Dataset Card for "huggingface-datasets"

This dataset is a snapshot of all public datasets in HuggingFace as of 04/24/2023. It is based on the dataset metadata that can be found at the following endpoint: https://huggingface.co/api/datasets/{dataset_id} Which contains information like the dataset name, its tags, description, and more. Please note that description is different from dataset card, which is what you are reading now :-). I would love to replace this dataset with one which… See the full description on the dataset page: https://huggingface.co/datasets/nkasmanoff/huggingface-datasets.

ftopal/huggingface-datasets-processed dataset hosted on Hugging Face and contributed by the HF Datasets community

This is the blind eval dataset of high-quality, diverse, human-written instructions with demonstrations. We will be using this for step 3 evaluations in our RLHF pipeline.

Dataset Card for DIALOGSum Corpus

  Dataset Description





  Links

Homepage: https://aclanthology.org/2021.findings-acl.449 Repository: https://github.com/cylnlp/dialogsum Paper: https://aclanthology.org/2021.findings-acl.449 Point of Contact: https://huggingface.co/knkarthick

  Dataset Summary

DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding… See the full description on the dataset page: https://huggingface.co/datasets/knkarthick/dialogsum.

Summary

databricks-dolly-15k is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. This dataset can be used for any purpose, whether academic or commercial, under the terms of the Creative Commons Attribution-ShareAlike 3.0 Unported… See the full description on the dataset page: https://huggingface.co/datasets/databricks/databricks-dolly-15k.

We curate a large corpus of legal and administrative data. The utility of this data is twofold: (1) to aggregate legal and administrative data sources that demonstrate different norms and legal standards for data filtering; (2) to collect a dataset that can be used in the future for pretraining legal-domain language models, a key direction in access-to-justice initiatives.

This replication package contains datasets and scripts related to the paper: "*How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study*"

## Root directory

- `statistics.r`: R script used to compute the correlation between usage and downloads, and the RQ1/RQ2 inter-rater agreements

- `modelsInfo.zip`: zip file containing all the downloaded model cards (in JSON format)

- `script`: directory containing all the scripts used to collect and process data. For further details, see README file inside the script directory.

## Dataset

- `Dataset/Dataset_HF-models-list.csv`: list of HF models analyzed

- `Dataset/Dataset_github-prj-list.txt`: list of GitHub projects using the *transformers* library

- `Dataset/Dataset_github-Prj_model-Used.csv`: contains usage pairs: project, model

- `Dataset/Dataset_prj-num-models-reused.csv`: number of models used by each GitHub project

- `Dataset/Dataset_model-download_num-prj_correlation.csv` contains, for each model used by GitHub projects: the name, the task, the number of reusing projects, and the number of downloads

## RQ1

- `RQ1/RQ1_dataset-list.txt`: list of HF datasets

- `RQ1/RQ1_datasetSample.csv`: sample set of models used for the manual analysis of datasets

- `RQ1/RQ1_analyzeDatasetTags.py`: Python script to analyze model tags for the presence of datasets. it requires to unzip the `modelsInfo.zip` in a directory with the same name (`modelsInfo`) at the root of the replication package folder. Produces the output to stdout. To redirect in a file fo be analyzed by the `RQ2/countDataset.py` script

- `RQ1/RQ1_countDataset.py`: given the output of `RQ2/analyzeDatasetTags.py` (passed as argument) produces, for each model, a list of Booleans indicating whether (i) the model only declares HF datasets, (ii) the model only declares external datasets, (iii) the model declares both, and (iv) the model is part of the sample for the manual analysis

- `RQ1/RQ1_datasetTags.csv`: output of `RQ2/analyzeDatasetTags.py`

- `RQ1/RQ1_dataset_usage_count.csv`: output of `RQ2/countDataset.py`

## RQ2

- `RQ2/tableBias.pdf`: table detailing the number of occurrences of different types of bias by model Task

- `RQ2/RQ2_bias_classification_sheet.csv`: results of the manual labeling

- `RQ2/RQ2_isBiased.csv`: file to compute the inter-rater agreement of whether or not a model documents Bias

- `RQ2/RQ2_biasAgrLabels.csv`: file to compute the inter-rater agreement related to bias categories

- `RQ2/RQ2_final_bias_categories_with_levels.csv`: for each model in the sample, this file lists (i) the bias leaf category, (ii) the first-level category, and (iii) the intermediate category

## RQ3

- `RQ3/RQ3_LicenseValidation.csv`: manual validation of a sample of licenses

- `RQ3/RQ3_{NETWORK-RESTRICTIVE|RESTRICTIVE|WEAK-RESTRICTIVE|PERMISSIVE}-license-list.txt`: lists of licenses with different permissiveness

- `RQ3/RQ3_prjs_license.csv`: for each project linked to models, among other fields it indicates the license tag and name

- `RQ3/RQ3_models_license.csv`: for each model, indicates among other pieces of info, whether the model has a license, and if yes what kind of license

- `RQ3/RQ3_model-prj-license_contingency_table.csv`: usage contingency table between projects' licenses (columns) and models' licenses (rows)

- `RQ3/RQ3_models_prjs_licenses_with_type.csv`: pairs project-model, with their respective licenses and permissiveness level

## scripts

Contains the scripts used to mine Hugging Face and GitHub. Details are in the enclosed README

📚 FineWeb-Edu

1.3 trillion tokens of the finest educational data the 🌐 web has to offer

Paper: https://arxiv.org/abs/2406.17557

  What is it?

📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version. To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We then… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu.

SmolLM-Corpus

This dataset is a curated collection of high-quality educational and synthetic data designed for training small language models. You can find more details about the models trained on this dataset in our SmolLM blog post.

  Dataset subsets





  Cosmopedia v2

Cosmopedia v2 is an enhanced version of Cosmopedia, the largest synthetic dataset for pre-training, consisting of over 39 million textbooks, blog posts, and stories generated by… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus.

In this huggingface discussion you can share what you used the dataset for. Derives from https://www.kaggle.com/datasets/rtatman/questionanswer-dataset?resource=download we generated our own subset using generate.py.

Dataset Card for allenai/wmt22_african

  Dataset Summary

This dataset was created based on metadata for mined bitext released by Meta AI. It contains bitext for 248 pairs for the African languages that are part of the 2022 WMT Shared Task on Large Scale Machine Translation Evaluation for African Languages.

  How to use the data

There are two ways to access the data:

Via the Hugging Face Python datasets library

from datasets import load_dataset dataset =… See the full description on the dataset page: https://huggingface.co/datasets/allenai/wmt22_african.

Dataset Card for H4 Stack Exchange Preferences Dataset

  Dataset Summary

This dataset contains questions and answers from the Stack Overflow Data Dump for the purpose of preference model training. Importantly, the questions have been filtered to fit the following criteria for preference models (following closely from Askell et al. 2021): have >=2 answers. This data could also be used for instruction fine-tuning and language model training. The questions are grouped with… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences.

Dataset Card for "imdb"

  Dataset Summary

Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.

  Supported Tasks and Leaderboards

More Information Needed

  Languages

More Information Needed

  Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/imdb.

Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs

  Overview

Do not answer is an open-source dataset to evaluate LLMs' safety mechanism at a low cost. The dataset is curated and filtered to consist only of prompts to which responsible language models do not answer. Besides human annotations, Do not answer also implements model-based evaluation, where a 600M fine-tuned BERT-like evaluator achieves comparable results with human and GPT-4.

  Instruction… See the full description on the dataset page: https://huggingface.co/datasets/LibrAI/do-not-answer.

We present CLAP NQ, a benchmark Long-form Question Answering dataset for the full RAG pipeline. CLAP NQ includes long answers with grounded gold passages from Natural Questions (NQ) and a corpus to perform either retrieval, generation, or the full RAG pipeline. The CLAP NQ answers are concise, 3x smaller than the full passage, and cohesive, with multiple pieces of the passage that are not contiguous. This is the annotated data for the generation portion of the RAG pipeline. For more… See the full description on the dataset page: https://huggingface.co/datasets/PrimeQA/clapnq.

detection-datasets/coco dataset hosted on Hugging Face and contributed by the HF Datasets community

Paper | Github | Dataset| Model 📣📣📣: Do check our new multilingual dataset CatQA here used in Safety Vectors:📣📣📣

As a part of our research efforts toward making LLMs more safe for public use, we create HarmfulQA i.e. a ChatGPT-distilled dataset constructed using the Chain of Utterances (CoU) prompt. More details are in our paper Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment HarmfulQA serves as both-a new LLM safety benchmark and an alignment dataset… See the full description on the dataset page: https://huggingface.co/datasets/declare-lab/HarmfulQA.

Dataset Card for VisIT-Bench

Dataset Description Links Dataset Structure Data Fields Data Splits Data Loading

Licensing Information Annotations Considerations for Using the Data Citation Information

  Dataset Description

VisIT-Bench is a dataset and benchmark for vision-and-language instruction following. The dataset is comprised of image-instruction pairs and corresponding example outputs, spanning a wide range of tasks, from simple object recognition to complex… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/VisIT-Bench.

Below, we provide access to the datasets used in and created for the EMNLP 2022 paper Large Language Models are Few-Shot Clinical Information Extractors.

  Task #1: Clinical Sense Disambiguation

For Task #1, we use the original annotations from the Clinical Acronym Sense Inventory (CASI) dataset, described in their paper. As is common, due to noisiness in the label set, we do not evaluate on the entire dataset, but only on a cleaner subset. For consistency, we use the subset defined… See the full description on the dataset page: https://huggingface.co/datasets/mitclinicalml/clinical-ie.

Dataset Card for WHOOPS!

Dataset Description Contribute Images to Extend WHOOPS! Languages

Dataset Data Fields Data Splits Data Loading

Licensing Information Annotations Considerations for Using the Data Citation Information

  Dataset Description

WHOOPS! is a dataset and benchmark for visual commonsense. The dataset is comprised of purposefully commonsense-defying images created by designers using publicly-available image generation tools like Midjourney. It contains… See the full description on the dataset page: https://huggingface.co/datasets/nlphuji/whoops.