Sentence Similarity
Transformers
Safetensors
sentence-transformers
Chinese
utu
feature-extraction
text-embeddings-inference
custom_code
Instructions to use tencent/Youtu-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/Youtu-Embedding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tencent/Youtu-Embedding", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use tencent/Youtu-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tencent/Youtu-Embedding", trust_remote_code=True) sentences = [ "那是 個快樂的人", "那是 條快樂的狗", "那是 個非常幸福的人", "今天是晴天" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e56cad9ac2a38f4b7de211953881b613c19e51838c05adf8f7984c139c2e8f5
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.