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license: cc-by-4.0 |
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task_categories: |
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- sentence-similarity |
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--- |
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This repository contains the datasets that are meant to be used with VIBE (Vector Index Benchmark for Embeddings): |
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https://github.com/vector-index-bench/vibe |
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The datasets can be downloaded manually from this repository, but the benchmark framework also downloads them automatically. |
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## Datasets |
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| Name | Type | n | d | Distance | |
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| [agnews-mxbai-1024-euclidean](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/agnews-mxbai-1024-euclidean.hdf5) | Text | 769,382 | 1024 | euclidean | |
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| [arxiv-nomic-768-normalized](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/arxiv-nomic-768-normalized.hdf5) | Text | 1,344,643 | 768 | any | |
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| [gooaq-distilroberta-768-normalized](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/gooaq-distilroberta-768-normalized.hdf5) | Text | 1,475,024 | 768 | any | |
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| [imagenet-clip-512-normalized](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/imagenet-clip-512-normalized.hdf5) | Image | 1,281,167 | 512 | any | |
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| [landmark-nomic-768-normalized](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/landmark-nomic-768-normalized.hdf5) | Image | 760,757 | 768 | any | |
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| [yahoo-minilm-384-normalized](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/yahoo-minilm-384-normalized.hdf5) | Text | 677,305 | 384 | any | |
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| [celeba-resnet-2048-cosine](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/celeba-resnet-2048-cosine.hdf5) | Image | 201,599 | 2048 | cosine | |
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| [ccnews-nomic-768-normalized](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/ccnews-nomic-768-normalized.hdf5) | Text | 495,328 | 768 | any | |
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| [codesearchnet-jina-768-cosine](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/codesearchnet-jina-768-cosine.hdf5) | Code | 1,374,067 | 768 | cosine | |
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| [glove-200-cosine](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/glove-200-cosine.hdf5) | Word | 1,192,514 | 200 | cosine | |
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| [landmark-dino-768-cosine](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/landmark-dino-768-cosine.hdf5) | Image | 760,757 | 768 | cosine | |
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| [simplewiki-openai-3072-normalized](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/simplewiki-openai-3072-normalized.hdf5) | Text | 260,372 | 3072 | any | |
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| [coco-nomic-768-normalized](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/coco-nomic-768-normalized.hdf5) | Text-to-Image | 282,360 | 768 | any | |
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| [imagenet-align-640-normalized](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/imagenet-align-640-normalized.hdf5) | Text-to-Image | 1,281,167 | 640 | any | |
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| [laion-clip-512-normalized](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/laion-clip-512-normalized.hdf5) | Text-to-Image | 1,000,448 | 512 | any | |
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| [yandex-200-cosine](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/yandex-200-cosine.hdf5) | Text-to-Image | 1,000,000 | 200 | cosine | |
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| [yi-128-ip](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/yi-128-ip.hdf5) | Attention | 187,843 | 128 | IP | |
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| [llama-128-ip](https://huggingface.co/datasets/vector-index-bench/vibe/blob/main/llama-128-ip.hdf5) | Attention | 256,921 | 128 | IP | |
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## Credit |
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The glove-200-cosine dataset uses embeddings from Glove (released under PDDL 1.0): |
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https://nlp.stanford.edu/projects/glove/ |
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The laion-clip-512-normalized dataset uses a subset of embeddings from LAION-400M (released under CC-BY 4.0): |
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https://laion.ai/blog/laion-400-open-dataset/ |
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The yandex-200-cosine dataset uses a subset of embeddings from Yandex Text2Image (released under CC-BY 4.0): |
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https://big-ann-benchmarks.com/neurips23.html |
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## Dataset structure |
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Each dataset is distributed as an HDF5 file. |
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The HDF5 files contain the following attributes: |
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- dimension: The dimensionality of the data. |
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- distance: The distance metric to use. |
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- point_type: The precision of the vectors, one of "float", "uint8", or "binary". |
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The HDF5 files contain the following HDF5 datasets: |
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- train: numpy array of size (n_corpus, dim) containing the embeddings used to build the vector index |
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- test: numpy array of size (n_test, dim) containing the test query embeddings |
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- neighbors: numpy array of size (n_test, 100) containing the IDs of the true 100 k-nn of each test query |
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- distances: numpy array of size (n_test, 100) containing the distances of the true 100 k-nn of each test query |
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- avg_distances: numpy array of size n_test containing the average distance from each test query to the corpus points |
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Additionally, the HDF5 files of OOD datasets contain the following HDF5 datasets: |
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- learn: numpy array of size (n_learn, dim) containing a larger sample from the query distribution |
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- learn_neighbors: numpy array of size (n_learn, 100) containing the true 100 k-nn (from the corpus) for each point in learn |
