Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use Codingchild/medical-bge-reranker-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Codingchild/medical-bge-reranker-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Codingchild/medical-bge-reranker-large") 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 Codingchild/medical-bge-reranker-large: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/Codingchild/medical-bge-reranker-large/resolve/main/training_args.bin
- Command line
-
hf download hf://Codingchild/medical-bge-reranker-large/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Codingchild/medical-bge-reranker-large/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 2cd5d1162c8a6a29097a6741d07a0710af293874cf423829fe118a8d5dd6d2b7
- Size of remote file:
- 5.24 kB
- SHA256:
- 6f690ff8a72bd4b883c8c174c1e891d65030100fd8a7043ae963e76e5abab8aa
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