Sentence Similarity
sentence-transformers
Safetensors
Japanese
modernbert
feature-extraction
mteb
japanese
retrieval
text-embeddings-inference
Instructions to use sionic-ai/comsat-embed-ja-0.3b-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sionic-ai/comsat-embed-ja-0.3b-preview with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sionic-ai/comsat-embed-ja-0.3b-preview") 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] - Notebooks
- Google Colab
- Kaggle
File size: 583 Bytes
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Copyright (c) 2026 Sionic AI
This model is a fine-tuned derivative of cl-nagoya/ruri-v3-310m
(https://huggingface.co/cl-nagoya/ruri-v3-310m).
The base model weights, tokenizer, and configuration are:
Copyright (c) the ruri-v3 authors (cl-nagoya)
Licensed under the Apache License, Version 2.0 (the "License").
You may obtain a copy of the License at:
http://www.apache.org/licenses/LICENSE-2.0
Modifications from the base model were made by Sionic AI.
The fine-tuned model weights are distributed under CC BY-NC 4.0 (see LICENSE).
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