Sicheng-Chroma/wikipedia-en-splade-bge

Wikipedia English with SPLADE and BGE-M3

Pre-computed SPLADE sparse and BGE-M3 dense embeddings for 6.4M English Wikipedia articles.

Direct Usage

HuggingFace automatically discovers parquet files. You can load this dataset directly:

from datasets import load_dataset

# Stream the entire dataset (recommended for large dataset)
dataset = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge", streaming=True)

# Load specific splits
train_dataset = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge", split="train")
test_dataset = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge", split="test")

# Load specific chunk
dataset = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge", 
                       data_files="train/train-00000-of-00007.parquet")

# Load multiple chunks
dataset = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge", 
                       data_files=["train/train-00000-of-00007.parquet", "train/train-00001-of-00007.parquet"])

Dataset Structure

Each row contains:

Field	Type	Description
text	string	Full Wikipedia article text
title	string	Article title
url	string	Wikipedia URL
sparse_embedding_indices	list[int32]	SPLADE indices (non-zero positions)
sparse_embedding_values	list[float32]	SPLADE values (weights)
dense_embedding	list[float32]	BGE-M3 1024-dim dense vector

Example Usage

from datasets import load_dataset

# Load the dataset (streaming recommended for large dataset)
ds = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge", streaming=True)

# Iterate through articles
for article in ds['train']:
    print(f"Title: {article['title']}")
    print(f"URL: {article['url']}")
    print(f"Text preview: {article['text'][:200]}...")
    print(f"Sparse embedding: {len(article['sparse_embedding_indices'])} non-zero dims")
    print(f"Dense embedding: {len(article['dense_embedding'])} dims")
    break

# Load into memory (for smaller chunks)
dataset = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge", 
                       data_files="train/train-00000-of-00007.parquet")
print(f"Loaded {len(dataset['train'])} articles")

File Organization

.
├── train/
│   ├── train-00000-of-00007.parquet - First 1M articles
│   ├── train-00001-of-00007.parquet - Next 1M articles
│   ├── train-00002-of-00007.parquet
│   ├── train-00003-of-00007.parquet
│   ├── train-00004-of-00007.parquet
│   ├── train-00005-of-00007.parquet
│   └── train-00006-of-00007.parquet - Final ~400K articles
└── test/
    └── test-00000-of-00001.parquet - 256 test queries

License

CC BY-SA 4.0 (inherited from Wikipedia)