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
Update README.md
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README.md
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---
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#
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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# parameter-psb
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A model for the identification of paramerter sentences in patents using PatentSBERTa
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('nategro/parameter-psb')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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