Datasets for Training Language Models

Here's the expanded and ranked list of 35+ publicly available and licensed datasets commonly used for training language models like GPT-3 and GPT-4. This list incorporates the additional datasets you mentioned, ensuring comprehensive coverage across domains. Each entry includes a link where applicable.
  1. Name: Common Crawl
    Description: A massive web-crawled dataset, providing diverse and extensive text from the open internet.
    Importance: Essential for covering a wide range of topics and diverse text styles.
    Link: https://commoncrawl.org
  2. Name: Wikipedia
    Description: A structured, multilingual encyclopedia.
    Importance: High-quality, curated, and factual data on a vast range of subjects.
    Link: https://www.wikipedia.org
  3. Name: BooksCorpus
    Description: A dataset of over 11,000 unpublished books.
    Importance: Provides long-form and diverse literary content.
    Link: Not officially hosted but widely used in NLP research.
  4. Name: OpenWebText
    Description: High-quality web content dataset sourced from websites linked on Reddit.
    Importance: A clean alternative to Common Crawl with high engagement content.
    Link: https://github.com/jcpeterson/openwebtext
  5. Name: GitHub Repositories
    Description: Public code repositories containing programming languages, documentation, and project descriptions.
    Importance: Crucial for training code and technical language understanding.
    Link: https://github.com
  6. Name: Project Gutenberg
    Description: A large collection of public domain books.
    Importance: Supplies high-quality, timeless literary texts.
    Link: https://www.gutenberg.org
  7. Name: C4 (Colossal Clean Crawled Corpus)
    Description: A cleaned version of Common Crawl focused on removing low-quality data.
    Importance: Critical for reducing noise in training data.
    Link: https://www.tensorflow.org/datasets/catalog/c4
  8. Name: The Pile
    Description: A 1TB dataset of diverse text sources curated by EleutherAI.
    Importance: Combines multiple datasets into a single resource, covering diverse topics.
    Link: https://pile.eleuther.ai
  9. Name: arXiv.org
    Description: Open-access repository of scientific research papers.
    Importance: Provides technical knowledge and scientific vocabulary.
    Link: https://arxiv.org
  10. Name: PubMed
    Description: A dataset of biomedical and life sciences literature.
    Importance: Indispensable for medical and scientific NLP tasks.
    Link: https://pubmed.ncbi.nlm.nih.gov
  11. Name: Google Books NGrams
    Description: Word and phrase frequency data extracted from digitized books.
    Importance: Provides historical insights into language trends.
    Link: https://books.google.com/ngrams
  12. Name: OpenCyc
    Description: A knowledge base of commonsense reasoning concepts.
    Importance: Enhances models' understanding of real-world semantics.
    Link: http://www.cyc.com/opencyc
  13. Name: Amazon Reviews
    Description: A dataset of product reviews and ratings.
    Importance: Useful for sentiment analysis and consumer behavior modeling.
    Link: https://nijianmo.github.io/amazon/index.html
  14. Name: SQuAD (Stanford Question Answering Dataset)
    Description: A dataset for machine comprehension and question-answering tasks.
    Importance: A gold standard for evaluating and fine-tuning QA models.
    Link: https://rajpurkar.github.io/SQuAD-explorer
  15. Name: Enron Email Dataset
    Description: A collection of over 500,000 emails from the Enron Corporation.
    Importance: A rich source for studying communication and dialogue modeling.
    Link: https://www.cs.cmu.edu/~enron
  16. Name: Microsoft Research Paraphrase Corpus
    Description: A dataset of sentence pairs labeled for semantic similarity.
    Importance: Useful for paraphrasing and semantic similarity tasks.
    Link: https://www.microsoft.com/en-us/download/details.aspx?id=52398

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