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README.md
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---
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---
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language:
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- en
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pretty_name: OpenSERP-V1
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task_categories:
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- text-generation
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size_categories:
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- 1B<n<10B
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---
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### Getting Started
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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.
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To access and utilize the OpenSERP-1B dataset, you can download it via HuggingFace with the following Python code:
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```python
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from datasets import load_dataset
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ds = load_dataset("SciPhi/OpenSERP-V1")
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# Optional, load just the "arxiv" dataset
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ds = load_dataset("SciPhi/OpenSERP-V1", "arxiv")
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```
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---
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A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/SciPhi/OpenSERP).
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### Dataset Summary
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OpenSERP is divided into a number of categories, similar to RedPajama-V1.
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| Dataset | Token Count |
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|----------------|-------------|
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| Books | x Billion |
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| ArXiv | x Billion |
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| Wikipedia | x Billion |
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| StackExchange | x Billion |
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| OpenMath | x Billion |
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| Filtered Crawl | x Billion |
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| Total | x Billion |
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### Languages
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English.
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## Dataset Structure
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The raw dataset structure is as follows:
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```json
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{
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"url": ...,
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"title": ...,
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"metadata": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...},
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"text_chunks": ...,
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"embeddings": ...,
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"dataset": "github" | "books" | "arxiv" | "wikipedia" | "stackexchange" | "open-math" | "filtered-rp2"
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}
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```
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The indexed dataset is structured as a qdrant database dump, each entry has meta data {"url", "vector"}.
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## Dataset Creation
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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.
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The embedding vectors have been indexed and made searchable via a qdrant database.
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### Source Data
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```
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@ONLINE{wikidump,
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author = "Wikimedia Foundation",
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title = "Wikimedia Downloads",
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url = "https://dumps.wikimedia.org"
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}
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```
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```
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@misc{paster2023openwebmath,
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title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text},
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author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba},
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year={2023},
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eprint={2310.06786},
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archivePrefix={arXiv},
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primaryClass={cs.AI}
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}
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```
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```
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@software{together2023redpajama,
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author = {Together Computer},
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title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset},
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month = April,
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year = 2023,
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url = {https://github.com/togethercomputer/RedPajama-Data}
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}
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```
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### License
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Please refer to the licenses of the data subsets you use.
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* [Open-Web (Common Crawl Foundation Terms of Use)](https://commoncrawl.org/terms-of-use/full/)
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* Books: [the_pile_books3 license](https://huggingface.co/datasets/the_pile_books3#licensing-information) and [pg19 license](https://huggingface.co/datasets/pg19#licensing-information)
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* [ArXiv Terms of Use](https://info.arxiv.org/help/api/tou.html)
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* [Wikipedia License](https://huggingface.co/datasets/wikipedia#licensing-information)
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* [StackExchange license on the Internet Archive](https://archive.org/details/stackexchange)
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<!--
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### Annotations
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#### Annotation process
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[More Information Needed]
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#### Who are the annotators?
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[More Information Needed]
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### Personal and Sensitive Information
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[More Information Needed]
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed]
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### Discussion of Biases
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[More Information Needed]
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### Other Known Limitations
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[More Information Needed]
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## Additional Information
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### Dataset Curators
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[More Information Needed]
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### Licensing Information
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[More Information Needed]
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### Citation Information
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[More Information Needed]
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### Contributions
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[More Information Needed]
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-->
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