Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
semantic-search
chinese
text-embeddings-inference
Instructions to use DMetaSoul/sbert-chinese-qmc-domain-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DMetaSoul/sbert-chinese-qmc-domain-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DMetaSoul/sbert-chinese-qmc-domain-v1") 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-qmc-domain-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DMetaSoul/sbert-chinese-qmc-domain-v1") model = AutoModel.from_pretrained("DMetaSoul/sbert-chinese-qmc-domain-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74badce7a5d5f0675cd3a0f788cdb3de3c6f5521ba0f93aa0eb0012588dd2849
- Size of remote file:
- 409 MB
- SHA256:
- 31b83455b0932145e893ff8bac2094ebc3e6808e5d2b0a162e5d0782bbd6a803
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