Datasets:

yelp
/

yelp_review_full

@@ -1,235 +1,4 @@
----
-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:[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.

+# Classify-Species-from-the-Orchid-Flowers
+CNN Classification Model to classify Species from the Orchid Flowers
+Build a CNN Classification Model to classify Species from the Orchid Flowers dataset (Use train and validation data for respective purpose, no testing needed)
+https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/0HNECY