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-v1.5 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-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
1122
Browse files
README.md
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## Contact
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If you encounter any issue, feel free to contact us via the email: yanshek.woo@gmail.com
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```
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## Citation
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Please cite the repo if you use the model or code in this repo.
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```
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@article{hu2025kalm,
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title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model},
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author={Hu, Xinshuo and Shan, Zifei and Zhao, Xinping and Sun, Zetian and Liu, Zhenyu and Li, Dongfang and Ye, Shaolin and Wei, Xinyuan and Chen, Qian and Hu, Baotian and others},
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journal={arXiv preprint arXiv:2501.01028},
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year={2025}
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}
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```
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## Contact
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If you encounter any issue, feel free to contact us via the email: yanshek.woo@gmail.com
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