metadata

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
  - crowdsourced
language:
  - id
multilinguality:
  - monolingual
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - multi-label-classification
license: apache-2.0
tags:
  - biology
pretty_name: 'Gatotkaca '
size_categories:
  - 10M<n<100M

Dataset Card for English quotes

I-Dataset Summary

english_quotes is a dataset of all the quotes retrieved from goodreads 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.

II-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).

III-Languages

The texts in the dataset are in English (en).

IV-Dataset Structure

Data Instances

A JSON-formatted example of a typical instance in the dataset:

{'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.

V-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 site: from goodreads 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):

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).

VI-Additional Informations

Dataset Curators

Abir ELTAIEF

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.