datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
yelp_academic_dataset_reviewyelp-rag-data
Yelp RAG Agent Data
Vector store and source data for the Yelp Business Intelligence Agent demo.
vectorstore/review_chunks.index — FAISS index (60,823 chunks)
vectorstore/review_chunks.pkl — chunk metadata + embeddings + business→indices map
yelp_reviews_sampled_50k.csv — 50k sampled Yelp reviews
yelp_academic_dataset_business.json — Yelp business metadata
Embedding model: sentence-transformers/all-MiniLM-L6-v2.
yelp-review-restaurant-aspect
Dataset Description
Source
This dataset is derived from a publicly available Yelp restaurant review dataset.
Domain
Restaurant reviews.
Subset
The dataset contains 6,000 Yelp restaurant reviews randomly sampled from a larger corpus and annotated for six predefined restaurant sentiment aspects: food, service, ambience, cleanliness, location, and price.
Size
Number of records: 6,000 reviews
Number of labels: 6 aspects
Labels… See the full description on the dataset page: https://huggingface.co/datasets/manmanluo/yelp-review-restaurant-aspect.Yelp-RestaurantsyelpA quick usage example of Yelp dataset.
install datasets library
%pip install datasets
import load_dataset
from datasets import load_dataset
Reading the Dataset
ds = load_dataset("recmeapp/yelp", "main_data")
Reading the App MetaData
app_metadata = load_dataset("recmeapp/yelp", "app_meta")
How many dialogs are there in different splits?
train_data = ds['train']
valid_data = ds['val']
test_data = ds['test']
print(f'There are… See the full description on the dataset page: https://huggingface.co/datasets/recmeapp/yelp.