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Datasets:
SciPhi
/
AgentSearch-V1

Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K<n<100K
ArXiv:
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metadata
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:

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.