Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1441905
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use AronowLab/bond-embed-v1-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AronowLab/bond-embed-v1-fp16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AronowLab/bond-embed-v1-fp16") sentences = [ "Treponema caused disease or disorder", "bejel", "tumor of ureter", "debrisoquine, ultrarapid metabolism of" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -155,7 +155,7 @@ Then you can load this model and run inference.
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("pankajrajdeo/bond-embed-
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# Run inference
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sentences = [
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'Light-chain amyloidosis',
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("pankajrajdeo/bond-embed-v1-fp16")
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# Run inference
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sentences = [
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'Light-chain amyloidosis',
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