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- ---
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- annotations_creators:
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- - crowdsourced
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- language_creators:
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- - crowdsourced
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- language:
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- - en
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- license:
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- - other
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- multilinguality:
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- - monolingual
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- size_categories:
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- - 100K<n<1M
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- source_datasets:
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- - original
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- task_categories:
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- - text-classification
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- task_ids:
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- - sentiment-classification
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- pretty_name: YelpReviewFull
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- license_details: yelp-licence
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- dataset_info:
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- config_name: yelp_review_full
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- features:
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- - name: label
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- dtype:
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- class_label:
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- names:
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- '0': 1 star
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- '1': 2 star
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- '2': 3 stars
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- '3': 4 stars
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- '4': 5 stars
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- - name: text
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- dtype: string
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- splits:
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- - name: train
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- num_bytes: 483811554
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- num_examples: 650000
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- - name: test
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- num_bytes: 37271188
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- num_examples: 50000
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- download_size: 322952369
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- dataset_size: 521082742
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- configs:
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- - config_name: yelp_review_full
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- data_files:
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- - split: train
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- path: yelp_review_full/train-*
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- - split: test
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- path: yelp_review_full/test-*
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- default: true
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- train-eval-index:
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- - config: yelp_review_full
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- task: text-classification
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- task_id: multi_class_classification
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- splits:
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- train_split: train
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- eval_split: test
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- col_mapping:
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- text: text
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- label: target
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- metrics:
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- - type: accuracy
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- name: Accuracy
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- - type: f1
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- name: F1 macro
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- args:
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- average: macro
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- - type: f1
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- name: F1 micro
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- args:
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- average: micro
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- - type: f1
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- name: F1 weighted
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- args:
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- average: weighted
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- - type: precision
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- name: Precision macro
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- args:
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- average: macro
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- - type: precision
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- name: Precision micro
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- args:
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- average: micro
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- - type: precision
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- name: Precision weighted
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- args:
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- average: weighted
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- - type: recall
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- name: Recall macro
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- args:
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- average: macro
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- - type: recall
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- name: Recall micro
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- args:
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- average: micro
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- - type: recall
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- name: Recall weighted
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- args:
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- average: weighted
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- ---
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- ---
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-
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- # Dataset Card for YelpReviewFull
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-
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- ## Table of Contents
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- - [Dataset Description](#dataset-description)
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- - [Dataset Summary](#dataset-summary)
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- - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- - [Languages](#languages)
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- - [Dataset Structure](#dataset-structure)
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- - [Data Instances](#data-instances)
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- - [Data Fields](#data-fields)
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- - [Data Splits](#data-splits)
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- - [Dataset Creation](#dataset-creation)
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- - [Curation Rationale](#curation-rationale)
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- - [Source Data](#source-data)
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- - [Annotations](#annotations)
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- - [Personal and Sensitive Information](#personal-and-sensitive-information)
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- - [Considerations for Using the Data](#considerations-for-using-the-data)
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- - [Social Impact of Dataset](#social-impact-of-dataset)
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- - [Discussion of Biases](#discussion-of-biases)
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- - [Other Known Limitations](#other-known-limitations)
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- - [Additional Information](#additional-information)
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- - [Dataset Curators](#dataset-curators)
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- - [Licensing Information](#licensing-information)
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- - [Citation Information](#citation-information)
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- - [Contributions](#contributions)
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-
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- ## Dataset Description
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-
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- - **Homepage:** [Yelp](https://www.yelp.com/dataset)
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- - **Repository:** [Crepe](https://github.com/zhangxiangxiao/Crepe)
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- - **Paper:** [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626)
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- - **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu)
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-
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- ### Dataset Summary
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-
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- The Yelp reviews dataset consists of reviews from Yelp.
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- It is extracted from the Yelp Dataset Challenge 2015 data.
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-
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- ### Supported Tasks and Leaderboards
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-
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- - `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment.
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-
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- ### Languages
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-
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- The reviews were mainly written in english.
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-
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- ## Dataset Structure
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-
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- ### Data Instances
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- A typical data point, comprises of a text and the corresponding label.
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- An example from the YelpReviewFull test set looks as follows:
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- ```
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- {
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- 'label': 0,
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- '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!'
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- }
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- ```
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-
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- ### Data Fields
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-
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- - '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".
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- - 'label': Corresponds to the score associated with the review (between 1 and 5).
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-
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- ### Data Splits
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-
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- 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.
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- In total there are 650,000 trainig samples and 50,000 testing samples.
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-
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- ## Dataset Creation
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-
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- ### Curation Rationale
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- 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).
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-
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- ### Source Data
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-
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- #### Initial Data Collection and Normalization
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- [More Information Needed]
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-
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- #### Who are the source language producers?
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- [More Information Needed]
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-
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- ### Annotations
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-
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- #### Annotation process
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- [More Information Needed]
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- #### Who are the annotators?
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- [More Information Needed]
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- ### Personal and Sensitive Information
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- [More Information Needed]
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- ## Considerations for Using the Data
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- ### Social Impact of Dataset
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- [More Information Needed]
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- ### Discussion of Biases
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- [More Information Needed]
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- ### Other Known Limitations
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-
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- [More Information Needed]
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-
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- ## Additional Information
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- ### Dataset Curators
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- [More Information Needed]
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- ### Licensing Information
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- 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).
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- ### Citation Information
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- Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
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-
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- ### Contributions
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- Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.
 
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+ CNN Classification Model to classify Species from the Orchid Flowers
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+ Build a CNN Classification Model to classify Species from the Orchid Flowers dataset (Use train and validation data for respective purpose, no testing needed)
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+ https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/0HNECY