Sentence Similarity
sentence-transformers
Safetensors
English
ultrabert
feature-extraction
mteb
long-context
custom_code
Eval Results (legacy)
Instructions to use PhysShell/UltraBERT-Embed-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use PhysShell/UltraBERT-Embed-150M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PhysShell/UltraBERT-Embed-150M", 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] - Notebooks
- Google Colab
- Kaggle
File size: 364 Bytes
be5c0e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"__version__": {
"pytorch": "2.14.0+cu130",
"sentence_transformers": "6.0.1",
"transformers": "5.16.1"
},
"default_prompt_name": null,
"model_type": "SentenceTransformer",
"prompts": {
"query": "Instruct: Given a question, retrieve passages that answer the question\nQuery: ",
"document": ""
},
"similarity_fn_name": "cosine"
}
|