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]
How to use NeuML/pubmedbert-base-embeddings with Transformers:
# pip install -U transformers accelerate # 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")
https://huggingface.co/NeuML/pubmedbert-base-embeddings/resolve/refs%2Fpr%2F1/tokenizer.json
hf download hf://NeuML/pubmedbert-base-embeddings@refs/pr/1/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/NeuML/pubmedbert-base-embeddings/resolve/refs%2Fpr%2F1/tokenizer.json
