datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
rotten_tomatoes
Dataset Card for "rotten_tomatoes"
Dataset Summary
Movie Review Dataset.
This is a dataset of containing 5,331 positive and 5,331 negative processed
sentences from Rotten Tomatoes movie reviews. This data was first used in Bo
Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for
sentiment categorization with respect to rating scales.'', Proceedings of the
ACL, 2005.
Supported Tasks and Leaderboards
More Information Needed
Languages… See the full description on the dataset page: https://huggingface.co/datasets/cornell-movie-review-data/rotten_tomatoes.yelp_review_full
Dataset Card for YelpReviewFull
Dataset Summary
The Yelp reviews dataset consists of reviews from Yelp.
It is extracted from the Yelp Dataset Challenge 2015 data.
Supported Tasks and Leaderboards
text-classification, sentiment-classification: The dataset is mainly used for text classification: given the text, predict the sentiment.
Languages
The reviews were mainly written in english.
Dataset Structure
Data Instances
A… See the full description on the dataset page: https://huggingface.co/datasets/Yelp/yelp_review_full.nrvbench-review
NR Video Editing Benchmark
This repository contains two non-rigid video editing benchmark subsets for evaluating instruction-driven video editing methods. Each row in metadata.csv corresponds to one editing instruction for a source video, with relative paths to the source video, extracted frames, binary masks, prompts, and evaluation questions.
The dataset card is written without author or institution identifiers so it can be used for anonymous review uploads. Before a non-anonymous… See the full description on the dataset page: https://huggingface.co/datasets/NRVBench/nrvbench-review.imagenet_hard_review_data_r2womens-clothing-ecommerce-reviews
Dataset Card for "womens-clothing-ecommerce-reviews"
Processed version of this dataset.
FIRM
FIRM: A Benchmark for Industrial Flexible-Object Robot Manipulation
FIRM is a benchmark for industrial flexible-object robot manipulation grounded in real-world industrial data. The benchmark focuses on manipulation tasks involving mixed-stiffness objects, including instruction manuals, power cables, sponge pads, tapes, and cardboard components. These objects exhibit bending, slipping, rolling, compression, elastic recovery, and flexible-rigid contact under production-line… See the full description on the dataset page: https://huggingface.co/datasets/firm-review/FIRM.document-review-data
Document Review Data
Private dataset for the Office/PDF title extraction review app and the current extractive title-training data package.
Current Title Extraction Dataset Surface
Canonical prefix:
datasets/title_extraction/
Effective datasets:
datasets/title_extraction/training/source4k_device_qwen_fp1000_v1/
datasets/title_extraction/evaluation/real_device_280_v1/
datasets/title_extraction/synthetic/controlled_synthetic_parse_v1/
The Dataset Viewer is… See the full description on the dataset page: https://huggingface.co/datasets/mannycooper/document-review-data.amazon_reviews_multiWe provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.
For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.
Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.tripadvisor-hotel-reviews
Dataset Card for "tripadvisor-hotel-reviews"
Dataset Summary
Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.
With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!
Citations on a scale from 1 to 5.
Languages
english
Citation Information
If you use this dataset in… See the full description on the dataset page: https://huggingface.co/datasets/argilla/tripadvisor-hotel-reviews.amazon_reviews_mcauley_1and5amazon_reviews_multi_enECommerce-Women-Clothing-Reviewstripadvisor_hotel_reviewsTTCW-Based-Review
TTCW Creative Writing Evaluation Dataset
If you use this dataset in your research, please cite our paper — it helps support ongoing academic work. Citation details are at the bottom of this page.
Dataset Description
Summary
A supervised fine-tuning (SFT) dataset for training LLMs to act as creative writing evaluators. Each example contains a creative story and four message-format columns representing different evaluation objectives — from… See the full description on the dataset page: https://huggingface.co/datasets/VibrantVista/TTCW-Based-Review.Womens_Clothing_E-Commerce_ReviewsDrivAerStar-Review
DrivAerStar: An Industrial-Grade CFD Dataset for Vehicle Aerodynamic Optimization
Vehicle aerodynamics optimization is fundamental to automotive engineering, drag reduction, noise minimization, and vehicle body stability through complex fluid dynamics simulations. Traditional approaches rely on computationally
expensive Computational Fluid Dynamics (CFD) simulations that limit design
exploration or simplified models that compromise accuracy. Machine learning
methods offer promising… See the full description on the dataset page: https://huggingface.co/datasets/drivaerstar/DrivAerStar-Review.amazon-reviews-2023-with-asin
Amazon Reviews 2023 (with ASIN)
A trimmed version of the McAuley-Lab/Amazon-Reviews-2023 dataset, retaining only the fields most relevant for NLP tasks while adding explicit product identification via parent_asin.
What's Different from the Original
The original dataset includes 10+ fields per review and requires a legacy loading script that is no longer supported by HuggingFace. This version:
Keeps only 4 fields: rating, title, text, and parent_asin
Is stored in… See the full description on the dataset page: https://huggingface.co/datasets/BarbaDLuca/amazon-reviews-2023-with-asin.app_reviews
Dataset Card for [Dataset Name]
Dataset Summary
It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews (extracted with specific text mining approaches)
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/sealuzh/app_reviews.drug-reviewshotel-reviews
Dataset Card for "hotel-reviews"
More Information needed
Data was obtained from here
amazon-reviews
Dataset Card for Amazon Reviews 2018
This dataset is a collection of title-review pairs collected from Amazon, as collected in Ni et al.. See Amazon Reviews 2018 for additional information.
This dataset can be used directly with Sentence Transformers to train embedding models.
Dataset Subsets
pair subset
Columns: "title", "review"
Column types: str, str
Examples:{
'title': "It doesn't fit my machine. I can't seem to ...",
'review': "It doesn't fit my… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/amazon-reviews.amazon-reviews-2023-trimmed
Amazon Product Reviews 2023 (Trimmed, 34 Categories)
This dataset is a trimmed and restructured version of the Amazon Product Reviews 2023 dataset by Julian McAuley and the UCSD Computer Science department.
It includes 34 product categories, each stored as a folder containing multiple sharded Parquet files for scalable access.
Only three fields are retained:
rating — The numerical review score (originally overall)
title — The review title (from summary)
text — The full review body… See the full description on the dataset page: https://huggingface.co/datasets/bagadbilla/amazon-reviews-2023-trimmed.yelp_review_fullagent_paper_reviewDrug-Review-DatasetUCI_drug_reviews
Data Description
This data comes from the UC Irvine Machine Learning Repository. It has been preprocessed to only contain reviews at least 13 or more words in length. The raw data for this
specific dataset can be found here. The base UCI ML url can be found
here.
imagenet_hard_review_datareviewsFollowing packages are necessary to compile the model in C:
sudo apt install gcc-7
sudo apt-get install build-essential
for i in range(1,21):
!wget https://huggingface.co/datasets/teragron/reviews/resolve/main/chunk_{i}.bin
git clone https://github.com/karpathy/llama2.c.git
cd llama2.c
pip install -r requirements.txt
Path: data/TinyStories_all_data
SWE-Review-Chat
SWE-Review-Chat: A Dataset of Code Review Conversations and Human-AI Collaboration in Agentic Code Review
Paper: https://arxiv.org/abs/2607.13196
GitHub: https://github.com/suzhenxzhong/SWE-Review-Chat
SWE-Review-Chat is a large-scale dataset of real-world code review conversations from pull requests of 207 popular GitHub projects, spanning the transition from human-centric to LLM-assisted and agentic code review by AI agents.
📊 Dataset Overview
Field… See the full description on the dataset page: https://huggingface.co/datasets/Suzhen/SWE-Review-Chat.dataset_for_review UNO-Bench: A Unified Benchmark for Exploring the Compositional Law Between Uni-modal and Omni-modal in Omni Models
👀 UNO-Bench Overview
Multimodal Large Languages models have been progressing from uni-modal understanding toward unifying visual, audio and language modalities, collectively termed omni models. However, the correlation between uni-modal and omni-modal remains unclear, which requires comprehensive evaluation to drive omni model's intelligence evolution. In… See the full description on the dataset page: https://huggingface.co/datasets/blue-tundra-42/dataset_for_review.