Instructions to use dongyang09/m3e-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dongyang09/m3e-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dongyang09/m3e-large") 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 dongyang09/m3e-large: direct link, hf CLI and curl.
- Browser
- Download file 1.3 GB
-
https://huggingface.co/dongyang09/m3e-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dongyang09/m3e-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dongyang09/m3e-large/resolve/main/pytorch_model.bin
1.3 GB
- Xet hash:
- 380fd536106a5d0b688989afbe7080b925a60f457a4d9f6eb9b79a2359a46a6f
- Size of remote file:
- 1.3 GB
- SHA256:
- 4f9930dbac74403217ab922e948114a4237eb8ea7fb63c6830050d9679289aa4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.