Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
English
bert
feature-extraction
patent-similarity
patent
text-embeddings-inference
Instructions to use mpi-inno-comp/paecter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mpi-inno-comp/paecter with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mpi-inno-comp/paecter") 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/paecter with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mpi-inno-comp/paecter") model = AutoModel.from_pretrained("mpi-inno-comp/paecter") - Inference
- Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model (#1)
Browse files- Adding `safetensors` variant of this model (7f4a7b5babef124b2e54b51cef5d3264db7ba22c)
Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>
- model.safetensors +3 -0
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