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 SentenceTransformer model = SentenceTransformer("NeuML/pubmedbert-base-splade") 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
Commit ·
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Parent(s): 378b290
Update README
Browse files
README.md
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- task:
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type: semantic-similarity
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name: Semantic Similarity
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metrics:
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- type: pearson_cosine
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value: 0.9422980731390805
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- task:
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type: semantic-similarity
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name: Semantic Similarity
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dataset:
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type: pubmed-similarity
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name: PubMed Similarity
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metrics:
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- type: pearson_cosine
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value: 0.9422980731390805
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