Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
bert
feature-extraction
patent-similarity
text-embeddings-inference
Instructions to use mpi-inno-comp/pat_specter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mpi-inno-comp/pat_specter with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mpi-inno-comp/pat_specter") 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 mpi-inno-comp/pat_specter with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mpi-inno-comp/pat_specter") model = AutoModel.from_pretrained("mpi-inno-comp/pat_specter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10efd95d52429bfc14e74bd969c1e13ca5b4fa3fcb8e10b91c4866adaa3c7605
- Size of remote file:
- 440 MB
- SHA256:
- 278f3753c2b7d4cbae67eca2e6f8055866394a9b2df284f85f81230d6dc5312c
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