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---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 43860000000
num_examples: 85000000
download_size: 24001057282
dataset_size: 43860000000
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
pretty_name: pretokenized,filtered,sorted subset of the Pile
size_categories:
- 10B<n<100B
source_datasets:
- the-pile
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: the-pile-cramming
---
# Dataset Card for "the_pile_WordPiecex32768_2efdb9d060d1ae95faf952ec1a50f020"
## Dataset Description
- **Repository:** https://github.com/JonasGeiping/cramming
- **Paper:** https://arxiv.org/abs/2212.14034
- **Raw Data Source Paper:** [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027)
- **Raw Data Source Datasheet:** [Datasheet for the Pile](https://arxiv.org/abs/2201.07311)
### Dataset Summary
This is a preprocessed, tokenized dataset for the cramming-project.
Use only with the tokenizer uploaded here.
This version is `2efdb9d060d1ae95faf952ec1a50f020`, which corresponds to a specific dataset construction setup, described below.
The raw data source is the Pile, a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality
datasets combined together.
### Languages
This dataset is in English (`EN`).
### Data Splits
This preprocessed subset contains only a train split.
## Dataset Creation
The configuration to create this dataset with the cramming project code (https://github.com/JonasGeiping/cramming) is
```
# This is a slice of the pile
name: the_pile
defaults:
- sources:
- the_pile
#
# Preprocessing
normalizer:
force_lowercase: True
strip_accents: True
force_english_keyboard: True
whitespace_escape: False
tokenizer: WordPiece
vocab_size: 32768
# Dataset Formation
seq_length: 128
include_cls_token_in_corpus: False
include_sep_token_in_corpus: True
use_type_ids: False
max_entries_in_raw_dataset: 16e6
max_seq_in_tokenized_dataset: 85e6
# Data Cleaning:
named_entity_simplification: False
remove_whitespaces: False
remove_trash: True
trash_cutoff: 0.25
deduplicate_entries: False
deduplication_threshold: 75
# Data Order:
ordering: sentence-length-curriculum
```
## Considerations for Using the Data
Limitations and bias:
This training data was further filtered and sorted beyond the normal preprocessing.
These modifications were not tested for unintended consequences.
## Additional Information
### Dataset Curators
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.
### Licensing Information
Please refer to the specific license depending on the subset you use at https://huggingface.co/datasets/EleutherAI/pile
### Citation Information
Filtered version for the cramming project:
```
@article{geiping_cramming_2022,
title = {Cramming: {{Training}} a {{Language Model}} on a {{Single GPU}} in {{One Day}}},
shorttitle = {Cramming},
author = {Geiping, Jonas and Goldstein, Tom},
year = {2022},
month = dec,
eprint = {2212.14034},
primaryclass = {cs},
publisher = {{arXiv}},
doi = {10.48550/arXiv.2212.14034},
url = {http://arxiv.org/abs/2212.14034},
urldate = {2023-01-10},
archiveprefix = {arxiv},
keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning},
journal = {arxiv:2212.14034[cs]}
}
```
Original Data Curation:
```
@article{gao2020pile,
title={The {P}ile: An 800{GB} dataset of diverse text for language modeling},
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},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
@article{biderman2022datasheet,
title={Datasheet for the pile},
author={Biderman, Stella and Bicheno, Kieran and Gao, Leo},
journal={arXiv preprint arXiv:2201.07311},
year={2022}
}
``` |