preprocessed point clouds and their corresponding GT layouts, multi-view images are cropped from the high-resolution panoramic images.
Folder Structure
Outlines of the dataset files:… See the full description on the dataset page: https://huggingface.co/datasets/manycore-research/SpatialGen-Testset.swesmith-sandboxes-with_teststestset_popqatestset_piqatest_sample_1000_part4test-scienceMindGuard-testset
MindGuard-testset: Expert-Annotated Evaluation Data for Mental Health AI Safety
MindGuard-testset is a clinically grounded benchmark dataset for evaluating safety classifiers in mental health AI systems. This dataset was developed by Sword Health in collaboration with licensed clinical psychologists to address the critical need for contextually appropriate safety measures in therapeutic AI applications.
Overview
MindGuard-testset contains 1,134 annotated user turns… See the full description on the dataset page: https://huggingface.co/datasets/swordhealth/MindGuard-testset.dolci-think-translation-tests
Dolci-Think Translation Tests
This dataset contains the inputs, outputs, reconstruction records, automatic
diagnostics, and retained COMET-QE evidence from a set of controlled translation
experiments over 340 English messages selected from
allenai/Dolci-Think-SFT-7B.
It is intended for inspecting the experiments and reproducing analyses, not as a
ready-made translation training set.
The central complication is that one source document does not always correspond
to one model… See the full description on the dataset page: https://huggingface.co/datasets/RfKnowledge/dolci-think-translation-tests.testset_mmluWorldRenderer-Testsetstress_tests_nli
Overview
Original dataset page here and dataset available here.
Dataset curation
Added new column label with encoded labels with the following mapping
{"entailment": 0, "neutral": 1, "contradiction": 2}
and the columns with parse information are dropped as they are not well formatted.
Also, the name of the file from which each instance comes is added in the column dtype.
Code to create the dataset
import pandas as pd
from datasets import Dataset, ClassLabel… See the full description on the dataset page: https://huggingface.co/datasets/pietrolesci/stress_tests_nli.test_sample_1000_part3testset_hellaswagtest_sample_1000_part2testset_winogrande-infilltest_sample_1000_part1cpp_unit_tests_benchmark_data