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cl-nagoya
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ruri-pt-base

Sentence Similarity
sentence-transformers
Safetensors
Japanese
bert
feature-extraction
Model card Files
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@@ -16,11 +16,8 @@ datasets:
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  # Ruri: Japanese General Text Embeddings
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-
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  ## Usage
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- ### Direct Usage (Sentence Transformers)
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-
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  First install the Sentence Transformers library:
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  ```bash
@@ -45,7 +42,7 @@ sentences = [
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  embeddings = model.encode(sentences, convert_to_tensor=True)
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  print(embeddings.size())
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- # [4, 1024]
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  similarities = F.cosine_similarity(embeddings.unsqueeze(0), embeddings.unsqueeze(1), dim=2)
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  print(similarities)
 
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  # Ruri: Japanese General Text Embeddings
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  ## Usage
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  First install the Sentence Transformers library:
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  ```bash
 
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  embeddings = model.encode(sentences, convert_to_tensor=True)
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  print(embeddings.size())
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+ # [4, 768]
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  similarities = F.cosine_similarity(embeddings.unsqueeze(0), embeddings.unsqueeze(1), dim=2)
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  print(similarities)