Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
100K - 1M
ArXiv:
License:
Update README.md
#8
by
dorsamnv
- opened
README.md
CHANGED
@@ -1,235 +1,4 @@
|
|
1 |
-
|
2 |
-
|
3 |
-
|
4 |
-
|
5 |
-
- crowdsourced
|
6 |
-
language:
|
7 |
-
- en
|
8 |
-
license:
|
9 |
-
- other
|
10 |
-
multilinguality:
|
11 |
-
- monolingual
|
12 |
-
size_categories:
|
13 |
-
- 100K<n<1M
|
14 |
-
source_datasets:
|
15 |
-
- original
|
16 |
-
task_categories:
|
17 |
-
- text-classification
|
18 |
-
task_ids:
|
19 |
-
- sentiment-classification
|
20 |
-
pretty_name: YelpReviewFull
|
21 |
-
license_details: yelp-licence
|
22 |
-
dataset_info:
|
23 |
-
config_name: yelp_review_full
|
24 |
-
features:
|
25 |
-
- name: label
|
26 |
-
dtype:
|
27 |
-
class_label:
|
28 |
-
names:
|
29 |
-
'0': 1 star
|
30 |
-
'1': 2 star
|
31 |
-
'2': 3 stars
|
32 |
-
'3': 4 stars
|
33 |
-
'4': 5 stars
|
34 |
-
- name: text
|
35 |
-
dtype: string
|
36 |
-
splits:
|
37 |
-
- name: train
|
38 |
-
num_bytes: 483811554
|
39 |
-
num_examples: 650000
|
40 |
-
- name: test
|
41 |
-
num_bytes: 37271188
|
42 |
-
num_examples: 50000
|
43 |
-
download_size: 322952369
|
44 |
-
dataset_size: 521082742
|
45 |
-
configs:
|
46 |
-
- config_name: yelp_review_full
|
47 |
-
data_files:
|
48 |
-
- split: train
|
49 |
-
path: yelp_review_full/train-*
|
50 |
-
- split: test
|
51 |
-
path: yelp_review_full/test-*
|
52 |
-
default: true
|
53 |
-
train-eval-index:
|
54 |
-
- config: yelp_review_full
|
55 |
-
task: text-classification
|
56 |
-
task_id: multi_class_classification
|
57 |
-
splits:
|
58 |
-
train_split: train
|
59 |
-
eval_split: test
|
60 |
-
col_mapping:
|
61 |
-
text: text
|
62 |
-
label: target
|
63 |
-
metrics:
|
64 |
-
- type: accuracy
|
65 |
-
name: Accuracy
|
66 |
-
- type: f1
|
67 |
-
name: F1 macro
|
68 |
-
args:
|
69 |
-
average: macro
|
70 |
-
- type: f1
|
71 |
-
name: F1 micro
|
72 |
-
args:
|
73 |
-
average: micro
|
74 |
-
- type: f1
|
75 |
-
name: F1 weighted
|
76 |
-
args:
|
77 |
-
average: weighted
|
78 |
-
- type: precision
|
79 |
-
name: Precision macro
|
80 |
-
args:
|
81 |
-
average: macro
|
82 |
-
- type: precision
|
83 |
-
name: Precision micro
|
84 |
-
args:
|
85 |
-
average: micro
|
86 |
-
- type: precision
|
87 |
-
name: Precision weighted
|
88 |
-
args:
|
89 |
-
average: weighted
|
90 |
-
- type: recall
|
91 |
-
name: Recall macro
|
92 |
-
args:
|
93 |
-
average: macro
|
94 |
-
- type: recall
|
95 |
-
name: Recall micro
|
96 |
-
args:
|
97 |
-
average: micro
|
98 |
-
- type: recall
|
99 |
-
name: Recall weighted
|
100 |
-
args:
|
101 |
-
average: weighted
|
102 |
-
---
|
103 |
-
---
|
104 |
-
|
105 |
-
# Dataset Card for YelpReviewFull
|
106 |
-
|
107 |
-
## Table of Contents
|
108 |
-
- [Dataset Description](#dataset-description)
|
109 |
-
- [Dataset Summary](#dataset-summary)
|
110 |
-
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
111 |
-
- [Languages](#languages)
|
112 |
-
- [Dataset Structure](#dataset-structure)
|
113 |
-
- [Data Instances](#data-instances)
|
114 |
-
- [Data Fields](#data-fields)
|
115 |
-
- [Data Splits](#data-splits)
|
116 |
-
- [Dataset Creation](#dataset-creation)
|
117 |
-
- [Curation Rationale](#curation-rationale)
|
118 |
-
- [Source Data](#source-data)
|
119 |
-
- [Annotations](#annotations)
|
120 |
-
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
121 |
-
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
122 |
-
- [Social Impact of Dataset](#social-impact-of-dataset)
|
123 |
-
- [Discussion of Biases](#discussion-of-biases)
|
124 |
-
- [Other Known Limitations](#other-known-limitations)
|
125 |
-
- [Additional Information](#additional-information)
|
126 |
-
- [Dataset Curators](#dataset-curators)
|
127 |
-
- [Licensing Information](#licensing-information)
|
128 |
-
- [Citation Information](#citation-information)
|
129 |
-
- [Contributions](#contributions)
|
130 |
-
|
131 |
-
## Dataset Description
|
132 |
-
|
133 |
-
- **Homepage:** [Yelp](https://www.yelp.com/dataset)
|
134 |
-
- **Repository:** [Crepe](https://github.com/zhangxiangxiao/Crepe)
|
135 |
-
- **Paper:** [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626)
|
136 |
-
- **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu)
|
137 |
-
|
138 |
-
### Dataset Summary
|
139 |
-
|
140 |
-
The Yelp reviews dataset consists of reviews from Yelp.
|
141 |
-
It is extracted from the Yelp Dataset Challenge 2015 data.
|
142 |
-
|
143 |
-
### Supported Tasks and Leaderboards
|
144 |
-
|
145 |
-
- `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment.
|
146 |
-
|
147 |
-
### Languages
|
148 |
-
|
149 |
-
The reviews were mainly written in english.
|
150 |
-
|
151 |
-
## Dataset Structure
|
152 |
-
|
153 |
-
### Data Instances
|
154 |
-
|
155 |
-
A typical data point, comprises of a text and the corresponding label.
|
156 |
-
|
157 |
-
An example from the YelpReviewFull test set looks as follows:
|
158 |
-
```
|
159 |
-
{
|
160 |
-
'label': 0,
|
161 |
-
'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!'
|
162 |
-
}
|
163 |
-
```
|
164 |
-
|
165 |
-
### Data Fields
|
166 |
-
|
167 |
-
- '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".
|
168 |
-
- 'label': Corresponds to the score associated with the review (between 1 and 5).
|
169 |
-
|
170 |
-
### Data Splits
|
171 |
-
|
172 |
-
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.
|
173 |
-
In total there are 650,000 trainig samples and 50,000 testing samples.
|
174 |
-
|
175 |
-
## Dataset Creation
|
176 |
-
|
177 |
-
### Curation Rationale
|
178 |
-
|
179 |
-
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).
|
180 |
-
|
181 |
-
### Source Data
|
182 |
-
|
183 |
-
#### Initial Data Collection and Normalization
|
184 |
-
|
185 |
-
[More Information Needed]
|
186 |
-
|
187 |
-
#### Who are the source language producers?
|
188 |
-
|
189 |
-
[More Information Needed]
|
190 |
-
|
191 |
-
### Annotations
|
192 |
-
|
193 |
-
#### Annotation process
|
194 |
-
|
195 |
-
[More Information Needed]
|
196 |
-
|
197 |
-
#### Who are the annotators?
|
198 |
-
|
199 |
-
[More Information Needed]
|
200 |
-
|
201 |
-
### Personal and Sensitive Information
|
202 |
-
|
203 |
-
[More Information Needed]
|
204 |
-
|
205 |
-
## Considerations for Using the Data
|
206 |
-
|
207 |
-
### Social Impact of Dataset
|
208 |
-
|
209 |
-
[More Information Needed]
|
210 |
-
|
211 |
-
### Discussion of Biases
|
212 |
-
|
213 |
-
[More Information Needed]
|
214 |
-
|
215 |
-
### Other Known Limitations
|
216 |
-
|
217 |
-
[More Information Needed]
|
218 |
-
|
219 |
-
## Additional Information
|
220 |
-
|
221 |
-
### Dataset Curators
|
222 |
-
|
223 |
-
[More Information Needed]
|
224 |
-
|
225 |
-
### Licensing Information
|
226 |
-
|
227 |
-
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).
|
228 |
-
|
229 |
-
### Citation Information
|
230 |
-
|
231 |
-
Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
|
232 |
-
|
233 |
-
### Contributions
|
234 |
-
|
235 |
-
Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.
|
|
|
1 |
+
# Classify-Species-from-the-Orchid-Flowers
|
2 |
+
CNN Classification Model to classify Species from the Orchid Flowers
|
3 |
+
Build a CNN Classification Model to classify Species from the Orchid Flowers dataset (Use train and validation data for respective purpose, no testing needed)
|
4 |
+
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/0HNECY
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|