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 model.safetensors from PhysShell/UltraBERT-Embed-150M: direct link, hf CLI and curl.
- Browser
- Download file 604 MB
-
https://huggingface.co/PhysShell/UltraBERT-Embed-150M/resolve/main/model.safetensors
- Command line
-
hf download hf://PhysShell/UltraBERT-Embed-150M/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/PhysShell/UltraBERT-Embed-150M/resolve/main/model.safetensors
604 MB
- Xet hash:
- 6bea65a931d77fae1bb730bdc273049767ce5b5a3807164873502fc739f160a7
- Size of remote file:
- 604 MB
- SHA256:
- da8be29564b69e4f90fc8f2683e1f369c3d3575e75f7fe798643e28de02e2b9c
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