Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K<n<100K
ArXiv:
Tags:
language: | |
- en | |
size_categories: | |
- 1B<n<10B | |
task_categories: | |
- text-generation | |
pretty_name: OpenSERP-V1 | |
configs: | |
- config_name: default | |
data_files: | |
- split: train | |
path: data/train-* | |
dataset_info: | |
features: | |
- name: id | |
dtype: int64 | |
- name: url | |
dtype: string | |
- name: title | |
dtype: string | |
- name: metadata | |
dtype: string | |
- name: dataset | |
dtype: string | |
- name: text_chunks | |
sequence: string | |
- name: embeddings | |
sequence: | |
sequence: float64 | |
splits: | |
- name: train | |
num_bytes: 40563228 | |
num_examples: 1000 | |
download_size: 34541852 | |
dataset_size: 40563228 | |
### 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: | |
```python | |
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](https://github.com/SciPhi/OpenSERP). | |
### 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: | |
```json | |
{ | |
"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. | |
* [Open-Web (Common Crawl Foundation Terms of Use)](https://commoncrawl.org/terms-of-use/full/) | |
* Books: [the_pile_books3 license](https://huggingface.co/datasets/the_pile_books3#licensing-information) and [pg19 license](https://huggingface.co/datasets/pg19#licensing-information) | |
* [ArXiv Terms of Use](https://info.arxiv.org/help/api/tou.html) | |
* [Wikipedia License](https://huggingface.co/datasets/wikipedia#licensing-information) | |
* [StackExchange license on the Internet Archive](https://archive.org/details/stackexchange) | |
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