Datasets:
language:
- en
pretty_name: OpenSERP-V1
task_categories:
- text-generation
size_categories:
- 1B<n<10B
Getting Started
The OpenSERP-V1 dataset includes full embeddings for over 50 million high-quality documents. This extensive collection encompasses the majority of content from sources like Arxiv, Wikipedia, Project Gutenberg, and includes quality-filtered CC data.
To access and utilize the OpenSERP-1B dataset, you can download it via HuggingFace with the following Python code:
from datasets import load_dataset
ds = load_dataset("SciPhi/OpenSERP-V1")
# Optional, load just the "arxiv" dataset
ds = load_dataset("SciPhi/OpenSERP-V1", "arxiv")
A full set of scripts to recreate the dataset from scratch can be found here.
Dataset Summary
OpenSERP is divided into a number of categories, similar to RedPajama-V1.
| Dataset | Token Count |
|---|---|
| Books | x Billion |
| ArXiv | x Billion |
| Wikipedia | x Billion |
| StackExchange | x Billion |
| OpenMath | x Billion |
| Filtered Crawl | x Billion |
| Total | x Billion |
Languages
English.
Dataset Structure
The raw dataset structure is as follows:
{
"url": ...,
"title": ...,
"metadata": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...},
"text_chunks": ...,
"embeddings": ...,
"dataset": "github" | "books" | "arxiv" | "wikipedia" | "stackexchange" | "open-math" | "filtered-rp2"
}
The indexed dataset is structured as a qdrant database dump, each entry has meta data {"url", "vector"}.
Dataset Creation
This dataset was created to allow make humanities most important knowledge locally searchable. It was created by filtering, cleaning, and augmenting locally publicly available datasets.
The embedding vectors have been indexed and made searchable via a qdrant database.
Source Data
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
@misc{paster2023openwebmath,
title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text},
author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba},
year={2023},
eprint={2310.06786},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
License
Please refer to the licenses of the data subsets you use.