---
license: mit
language:
- en
---
# Ettin Mid-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 2 of 3**: Higher-quality filtered data with context extension (250B tokens) used for mid-training of Ettin models.

This dataset contains the mid-training phase data used to train all [Ettin encoder and decoder models](https://huggingface.co/collections/jhu-clsp/encoders-vs-decoders-the-ettin-suite-686303e16142257eed8e6aeb). This phase focuses on **higher-quality filtered data** and **context length extension to 8K tokens**. 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 (Dolmino) | 175.5 | 70.4% | High-quality filtered web crawl |
| Starcoder | 38.4 | 15.4% | Code repositories and files |
| Math (Dolmino) | 10.4 | 4.2% | Mathematical content (filtered) |
| PeS2o | 8.3 | 3.3% | Scientific papers |
| Reddit | 6.2 | 2.5% | Social discussion threads |
| Arxiv | 4.1 | 1.6% | Academic preprints |
| StackExchange (Dolmino) | 2.7 | 1.1% | Q&A forums (filtered) |
| Tulu Flan | 2.4 | 1.0% | Instruction-following data |
| Books | 0.8 | 0.3% | Literature and reference books |
| Wikipedia | 0.5 | 0.2% | Encyclopedia articles |
| **Total** | **249.3** | **100.0%** | Quality-focused mixture |

## 🔧 Key Changes from Pre-training

### Data Quality Improvements
- **Filtered DCLM**: Using Dolmino-filtered version instead of raw DCLM
- **Enhanced Math**: Dolmino-filtered mathematical content  
- **Curated StackExchange**: Higher-quality Q&A content
- **Removed Noisy Sources**: Dropped CC Head, CC News, and general StackExchange

### Technical Improvements
- **Context Extension**: Increased from 1K to 8K token sequences
- **RoPE Updates**: Modified positional encoding for longer context
- **Learning Schedule**: Inverse square root decay from peak LR

## 🚀 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-extension-data',
    local='/tmp/ettin-extension-data',
    shuffle=True
)

# Access samples (note: these will be longer sequences)
for sample in dataset:
    text = sample['text']  # Up to 8K tokens
    # Process your data...
```

## 📁 Structure

Each folder contains filtered, higher-quality data sources in MDS format:
- `arxiv/` - Academic papers from ArXiv
- `books/` - Literature and reference books
- `dclm_dolmino/` - Dolmino-filtered web crawl data (primary source)
- `math_dolmino/` - Filtered mathematical content
- `pes2o/` - Scientific papers
- `reddit/` - Reddit discussion threads
- `stackexchange_dolmino/` - Filtered 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 1**: [Pre-training Data](https://huggingface.co/datasets/jhu-clsp/ettin-pretraining-data) (1.7T 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}, 
}
```