Sentence Similarity
sentence-transformers
Safetensors
Model2Vec
Transformers
English
feature-extraction
embeddings
static-embeddings
Instructions to use NeuML/pubmedbert-base-embeddings-1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/pubmedbert-base-embeddings-1M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/pubmedbert-base-embeddings-1M") 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-1M with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NeuML/pubmedbert-base-embeddings-1M", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 278 Bytes
f099815 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | [
{
"idx": 0,
"name": "0",
"path": ".",
"type": "sentence_transformers.models.StaticEmbedding"
},
{
"idx": 1,
"name": "1",
"path": "1_Normalize",
"type": "sentence_transformers.models.Normalize"
}
] |