Dataset Card for Dataset Name

  Dataset Summary

This dataset contains the 20 trending repositories of each type: models, datasets, and space, on Hugging Face, every day. Each type can be loaded from its own dataset config.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

Not relevant.

  Dataset Structure





  Data Instances

The dataset contains three configurations: models: the history of trending models on Hugging… See the full description on the dataset page: https://huggingface.co/datasets/severo/trending-repos.

Contex

The Hugging Face Hub hosts many models for a variety of machine learning tasks. Models are stored in repositories, so they benefit from all the features possessed by every repo on the Hugging Face Hub.

Data Source Link: huggingface.co/models

Attribute Information

This dataset contains different variants of the RoBERTa and XLM-RoBERTa model by Meta AI available on Hugging Face's model repository.

By making it a dataset, it is significantly faster to load the weights since you can directly attach a Kaggle dataset to the notebook rather than downloading the data every time. See the speed comparison notebook. Another benefit of loading models as a dataset is that it can be used in competitions that require internet access to be "off".

For more information on usage visit the roberta hugging face docs and the xlm-roberta hugging face docs.

Usage

To use this dataset, attach it to your notebook and specify the path to the dataset. For example:

from transformers import AutoTokenizer, AutoModelForPreTraining
​
MODEL_DIR = "/kaggle/input/huggingface-roberta/"
​
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR + "roberta-base")
model = AutoModelForPreTraining.from_pretrained(MODEL_DIR + "roberta-base")

Acknowledgements All the copyrights and IP relating to RoBERTa and XLM-RoBERTa belong to the original authors (Liu et al. and Conneau et al.) and Meta AI. All copyrights relating to the transformers library belong to Hugging Face. Please reach out directly to the authors if you have questions regarding licenses and usage.

jsulz/hub-repo-stats dataset hosted on Hugging Face and contributed by the HF Datasets community

Huggingface Hub: Models, Datasets, and Spaces

Dataset Overview

This comprehensive dataset contains detailed information about all the models, datasets, and spaces available on the Huggingface Hub. It is an essential resource for anyone looking to explore the extensive range of tools and datasets available for machine learning and AI research.

Key Features

• Comprehensive Data: Includes exhaustive details on all models, datasets, and spaces from the Huggingface Hub.
• Permissible Models: A specialized subset is provided in a separate CSV file, focusing exclusively on models that are permissible for use.
• Regularly Updated: The dataset is refreshed weekly to ensure the latest information is always available.

Last Update

• Date: December 26, 2023

Update Frequency

• Frequency: Weekly

Dataset Contents

1. Models: Detailed listings of all models available on Huggingface Hub.
2. Datasets: Comprehensive information on datasets hosted on the Hub.
3. Spaces: An overview of the different spaces and their functionalities.
4. Permissible Models CSV: A smaller, curated list of models that are cleared for use.

Usage

This dataset is ideal for researchers, developers, and AI enthusiasts who are looking for a one-stop repository of models, datasets, and spaces from the Huggingface Hub. It provides a holistic view and simplifies the task of finding the right tools for various machine learning and AI projects.

Note: This dataset is not officially affiliated with or endorsed by the Huggingface organization.

This repository contains the mapping from integer id's to actual label names (in HuggingFace Transformers typically called id2label) for several datasets. Current datasets include:

ImageNet-1k ImageNet-22k (also called ImageNet-21k as there are 21,843 classes) COCO detection 2017 COCO panoptic 2017 ADE20k (actually, the MIT Scene Parsing benchmark, which is a subset of ADE20k) Cityscapes VQAv2 Kinetics-700 RVL-CDIP PASCAL VOC Kinetics-400 ...

You can read in a label file as follows (using… See the full description on the dataset page: https://huggingface.co/datasets/huggingface/label-files.

https://huggingface.co/landing/assets/transformers-docs/huggingface_logo.svg" alt="HuggingFace">

Dataset containing metadata information of all the publicly uploaded models(10,000+) available on HuggingFace model hub Data was collected between 15-20th June 2021.

Dataset was generated using huggingface_hub APIs provided by huggingface team.

Update v3:

• Added Downloads last month metric
• Added library name

Contents:

• huggingface_models.csv : Primary file which contains metadata information like model name, tags, last modified and filenames
• huggingface_modelcard_readme.csv : Detailed file containing README.md contents if available for a particular model. Content is in markdown format. modelId column joins both the files together. ### huggingface_models.csv
• modelId: ID of the model as present on HF website
• lastModified: Time when this model was last modified
• tags: Tags associated with the model (provided by mantainer)
• pipeline_tag: If exists, denotes which pipeline this model could be used with
• files: List of available files in the model repo
• publishedBy: Custom column derived from modelID, specifying who published this model
• downloads_last_month: Number of times the model has been downloaded in last month.
• library: Name of library the model belongs to eg: transformers, spacy, timm etc. ### huggingface_modelcard_readme.csv
• modelId: ID of the model as available on HF website
• modelCard: Readme contents of a model (referred to as modelCard in HuggingFace ecoystem). It contains useful information on how the model was trained, benchmarks and author notes. ### Inspiration: The idea of analyzing publicly available models on HugginFace struck me while I was attending a livesession of the amazing transformers course by @LysandreJik. Soon after, I tweeted the team and asked for permission to create such a dataset. Special shoutout to @osanseviero for encouraging and pointing me in the right direction.

This is my first dataset upload on Kaggle. I hope you like it. :)

RTL-Repo Benchmark

This repository contains the data for the RTL-Repo benchmark introduced in the paper RTL-Repo: A Benchmark for Evaluating LLMs on Large-Scale RTL Design Projects.

  👋 Overview

RTL-Repo is a benchmark for evaluating LLMs' effectiveness in generating Verilog code autocompletions within large, complex codebases. It assesses the model's ability to understand and remember the entire Verilog repository context and generate new code that is correct, relevant… See the full description on the dataset page: https://huggingface.co/datasets/ahmedallam/RTL-Repo.

This repository contains the data used in the paper title "On the Suitability of Hugging Face Hub for Empirical Studies". For RQ1 we share the survey responses and the interview transcription, while for RQ2 we share the link to the repository where the data is hosted.

• For RQ1, the survey responses are in an Excel file titled "Survey Responses Public.xlsx". The transcriptions of each interview are in a Word file titled "Transcription-intvw-slot-N.docx".
• For RQ2, we collected the data of the HFCommunity release of October 2023. It can be found in its website. We also share the DOI of the dump.

This dataset contains different variants of the MobileBERT model by Google available on Hugging Face's model repository.

By making it a dataset, it is significantly faster to load the weights since you can directly attach a Kaggle dataset to the notebook rather than downloading the data every time. See the speed comparison notebook. Another benefit of loading models as a dataset is that it can be used in competitions that require internet access to be "off".

For more information on usage visit the mobilebert hugging face docs.

Usage

To use this dataset, attach it to your notebook and specify the path to the dataset. For example:

from transformers import AutoTokenizer, AutoModelForPreTraining
​
MODEL_DIR = "/kaggle/input/huggingface-google-mobilebert/"
​
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
model = AutoModelForPreTraining.from_pretrained(MODEL_DIR)

Acknowledgements All the copyrights and IP relating to MobileBERT belong to the original authors (Sun et al.) and Google. All copyrights relating to the transformers library belong to Hugging Face. Please reach out directly to the authors if you have questions regarding licenses and usage.

Context

FineWeb 2 is the second iteration of the popular 🍷 FineWeb dataset, bringing high quality pretraining data to over 1000 🗣️ languages. For the actual data, please see the HuggingFace repository.

This is the second iteration of the popular 🍷 FineWeb dataset, bringing high quality pretraining data to over 1000 🗣️ languages.

The 🥂 FineWeb2 dataset is fully reproducible, available under the permissive ODC-By 1.0 license and extensively validated through hundreds of ablation experiments.

In particular, on the set of 9 diverse languages we used to guide our processing decisions, 🥂 FineWeb2 outperforms other popular pretraining datasets covering multiple languages (such as CC-100, mC4, CulturaX or HPLT, while being substantially larger) and, in some cases, even performs better than some datasets specifically curated for a single one of these languages, in our diverse set of carefully selected evaluation tasks: FineTasks.

The dataset is also listed on HF, here is official HF Page.

"My focus is on sharing this valuable open-source dataset to help AI and ML practitioners easily find resources on Kaggle."

The detailed information about FW2 is listed in README.md file below ↓

Acknowledgement

Hugging Face FW

KTDA-Datasets

This dataset card aims to describe the datasets used in the KTDA.

  Install

pip install huggingface-hub

  Usage

Step 1: Download datasets

huggingface-cli download --repo-type dataset XavierJiezou/ktda-datasets --local-dir data --include grass.zip huggingface-cli download --repo-type dataset XavierJiezou/ktda-datasets --local-dir data --include cloud.zip

Step 2: Extract datasets

unzip grass.zip -d grass unzip cloud.zip -d l8_biome… See the full description on the dataset page: https://huggingface.co/datasets/XavierJiezou/ktda-datasets.

sonyashijin/rtl-repo-curated dataset hosted on Hugging Face and contributed by the HF Datasets community

tdross/test-repo dataset hosted on Hugging Face and contributed by the HF Datasets community

Adanato/test-repo dataset hosted on Hugging Face and contributed by the HF Datasets community

samfred2/my-target-repo dataset hosted on Hugging Face and contributed by the HF Datasets community

Guide: How to share your data on the BoAmps repository

This guide explains step by step how to share BoAmps format reports on this public Hugging Face repository.

  Prerequisites

Before starting, make sure you have:

A Hugging Face account The files you want to upload

  Method 1: Hugging Face Web Interface

Log in to Hugging Face

Go to the boamps dataset

Navigate to the files: Click on "Files and versions" then on the "data" folder

Click on "Contribute" then… See the full description on the dataset page: https://huggingface.co/datasets/boavizta/open_data_boamps.

tstone87/repo dataset hosted on Hugging Face and contributed by the HF Datasets community

Dear researchers and engineers, you're accessing a dataset that would cost millions of dollars to build and took millions of nerves to negotiate favorable terms for its use. Your support, by liking the repositories and upvoting the collection, costs nothing but gives us valuable motivation to continue our contributions to the community. We reserve the right not to approve the request if you don't support our efforts. Thank you very much for collaboration!

  HISTAI Dataset

HISTAI is a… See the full description on the dataset page: https://huggingface.co/datasets/histai/HISTAI-metadata.

cityTS/gxucpc-repo-v1 dataset hosted on Hugging Face and contributed by the HF Datasets community