Feature Extraction
sentence-transformers
PyTorch
Chinese
English
bert
sentence-similarity
mteb
RAG
Eval Results (legacy)
text-embeddings-inference
Instructions to use DMetaSoul/Dmeta-embedding-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DMetaSoul/Dmeta-embedding-zh with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DMetaSoul/Dmeta-embedding-zh") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
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您也可以联系我们:赵中昊 <zhongh@dmetasoul.com>, 肖文斌 <xiaowenbin@dmetasoul.com>, 孙凯 <sunkai@dmetasoul.com>
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同时我们也开通了微信群,可扫码加入我们,一起共建 AIGC 技术生态!
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<image src="https://huggingface.co/DMetaSoul/Dmeta-embedding/resolve/main/weixin.jpeg" style="display: block; margin-left: auto; margin-right: auto; width: 256px; height: 358px;"/>
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您也可以联系我们:赵中昊 <zhongh@dmetasoul.com>, 肖文斌 <xiaowenbin@dmetasoul.com>, 孙凯 <sunkai@dmetasoul.com>
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同时我们也开通了微信群,可扫码加入我们(人数超200了,先加管理员再拉进群),一起共建 AIGC 技术生态!
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<image src="https://huggingface.co/DMetaSoul/Dmeta-embedding/resolve/main/weixin.jpeg" style="display: block; margin-left: auto; margin-right: auto; width: 256px; height: 358px;"/>
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