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:
# pip install -U transformers accelerate # 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
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# pat_specter
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This is a [sentence-transformers](https://www.SBERT.net) model. This model is fine-tuned on patent texts, leveraging SPECTER 2.0 as a base, which is provided by Allen Institute for AI. It maps patent text to a 768 dimensional dense vector space and can be used for patent-specific downstream tasks.
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## Usage (Sentence-Transformers)
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# pat_specter
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This is a [sentence-transformers](https://www.SBERT.net) model. This model is fine-tuned on patent texts, leveraging SPECTER 2.0 as a base, which is provided by Allen Institute for AI. It maps patent text to a 768 dimensional dense vector space and can be used for patent-specific downstream tasks.
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However, it is noteworthy that [PaECTER](https://huggingface.co/mpi-inno-comp/paecter) outperforms this model in terms of performance.
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## Usage (Sentence-Transformers)
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