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
Download config_sentence_transformers.json from PhysShell/UltraBERT-Embed-150M: direct link, hf CLI and curl.
- Browser
- Download file 364 Bytes
-
https://huggingface.co/PhysShell/UltraBERT-Embed-150M/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://PhysShell/UltraBERT-Embed-150M/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/PhysShell/UltraBERT-Embed-150M/resolve/main/config_sentence_transformers.json
364 Bytes
| { | |
| "__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" | |
| } | |