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HIT-TMG
/
KaLM-embedding-multilingual-mini-instruct-v2

Feature Extraction
sentence-transformers
Safetensors
English
Chinese
qwen2
MTEB
CMTEB
Transformers
Retrieval
STS
Classification
Clustering
custom_code
Eval Results
text-embeddings-inference
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xet
Community
4

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]
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Add evaluation results for model HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2 revision d2a21c232dc712ae8230af56d1027cf21b7864bf

#4 opened 2 months ago by
Samoed

Again at the top of the Rag benchmark

👍 2
5
#2 opened 8 months ago by
LPN64

Add exported onnx model 'model.onnx'

#1 opened 9 months ago by
JanN989
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