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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b635a27f78c3522c1d1c4fd3ea6c025a4b826977c1188c35afc91c1fbecf53fb
- Size of remote file:
- 438 MB
- SHA256:
- 929ddc16369bb4ff6f8e92a6ee9f5e0748f2bef33989b5617533dd50beebe4ef
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