Dolma: an Open Corpus of Three Trillion Tokens for Language Model
  Pretraining Research
Language models have become a critical technology to tackling a wide range of
natural language processing tasks, yet many details about how the
best-performing language models were developed are not reported. In particular,
information about their pretraining corpora is seldom discussed: commercial
language models rarely provide any information about their data; even open
models rarely release datasets they are trained on, or an exact recipe to
reproduce them. As a result, it is challenging to conduct certain threads of
language modeling research, such as understanding how training data impacts
model capabilities and shapes their limitations. To facilitate open research on
language model pretraining, we release Dolma, a three trillion tokens English
corpus, built from a diverse mixture of web content, scientific papers, code,
public-domain books, social media, and encyclopedic materials. In addition, we
open source our data curation toolkit to enable further experimentation and
reproduction of our work. In this report, we document Dolma, including its
design principles, details about its construction, and a summary of its
contents. We interleave this report with analyses and experimental results from
training language models on intermediate states of Dolma to share what we have
learned about important data curation practices, including the role of content
or quality filters, deduplication, and multi-source mixing. Dolma has been used
to train OLMo, a state-of-the-art, open language model and framework designed
to build and study the science of language modeling.
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Dolma: an Open Corpus of Three Trillion Tokens for Language Model
  Pretraining Research
Language models have become a critical technology to tackling a wide range of
natural language processing tasks, yet many details about how the
best-performing language models were developed are not reported. In particular,
information about their pretraining corpora is seldom discussed: commercial
language models rarely provide any information about their data; even open
models rarely release datasets they are trained on, or an exact recipe to
reproduce them. As a result, it is challenging to conduct certain threads of
language modeling research, such as understanding how training data impacts
model capabilities and shapes their limitations. To facilitate open research on
language model pretraining, we release Dolma, a three trillion tokens English
corpus, built from a diverse mixture of web content, scientific papers, code,
public-domain books, social media, and encyclopedic materials. In addition, we
open source our data curation toolkit to enable further experimentation and
reproduction of our work. In this report, we document Dolma, including its
design principles, details about its construction, and a summary of its
contents. We interleave this report with analyses and experimental results from
training language models on intermediate states of Dolma to share what we have
learned about important data curation practices, including the role of content
or quality filters, deduplication, and multi-source mixing. Dolma has been used
to train OLMo, a state-of-the-art, open language model and framework designed
to build and study the science of language modeling.
Dolma, a three trillion token English corpus, and its open-source data curation toolkit facilitate open research in language model pretraining by providing detailed insights into data curation practices.
language models Dolma data curation English corpus web content scientific papers public-domain books social media encyclopedic materials data curation toolkit OLMo language model pretraining content filters deduplication multi-source mixing