Datasets:

NotHotTryHard
/

wikipedia-en-harrier-0.6b-emb

Parquet error: Scan size limit exceeded: attempted to read 4104695597 bytes, limit is 300000000 bytes Make sure that 1. the Parquet files contain a page index to enable random access without loading entire row groups2. otherwise use smaller row-group sizes when serializing the Parquet files

Error code:   TooBigContentError

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Wikipedia EN Chunks + Harrier 0.6B Embeddings

Pre-computed dense embeddings for 23.7M English Wikipedia chunks using microsoft/harrier-oss-v1-0.6b (384-dim).

Related Datasets

Dataset	Description
NotHotTryHard/wikipedia-en-harrier-270m-emb	Same chunks, embedded with the smaller Harrier 270m model
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 (6.4M articles)
Chunking: 200-word sliding window, 50-word overlap, min 50 characters
Total chunks: ~23,758,035

Embeddings

Model: microsoft/harrier-oss-v1-0.6b
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

from datasets import load_dataset

ds = load_dataset("NotHotTryHard/wiki-en-harrier-0.6b", split="train")
print(ds[0])
# {'chunk_id': 0, 'article_title': 'Anarchism', 'text': '...', 'embedding': [0.012, ...]}

Building a FAISS Index

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-v1-0.6b, parallel GPU shards)
  -> export_parquet.py -> this dataset

License

Wikipedia content is under CC BY-SA 4.0.

Downloads last month: 23