Upload Wikipedia SPLADE + BGE-M3 embeddings

.ipynb_checkpoints/metadata-checkpoint.json

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+{
+  "total_records": 6406711,
+  "num_chunks": 7,
+  "chunk_size": 1000000,
+  "max_chars": 8000,
+  "sparse_model": "prithivida/Splade_PP_en_v1",
+  "dense_model": "BAAI/bge-m3",
+  "processing_time_seconds": 19665.465597867966,
+  "docs_per_second": 325.78486220507205,
+  "resumed_from": null,
+  "completed": true
+}

README.md

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+---
+license: cc-by-sa-4.0
+task_categories:
+- text-retrieval
+- sentence-similarity
+language:
+- en
+tags:
+- wikipedia
+- embeddings
+- splade
+- bge-m3
+- dense-retrieval
+- sparse-retrieval
+- hybrid-search
+size_categories:
+- 1M<n<10M
+---
+
+# Wikipedia English with SPLADE and BGE-M3
+
+Pre-computed SPLADE sparse and BGE-M3 dense embeddings for 6.4M English Wikipedia articles.
+
+## Dataset Description
+
+- **Source**: [HuggingFaceFW/clean-wikipedia](https://huggingface.co/datasets/HuggingFaceFW/clean-wikipedia) (English)
+- **Size**: 6,406,711 documents
+- **Format**: 7 Parquet files (~1M records each)
+- **Total Size**: ~15-20GB
+- **Embeddings**: 
+  - **Sparse**: [SPLADE PP](https://huggingface.co/prithivida/Splade_PP_en_v1) - ~265 non-zero dims
+  - **Dense**: [BGE-M3](https://huggingface.co/BAAI/bge-m3) - 1024 dimensions
+
+## Direct Usage
+
+HuggingFace automatically discovers parquet files. You can load this dataset directly:
+
+```python
+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 split/chunk
+dataset = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge", 
+                       data_files="train-00000-of-00007.parquet")
+
+# Load multiple chunks
+dataset = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge", 
+                       data_files=["train-00000-of-00007.parquet", "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
+
+```python
+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-00000-of-00007.parquet")
+print(f"Loaded {len(dataset['train'])} articles")
+```
+
+## Files
+
+The dataset consists of 7 parquet files in standard HuggingFace format:
+- `train-00000-of-00007.parquet` - First 1M articles
+- `train-00001-of-00007.parquet` - Next 1M articles
+- ...
+- `train-00006-of-00007.parquet` - Final ~400K articles
+
+HuggingFace automatically recognizes this naming pattern and combines all files into a single dataset.
+
+## Processing Details
+
+- **Text Truncation**: First 8,000 characters used for embeddings
+- **Full Text**: Original complete text is preserved
+- **Processing Time**: ~5.5 hours
+- **Concurrent Processing**: 8 async batches
+
+## License
+
+CC BY-SA 4.0 (inherited from Wikipedia)

metadata.json

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+{
+  "total_records": 6406711,
+  "num_chunks": 7,
+  "chunk_size": 1000000,
+  "max_chars": 8000,
+  "sparse_model": "prithivida/Splade_PP_en_v1",
+  "dense_model": "BAAI/bge-m3",
+  "processing_time_seconds": 19665.465597867966,
+  "docs_per_second": 325.78486220507205,
+  "resumed_from": null,
+  "completed": true
+}

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