---
dataset_info:
  features:
    - name: chunk_id
      dtype: int64
    - name: article_title
      dtype: string
    - name: text
      dtype: string
    - name: embedding
      sequence:
        dtype: float32
      length: 384
license: cc-by-sa-4.0
task_categories:
  - feature-extraction
  - text-retrieval
tags:
  - wikipedia
  - embeddings
  - dense-retrieval
  - fact-checking
  - faiss
  - harrier
language:
  - en
size_categories:
  - 10M<n<100M
pretty_name: Wikipedia EN Chunks + Harrier 270M Embeddings
---

# Wikipedia EN Chunks + Harrier 270M Embeddings

Pre-computed dense embeddings for **23.7M English Wikipedia chunks** using [microsoft/harrier-oss-v1-270m](https://huggingface.co/microsoft/harrier-oss-v1-270m) (384-dim).

## Related Datasets

| Dataset | Description |
|---|---|
| [NotHotTryHard/wikipedia-en-harrier-270m-emb](https://huggingface.co/datasets/NotHotTryHard/wikipedia-en-harrier-270m-emb) | Same chunks, embedded with the larger **Harrier 270m** model |
| [NotHotTryHard/wikipedia-en-harrier-0.6b-emb](https://huggingface.co/datasets/NotHotTryHard/wikipedia-en-harrier-0.6b-emb) | Same chunks, embedded with the larger **Harrier 0.6B** model |

## Dataset Details

### Source

- **Wikipedia dump**: [wikimedia/wikipedia 20231101.en](https://huggingface.co/datasets/wikimedia/wikipedia) (6.4M articles)
- **Chunking**: 200-word sliding window, 50-word overlap, min 50 characters
- **Total chunks**: ~23,758,035

### Embeddings

- **Model**: `microsoft/harrier-oss-v1-270m`
- **Dimension**: 384
- **Normalization**: L2-normalized
- **Precision**: float32

### Schema

| Column | Type | Description |
|---|---|---|
| `chunk_id` | int64 | Unique chunk identifier (sequential) |
| `article_title` | string | Wikipedia article title |
| `text` | string | Chunk text (~200 words) |
| `embedding` | list[float32] x 384 | L2-normalized dense vector |

### Storage

- **Format**: Parquet shards with ZSTD compression
- **Naming**: `data/train-XXXXX-of-NNNNN.parquet`

## Usage

```python
from datasets import load_dataset

ds = load_dataset("NotHotTryHard/wiki-en-harrier-270m", split="train")
print(ds[0])
# {'chunk_id': 0, 'article_title': 'Anarchism', 'text': '...', 'embedding': [0.012, ...]}
```

### Building a FAISS Index

```python
import numpy as np
import faiss

embeddings = np.array(ds["embedding"], dtype=np.float32)
index = faiss.IndexFlatIP(384)
index.add(embeddings)
```

## Pipeline

```
Wikipedia 20231101.en (6.4M articles)
  -> chunk.py (200w window, 50w overlap)
  -> 23.7M chunks in SQLite
  -> embed.py (harrier-oss-v1-270m, parallel GPU shards)
  -> export_parquet.py -> this dataset
```

## License

Wikipedia content is under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).