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---
dataset_info:
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features:
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dtype: string
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- config_name: peS2o
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- config_name: redpajama_books
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- config_name: stackexchange
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- config_name: uspto
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- config_name: wiki
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configs:
- config_name: all
data_files:
- split: train
path: all/train-*
- config_name: c4_realnews
data_files:
- split: train
path: c4_realnews/train-*
- config_name: openwebtext
data_files:
- split: train
path: openwebtext/train-*
- config_name: peS2o
data_files:
- split: train
path: peS2o/train-*
- config_name: redpajama_books
data_files:
- split: train
path: redpajama_books/train-*
- config_name: stackexchange
data_files:
- split: train
path: stackexchange/train-*
- config_name: uspto
data_files:
- split: train
path: uspto/train-*
- config_name: wiki
data_files:
- split: train
path: wiki/train-*
task_categories:
- text-generation
language:
- en
size_categories:
- 10M<n<100M
---
A small, aggressively cleaned and de-duped pre-training corpus for academic settings. It aims to recreate something akin to [The Pile](https://huggingface.co/datasets/EleutherAI/pile) but prioritizes quality for the constrained token budget academic researchers live with.
It has seven config subsets and an eighth `all` subset that combines them for a total of ~91B tokens (GPT2 Tokenizer estimate). These splits are as follows:
1. `c4_realnews`: The RealNews domain subset of the C4 dataset containing news articles.
2. `openwebtext`: The OpenWebText dataset containing the contents of the links mentioned in Reddit posts with at least 3 upvotes.
3. `peS2o`: The PeS2o dataset containing academic articles from Semantic Scholar.
4. `redpajama_books`: The books subset of RedPajama V1.
5. `stackexchange`: The EN StackExchange non-code subset of the BigScience ROOTs dataset.
6. `uspto`: The EN USPTO patent applications contents' subset of the BigScience ROOTs dataset.
7. `wiki`: The EN Wiki subset of the BigScience ROOTs dataset.
The following processing and filtering steps have been applied:
1. Removed citation text and bibliography information for academic texts.
2. Ran a perplexity filter using a KenLM model trained on the EN OSCAR corpus and removed documents with a perplexity of more than 325 and less than 7.
3. Removed samples which have a repeating <=4-gram proportion of 15%.
4. Removed samples which have lower than 99% confidence of being EN using the lingua language detector.
5. Performed an aggressive MinHash de-dupe using a shingle size of 8 and a low threshold of 0.5. |