Datasets:

Ashu9675
/

space-llm-training-data

license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - space
  - astronomy
  - astrophysics
  - cosmology
  - training-data
  - llm-training
size_categories:
  - 1B<n<10B

Space LLM Training Data (~1.27 Billion Tokens)

A curated dataset of space and astronomy text for training language models, containing approximately 1.27 billion tokens collected from academic papers, arXiv abstracts, and educational web content.

Dataset Summary

File	Size	Est. Tokens	Source
`jsalt_astroph_full.txt`	2.88 GB	~862M	271K full astrophysics papers (abstract + introduction + conclusions)
`arxiv_astro_full.txt`	360 MB	~108M	284K arXiv paper abstracts (8 space categories)
`fineweb_space_combined.txt`	1.02 GB	~305M	95K space-related educational web texts
Total	4.16 GB	~1.27B

Sources

1. JSALT Astrophysics Dataset (`jsalt_astroph_full.txt`)

Source: charlieoneill/jsalt-astroph-dataset
Content: Full paper text (abstract, introduction, conclusions) from 271,544 astrophysics papers
Categories: astro-ph (all subcategories)
Format: Documents separated by ---

2. arXiv Space Categories (`arxiv_astro_full.txt`)

Source: permutans/arxiv-papers-by-subject
Content: Title + abstract from 283,699 papers across 8 space-related categories:
- astro-ph.CO (Cosmology & Nongalactic Astrophysics)
- astro-ph.GA (Galaxy Astrophysics)
- astro-ph.HE (High Energy Astrophysical Phenomena)
- astro-ph.IM (Instrumentation & Methods)
- astro-ph.SR (Solar & Stellar Astrophysics)
- astro-ph.EP (Earth & Planetary Astrophysics)
- gr-qc (General Relativity & Quantum Cosmology)
- physics.space-ph (Space Physics)
Format: Documents separated by ---

3. FineWeb-Edu Space Filtered (`fineweb_space_combined.txt`)

Source: HuggingFaceFW/fineweb-edu
Content: ~95K educational web texts filtered for space/astronomy keywords
Filtering keywords: astronomy, astrophysics, black hole, galaxy, cosmology, planet, solar system, dark matter, supernova, nasa, orbit, telescope, universe, big bang, stellar, exoplanet, space, rocket, spacecraft, moon, satellite, nebula, quasar, etc.
Format: Documents separated by ---

Intended Use

This dataset is designed for training and fine-tuning language models focused on space, astronomy, and astrophysics domains. It was specifically curated for the Space LLM project.

Token Count Estimation

Token counts are estimated using the heuristic of ~3.5 characters per token for mixed academic and web text.

How to Use

from datasets import load_dataset

# Load from HuggingFace
dataset = load_dataset("Ashu9675/space-llm-training-data")

# Or read the text files directly
with open("jsalt_astroph_full.txt") as f:
    documents = f.read().split("\n---\n")

License

arXiv content: MIT (as per source dataset licenses)
FineWeb-Edu content: ODC-BY (as per FineWeb-Edu license)
Combined dataset: MIT

Limitations

Token counts are estimates, not exact
Some documents may contain LaTeX formatting from academic papers
FineWeb content is keyword-filtered and may include some false positives
The jsalt dataset includes only abstract, introduction, and conclusions (not full paper body)