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
caps and link
Browse files
README.md
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license: apache-2.0
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Built upon Google's BERT for Patents as its base model, it generates 1024-dimensional dense vector embeddings from patent text.
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These vectors encapsulate the semantic essence of the given patent text, making it highly suitable for various downstream tasks related to patent analysis.
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## Applications
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* Semantic Search
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* Prior Art Search
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license: apache-2.0
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# PaECTER - a Patent Similarity Model
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PaECTER (Patent Embeddings using Citationinformed TransformERs) is a patent similarity model.
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Built upon Google's BERT for Patents as its base model, it generates 1024-dimensional dense vector embeddings from patent text.
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These vectors encapsulate the semantic essence of the given patent text, making it highly suitable for various downstream tasks related to patent analysis.
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Paper: https://arxiv.org/pdf/2402.19411
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## Applications
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* Semantic Search
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* Prior Art Search
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