| --- |
| 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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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](https://huggingface.co/Ashu9675/space-llm-10m) 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 |
|
|
| ```python |
| 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) |
|
|
