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Error code: ConfigNamesError Exception: DataFilesNotFoundError Message: No (supported) data files found in jankovicsandras/nowiki-faiss-sbert-202309 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 jankovicsandras/nowiki-faiss-sbert-202309
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This is a FAISS vectordb from a Norwegian Wikipedia dump from 2023-09 embedded with NbAiLab/nb-sbert-base.
This can be used to augment a chatbot with RAG (norwegian bokmål language).
Only the article abstracts are processed, but they seemed detailed enough. The 'url' in the metadata points to the original article. Each abstract is embedded as a 768 dimensional vector with this model: NbAiLab/nb-sbert-base
Example usage (Python):
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
import time
embedder = HuggingFaceEmbeddings(model_name='NbAiLab/nb-sbert-base')
qs = [
'Kven er Beyonce?',
'Hva skjedde i 2012?',
'Hvilke musikkfestivalar kan du anbefale?',
]
db = FAISS.load_local('nowiki_faiss_sbert_all', embedder, allow_dangerous_deserialization=True)
starttime=time.time()
for q in qs :
print('----\n',q)
r = db.similarity_search_with_score(q)
print(r)
print('questions took ',time.time()-starttime,' s. ')
More info about the Wikipedia source:
https://dumps.wikimedia.org/other/enterprise_html/
License and guidelines:
https://dumps.wikimedia.org/legal.html
https://foundation.wikimedia.org/wiki/Legal:Developer_app_guidelines
Embedder model:
https://huggingface.co/NbAiLab/nb-sbert-base
FAISS vectordb:
https://python.langchain.com/docs/integrations/vectorstores/faiss
license: other license_name: wikimedia license_link: https://dumps.wikimedia.org/legal.html
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