Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use AnnaWegmann/Style-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AnnaWegmann/Style-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AnnaWegmann/Style-Embedding") 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 AnnaWegmann/Style-Embedding with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AnnaWegmann/Style-Embedding") model = AutoModel.from_pretrained("AnnaWegmann/Style-Embedding") - Inference
- Notebooks
- Google Colab
- Kaggle
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## Citing & Authors
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```
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@inproceedings{wegmann-etal-2022-author,
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title = "Same Author or Just Same Topic? Towards Content-Independent Style Representations",
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## Citing & Authors
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Feel free to call my model CISR (Content Independent Style Representations) to save space in articles, as done by others.
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```
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@inproceedings{wegmann-etal-2022-author,
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title = "Same Author or Just Same Topic? Towards Content-Independent Style Representations",
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