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/Qwen3-Embedding-0.6B/model.safetensors from AutoDataBench/Retrieval-resources: direct link, hf CLI and curl.
- Browser
- Download file 1.19 GB
-
https://huggingface.co/AutoDataBench/Retrieval-resources/resolve/main/models/Qwen3-Embedding-0.6B/model.safetensors
- Command line
-
hf download hf://AutoDataBench/Retrieval-resources/models/Qwen3-Embedding-0.6B/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/AutoDataBench/Retrieval-resources/resolve/main/models/Qwen3-Embedding-0.6B/model.safetensors
1.19 GB
- Xet hash:
- 50e3c282187ba05fed66edb58ffbba6f4ce258bd2d3dcda860b8dc125e041bab
- Size of remote file:
- 1.19 GB
- SHA256:
- 0437e45c94563b09e13cb7a64478fc406947a93cb34a7e05870fc8dcd48e23fd
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