Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
semantic-search
chinese
text-embeddings-inference
Instructions to use DMetaSoul/sbert-chinese-general-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DMetaSoul/sbert-chinese-general-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DMetaSoul/sbert-chinese-general-v2") 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] - Transformers
How to use DMetaSoul/sbert-chinese-general-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DMetaSoul/sbert-chinese-general-v2") model = AutoModel.from_pretrained("DMetaSoul/sbert-chinese-general-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from DMetaSoul/sbert-chinese-general-v2: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/DMetaSoul/sbert-chinese-general-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://DMetaSoul/sbert-chinese-general-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/DMetaSoul/sbert-chinese-general-v2/resolve/main/pytorch_model.bin
409 MB
- Xet hash:
- a42110f4f91dbd96e71e255af9e0d4f3108738a3522c34bde3d9e352a67f574a
- Size of remote file:
- 409 MB
- SHA256:
- 7a87f5e2e093bd073d275c3d95f68a5b997ffaad85a4384fe3c311820e4d2764
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