restaurants
cs_restaurantsThe task is generating responses in the context of a (hypothetical) dialogue
system that provides information about restaurants. The input is a basic
intent/dialogue act type and a list of slots (attributes) and their values.
The output is a natural language sentence.setfit-absa-semeval-restaurants
Dataset Card for "tomaarsen/setfit-absa-semeval-restaurants"
Dataset Summary
This dataset contains the manually annotated restaurant reviews from SemEval-2014 Task 4, in the format as
understood by SetFit ABSA.
For more details, see https://aclanthology.org/S14-2004/
Data Instances
An example of "train" looks as follows.
{"text": "But the staff was so horrible to us.", "span": "staff", "label": "negative", "ordinal": 0}
{"text": "To be completely fair, the only… See the full description on the dataset page: https://huggingface.co/datasets/tomaarsen/setfit-absa-semeval-restaurants.cs_restaurants
Dataset Card for Czech Restaurant
Dataset Summary
This is a dataset for NLG in task-oriented spoken dialogue systems with Czech as the target language. It originated as a translation of the English San Francisco Restaurants dataset by Wen et al. (2015). The domain is restaurant information in Prague, with random/fictional values. It includes input dialogue acts and the corresponding outputs in Czech.
Supported Tasks and Leaderboards
other-intent-to-text:… See the full description on the dataset page: https://huggingface.co/datasets/community-datasets/cs_restaurants.semeval2014_restaurants
Dataset Card for "semeval2014_restaurants"
More Information needed
whatscooking.restaurants
Whatscooking.restaurants
Overview
This dataset provides detailed information about various restaurants, including their location, cuisine, ratings, and other attributes. It is particularly useful for applications in food and beverage industry analysis, recommendation systems, and geographical studies.
Dataset Structure
Each record in the dataset represents a single restaurant and contains the following fields:
_id: A unique identifier for the restaurant… See the full description on the dataset page: https://huggingface.co/datasets/MongoDB/whatscooking.restaurants.restaurants_reviews
Dataset Summary
The dataset contains user reviews about restaurants.
In total it contains 47,139 reviews. A review tagged with the general sentiment and sentiments on 3 aspects: food, interior, service.
Data Fields
Each sample contains the following fields:
review_id;
general;
food;
interior;
service;
text review text.
Python
import pandas as pd
df = pd.read_json('restaurants_reviews.jsonl', lines=True)
df.sample(5)