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
Update README.md
Browse filesFix typo in README, closes #1
README.md
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Alternatively, the model can be loaded with [sentence-transformers](https://www.SBERT.net).
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```python
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from sentence_transformers import
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model =
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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Alternatively, the model can be loaded with [sentence-transformers](https://www.SBERT.net).
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```python
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from sentence_transformers import SparseEncoder
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SparseEncoder("neuml/pubmedbert-base-splade")
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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