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
Update README.md
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README.md
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@@ -13,7 +13,7 @@ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentence
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for more info see [Style-Embeddings](https://github.com/nlpsoc/Style-Embeddings)
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see paper at [https://aclanthology.org/2022.repl4nlp-1.26/](https://aclanthology.org/2022.repl4nlp-1.26/)
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## Usage (Sentence-Transformers)
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for more info see [Style-Embeddings](https://github.com/nlpsoc/Style-Embeddings)
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see published paper at [https://aclanthology.org/2022.repl4nlp-1.26/](https://aclanthology.org/2022.repl4nlp-1.26/) and arxiv paper at [https://arxiv.org/abs/2204.04907](https://arxiv.org/abs/2204.04907).
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## Usage (Sentence-Transformers)
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