Feature Extraction
sentence-transformers
Safetensors
English
Chinese
qwen2
Retrieval
STS
Classification
Clustering
Reranking
vllm
custom_code
text-embeddings-inference
Instructions to use KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5", 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: 320 Bytes
1ee05ba d1f492d 1ee05ba | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"__version__": {
"sentence_transformers": "3.4.1",
"transformers": "4.45.0",
"pytorch": "2.1.2+cu121"
},
"prompts": {
"query": "Instruct: Given a query, retrieve documents that answer the query \n Query: ",
"document": ""
},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |