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Update README.md
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README.md
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num_examples: 43166767
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download_size: 12187746609
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dataset_size: 22274051772
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---
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# Dataset Card for
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-
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num_examples: 43166767
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download_size: 12187746609
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dataset_size: 22274051772
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annotations_creators:
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- no-annotation
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language_creators:
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- found
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language:
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- en
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license: other
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multilinguality:
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- monolingual
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pretty_name: pretokenized,filtered,sorted subset of the Pile
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size_categories:
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- 10B<n<100B
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source_datasets:
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- the-pile
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task_categories:
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- text-generation
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- fill-mask
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task_ids:
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- language-modeling
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- masked-language-modeling
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paperswithcode_id: the-pile-cramming
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---
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# Dataset Card for the_pile_WordPiecex32768_97b8e776baafb99c3892e6572a9f51b3
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This is a preprocessed, tokenized dataset for the cramming-project.
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Use only with the tokenizer uploaded here.
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This version is `97b8e776baafb99c3892e6572a9f51b3`, which corresponds to a specific dataset construction setup, described below.
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The raw data source is the Pile, a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality
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datasets combined together.
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## Dataset Description
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- **Repository:** https://github.com/JonasGeiping/cramming
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- **Paper:** https://arxiv.org/abs/2212.14034
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- **Raw Data Source Paper:** [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027)
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- **Raw Data Source Datasheet:** [Datasheet for the Pile](https://arxiv.org/abs/2201.07311)
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### Languages
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This dataset is in tokenized English (`EN`).
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### Data Splits
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This preprocessed subset contains only a train split.
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## Dataset Creation
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The configuration to create this dataset with the cramming project code (https://github.com/JonasGeiping/cramming) is
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```
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name: the_pile
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defaults:
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- sources:
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- the_pile
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# Preprocessing
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normalizer:
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force_lowercase: True
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strip_accents: True
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force_english_keyboard: True
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whitespace_escape: False
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tokenizer: WordPiece
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vocab_size: 32768
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# Dataset Formation
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seq_length: 128
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include_cls_token_in_corpus: False
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include_sep_token_in_corpus: True
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use_type_ids: False
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max_entries_in_raw_dataset: 16e6
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max_seq_in_tokenized_dataset: 85e6
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# Data Cleaning:
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named_entity_simplification: False
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remove_whitespaces: False
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remove_trash: True
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trash_cutoff: 0.25
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deduplicate_entries: False
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deduplication_threshold: 75
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# Data Order:
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ordering: sentence-length-curriculum
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```
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## Considerations for Using the Data
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Limitations and bias:
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This training data was further filtered and sorted beyond the normal preprocessing.
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These modifications were not tested for unintended consequences.
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## Additional Information
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### Dataset Curators
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This dataset is a filtered, sorted and preprocessed subset of the the-Pile made by Jonas Geiping . The original dataset was primarily curated by Leo Gao and Stella Biderman, with assistance from other authors of the Pile paper.
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### Licensing Information
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Please refer to the specific license depending on the subset you use at https://huggingface.co/datasets/EleutherAI/pile
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### Citation Information
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Filtered version for the cramming project:
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```
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@article{geiping_cramming_2022,
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title = {Cramming: {{Training}} a {{Language Model}} on a {{Single GPU}} in {{One Day}}},
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shorttitle = {Cramming},
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author = {Geiping, Jonas and Goldstein, Tom},
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year = {2022},
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month = dec,
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eprint = {2212.14034},
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primaryclass = {cs},
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publisher = {{arXiv}},
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doi = {10.48550/arXiv.2212.14034},
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url = {http://arxiv.org/abs/2212.14034},
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urldate = {2023-01-10},
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archiveprefix = {arxiv},
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keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning},
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journal = {arxiv:2212.14034[cs]}
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}
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```
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Original Data Curation:
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```
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@article{gao2020pile,
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title={The {P}ile: An 800{GB} dataset of diverse text for language modeling},
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author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others},
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journal={arXiv preprint arXiv:2101.00027},
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year={2020}
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}
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@article{biderman2022datasheet,
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title={Datasheet for the pile},
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author={Biderman, Stella and Bicheno, Kieran and Gao, Leo},
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journal={arXiv preprint arXiv:2201.07311},
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year={2022}
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}
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```
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