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 training_args.bin from Labib11/PMC_bge_800: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/Labib11/PMC_bge_800/resolve/main/training_args.bin
- Command line
-
hf download hf://Labib11/PMC_bge_800/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Labib11/PMC_bge_800/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 168fe35236a48e8a87334adcb9ae41be1b8028dfc8d6e29f412cfc88c24803bb
- Size of remote file:
- 5.24 kB
- SHA256:
- 3da4c15b601d6f576dcd179cd8b78a831ce9b72d07fa3dd500671fb02c60c38b
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