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
Why choose qwen2-0.5B as backbone model?
4
#4 opened over 1 year ago
by
alan
What's the instruction you added for each task in training?
4
#3 opened over 1 year ago
by
nachtsky1077
Any support for onnx?
1
#2 opened over 1 year ago
by
cseeeee
Model weights release?
3
#1 opened over 1 year ago
by
rbhatia46