The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Recent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale language models. With this in mind, we present \textit{the Pile}: an 825 GiB English text corpus targeted at training large-scale language models. The Pile is constructed from 22 diverse high-quality subsets -- both existing and newly constructed -- many of which derive from academic or professional sources. Our evaluation of the untuned performance of GPT-2 and GPT-3 on the Pile shows that these models struggle on many of its components, such as academic writing. Conversely, models trained on the Pile improve significantly over both Raw CC and CC-100 on all components of the Pile, while improving performance on downstream evaluations. Through an in-depth exploratory analysis, we document potentially concerning aspects of the data for prospective users. We make publicly available the code used in its construction.
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Colab
Tasks
The Pile
C4
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| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Language Modelling | The Pile | GPT-3 Davinci 175B (pre-trained) | Bits per byte | 0.7177 | # 3 | |
| Language Modelling | The Pile | GPT-2 Small 124M (pre-trained) | Bits per byte | 1.2253 | # 12 | |
| Language Modelling | The Pile | GPT-3 Curie 6.7B (pre-trained) | Bits per byte | 0.7980 | # 5 | |
| Language Modelling | The Pile | GPT-3 Babbage 1.3B (pre-trained) | Bits per byte | 0.8718 | # 7 | |
| Language Modelling | The Pile | GPT-3 Ada 350M (pre-trained) | Bits per byte | 0.9631 | # 8 | |
| Language Modelling | The Pile | GPT-2 XL 1.5B (pre-trained) | Bits per byte | 1.0468 | # 9 | |
| Language Modelling | The Pile | GPT-2 Large 774M (pre-trained) | Bits per byte | 1.0828 | # 10 | |
| Language Modelling | The Pile | GPT-2 Medium 355M (pre-trained) | Bits per byte | 1.0928 | # 11 |
