Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Danish
bert
feature-extraction
text-embeddings-inference
Instructions to use KennethTM/MiniLM-L6-danish-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KennethTM/MiniLM-L6-danish-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KennethTM/MiniLM-L6-danish-encoder") 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
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# MiniLM-L6-danish-encoder
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This is a lightweight (~22 M parameters) [sentence-transformers](https://www.SBERT.net) model for Danish NLP: It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for tasks like clustering or semantic search.
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*New version available, trained on more data and otherwise identical [KennethTM/MiniLM-L6-danish-encoder-v2](https://huggingface.co/KennethTM/MiniLM-L6-danish-encoder-v2)*
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# MiniLM-L6-danish-encoder
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This is a lightweight (~22 M parameters) [sentence-transformers](https://www.SBERT.net) model for Danish NLP: It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for tasks like clustering or semantic search.
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