Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use seongwoon/patent_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use seongwoon/patent_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seongwoon/patent_embedding") 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 seongwoon/patent_embedding with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("seongwoon/patent_embedding") model = AutoModel.from_pretrained("seongwoon/patent_embedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 663509a02063e601ebc14c90dfdb7a5a5615b391d09cdfa2775c34d545f58d19
- Size of remote file:
- 440 MB
- SHA256:
- 52f4b84ff5c63e20d74eb74975d5d71687f3b104c5f9b7a630d9f08a35e3610d
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