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---
license: odc-by
dataset_info:
- config_name: gutenberg
features:
- name: added
dtype: string
- name: attributes
struct:
- name: bff_duplicate_paragraph_spans_decontamination
sequence: 'null'
- name: created
dtype: string
- name: id
dtype: string
- name: metadata
struct:
- name: gutenberg_metadata_available
dtype: bool
- name: issued_or_updated_available
dtype: bool
- name: length
dtype: int64
- name: pipeline
dtype: string
- name: provenance
dtype: string
- name: purl.org/dc/terms/issued
dtype: timestamp[s]
- name: purl.org/dc/terms/publisher
dtype: string
- name: purl.org/dc/terms/rights
dtype: string
- name: purl.org/dc/terms/title
dtype: string
- name: rdf_available
dtype: bool
- name: www.gutenberg.org/2009/pgterms/downloads
dtype: int64
- name: purl.org/dc/terms/tableOfContents
dtype: string
- name: purl.org/dc/terms/alternative
dtype: string
- name: purl.org/dc/terms/description
dtype: string
- name: source
dtype: string
- name: text
dtype: string
- name: version
dtype: string
splits:
- name: train
num_bytes: 25032585417
num_examples: 68319
download_size: 15328016129
dataset_size: 25032585417
- config_name: wikipedia
features:
- name: added
dtype: string
- name: attributes
struct:
- name: bff_duplicate_paragraph_spans_decontamination
sequence: 'null'
- name: created
dtype: string
- name: id
dtype: string
- name: metadata
struct:
- name: length
dtype: int64
- name: provenance
dtype: string
- name: revid
dtype: string
- name: url
dtype: string
- name: source
dtype: string
- name: text
dtype: string
- name: version
dtype: string
splits:
- name: train
num_bytes: 16769628203
num_examples: 6543381
download_size: 9557680471
dataset_size: 16769628203
configs:
- config_name: gutenberg
data_files:
- split: train
path: gutenberg/*
- config_name: wikipedia
data_files:
- split: train
path: wikipedia/*
task_categories:
- text-generation
language:
- en
tags:
- wikipedia
- books
- gutenberg
pretty_name: Wikipedia + Gutenberg
size_categories:
- 100K<n<1M
---
# Dataset Card for Dataset Name
Pre-training corpus for seed models in "Scalable Data Ablation Approximations for Language Models through Modular Training and Merging", to be presented at EMNLP 2024.
## Dataset Details
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