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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README.md
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The maximum sequence length is 512 tokens.
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The model was not pre-trained from scratch but adapted from the English version with a [Danish tokenizer](https://huggingface.co/KennethTM/bert-base-uncased-danish).
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Trained on ELI5 and SQUAD data machine translated from English to Danish.
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The maximum sequence length is 512 tokens.
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The model was not pre-trained from scratch but adapted from the English version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) with a [Danish tokenizer](https://huggingface.co/KennethTM/bert-base-uncased-danish).
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Trained on ELI5 and SQUAD data machine translated from English to Danish.
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