Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use NeuML/pubmedbert-base-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/pubmedbert-base-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/pubmedbert-base-embeddings") 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] - Transformers
How to use NeuML/pubmedbert-base-embeddings with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("NeuML/pubmedbert-base-embeddings") model = AutoModel.from_pretrained("NeuML/pubmedbert-base-embeddings") - Inference
- Notebooks
- Google Colab
- Kaggle
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## More Information
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and/or [this paper](https://github.com/neuml/papers/blob/master/pubmedbert-embeddings/pubmedbert-embeddings.pdf).
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## More Information
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Read more about PubMedBERT Embeddings in [this article](https://medium.com/neuml/embeddings-for-medical-literature-74dae6abf5e0) and [this paper](https://github.com/neuml/papers/blob/master/pubmedbert-embeddings/pubmedbert-embeddings.pdf).
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