Sentence Similarity
sentence-transformers
PyTorch
English
distilbert
sentence similarity
text-embeddings-inference
Instructions to use Sakil/sentence_similarity_semantic_search with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Sakil/sentence_similarity_semantic_search with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Sakil/sentence_similarity_semantic_search") 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] - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -40,7 +40,7 @@ from torch.utils.data import DataLoader
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from sentence_transformers import SentenceTransformer, util
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model_name="Sakil/sentence_similarity_semantic_search"
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sentences = ['A man is eating food.',
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'A man is eating a piece of bread.',
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'The girl is carrying a baby.',
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from sentence_transformers import SentenceTransformer, util
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model_name="Sakil/sentence_similarity_semantic_search"
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model = SentenceTransformer(model_name)
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sentences = ['A man is eating food.',
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'A man is eating a piece of bread.',
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'The girl is carrying a baby.',
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