Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use nategro/parameter-psb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nategro/parameter-psb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nategro/parameter-psb") 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 nategro/parameter-psb with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("nategro/parameter-psb") model = AutoModelForMultimodalLM.from_pretrained("nategro/parameter-psb") - Notebooks
- Google Colab
- Kaggle
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## Citing & Authors
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The following pre-trained model was used: [`AI-Growth-Lab/PatentSBERTa
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The following pre-trained model was used: [`AI-Growth-Lab/PatentSBERTa`](https://huggingface.co/AI-Growth-Lab/PatentSBERTa)
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