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:
- cbe93fb625442768f20abe538d8f91045116a8f32de381bd49dfd95126a65f9f
- Size of remote file:
- 3.39 kB
- SHA256:
- 04d8990cb13f388af23f5fb4e64f85d9dddb9e042f529ff7a285e211d82345d0
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