Instructions to use andersborges/model2vecdk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use andersborges/model2vecdk with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("andersborges/model2vecdk") - sentence-transformers
How to use andersborges/model2vecdk with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("andersborges/model2vecdk") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6cd239aeee6cb834a9a154c96ef3234335945d3a63c3ba12fdca2a27af47ca14
- Size of remote file:
- 192 MB
- SHA256:
- 28779c5795528b4a400a0abf91972a71463cb891847f704367c92c616435448f
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