Create README.md

README.md

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+#  Dataset Card for English quotes
+#### Dataset Summary
+english_quotes is a dataset of English quotes 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.
+
+#### Supported Tasks and Leaderboards
+- Multi-label text classification : The dataset can be used to train a model for text-classification, which consists of classifying quotes by author as well as by topic (using tags). Success on this task is typically measured by achieving a high or low accuracy.
+- Text-generation : The dataset can be used to train a model to generate quotes by fine-tuning an existing pretrained model on the corpus composed of all quotes (or quotes by author).
+
+#### 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):
+```python
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+import json
+from collections import OrderedDict
+
+page = requests.get('https://www.goodreads.com/quotes')
+if page.status_code == 200:
+    pageParsed = BeautifulSoup(page.content, 'html5lib')
+    
+# Define a function that retrieves information about each HTML quote code in a dictionary form.
+def extract_data_quote(quote_html):
+        quote = quote_html.find('div',{'class':'quoteText'}).get_text().strip().split('\n')[0]
+        author = quote_html.find('span',{'class':'authorOrTitle'}).get_text().strip()
+        if quote_html.find('div',{'class':'greyText smallText left'}) is not None:
+            tags_list = [tag.get_text() for tag in quote_html.find('div',{'class':'greyText smallText left'}).find_all('a')]
+            tags = list(OrderedDict.fromkeys(tags_list))
+            if 'attributed-no-source' in tags:
+                tags.remove('attributed-no-source')
+        else:
+            tags = None
+        data = {'quote':quote, 'author':author, 'tags':tags}
+        return data
+
+# Define a function that retrieves all the quotes on a single page. 
+def get_quotes_data(page_url):
+    page = requests.get(page_url)
+    if page.status_code == 200:
+        pageParsed = BeautifulSoup(page.content, 'html5lib')
+        quotes_html_page = pageParsed.find_all('div',{'class':'quoteDetails'})
+        return [extract_data_quote(quote_html) for quote_html in quotes_html_page]
+
+# Retrieve data from the first page.
+data = get_quotes_data('https://www.goodreads.com/quotes')
+
+# Retrieve data from all pages.
+for i in range(2,101):
+    print(i)
+    url = f'https://www.goodreads.com/quotes?page={i}'
+    data_current_page = get_quotes_data(url)
+    if data_current_page is None:
+        continue
+    data = data + data_current_page
+
+data_df = pd.DataFrame.from_dict(data)
+for i, row in data_df.iterrows():
+    if row['tags'] is None:
+        data_df = data_df.drop(i)
+# Produce the data in a JSON format.
+data_df.to_json('C:/Users/Abir/Desktop/quotes.jsonl',orient="records", lines =True,force_ascii=False)
+# 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 :
+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 for adding this dataset.