Sentence Similarity
sentence-transformers
Safetensors
Model2Vec
Transformers
English
feature-extraction
embeddings
static-embeddings
Instructions to use NeuML/pubmedbert-base-embeddings-2M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/pubmedbert-base-embeddings-2M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/pubmedbert-base-embeddings-2M") 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-2M with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NeuML/pubmedbert-base-embeddings-2M", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": ".", | |
| "type": "sentence_transformers.models.StaticEmbedding" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |