Feature Extraction
sentence-transformers
Safetensors
English
Chinese
qwen2
MTEB
CMTEB
Transformers
Retrieval
STS
Classification
Clustering
custom_code
Eval Results
text-embeddings-inference
Instructions to use HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2", 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
| { | |
| "__version__": { | |
| "sentence_transformers": "3.4.1", | |
| "transformers": "4.45.0", | |
| "pytorch": "2.1.2+cu121" | |
| }, | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |