Instructions to use TencentBAC/Conan-embedding-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TencentBAC/Conan-embedding-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TencentBAC/Conan-embedding-v1") 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 pytorch_model.bin from TencentBAC/Conan-embedding-v1: direct link, hf CLI and curl.
- Browser
- Download file 1.3 GB
-
https://huggingface.co/TencentBAC/Conan-embedding-v1/resolve/refs%2Fpr%2F12/pytorch_model.bin
- Command line
-
hf download hf://TencentBAC/Conan-embedding-v1@refs/pr/12/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TencentBAC/Conan-embedding-v1/resolve/refs%2Fpr%2F12/pytorch_model.bin
1.3 GB
- Xet hash:
- c04a69732ea1474860a28dea40e70523f037384e4a60faea02c8047374a45747
- Size of remote file:
- 1.3 GB
- SHA256:
- 4c97e006299fe7b2c369e305d7dd537af3cce5336d0f2252366701899c25678e
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