Sentence Similarity
sentence-transformers
Safetensors
German
English
multilingual
xlm-roberta
feature-extraction
retrieval
semantic-search
ifc
lca
text-embeddings-inference
Instructions to use Hygros-LCA/bge-m3-ifc-kbob-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hygros-LCA/bge-m3-ifc-kbob-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hygros-LCA/bge-m3-ifc-kbob-finetuned") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 431a4045e0ae408bcebeab098798bc2fb2d555b2dd91ba179851249a778c2988
- Size of remote file:
- 17.1 MB
- SHA256:
- d2174406fd56fd8d5b49b5a7bc51c2a8a7986ceacaed6ad9f3ee57fbc799b84a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.