Update README to reflect new dataset structure with train/ and test/ directories

README.md

@@ -1,23 +1,20 @@
 ---
-license: mit
-dataset_info:
-  features:
-  - name: doc_id
-    dtype: string
-  - name: title
-    dtype: string
-  - name: text
-    dtype: string
-  - name: emb
-    sequence: float32
-  splits:
-  - name: train
-    num_bytes: 52899390680
-    num_examples: 6406711
-  - name: test
-    num_examples: 256
-  download_size: 28699669151
-  dataset_size: 52899390680
+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
 configs:
 - config_name: default
   data_files:
@@ -27,48 +24,80 @@ configs:
     path: test/*.parquet
 ---
 
-# Wikipedia EN SPLADE + BGE Dataset
+# Wikipedia English with SPLADE and BGE-M3
 
-This dataset contains Wikipedia articles with SPLADE sparse embeddings and BGE dense embeddings for hybrid search applications.
+Pre-computed SPLADE sparse and BGE-M3 dense embeddings for 6.4M English Wikipedia articles.
 
-## Dataset Structure
-
-The dataset is organized into two splits:
-
-### Train Split
-- **Location**: `train/` directory
-- **Files**: 7 parquet files (`train-00000-of-00007.parquet` through `train-00006-of-00007.parquet`)
-- **Size**: 6,406,711 documents
-- **Content**: Wikipedia articles with precomputed embeddings
+## Dataset Description
 
-### Test Split
-- **Location**: `test/` directory
-- **Files**: 1 parquet file (`test-00000-of-00001.parquet`)
-- **Size**: 256 queries
-- **Content**: Test queries for evaluation
+- **Source**: [HuggingFaceFW/clean-wikipedia](https://huggingface.co/datasets/HuggingFaceFW/clean-wikipedia) (English)
+- **Size**: 
+  - **Train**: 6,406,711 documents
+  - **Test**: 256 queries
+- **Format**: 
+  - **Train**: 7 Parquet files (~1M records each) in `train/` directory
+  - **Test**: 1 Parquet file in `test/` directory
+- **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
 
-## Features
+## Direct Usage
 
-Each record contains:
-- `doc_id`: Unique document identifier
-- `title`: Article title
-- `text`: Full article text
-- `emb`: Precomputed embeddings (SPLADE sparse + BGE dense)
-
-## Usage
+HuggingFace automatically discovers parquet files. You can load this dataset directly:
 
 ```python
 from datasets import load_dataset
 
-# Load the full dataset
-dataset = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge")
+# 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")
 
-# Stream the dataset to avoid loading everything into memory
-dataset = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge", streaming=True)
+# 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
+
+```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/train-00000-of-00007.parquet")
+print(f"Loaded {len(dataset['train'])} articles")
 ```
 
 ## File Organization
@@ -76,30 +105,17 @@ dataset = load_dataset("Sicheng-Chroma/wikipedia-en-splade-bge", streaming=True)
 ```
 .
 ├── train/
-│   ├── train-00000-of-00007.parquet
-│   ├── train-00001-of-00007.parquet
+│   ├── 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
+│   └── train-00006-of-00007.parquet - Final ~400K articles
 └── test/
-    └── test-00000-of-00001.parquet
+    └── test-00000-of-00001.parquet - 256 test queries
 ```
 
 ## License
 
-This dataset is released under the MIT license.
-
-## Citation
-
-If you use this dataset, please cite:
-
-```bibtex
-@dataset{wikipedia_splade_bge,
-  title={Wikipedia EN with SPLADE and BGE Embeddings},
-  author={Sicheng-Chroma},
-  year={2024},
-  publisher={Hugging Face}
-}
-```
+CC BY-SA 4.0 (inherited from Wikipedia)