Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
100K - 1M
ArXiv:
License:
annotations_creators: | |
- crowdsourced | |
language_creators: | |
- crowdsourced | |
language: | |
- en | |
license: | |
- other | |
license_details: yelp-licence | |
multilinguality: | |
- monolingual | |
size_categories: | |
- 100K<n<1M | |
source_datasets: | |
- original | |
task_categories: | |
- text-classification | |
task_ids: | |
- sentiment-classification | |
paperswithcode_id: null | |
pretty_name: YelpReviewFull | |
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_info: | |
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 | |
config_name: yelp_review_full | |
splits: | |
- name: test | |
num_bytes: 37271188 | |
num_examples: 50000 | |
- name: train | |
num_bytes: 483811554 | |
num_examples: 650000 | |
download_size: 196146755 | |
dataset_size: 521082742 | |
--- | |
# 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:[email protected]) | |
### 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 ([email protected]) 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. |