Sentence Similarity
sentence-transformers
Safetensors
qwen2
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1 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-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Any support for onnx?
#2
by cseeeee - opened
No description provided.
I'm sorry, but we haven't tried the ONNX format yet.
However, we are using the native Hugging Face model and parameters, so you can try the conventional ONNX conversion method.