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
Download pytorch_model.bin from NeuML/pubmedbert-base-embeddings: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/NeuML/pubmedbert-base-embeddings/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NeuML/pubmedbert-base-embeddings/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NeuML/pubmedbert-base-embeddings/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 90d334657fce954df37b785ed8aa6ab0db815218b462fc9accdc8a5c33dbb4d3
- Size of remote file:
- 438 MB
- SHA256:
- 0bdb9787bcb608f0e4dbfa2724821b7d66a66be79508bff915a9d2e3fe1f3853
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