Datasets:
yelp_review_full

	---
	annotations_creators:
	- crowdsourced
	language_creators:
	- crowdsourced
	language:
	- en
	license:
	- other
	multilinguality:
	- monolingual
	size_categories:
	- 100K<n<1M
	source_datasets:
	- original
	task_categories:
	- text-classification
	task_ids:
	- sentiment-classification
	pretty_name: YelpReviewFull
	license_details: yelp-licence
	dataset_info:
	config_name: yelp_review_full
	features:
	- name: label
	dtype:
	class_label:
	names:
	'0': 1 star
	'1': 2 star
	'2': 3 stars
	'3': 4 stars
	'4': 5 stars
	- name: text
	dtype: string
	splits:
	- name: train
	num_bytes: 483811554
	num_examples: 650000
	- name: test
	num_bytes: 37271188
	num_examples: 50000
	download_size: 322952369
	dataset_size: 521082742
	configs:
	- config_name: yelp_review_full
	data_files:
	- split: train
	path: yelp_review_full/train-*
	- split: test
	path: yelp_review_full/test-*
	default: true
	train-eval-index:
	- config: yelp_review_full
	task: text-classification
	task_id: multi_class_classification
	splits:
	train_split: train
	eval_split: test
	col_mapping:
	text: text
	label: target
	metrics:
	- type: accuracy
	name: Accuracy
	- type: f1
	name: F1 macro
	args:
	average: macro
	- type: f1
	name: F1 micro
	args:
	average: micro
	- type: f1
	name: F1 weighted
	args:
	average: weighted
	- type: precision
	name: Precision macro
	args:
	average: macro
	- type: precision
	name: Precision micro
	args:
	average: micro
	- type: precision
	name: Precision weighted
	args:
	average: weighted
	- type: recall
	name: Recall macro
	args:
	average: macro
	- type: recall
	name: Recall micro
	args:
	average: micro
	- type: recall
	name: Recall weighted
	args:
	average: weighted
	---
	---

	# Dataset Card for YelpReviewFull

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: [Yelp](https://www.yelp.com/dataset)
	- Repository: [Crepe](https://github.com/zhangxiangxiao/Crepe)
	- Paper: [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626)
	- Point of Contact: [Xiang Zhang](mailto:xiang.zhang@nyu.edu)

	### 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 typical data point, comprises of a text and the corresponding label.

	An example from the YelpReviewFull test set looks as follows:
	```
	{
	'label': 0,
	'text': 'I got \'new\' tires from them and within two weeks got a flat. I took my car to a local mechanic to see if i could get the hole patched, but they said the reason I had a flat was because the previous patch had blown - WAIT, WHAT? I just got the tire and never needed to have it patched? This was supposed to be a new tire. \\nI took the tire over to Flynn\'s and they told me that someone punctured my tire, then tried to patch it. So there are resentful tire slashers? I find that very unlikely. After arguing with the guy and telling him that his logic was far fetched he said he\'d give me a new tire \\"this time\\". \\nI will never go back to Flynn\'s b/c of the way this guy treated me and the simple fact that they gave me a used tire!'
	}
	```

	### Data Fields

	- 'text': The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
	- 'label': Corresponds to the score associated with the review (between 1 and 5).

	### Data Splits

	The Yelp reviews full star dataset is constructed by randomly taking 130,000 training samples and 10,000 testing samples for each review star from 1 to 5.
	In total there are 650,000 trainig samples and 50,000 testing samples.

	## Dataset Creation

	### Curation Rationale

	The Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the Yelp Dataset Challenge 2015. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed]

	#### Who are the source language producers?

	[More Information Needed]

	### Annotations

	#### Annotation process

	[More Information Needed]

	#### Who are the annotators?

	[More Information Needed]

	### Personal and Sensitive Information

	[More Information Needed]

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed]

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	[More Information Needed]

	## Additional Information

	### Dataset Curators

	[More Information Needed]

	### Licensing Information

	You can check the official [yelp-dataset-agreement](https://s3-media3.fl.yelpcdn.com/assets/srv0/engineering_pages/bea5c1e92bf3/assets/vendor/yelp-dataset-agreement.pdf).

	### Citation Information

	Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

	### Contributions

	Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.