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
Add evaluation results for model HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2 revision d2a21c232dc712ae8230af56d1027cf21b7864bf
#4 opened 3 months ago
by
Samoed
Again at the top of the Rag benchmark
👍 2
5
#2 opened 9 months ago
by
LPN64
Add exported onnx model 'model.onnx'
#1 opened 10 months ago
by
JanN989