metadata

license: other
license_name: impact-license-medium-risk
license_link: https://allenai.org/licenses/impact-mr
viewer: false
task_categories:
  - text-generation
language:
  - en
tags:
  - language-modeling
  - casual-lm
  - llm
pretty_name: Dolma
size_categories:
  - n>1T
extra_gated_prompt: >-
  Access to this dataset is automatically granted upon accepting the [**AI2
  ImpACT License - Medium Risk Artifacts (“MR
  Agreement”)**](https://allenai.org/licenses/impact-mr) and completing all
  fields below.
extra_gated_fields:
  Your full name: text
  Organization or entity you are affiliated with: text
  State or country you are located in: text
  Contact email: text
  Please describe your intended use of the medium risk artifact(s): text
  I AGREE to the terms and conditions of the MR Agreement above: checkbox
  I AGREE to AI2’s use of my information for legal notices and administrative matters: checkbox
  I CERTIFY that the information I have provided is true and accurate: checkbox

Dolma

Dolma is a dataset of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials. It is openly released under AI2’s ImpACT license as a medium risk artifact.

More information:

https://github.com/allenai/dolma/blob/main/docs/assets/dolma-datasheet-v0.1.pdf

Read Dolma manuscript and its Data Sheet on ArXiv;
Review Dolma's ImpACT license for medium risk artifacts;
Explore the open source tools we created to curate Dolma.
Want to request removal of personal data? Use this form to notify us of documents containing PII about a specific user.

To learn more about the toolkit used to create Dolma, including how to replicate this dataset, head over our GitHub project page!

Versions

At the moment, there are five versions of Dolma available:

Version	Default?	Release Date	Size (gzip)	Description
`v1_6`	✅	2024-01-31	5.4 TB	The latest version of Dolma, with 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials.
`v1_6-sample`		2024-01-31	16.4 GB	A smaller sample of Dolma, with roughly 10 billion tokens. Useful for data exploration.
`v1_5`		2023-10-31	6.4 TB	The version of Dolma used to train OLMo-1B. Roughly 3 trillion tokens.
`v1_5-sample`		2023-10-31	2.9 TB	A sample of roughly 1.9 trillion tokens used to train OLMo-7B
`v1`		2023-08-18	6.0 TB	The first version of Dolma.

(Size difference between v1_6 and previous version is due to different set of metadata included in files: we removed redundant metadata in v1_6.)

Summary Statistics (v1.6)

Source	Doc Type	UTF-8 bytes (GB)	Documents (millions)	Unicode words (billions)	Llama tokens (billions)
Common Crawl	web pages	9,022	3,370	1,775	2,281
The Stack	code	1,043	210	260	411
C4	web pages	790	364	153	198
Reddit	social media	339	377	72	89
PeS2o	STEM papers	268	38.8	50	70
Project Gutenberg	books	20.4	0.056	4.0	6.0
Wikipedia, Wikibooks	encyclopedic	16.2	6.2	3.7	4.3
Total		11,519	4,367	2,318	3,059

Download

The fastest way to download Dolma is to clone this repository and use the files in the url directory. We recommend using wget in parallel mode to download the files. For example:

DATA_DIR="<path_to_your_data_directory>"
PARALLEL_DOWNLOADS="<number_of_parallel_downloads>"
DOLMA_VERSION="<version_of_dolma_to_download>"

git clone https://huggingface.co/datasets/allenai/dolma
mkdir -p "${DATA_DIR}"


cat "dolma/urls/${DOLMA_VERSION}.txt" | xargs -n 1 -P "${PARALLEL_DOWNLOADS}" wget -q -P "$DATA_DIR"

Then, to load this data using HuggingFace's datasets library, you can use the following code:

import os
from datasets import load_dataset

os.environ["DATA_DIR"] = "<path_to_your_data_directory>"
dataset = load_dataset("allenai/dolma", split="train")

Bibtex

If you use our dataset or tooling, please cite us at:

@article{dolma,
  title = {{Dolma: An Open Corpus of Three Trillion Tokens for Language Model Pretraining Research}},
  author = {
    Luca Soldaini and Rodney Kinney and Akshita Bhagia and Dustin Schwenk and David Atkinson and
    Russell Authur and Ben Bogin and Khyathi Chandu and Jennifer Dumas and Yanai Elazar and
    Valentin Hofmann and Ananya Harsh Jha and Sachin Kumar and Li Lucy and Xinxi Lyu and Ian Magnusson and
    Jacob Morrison and Niklas Muennighoff and Aakanksha Naik and Crystal Nam and Matthew E. Peters and
    Abhilasha Ravichander and Kyle Richardson and Zejiang Shen and Emma Strubell and Nishant Subramani and
    Oyvind Tafjord and Evan Pete Walsh and Hannaneh Hajishirzi and Noah A. Smith and Luke Zettlemoyer and
    Iz Beltagy and Dirk Groeneveld and Jesse Dodge and Kyle Lo
},
  year = {2024},
  journal={arXiv preprint},
}