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

- Xet hash:
- 68f65b621e17fd363cb2bb4a86f9dfdb5e13cf6a12fec47ca0f479e28bd63af3
- Size of remote file:
- 273 kB
- SHA256:
- 7e526bc1aa3cdb42c25755a5ee9215bda96ac823625ac5c26655dc84832db77d
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