Sentence Similarity
sentence-transformers
English
bert
ctranslate2
int8
float16
feature-extraction
text-embeddings-inference
Instructions to use michaelfeil/ct2fast-all-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use michaelfeil/ct2fast-all-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("michaelfeil/ct2fast-all-MiniLM-L6-v2") 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
- Xet hash:
- 5f95a9b0ce07eefbdc8c0ed2353b6624de6a2f61d8c996c050b339633af1828f
- Size of remote file:
- 45.4 MB
- SHA256:
- 8e02198a1a1480129f35fede1751d0406a43e5ea8e7abb618ac58285e974cd6e
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