Sentence Similarity
sentence-transformers
PyTorch
Swedish
bert
feature-extraction
text-embeddings-inference
Instructions to use jzju/sbert-sv-lim2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jzju/sbert-sv-lim2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jzju/sbert-sv-lim2") sentences = [ "Mannen åt mat.", "Han förtärde en närande och nyttig måltid.", "Det var ett sunkigt hak med ganska gott käk.", "Han inmundigade middagen tillsammans med ett glas rödvin.", "Potatischips är jättegoda.", "Tryck på knappen för att få tala med kundsupporten." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [6, 6] - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert
This new file is equivalent to pytorch_model.bin but safe in the sense that
no arbitrary code can be put into it.
These files also happen to load much faster than their pytorch counterpart:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb
The widgets on your model page will run using this model even if this is not merged
making sure the file actually works.
If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions
Feel free to ignore this PR.