Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert_hash
feature-extraction
custom_code
Instructions to use NeuML/biomedbert-hash-nano-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/biomedbert-hash-nano-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/biomedbert-hash-nano-embeddings", trust_remote_code=True) 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/biomedbert-hash-nano-embeddings with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NeuML/biomedbert-hash-nano-embeddings", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "SentenceTransformer", | |
| "__version__": { | |
| "sentence_transformers": "5.1.1", | |
| "transformers": "4.56.2", | |
| "pytorch": "2.8.0+cu128" | |
| }, | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |