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olmer
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wiki_mpnet_index

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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    ImportError
Message:      To be able to use olmer/wiki_mpnet_index, you need to install the following dependencies: faiss, sentence_transformers.
Please install them using 'pip install faiss sentence_transformers' for instance.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1914, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1880, in dataset_module_factory
                  return HubDatasetModuleFactoryWithScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1504, in get_module
                  local_imports = _download_additional_modules(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 354, in _download_additional_modules
                  raise ImportError(
              ImportError: To be able to use olmer/wiki_mpnet_index, you need to install the following dependencies: faiss, sentence_transformers.
              Please install them using 'pip install faiss sentence_transformers' for instance.

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Semantic search over the 44 million of English Wikipedia paragraphs using sentence transformers encoder.

The dataset contains:

  • 43 911 155 paragraphs from 6 458 670 wikipedia articles stored in a zip archive;
  • FAISS index with the embeddings;
  • Retriever module for semantic search over the paragraphs.

The size of each paragraph varies from 20 to 2000 characters.
The embedding vector size is 768.
The index is 4-bit-quantized 2-level IVF16384_HNSW32 constructed with the FAISS library.
Sentence encoder: all-mpnet-base-v2.

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