Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use Labib11/PMC_bge_1600 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Labib11/PMC_bge_1600 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Labib11/PMC_bge_1600") 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_1600: direct link, hf CLI and curl.
- Browser
- Download file 2.67 GB
-
https://huggingface.co/Labib11/PMC_bge_1600/resolve/main/optimizer.pt
- Command line
-
hf download hf://Labib11/PMC_bge_1600/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Labib11/PMC_bge_1600/resolve/main/optimizer.pt
2.67 GB
- Xet hash:
- 38ca1881ad5cdcaa439f5349e75c1d6f2dacefe2d387c62a5faef83e80bce849
- Size of remote file:
- 2.67 GB
- SHA256:
- 19a10c6a1e7dcb1f7668fa8bdf0ff40ad9db6deb3cde8ca4a45d7d63696073ce
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