Datasets:

Abirate
/

english_quotes

@@ -1,4 +1,4 @@
-#  Dataset Card for English quotes
 #### Dataset Summary
 english_quotes is a dataset of all the quotes retrieved from [goodreads quotes](https://www.goodreads.com/quotes). This dataset can be used for multi-label text classification and text generation. The content of each quote is in English and concerns the domain of datasets for NLP and beyond.
@@ -9,35 +9,35 @@ english_quotes is a dataset of all the quotes retrieved from [goodreads quotes](
 #### Languages
 The texts in the dataset are in English (en).
-#  Dataset Structure
-####  Data Instances
 A JSON-formatted example of a typical instance in the dataset:
 ```python
 {'author': 'Ralph Waldo Emerson',
  'quote': '“To be yourself in a world that is constantly trying to make you something else is the greatest accomplishment.”',
  'tags': ['accomplishment', 'be-yourself', 'conformity', 'individuality']}
   ```
- ####  Data Fields
  - **author** : The author of the quote.
  - **quote** : The text of the quote.
  - **tags**:  The tags could be characterized as topics around the quote.
-  ####  Data Splits
 I kept the dataset as one block (train), so it can be shuffled and split by users later using methods of the hugging face dataset library like the (.train_test_split()) method.
-#  Dataset Creation
-####  Curation Rationale
 I want to share my datasets (created by web scraping and additional cleaning treatments) with the HuggingFace community so that they can use them in NLP tasks to advance artificial intelligence.
-####  Source Data
 The source of Data is [goodreads](https://www.goodreads.com/?ref=nav_home) site: from [goodreads quotes](https://www.goodreads.com/quotes)
-####  Initial Data Collection and Normalization
 The data collection process is web scraping using BeautifulSoup and Requests libraries.
 The data is slightly modified after the web scraping: removing all quotes with "None" tags, and the tag "attributed-no-source" is removed from all tags, because it has not added value to the topic of the quote.
-####  Who are the source Data producers ?
 The data is machine-generated (using web scraping) and subjected to human additional treatment.
 below, I provide the script I created to scrape the data (as well as my additional treatment):
@@ -95,18 +95,18 @@ data_df.to_json('C:/Users/Abir/Desktop/quotes.jsonl',orient="records", lines =Tr
 # Then I used the familiar process to push it to the Hugging Face hub.
 ```
-####  Annotations
 Annotations are part of the initial data collection (see the script above).
-#  Additional Information
-####  Dataset Curators
 Abir ELTAIEF :
 [@AbirEltaief](https://tn.linkedin.com/in/abir-eltaief-pmp%C2%AE-469048115)
-####  Licensing Information
 This work is licensed under a Creative Commons Attribution 4.0 International License (all software and libraries used for web scraping are made available under this Creative Commons Attribution license).
-####  Contributions
 Thanks to [@Abirate](https://tn.linkedin.com/in/abir-eltaief-pmp%C2%AE-469048115)
  for adding this dataset.

+# **I-Dataset Card for English quotes**
 #### Dataset Summary
 english_quotes is a dataset of all the quotes retrieved from [goodreads quotes](https://www.goodreads.com/quotes). This dataset can be used for multi-label text classification and text generation. The content of each quote is in English and concerns the domain of datasets for NLP and beyond.
 #### Languages
 The texts in the dataset are in English (en).
+# **II-Dataset Structure**
+#### Data Instances
 A JSON-formatted example of a typical instance in the dataset:
 ```python
 {'author': 'Ralph Waldo Emerson',
  'quote': '“To be yourself in a world that is constantly trying to make you something else is the greatest accomplishment.”',
  'tags': ['accomplishment', 'be-yourself', 'conformity', 'individuality']}
   ```
+ #### Data Fields
  - **author** : The author of the quote.
  - **quote** : The text of the quote.
  - **tags**:  The tags could be characterized as topics around the quote.
+  #### Data Splits
 I kept the dataset as one block (train), so it can be shuffled and split by users later using methods of the hugging face dataset library like the (.train_test_split()) method.
+# **III-Dataset Creation**
+#### Curation Rationale
 I want to share my datasets (created by web scraping and additional cleaning treatments) with the HuggingFace community so that they can use them in NLP tasks to advance artificial intelligence.
+#### Source Data
 The source of Data is [goodreads](https://www.goodreads.com/?ref=nav_home) site: from [goodreads quotes](https://www.goodreads.com/quotes)
+#### Initial Data Collection and Normalization
 The data collection process is web scraping using BeautifulSoup and Requests libraries.
 The data is slightly modified after the web scraping: removing all quotes with "None" tags, and the tag "attributed-no-source" is removed from all tags, because it has not added value to the topic of the quote.
+#### Who are the source Data producers ?
 The data is machine-generated (using web scraping) and subjected to human additional treatment.
 below, I provide the script I created to scrape the data (as well as my additional treatment):
 # Then I used the familiar process to push it to the Hugging Face hub.
 ```
+#### Annotations
 Annotations are part of the initial data collection (see the script above).
+# **IV-Additional Informations**
+#### Dataset Curators
 Abir ELTAIEF :
 [@AbirEltaief](https://tn.linkedin.com/in/abir-eltaief-pmp%C2%AE-469048115)
+#### Licensing Information
 This work is licensed under a Creative Commons Attribution 4.0 International License (all software and libraries used for web scraping are made available under this Creative Commons Attribution license).
+#### Contributions
 Thanks to [@Abirate](https://tn.linkedin.com/in/abir-eltaief-pmp%C2%AE-469048115)
  for adding this dataset.