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.
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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
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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
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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Description: A dataset of over 11,000 unpublished books.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.
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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
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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
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Name: Project Gutenberg
Description: A large collection of public domain books.
Importance: Supplies high-quality, timeless literary texts.
Link: https://www.gutenberg.org
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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
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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
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Name: arXiv.org
Description: Open-access repository of scientific research papers.
Importance: Provides technical knowledge and scientific vocabulary.
Link: https://arxiv.org
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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
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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
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Name: OpenCyc
Description: A knowledge base of commonsense reasoning concepts.
Importance: Enhances models' understanding of real-world semantics.
Link: http://www.cyc.com/opencyc
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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
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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
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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
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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
Usefull links
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