Instructions to use AutoDataBench/Retrieval-resources with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AutoDataBench/Retrieval-resources with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AutoDataBench/Retrieval-resources") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download models/MiniLM-L6-H384-uncased/flax_model.msgpack from AutoDataBench/Retrieval-resources: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/AutoDataBench/Retrieval-resources/resolve/main/models/MiniLM-L6-H384-uncased/flax_model.msgpack
- Command line
-
hf download hf://AutoDataBench/Retrieval-resources/models/MiniLM-L6-H384-uncased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/AutoDataBench/Retrieval-resources/resolve/main/models/MiniLM-L6-H384-uncased/flax_model.msgpack
90.9 MB
- Xet hash:
- 7e4cd183b1a342780ac0aed78f73df888cbdf65f6785616adbf642929d66276d
- Size of remote file:
- 90.9 MB
- SHA256:
- cc64ec084e314b03470e11b4c5170bb9a64db946dc738768251ce188933dbab8
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