Top LLM Datasets

Abdulkader Helwan
4 min readApr 3, 2024
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Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP). Their ability to learn from massive amounts of text data allows them to perform a wide range of tasks, from generating human-quality text to translating languages. However, the success of LLMs hinges on the quality and quantity of the data they are trained on.

This article delves into some of the most valuable datasets available for LLM training. We’ll explore datasets across various domains, providing a comprehensive resource for researchers and practitioners seeking to push the boundaries of LLM capabilities.

General Pre-Training Corpora:

  • LAION-2B-en (https://huggingface.co/datasets/laion/laion2B-en): This colossal dataset boasts 260 billion words and 13 billion web page crawls, offering a diverse collection of text and code for pre-training LLMs.
  • CCAW (https://ar5iv.labs.arxiv.org/html/2101.00027): The Pile, a collaborative effort by Google AI, Facebook AI, and others, offers the Common Crawl Archive Web (CCAW) dataset, containing a massive corpus of web crawl data for LLM pre-training.
  • WikiText-103 (https://huggingface.co/datasets/wikitext): This dataset consists of 103 gigabytes of text extracted from Wikipedia, providing a well-structured and informative corpus for various NLP tasks.
  • BookCorpus (https://huggingface.co/datasets/bookcorpus): Dive into the world of literature with BookCorpus, a collection of English books containing over 10,000 works. This dataset is perfect for training LLMs for tasks like text summarization and book recommendation.

Domain-Specific Datasets:

  • PubMed Abstracts (https://huggingface.co/datasets/pubmed): Venture into the realm of biomedicine with PubMed Abstracts, a collection of abstracts from biomedical research papers. This dataset allows you to train LLMs for tasks like scientific document analysis and information retrieval in the medical domain.
  • I2B2 2012 Shared Task Clinical De-identification (English) (https://huggingface.co/obi/deid_bert_i2b2): Ensure patient privacy while training LLMs for healthcare applications. This dataset focuses on de-identifying patient information in clinical text, safeguarding sensitive data.
  • CheXpert (https://huggingface.co/datasets/keremberke/chest-xray-classification): Train LLMs to become medical imaging experts with CheXpert. This dataset includes chest X-ray images along with associated radiology reports, enabling tasks like medical image classification and automated report generation.
  • WebText (https://huggingface.co/datasets/the_pile_openwebtext2): Explore the world of online interactions with WebText, a dataset from The Pile containing a massive collection of text and code extracted from web conversations. This dataset is ideal for training LLMs to understand informal language and online communication styles.
  • SQUAD (https://huggingface.co/datasets/rajpurkar/squad): Hone your LLM’s question-answering capabilities with SQUAD (Stanford Question Answering Dataset). This dataset provides a collection of question-answer pairs based on Wikipedia passages.

Conversational Datasets:

  • OpenSubtitles (https://huggingface.co/datasets/open_subtitles): Immerse your LLM in movie magic with OpenSubtitles, a collection of movie dialogue subtitles in various languages. This dataset helps train LLMs for tasks like conversation generation and sentiment analysis.
  • DailyDialog (https://huggingface.co/datasets/daily_dialog): Engage in casual conversations with DailyDialog, a dataset containing multi-turn dialogues extracted from online forums. This dataset is valuable for training chatbots and conversational language models.
  • Switchboard Dialogue Corpus (https://www.ldc.upenn.edu/): Explore telephone conversations with Switchboard, a collection of transcribed conversations between people discussing various topics. This dataset helps LLMs understand turn-taking and natural conversation flow.

Code Datasets:

  • Python (https://huggingface.co/docs/datasets/en/loading): Train your LLM to become a coding whiz with the Python dataset, containing a collection of Python code. This dataset allows you to explore tasks like code generation and code completion.
  • Java (https://huggingface.co/CAUKiel/JavaBERT): Similar to Python, the Java dataset offers a collection of Java code for training LLMs. This opens doors for tasks like code analysis and automatic code generation in Java.
  • Starcoder (https://huggingface.co/bigcode/starcoder): Deepen your LLM’s understanding of programming with Starcoder, a dataset built from 783 GB of code written in 86 programming languages.

Additional Considerations:

  • Dataset Size: The size of the dataset significantly impacts the performance of your LLM. Larger datasets often lead to better results but require more computational resources for training.
  • Dataset Quality: Ensure the quality of the data you’re using. Datasets with errors, biases, or inconsistencies can negatively impact the performance of your LLM.
  • Data Preprocessing: Many datasets require preprocessing before training your LLM. This might involve cleaning the text, tokenization, and handling missing values.
  • Computational Resources: Training LLMs on large datasets can be computationally expensive. Consider factors like GPU availability and memory limitations when choosing a dataset.

The quest for the perfect dataset for LLM training is an ongoing exploration. This guide provides a glimpse into some of the most valuable resources available, but the best choice ultimately depends on your specific goals and constraints. By carefully selecting and prepping your data, you can empower your LLM to unlock its full potential and push the boundaries of NLP capabilities.

Beyond the Datasets:

This blog post focused on showcasing valuable datasets for LLM training. However, the LLM ecosystem is constantly evolving. Here are some additional resources to stay updated on the latest advancements:

By exploring these resources and actively participating in the LLM research community, you can contribute to the development of ever-more powerful and versatile language models.

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