---
language:
- en
license: mit
task_categories:
- text-generation
- fill-mask
- text-classification
- retrieval
tags:
- pretraining
- language-modeling
- encoder
- decoder
- foundation-model
- transformer
---

# Ettin Pre-training Data

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Paper](https://img.shields.io/badge/Paper-Arxiv-red)](https://arxiv.org/abs/2507.11412)
[![Models](https://img.shields.io/badge/🤗%20Hugging%20Face-12%20Models-blue)](https://huggingface.co/jhu-clsp)
[![GitHub](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/jhu-clsp/ettin-encoder-vs-decoder)

> **Phase 1 of 3**: Diverse pre-training data mixture (1.7T tokens) used to train the Ettin model suite.

This dataset contains the pre-training phase data used to train all [Ettin encoder and decoder models](https://huggingface.co/jhu-clsp). The data is provided in **MDS format** ready for use with [Composer](https://github.com/mosaicml/composer) and the [ModernBERT training repository](https://github.com/answerdotai/ModernBERT).

## 📊 Data Composition

| Data Source | Tokens (B) | Percentage | Description |
|:------------|:-----------|:-----------|:------------|
| DCLM | 837.2 | 49.1% | High-quality web crawl data |
| CC Head | 356.6 | 20.9% | Common Crawl head documents |
| Starcoder | 263.9 | 15.5% | Code repositories and files |
| Reddit | 80.3 | 4.7% | Social discussion threads |
| PeS2o | 57.3 | 3.4% | Scientific papers |
| Arxiv | 28.0 | 1.6% | Academic preprints |
| StackExchange | 19.6 | 1.2% | Q&A forums |
| Tulu Flan | 16.6 | 1.0% | Instruction-following data |
| Open-Web-Math | 12.7 | 0.7% | Mathematical content |
| Algebraic StackExchange | 12.6 | 0.7% | Math Q&A |
| CC News | 7.3 | 0.4% | News articles |
| Wikipedia | 7.3 | 0.4% | Encyclopedia articles |
| **Total** | **1,704.7** | **100.0%** | Diverse mixture for foundation training |

## 🚀 Usage

For pre-training, see the ModernBERT repo: https://github.com/AnswerDotAI/ModernBERT

### Direct Access

```python
from streaming import StreamingDataset

# Load the streaming dataset
dataset = StreamingDataset(
    remote='https://huggingface.co/datasets/jhu-clsp/ettin-pretraining-data',
    local='/tmp/ettin-pretraining-data',
    shuffle=True
)

# Access samples
for sample in dataset:
    text = sample['text']
    # Process your data...
```

## 📁 Structure

Each folder contains one data source in MDS (Mosaic Data Shard) format:
- `arxiv/` - Academic papers from ArXiv
- `books/` - Literature and reference books  
- `cc_head/` - High-quality Common Crawl documents
- `cc_news/` - News articles from Common Crawl
- `dclm/` - DataComp-LM filtered web data
- `open_web_math/` - Mathematical web content
- `algebraic_stackexchange/` - Math Q&A from StackExchange
- `pes2o/` - Scientific papers (PeS2o dataset)
- `reddit/` - Reddit discussion threads
- `stackexchange/` - General StackExchange Q&A
- `starcoder/` - Code from GitHub repositories
- `tulu_flan/` - Instruction-following examples
- `wikipedia/` - Wikipedia articles

## 🔗 Related Resources

- **Models**: [Ettin Model Suite](https://huggingface.co/collections/jhu-clsp/encoders-vs-decoders-the-ettin-suite-686303e16142257eed8e6aeb) (17M-1B parameters)
- **Phase 2**: [Mid-training Data](https://huggingface.co/datasets/jhu-clsp/ettin-extension-data) (250B tokens)
- **Phase 3**: [Decay Phase Data](https://huggingface.co/datasets/jhu-clsp/ettin-decay-data) (50B tokens)
- **Training Order**: [Batch-level Data Order](https://huggingface.co/datasets/jhu-clsp/ettin-data-order)
- **Paper**: [Arxiv link](https://arxiv.org/abs/2507.11412)
- **Code**: [GitHub Repository](https://github.com/jhu-clsp/ettin-encoder-vs-decoder)

## Citation

```bibtex
@misc{weller2025seqvsseqopen,
      title={Seq vs Seq: An Open Suite of Paired Encoders and Decoders}, 
      author={Orion Weller and Kathryn Ricci and Marc Marone and Antoine Chaffin and Dawn Lawrie and Benjamin Van Durme},
      year={2025},
      eprint={2507.11412},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.11412}, 
}
```