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/pytorch_model.bin 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/pytorch_model.bin
- Command line
-
hf download hf://AutoDataBench/Retrieval-resources/models/MiniLM-L6-H384-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AutoDataBench/Retrieval-resources/resolve/main/models/MiniLM-L6-H384-uncased/pytorch_model.bin
90.9 MB
- Xet hash:
- cc17df7afc7cdd8f734c04ae2ab39ca0a52b395adc0be34bf7fca0b30eb12014
- Size of remote file:
- 90.9 MB
- SHA256:
- ab5048f3effe06bccb318ee923df90cba4e918b309a0ac6b00c654ede0768b87
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