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
File size: 485 Bytes
bdea6d0 af75341 bdea6d0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"max_length": 128,
"model_max_length": 512,
"pad_to_multiple_of": null,
"pad_token": "[PAD]",
"pad_token_type_id": 0,
"padding_side": "right",
"sep_token": "[SEP]",
"stride": 0,
"tokenizer_class": "PreTrainedTokenizerFast",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]",
"vocab": "bert-uncased-tokenizer-danish/vocab.txt"
}
|