Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use Labib11/PMC_bge_800 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Labib11/PMC_bge_800 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Labib11/PMC_bge_800") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from Labib11/PMC_bge_800: direct link, hf CLI and curl.
- Browser
- Download file 2.67 GB
-
https://huggingface.co/Labib11/PMC_bge_800/resolve/main/optimizer.pt
- Command line
-
hf download hf://Labib11/PMC_bge_800/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Labib11/PMC_bge_800/resolve/main/optimizer.pt
2.67 GB
- Xet hash:
- 7b6ef6712ca6bc7ebf6a1991193c21d9b526229198bdc643c7cebbe51a9e1fee
- Size of remote file:
- 2.67 GB
- SHA256:
- b50b33531353b1a9a9544f9c6148882239ad161e1987928dbfe2792e3f8b8330
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