domyn/Domyn-Edge-Base-checkpoints
Text Generation • Updated • 113
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text string | token_length int64 | src_path string |
|---|---|---|
### Code:
```python
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt # data visualization
import seaborn as sns
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clickin... | 1,819 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
### Code:
```python
# **Comparison between different encoding methods**
# * LabelEncoder (sklearn)
# * OrdinalEncoder (sklearn)
# * One-hot Encoder (sklearn)
# * get_dummies (pandas)
# * map (pandas)
# The "titanic disaster" dataset is used for demonstration.
import numpy as np
import pandas as pd
import sklearn
pd... | 724 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
# Students Performance in Exams
### Context
Marks secured by the students
### Content
This data set consists of the marks secured by the students in various subjects.
Kaggle dataset identifier: students-performance-in-exams
### Code:
```python
import numpy as np # linear algebra
import pandas as pd # data pro... | 1,669 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
# Healthy Diet Breakdown
This is a small dataset that contains information about food groups along with serving information. The dataset contains 8 food items: Vegetables, Fruits, Grains, Dairy, meat, fish, nuts, and fats. For each food group, the following information is provided:
- Serving Per Day
- Serving Per We... | 9,883 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
# 120 years of Olympic history: athletes and results
### Context
This is a historical dataset on the modern Olympic Games, including all the Games from Athens 1896 to Rio 2016. I scraped this data from www.sports-reference.com in May 2018. The R code I used to [scrape](https://github.com/rgriff23/Olympic_history/blo... | 3,643 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
# Health care: Heart attack possibility
**About data set**
This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to
this date.The "target" field refers to the presence of hear... | 7,954 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
### Code:
```python
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory... | 632 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
# Sample Data for WordAndDoc2vec
Kaggle dataset identifier: sample-data-wordanddoc2vec
### Code:
```python
from IPython.display import SVG, Image
import os.path
import sys
import re
import itertools
import csv
import datetime
import pickle
import random
from collections import defaultdict, Counter
import gc
import ... | 6,137 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
# NYC Open Data
### Context
NYC Open Data is an opportunity to engage New Yorkers in the information that is produced and used by City government. We believe that every New Yorker can benefit from Open Data, and Open Data can benefit from every New Yorker. Source: https://opendata.cityofnewyork.us/overview/
### Co... | 782 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
### Code:
```python
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
train_df = pd.read_csv("../input/titanic/train.csv")
test_df = pd.read_csv("../input/titanic/test.csv")
train_df.head()
test_df.head()
X = train_df["Pclass"].values.reshape(-1, 1)
y = train_df["Survived"].... | 243 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
### Code:
```python
import numpy as np
from keras import backend as K
from keras import models
from keras import layers
def make_data(batch_size, input_size, index):
"""
batch_size : Total batch size
input_size : Total input size, values are random number except at the attention_index
index : value... | 800 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
# Predict CLTV of a customer
Problem Statement
VahanBima is one of the leading insurance companies in India. It provides motor vehicle insurances at best prices with 24/7 claim settlement. It offers different types of policies for both personal and commercial vehicles. It has established its brand across different... | 2,424 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
# weight-height.csv
Kaggle dataset identifier: weight-height
### Code:
```python
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or press... | 1,116 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
# Global Terrorism Database
# Context
Information on more than 180,000 Terrorist Attacks
The Global Terrorism Database (GTD) is an open-source database including information on terrorist attacks around the world from 1970 through 2017. The GTD includes systematic data on domestic as well as international terrorist ... | 11,145 | coding/issues_kaggle_notebooks/source=kaggle/9_ba9709b170b24067b526e441eeb8a169_000207_000000-0.parquet |
End of preview.
Domyn Edge Pretraining Mix 8.4T
Phased release. Domyn Edge is a rolling release in three bundles. The first, released on October 20, 2026, includes this dataset,
domyn/Domyn-Edge-Baseanddomyn/Domyn-Edge-SFT. The data mixes for pre-training stages 2 to 6 follow in a later bundle. For an overview, see the launch blog post.
This is the stage 1 pre-training mix of Domyn Edge Base: 8.44 trillion tokens of web text, code, scientific papers, math and multilingual text. All counts refer to the tokens seen during training, measured with the Domyn Edge tokenizer.
This release contains 7.59 trillion of those tokens. Only the stack-edu code is withheld until we can provide per-file attribution for it; see Withheld material.
Composition
The mix is drawn from five source families: web, code, STEM, multilingual and synthetic.
| Family | Source | Tokens | Share | In this release |
|---|---|---|---|---|
| Web | Web crawl | 3.65T | 43.20% | yes |
| Web | Wikipedia | 4.0B | 0.05% | yes |
| Synthetic | Synthetic | 1.68T | 19.91% | yes |
| STEM | Science PDFs | 1.16T | 13.74% | yes |
| STEM | Math | 130B | 1.54% | yes |
| STEM | Research papers | 78B | 0.93% | yes |
| Multilingual | Multilingual | 880B | 10.43% | yes |
| Code | Code | 860B | 10.20% | issues and Kaggle notebooks only |
Science PDFs and research papers were upsampled. The repeated copies are in the upsampling/
subfolders of data/science_pdfs/ and data/research_papers/, and they are included in the token
count.
Web crawl
| Source | Config | Tokens | Share of web crawl | Share of mix |
|---|---|---|---|---|
| Dolma CC | crawl_dolma-cc |
3.55T | 97.5% | 42.11% |
| Common Crawl, Domyn pipeline | crawl_fineweb |
92.4B | 2.5% | 1.10% |
Synthetic
| Source | Config | Tokens | Share of synthetic | Share of mix |
|---|---|---|---|---|
| Pre-training synthetic | synthetic_pretraining_synthetic |
1.05T | 62.8% | 12.50% |
| FinePhrase | synthetic_finephrase |
625.0B | 37.2% | 7.41% |
Nemotron Wiki-Rewrite (7.9B tokens of the pre-training synthetic data) is available in a separate
config, synthetic_wiki_rewrite; see Wikipedia attribution.
Code
All code except the issues and Kaggle notebooks is withheld from this release; see Withheld material. The table shows the full training mix.
| Source | Tokens | Share of code | Share of mix |
|---|---|---|---|
| Python | 218.8B | 25.4% | 2.59% |
| Java | 175.1B | 20.3% | 2.07% |
| C++ | 131.9B | 15.3% | 1.56% |
| Markdown | 109.2B | 12.7% | 1.29% |
| JavaScript | 56.6B | 6.6% | 0.67% |
| C# | 39.6B | 4.6% | 0.47% |
| C | 25.9B | 3.0% | 0.31% |
| PHP | 19.2B | 2.2% | 0.23% |
| Shell | 16.7B | 1.9% | 0.20% |
| TypeScript | 14.7B | 1.7% | 0.17% |
| SQL | 14.0B | 1.6% | 0.17% |
| Rust | 9.1B | 1.1% | 0.11% |
| Swift | 9.1B | 1.1% | 0.11% |
| Ruby | 8.5B | 1.0% | 0.10% |
Issues and Kaggle notebooks (code_Issues-Kaggle) |
7.7B | 0.9% | 0.09% |
| Go | 4.3B | 0.5% | 0.05% |
The Python, Java and C++ data comes from a version of stack-edu in which we added reasoning traces,
in <think> tags, to most documents. The other stack-edu languages are unchanged.
Multilingual
| Language | Tokens | Share of multilingual | Share of mix |
|---|---|---|---|
| Spanish | 116.4B | 13.2% | 1.38% |
| Italian | 115.9B | 13.2% | 1.37% |
| German | 114.9B | 13.1% | 1.36% |
| French | 112.7B | 12.8% | 1.34% |
| Portuguese | 92.1B | 10.5% | 1.09% |
| Russian | 63.0B | 7.2% | 0.75% |
| Chinese | 63.0B | 7.2% | 0.75% |
| Japanese | 42.0B | 4.8% | 0.50% |
| Turkish | 42.0B | 4.8% | 0.50% |
| Dutch | 42.0B | 4.8% | 0.50% |
| Indonesian | 42.0B | 4.8% | 0.50% |
| Arabic | 33.9B | 3.9% | 0.40% |
Each language has a high-quality subset (multilingual_<lang>_hq, from FineWeb2-HQ) and a
lower-quality subset (multilingual_<lang>_lowq, from HPLT 3.0), except Russian and Chinese, which
have only the high-quality subset. The FineWiki data for Indonesian, Japanese, Dutch, Portuguese and
Turkish is available in the wiki config, together with English Wikipedia; see
Wikipedia attribution.
Statistics
| Trained on | In this release | |
|---|---|---|
| Tokens | 8,438,903,495,207 | 7,586,205,883,648 |
| Documents | 5,881,410,200 | 5,121,854,871 |
How to use
Each source is available as a named config:
from datasets import load_dataset
italian = load_dataset(
"domyn/Domyn-Edge-Pretraining-Mix-8.4T",
"multilingual_ita_hq",
split="train",
streaming=True,
)
The default config loads the whole release except the Wikipedia data, which has three additional
columns and is available in two separate configs, wiki and synthetic_wiki_rewrite. To load the
full release, load default and both of these configs.
Data is stored under data/ as Parquet files of about 1 GB each, compressed with Snappy. Code,
crawl, synthetic and multilingual data are grouped in subfolders (data/code/, data/crawl/,
data/synthetic/, data/multilingual/); math, research_papers and science_pdfs are at the
top level of data/, and Wikipedia text is in data/wiki/ and data/synthetic/wiki_rewrite/.
Columns
| Column | Type | Description |
|---|---|---|
text |
string | The document. |
token_length |
int64 | Length of the document in Domyn Edge tokens. |
src_path |
string | The upstream corpus file the document came from, so any document can be traced back to its source. |
The Wikipedia configs have three additional columns, described in Wikipedia attribution.
Wikipedia attribution
Wikipedia text is licensed under CC BY-SA 4.0, which requires attribution to the contributors of
each article. The wiki and synthetic_wiki_rewrite configs include three additional columns that
identify the source of each document:
| Column | Type | Description |
|---|---|---|
source_id |
string | The upstream record: enwiki/<page id> for English Wikipedia, <wiki>/<page id> for FineWiki (for example ptwiki/25726), or the NVIDIA record uuid for Wiki-Rewrite. |
source_url |
string | The article URL. Null for Wiki-Rewrite. |
revision_id |
string | The article revision the text was taken from. Null for Wiki-Rewrite. |
To view a revision, open https://<language>.wikipedia.org/w/index.php?oldid=<revision_id>; the
contributors are listed in the article history. Wiki-Rewrite rows are attributed to the NVIDIA
record they come from, because NVIDIA does not link its records to the original articles. For
1,934 Wiki-Rewrite rows, all three columns are null, because the same text appears in more than one
NVIDIA record. Refer to ATTRIBUTION.md for details.
Sources
| Source | Link | Notes |
|---|---|---|
| Dolma 3 mix (6T) | allenai/dolma3_mix-6T | Most of the web crawl, the olmOCR science PDFs, the stack-edu code, FineMath, the RedPajama proof-pile arXiv papers and the Dolma 1.7 Wikipedia text. The code is withheld. |
| Common Crawl, Domyn pipeline | commoncrawl.org | Our FineWeb-style pipeline, run on CC-MAIN-2025-47. |
| FineWeb2-HQ | epfml/FineWeb2-HQ | High-quality multilingual subsets. |
| HPLT 3.0 | HPLT/HPLT3.0 | Lower-quality multilingual subsets. |
| FineWiki | HuggingFaceFW/finewiki | Five languages, in the wiki config. |
| FinePhrase | HuggingFaceFW/finephrase | |
| SYNTH | PleIAs/SYNTH | |
| Nemotron CC Math v1 | nvidia/Nemotron-CC-Math-v1 | The 4plus split, with reasoning traces we added. |
| Nemotron Pretraining Code v2 | nvidia/Nemotron-Pretraining-Code-v2 | |
| Nemotron Pretraining Specialized v1 | nvidia/Nemotron-Pretraining-Specialized-v1 | The Wiki-Rewrite subset is in the synthetic_wiki_rewrite config. |
| Nemotron Pretraining Specialized v1.1 | nvidia/Nemotron-Pretraining-Specialized-v1.1 | |
| OPC annealing corpus | OpenCoder-LLM/opc-annealing-corpus | |
| Swallow Code v2 | tokyotech-llm/swallow-code-v2 | |
| Issues and Kaggle notebooks | HuggingFaceTB/issues-kaggle-notebooks | The only code in this release. |
Withheld material
The composition tables above describe the full training mix. One part is not in this release:
| Withheld | Tokens | Share of mix | Reason |
|---|---|---|---|
| Stack-edu code | 852.7B | 10.10% | per-file attribution |
We will add it to this repository once we have restored the attribution metadata its licenses require.
Stack-edu code. Source code requires per-file attribution, and we have not yet assembled the identifiers and metadata for every file. In the meantime, most of this code is available upstream in allenai/dolma3_mix-6T and HuggingFaceTB/stack-edu, since we used those subsets almost unchanged. Python, Java and C++ are the exception: their documents interleave our reasoning traces with the original code, and we are separating the two before release.
Reasoning traces
The reasoning traces inside <think> tags were generated by us and are released under the Apache
License 2.0. In this release they appear only in the math data, derived from the Nemotron CC Math v1
4plus split; the traces for Python, Java and C++ are part of the withheld code. The Apache 2.0
grant covers the traces only, while the upstream documents each trace is attached to are covered by
the license of their respective source.
License
Our contributions to this dataset, including the curation and the reasoning traces, are released under the Apache License 2.0. Upstream corpora maintain their original license terms.
Read ATTRIBUTION.md before redistributing any part of this data. It lists every
upstream dataset with its publisher, repository and license. The LICENSES/ folder contains the
full text of each license: ODC-BY 1.0, CC BY 4.0, CC BY-SA 4.0, CC0 1.0, GFDL 1.3, Apache 2.0 and
the NVIDIA Data Agreement for Model Training.
Citation
The technical report is forthcoming in November 2026. Until it is published, please cite the dataset:
@misc{domyn_edge_pretraining_mix_2026,
title = {Domyn Edge Pretraining Mix},
author = {Domyn},
year = {2026},
note = {Technical report, forthcoming in November 2026.},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/domyn/Domyn-Edge-Pretraining-Mix-8.4T}
}
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