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
new experiment
Browse files- pytorch_model.bin +1 -1
- sentence_bert_config.json +1 -1
- tokenizer.json +1 -1
pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:7bda4e47f2ff0e524b10823df319ed26fa1a91b1819368d2a7a138fb9cc85fad
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sentence_bert_config.json
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"max_seq_length":
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"max_seq_length": 512,
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"do_lower_case": false
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tokenizer.json
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"version": "1.0",
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"truncation": {
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"direction": "Right",
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"max_length":
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"strategy": "LongestFirst",
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"stride": 0
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"version": "1.0",
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"truncation": {
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"direction": "Right",
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"max_length": 512,
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"strategy": "LongestFirst",
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"stride": 0
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