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Cannot get the config names for the dataset.
Error code: ConfigNamesError Exception: DataFilesNotFoundError Message: No (supported) data files found in Cohere/BinaryVectorDB 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 1873, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1854, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1245, in get_module module_name, default_builder_kwargs = infer_module_for_data_files( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 595, in infer_module_for_data_files raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else "")) datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in Cohere/BinaryVectorDB
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YAML Metadata
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empty or missing yaml metadata in repo card
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Pre-build Binary Vector Databases
Here we ship different pre-build Binary Vector databases.
See the Github readme what Binary Vector Database is and why it can save you a lot of memory (and money) when you need to scale to tens or hundred millions of embeddings.
Available Datasets
We have Wikipedia available in all 300+ languages.
See Files for the different files. These are simple .zip files you can download and unzip locally.
Note for the English Wikipedia: English Wikipedia is due to its size distributed as 2 pars: wikipedia-2023-11-en-part-1.zip wikipedia-2023-11-en-part-2.zip
Unzip both to be able to load the Binary Vector Database for the English Wikipedia (42M embeddings).
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