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 2_Dense/pytorch_model.bin from TencentBAC/Conan-embedding-v1: direct link, hf CLI and curl.
- Browser
- Download file 7.35 MB
-
https://huggingface.co/TencentBAC/Conan-embedding-v1/resolve/refs%2Fpr%2F21/2_Dense/pytorch_model.bin
- Command line
-
hf download hf://TencentBAC/Conan-embedding-v1@refs/pr/21/2_Dense/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TencentBAC/Conan-embedding-v1/resolve/refs%2Fpr%2F21/2_Dense/pytorch_model.bin
7.35 MB
- Xet hash:
- 92615e7a6ee79fc8f453ab79d6cbb0c863a251bb2273a744242621f129d1210f
- Size of remote file:
- 7.35 MB
- SHA256:
- 60cf9fa06b192d0fff51e14b16ba64b11444689642d7179efd45a6178d1ca1cc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.