Instructions to use uer/sbert-base-chinese-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use uer/sbert-base-chinese-nli with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("uer/sbert-base-chinese-nli") sentences = [ "那个人很开心", "那个人非常开心", "那只猫很开心", "那个人在吃东西" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use uer/sbert-base-chinese-nli with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("uer/sbert-base-chinese-nli") model = AutoModel.from_pretrained("uer/sbert-base-chinese-nli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update
Browse files- config.json +0 -1
- pytorch_model.bin +2 -2
config.json
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"_name_or_path": "sbert",
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"BertModel"
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pytorch_model.bin
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size 409154799
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