README.md

---
language:
- en
license: other
task_categories:
- text-classification
- feature-extraction
tags:
- regmix
- embeddings
- clustering
- stella
size_categories:
- 100K<n<1M
---

# RegMix Data Sample (Stella Embeddings)

Mirror of [`sail/regmix-data-sample`](https://huggingface.co/datasets/sail/regmix-data-sample) with precomputed **1024-dim L2-normalized** embeddings from [`Marqo/dunzhang-stella_en_400M_v5`](https://huggingface.co/Marqo/dunzhang-stella_en_400M_v5).

## Dataset Description

This dataset preserves the original RegMix domain JSONL text and adds Stella embeddings organized for downstream clustering and mixture analysis.

- **Source**: sail/regmix-data-sample
- **Embedding model**: Marqo/dunzhang-stella_en_400M_v5
- **Embedding dim**: 1024
- **Max token length**: 512
- **Pooling**: mean over token hidden states
- **Normalization**: L2
- **Total rows**: 2,345,364

### Domains

- `train/arxiv`: 79,387 rows
- `train/dm_mathematics`: 64,147 rows
- `train/enron_emails`: 30,905 rows
- `train/europarl`: 4,348 rows
- `train/freelaw`: 169,944 rows
- `train/github`: 300,000 rows
- `train/gutenberg_pg_19`: 2,276 rows
- `train/hackernews`: 52,544 rows
- `train/nih_exporter`: 58,972 rows
- `train/philpapers`: 2,024 rows
- `train/pile_cc`: 300,000 rows
- `train/pubmed_abstracts`: 300,000 rows
- `train/pubmed_central`: 150,000 rows
- `train/stackexchange`: 84,651 rows
- `train/ubuntu_irc`: 623 rows
- `train/uspto_backgrounds`: 300,000 rows
- `train/wikipedia_en`: 265,170 rows
- `validation/arxiv`: 2,406 rows
- `validation/dm_mathematics`: 1,922 rows
- `validation/enron_emails`: 1,010 rows
- `validation/europarl`: 157 rows
- `validation/freelaw`: 5,101 rows
- `validation/github`: 18,195 rows
- `validation/gutenberg_pg_19`: 80 rows
- `validation/hackernews`: 1,632 rows
- `validation/nih_exporter`: 1,884 rows
- `validation/philpapers`: 64 rows
- `validation/pile_cc`: 52,790 rows
- `validation/pubmed_abstracts`: 29,895 rows
- `validation/pubmed_central`: 5,911 rows
- `validation/stackexchange`: 30,378 rows
- `validation/ubuntu_irc`: 22 rows
- `validation/uspto_backgrounds`: 11,415 rows
- `validation/wikipedia_en`: 17,511 rows

## Schema

Each parquet shard under `data/{split}/{domain}.parquet` contains:

| Column | Type | Description |
|---|---|---|
| `example_id` | int64 | Stable join key |
| `split` | string | `train` or `validation` |
| `domain` | string | RegMix domain name |
| `source_file` | string | Original JSONL filename |
| `line_idx` | int32 | Line index in source file |
| `text` | string | Original document text |
| `text_char_len` | int32 | Character length of text |
| `embedding` | list[float32] | L2-normalized Stella vector |

`indices/global_index.parquet` maps each `example_id` to its shard path and row index.

## Usage

### Load with Hugging Face Datasets

```python
from datasets import load_dataset
import numpy as np

ds = load_dataset("quinnlue/regmix-data-sample-stella", split="train")
emb = np.stack(ds["embedding"], dtype=np.float32)  # [N, 1024]
ids = np.array(ds["example_id"], dtype=np.int64)
domains = ds["domain"]
```

### Load a single domain

```python
from datasets import load_dataset

arxiv = load_dataset(
    "quinnlue/regmix-data-sample-stella",
    data_files={"train": "data/train/arxiv.parquet"},
    split="train",
)
```

### Clustering starter (spherical k-means)

```python
from datasets import load_dataset
import numpy as np
from sklearn.cluster import KMeans

ds = load_dataset("quinnlue/regmix-data-sample-stella", split="train")
emb = np.stack(ds["embedding"], dtype=np.float32)
ids = np.array(ds["example_id"], dtype=np.int64)

kmeans = KMeans(n_clusters=128, n_init=10, random_state=0)
cluster_ids = kmeans.fit_predict(emb)

assignments = {int(example_id): int(cluster_id) for example_id, cluster_id in zip(ids, cluster_ids)}
```

## Citation

If you use this dataset, please cite the RegMix paper:

```bibtex
@article{liu2024regmix,
  title={RegMix: Data Mixture as Regression for Language Model Pre-training},
  author={Liu, Qian and Zheng, Xiaosen and Muennighoff, Niklas and Zeng, Guangtao and Dou, Longxu and Pang, Tianyu and Jiang, Jing and Lin, Min},
  journal={arXiv preprint arXiv:2407.01492},
  year={2024}
}
```