Sentence Similarity
sentence-transformers
Safetensors
English
bert
sparse-encoder
sparse
splade
Generated from Trainer
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use NeuML/pubmedbert-base-splade with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/pubmedbert-base-splade with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("NeuML/pubmedbert-base-splade") queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
config_sentence_transformers.json : sentence_similarity_fn_name
#2
by nadshe - opened
Is there are reason the default similarity name is "cosine" and not "dot"? Was this model not trained with the dot function for scoring?
{
"model_type": "SparseEncoder",
"__version__": {
"sentence_transformers": "5.0.0",
"transformers": "4.52.4",
"pytorch": "2.6.0+cu124"
},
"prompts": {
"query": "",
"document": ""
},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
}
Hello. It's designed to work with cosine similarity (or dot similarity with normalized vectors).