Sentence Similarity
sentence-transformers
Safetensors
Transformers
Japanese
llama
feature-extraction
mirei
llm2vec
text-embedding
embeddings
retrieval
custom_code
text-embeddings-inference
Instructions to use iamtatsuki05/Sentence-Sarashina-Bi-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use iamtatsuki05/Sentence-Sarashina-Bi-0.5B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("iamtatsuki05/Sentence-Sarashina-Bi-0.5B", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use iamtatsuki05/Sentence-Sarashina-Bi-0.5B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("iamtatsuki05/Sentence-Sarashina-Bi-0.5B", trust_remote_code=True) model = AutoModel.from_pretrained("iamtatsuki05/Sentence-Sarashina-Bi-0.5B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -96,7 +96,7 @@ This model is distributed under the [MIT License](https://opensource.org/license
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```tex
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@article{MIREI
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title={同一条件下における Encoder/Decoderアーキテクチャ
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author={岡田 龍樹 and 杉本 徹},
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journal={言語処理学会第 32 回年次大会 (NLP2026)},
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year={2026}
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```tex
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@article{MIREI
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title={同一条件下における Encoder/Decoder アーキテクチャによる文埋め込みの性能分析},
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author={岡田 龍樹 and 杉本 徹},
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journal={言語処理学会第 32 回年次大会 (NLP2026)},
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year={2026}
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