Add dataset card

README.md

@@ -0,0 +1,151 @@
+---
+pretty_name: Multimodal Embedding 100M
+license: cc-by-4.0
+size_categories:
+- 100M<n<1B
+task_categories:
+- feature-extraction
+- sentence-similarity
+tags:
+- multimodal
+- embeddings
+- vector-search
+- nearest-neighbors
+- ann-benchmark
+- laion
+- parquet
+- xet
+---
+
+# Multimodal Embedding 100M
+
+This dataset contains a 100M-row multimodal embedding corpus derived from LAION-400M-style embedding data. It is intended for large-scale vector database ingestion, ANN index construction, nearest-neighbor search, and retrieval benchmark experiments.
+
+The dataset is stored as Parquet files and organized to keep each directory below Hugging Face's per-directory file-count limit.
+
+## Dataset Summary
+
+| Split | Rows | Files | Path |
+| --- | ---: | ---: | --- |
+| `train` | 100,000,000 | 16,824 | `train/shard-00/*.parquet` ... `train/shard-09/*.parquet` |
+| `test` | 10,000 | 2 | `test/*.parquet` |
+| `neighbors` | 10,000 | 1 | `neighbors/neighbors.parquet` |
+
+Total: 100,020,000 rows across 16,827 Parquet files.
+
+## Directory Layout
+
+```text
+.
+├── train/
+│   ├── shard-00/
+│   ├── shard-01/
+│   ├── shard-02/
+│   ├── shard-03/
+│   ├── shard-04/
+│   ├── shard-05/
+│   ├── shard-06/
+│   ├── shard-07/
+│   ├── shard-08/
+│   └── shard-09/
+├── test/
+└── neighbors/
+```
+
+Train shard file distribution:
+
+| Shard | Files |
+| --- | ---: |
+| `shard-00` | 1,683 |
+| `shard-01` | 1,683 |
+| `shard-02` | 1,683 |
+| `shard-03` | 1,683 |
+| `shard-04` | 1,683 |
+| `shard-05` | 1,683 |
+| `shard-06` | 1,683 |
+| `shard-07` | 1,683 |
+| `shard-08` | 1,683 |
+| `shard-09` | 1,677 |
+
+## Schema
+
+### `train` and `test`
+
+| Column | Type | Description |
+| --- | --- | --- |
+| `id` | `int64` | Numeric sample id. |
+| `key` | `string` | Original sample key. |
+| `emb` | `fixed_size_list<float>[4096]` | 4096-dimensional embedding vector. |
+
+### `neighbors`
+
+| Column | Type | Description |
+| --- | --- | --- |
+| `id` | `int64` | Query/sample id. |
+| `neighbors` | `list<int64>` | 100 nearest-neighbor ids for the query/sample. |
+| `scores` | `list<float>` | 100 similarity scores aligned with `neighbors`. |
+
+## Loading
+
+For large-scale use, streaming mode is recommended:
+
+```python
+from datasets import load_dataset
+
+data_files = {
+    "train": "train/shard-*/*.parquet",
+    "test": "test/*.parquet",
+    "neighbors": "neighbors/*.parquet",
+}
+
+ds = load_dataset(
+    "WenxingZhu/multimodal-embedding-100M",
+    data_files=data_files,
+    streaming=True,
+)
+
+row = next(iter(ds["train"]))
+print(row["id"], row["key"], len(row["emb"]))
+```
+
+To load a small evaluation split without streaming:
+
+```python
+from datasets import load_dataset
+
+test = load_dataset(
+    "WenxingZhu/multimodal-embedding-100M",
+    data_files={"test": "test/*.parquet"},
+    split="test",
+)
+
+print(test)
+```
+
+## Intended Uses
+
+- Large-scale vector database ingestion and indexing tests.
+- ANN search and recall evaluation.
+- Embedding storage and retrieval system benchmarking.
+- Multimodal embedding pipeline validation.
+
+## Limitations
+
+- This repository contains embeddings and nearest-neighbor metadata, not original images or captions.
+- The embeddings are provided as precomputed vectors; the model and preprocessing pipeline used to generate them should be validated before comparing results across datasets.
+- Users should verify that the dataset is suitable for their benchmark methodology, especially when using the `neighbors` split as retrieval ground truth.
+
+## License and Attribution
+
+The original LAION-400M metadata dataset is distributed under CC-BY 4.0. Users are responsible for complying with the applicable license terms and for validating downstream usage requirements.
+
+If you use this dataset in a benchmark or experiment, please cite the original LAION-400M work:
+
+```bibtex
+@article{schuhmann2021laion400m,
+  title={LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs},
+  author={Schuhmann, Christoph and Vencu, Richard and Beaumont, Romain and Kaczmarczyk, Robert and Mullis, Clayton and Katta, Aarush and Coombes, Theo and Jitsev, Jenia and Komatsuzaki, Aran},
+  journal={arXiv preprint arXiv:2111.02114},
+  year={2021}
+}
+```