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
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Download models/MiniLM-L6-H384-uncased/README.md from AutoDataBench/Retrieval-resources: direct link, hf CLI and curl.
- Browser
- Download file 215 Bytes
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https://huggingface.co/AutoDataBench/Retrieval-resources/resolve/a4efa447e52155c8b38b790aaee35db4b8297c8e/models/MiniLM-L6-H384-uncased/README.md
- Command line
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hf download hf://AutoDataBench/Retrieval-resources@a4efa447e52155c8b38b790aaee35db4b8297c8e/models/MiniLM-L6-H384-uncased/README.md
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curl -L -o README.md https://huggingface.co/AutoDataBench/Retrieval-resources/resolve/a4efa447e52155c8b38b790aaee35db4b8297c8e/models/MiniLM-L6-H384-uncased/README.md
215 Bytes
metadata
license: mit
MiniLM: 6 Layer Version
This is a 6 layer version of microsoft/MiniLM-L12-H384-uncased by keeping only every second layer.