Sentence Similarity
sentence-transformers
Safetensors
Transformers
Japanese
modernbert
feature-extraction
mirei
masked-lm
text-embedding
embeddings
retrieval
text-embeddings-inference
Instructions to use iamtatsuki05/Sentence-ModernBERT-JP-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use iamtatsuki05/Sentence-ModernBERT-JP-0.5B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("iamtatsuki05/Sentence-ModernBERT-JP-0.5B") 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-ModernBERT-JP-0.5B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("iamtatsuki05/Sentence-ModernBERT-JP-0.5B") model = AutoModel.from_pretrained("iamtatsuki05/Sentence-ModernBERT-JP-0.5B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 729f4b16b24503bb73c26083537538159ba3b17dbe0d2abace9b7e15b166d281
- Size of remote file:
- 1.83 MB
- SHA256:
- 008293028e1a9d9a1038d9b63d989a2319797dfeaa03f171093a57b33a3a8277
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