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language:
- en
pretty_name: NeoCorpus
configs:
- config_name: foundation
data_dir: foundation
- config_name: specialized
data_dir: specialized
task_categories:
- text-generation
tags:
- pretraining
- language-modeling
- english
license: cc-by-nc-4.0
---
# NeoCorpus
**NeoCorpus** is a multi-stage pretraining corpus for the NeoLM
family of decoder-only language models trained from scratch.
## Available Configurations
| Configuration | Description | Status |
|---|---|---|
| `foundation` | General-purpose foundation pretraining | Available |
| `specialized` | Specialized continued pretraining | Planned |
| `decay` | Final high-quality pretraining | Planned |
## Foundation Dataset
The `foundation` configuration contains an English-language subset
derived from Andrej Karpathy's globally shuffled NVIDIA Nemotron-ClimbMix.
### Dataset Statistics
| Split | Documents |
|---|---:|
| Train | 1,889,952 |
| Validation | 5,000 |
| Test | 20,000 |
| **Total** | **1,914,952** |
### Token Statistics
Estimated using the previous NeoLM ByteLevel BPE tokenizer
(vocabulary size 32,000), including one planned EOS per document.
| Split | Estimated Tokens |
|---|---:|
| Train | 1,199,827,981 |
| Validation | 3,223,381 |
| Test | 12,650,844 |
Final token counts will be recalculated using the tokenizer
trained for the next NeoLM model.
### Source and Provenance
- Original corpus: [NVIDIA Nemotron-ClimbMix](https://huggingface.co/datasets/nvidia/Nemotron-ClimbMix)
- Shuffled source: [karpathy/climbmix-400b-shuffle](https://huggingface.co/datasets/karpathy/climbmix-400b-shuffle)
- Source revision: `915333b4f8b8684f39aeaafea600fea6f43fb703`
- Collection seed: `1337`
- Collection method: sequential traversal of globally shuffled source shards
NeoCorpus is an independent derivative dataset, not affiliated
with NVIDIA or Andrej Karpathy.
### Data Preparation
1. Stream documents from the pinned source revision.
2. Strip edge whitespace while retaining internal formatting.
3. Filter empty documents and lengths outside 100–100,000 characters.
4. Exclude reserved tokenizer markers.
5. Reserve 5,000 validation and 20,000 test documents.
6. Collect approximately 1.2 billion training tokens.
7. Remove exact-text duplicates from the training set,
prioritizing validation and test holdouts.
8. Preserve provenance and estimated token counts.
483 training documents were removed by exact deduplication.
Near-duplicate deduplication was not performed.
### Features
| Field | Description |
|---|---|
| `doc_id` | Stable document identifier |
| `text` | Document content |
| `source` | Original dataset identifier |
| `source_shard` | Original source shard |
| `token_count` | Estimated tokens including planned EOS |
## Usage
from datasets import load_dataset
ds = load_dataset(
"MaagDeveloper/NeoCorpus",
"foundation",
split="train",
)
The next NeoLM tokenizer will be trained on the training split only.
## Limitations
The dataset may contain factual errors, biases, unsafe text,
personally identifiable information, and near-duplicates.
It has not undergone exhaustive manual quality review.
Source licensing and redistribution obligations require review
before public release.
## Licensing and Redistribution
NeoCorpus is an independently curated derivative of [NVIDIA Nemotron-ClimbMix](https://huggingface.co/datasets/nvidia/Nemotron-ClimbMix), obtained through [Andrej Karpathy's globally shuffled redistribution](https://huggingface.co/datasets/karpathy/climbmix-400b-shuffle).
The original NVIDIA dataset is published under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license. NeoCorpus is redistributed subject to the applicable attribution and non-commercial conditions of that license.
The following modifications were made:
- Selection of a subset from globally shuffled source shards.
- Document length filtering and whitespace normalization.
- Removal of exact duplicate training documents.
- Creation of separate training, validation, and test splits.
- Addition of document identifiers, provenance metadata, and estimated token counts.
The original NVIDIA dataset and Karpathy's redistribution are credited in the Attribution section below.
NeoCorpus is an independent, non-commercial research project and is not officially affiliated with NVIDIA or Andrej Karpathy.
This licensing notice does not grant additional rights over any third-party material contained in the source corpus. Users are responsible for complying with applicable licenses and rights.
## Attribution
NeoCorpus builds upon NVIDIA Nemotron-ClimbMix and
Andrej Karpathy's shuffled ClimbMix dataset used for nanochat.
- [NVIDIA Nemotron-ClimbMix](https://huggingface.co/datasets/nvidia/Nemotron-ClimbMix)
- [Karpathy ClimbMix Shuffle](https://huggingface.co/datasets/karpathy/climbmix-400b-shuffle)
- [nanochat](https://github.com/karpathy/nanochat)
This project is independent and is not officially affiliated
with NVIDIA or Andrej Karpathy.
## Temporary Specialized Configuration
**WARNING: TEST DATA — NOT FOR MODEL TRAINING.**
The `specialized` configuration currently contains only 10
synthetic, original short stories per split.
These 30 documents are temporary placeholders used to verify
multi-configuration behavior in the Hugging Face Dataset Viewer
and the `datasets` Python library.
They will be replaced with the real specialized corpus.
The `token_count` field is null for these placeholder rows.
|