Instructions to use blackcodetavern/gbert-large-paraphrase-cosine-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use blackcodetavern/gbert-large-paraphrase-cosine-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("blackcodetavern/gbert-large-paraphrase-cosine-onnx") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use blackcodetavern/gbert-large-paraphrase-cosine-onnx with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("blackcodetavern/gbert-large-paraphrase-cosine-onnx") model = AutoModel.from_pretrained("blackcodetavern/gbert-large-paraphrase-cosine-onnx") - setfit
How to use blackcodetavern/gbert-large-paraphrase-cosine-onnx with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("blackcodetavern/gbert-large-paraphrase-cosine-onnx") - Notebooks
- Google Colab
- Kaggle
German BERT large paraphrase cosine
This is a sentence-transformers model. It maps sentences & paragraphs (text) into a 1024 dimensional dense vector space. The model is intended to be used together with SetFit to improve German few-shot text classification.
This is the ONNX-Version of deutsche-telekom/gbert-large-paraphrase-cosine
Licensing
Copyright (c) 2023 Philip May, Deutsche Telekom AG
Copyright (c) 2022 deepset GmbH
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License by reviewing the file
LICENSE in the repository.
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