This is a mirror to the example dataset "The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation" paper by Manco et al. Project page on Github: https://github.com/mulab-mir/song-describer-dataset Dataset on Zenodoo: https://zenodo.org/records/10072001 Explore the dataset on your local machine: import datasets from renumics import spotlight

ds = datasets.load_dataset('renumics/song-describer-dataset') spotlight.show(ds)

Dataset Card

This dataset contains a single huggingface split, named 'all_samples'. The samples contains a single huggingface feature, named called "sample". Samples are instances of plaid.containers.sample.Sample. Mesh objects included in samples follow the CGNS standard, and can be converted in Muscat.Containers.Mesh.Mesh. Example of commands: fromdatasets import load_dataset from plaid.containers.sample import Sample import pickle

Load the dataset

hf_dataset =… See the full description on the dataset page: https://huggingface.co/datasets/PLAID-datasets/2D_ElastoPlastoDynamics.

Terms of Use

By using the dataset, you agree to comply with the dataset license (CC-by-4.0-Deed).

  Download Instructions

To download one file, please use from huggingface_hub import hf_hub_download

Path of the directory where the data will be downloaded in your local machine

local_directory = 'LOCAL_DIRECTORY'

Relative path of the file in the repository

filepath = 'FILE_PATH'

repo_id = "climateset/climateset" repo_type = "dataset" hf_hub_download(repo_id=repo_id… See the full description on the dataset page: https://huggingface.co/datasets/climateset/climateset.

Dataset Card for LAMBADA

  Dataset Summary

The LAMBADA evaluates the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local… See the full description on the dataset page: https://huggingface.co/datasets/cimec/lambada.

Partial data from SimXRD (the original dataset is too large to be shared on Hugging Face). Sample data provided for reviewers.

1. Point to a local or remote Croissant file

import mlcroissant as mlc
url = "https://huggingface.co/datasets/caobin/SimXRDreview/raw/main/simxrd_croissant.json"

2. Inspect metadata

dataset_info = mlc.Dataset(url).metadata.to_json print(dataset_info)

from dataset.parse importload_dataset,bar_progress # defined in our github :… See the full description on the dataset page: https://huggingface.co/datasets/caobin/SimXRDreview.

Fastmap evaluation suite.

You only need the databases to run fastmap. Download the images if you want to produce colored point cloud. Download the subset of data you want to your local directory. huggingface-cli download whc/fastmap_sfm --repo-type dataset --local-dir ./ --include 'databases/tnt_*' 'ground_truths/tnt_*'

or use the python interface from huggingface_hub import hf_hub_download, snapshot_download snapshot_download( repo_id="whc/fastmap_sfm", repo_type='dataset'… See the full description on the dataset page: https://huggingface.co/datasets/whc/fastmap_sfm.

CelebA dataset

A copy of celeba dataset. https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html

  How to use

Download data

huggingface-cli download --local-dir /path/to/datasets/celeba --repo-type dataset Yuehao/celeba unzip /path/to/datasets/celeba/img_align_celeba.zip -d /path/to/datasets/celeba

Load data via torchvision.datasets.CelebA

torchvision.datasets.CelebA(root='/path/to/datasets')

How to install?

!pip install datasets -q from huggingface_hub import snapshot_download import pandas as pd import matplotlib.pyplot as plt

First step: download an entire datatset

snapshot_download(repo_id="Aborevsky01/CLEVR-BT-DB", repo_type="dataset", local_dir='path-to-your-local-dir')

Second step: unarchive the images for VQA

!unzip [path-to-your-local-dir]/[type-of-task]/images.zip

Example of the triplet (image - question -… See the full description on the dataset page: https://huggingface.co/datasets/Aborevsky01/CLEVR-BT-DB.