Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use AI-Growth-Lab/PatentSBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AI-Growth-Lab/PatentSBERTa with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AI-Growth-Lab/PatentSBERTa") 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 AI-Growth-Lab/PatentSBERTa with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AI-Growth-Lab/PatentSBERTa") model = AutoModel.from_pretrained("AI-Growth-Lab/PatentSBERTa", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
53fbbaa
1
Parent(s): bb3a7cd
Update README.md
Browse files
README.md
CHANGED
|
@@ -129,4 +129,9 @@ SentenceTransformer(
|
|
| 129 |
|
| 130 |
## Citing & Authors
|
| 131 |
|
| 132 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 129 |
|
| 130 |
## Citing & Authors
|
| 131 |
|
| 132 |
+
@article{bekamiri2021patentsberta,
|
| 133 |
+
title={PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT},
|
| 134 |
+
author={Bekamiri, Hamid and Hain, Daniel S and Jurowetzki, Roman},
|
| 135 |
+
journal={arXiv preprint arXiv:2103.11933},
|
| 136 |
+
year={2021}
|
| 137 |
+
}
|