Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use embedingHF/Sentence_Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use embedingHF/Sentence_Transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("embedingHF/Sentence_Transformer") 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 embedingHF/Sentence_Transformer with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("embedingHF/Sentence_Transformer") model = AutoModel.from_pretrained("embedingHF/Sentence_Transformer") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -48,7 +48,7 @@ Then you can use the model like this:
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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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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('embeddingHF/Detector_model')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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