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
File size: 102 Bytes
d4337b2 | 1 2 3 4 5 6 7 8 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "llm2vec_st.LLM2VecTransformer"
}
] |