Sentence Similarity
sentence-transformers
Safetensors
Danish
Swedish
Norwegian
llama
feature-extraction
text-embedding
embeddings
information-retrieval
beir
text-classification
text-clustering
llm2vec
custom_code
text-embeddings-inference
Instructions to use jealk/TTC-L2V-supervised-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jealk/TTC-L2V-supervised-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jealk/TTC-L2V-supervised-2", trust_remote_code=True) 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
| { | |
| "prompts": { | |
| "query": "Givet et sp\u00f8rgsm\u00e5l, find relevante tekstudsnit, der besvarer det:" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |