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
- Shuffled source: 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
- Stream documents from the pinned source revision.
- Strip edge whitespace while retaining internal formatting.
- Filter empty documents and lengths outside 100–100,000 characters.
- Exclude reserved tokenizer markers.
- Reserve 5,000 validation and 20,000 test documents.
- Collect approximately 1.2 billion training tokens.
- Remove exact-text duplicates from the training set, prioritizing validation and test holdouts.
- 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, obtained through Andrej Karpathy's globally shuffled redistribution.
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.
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.