Instructions to use cl-nagoya/sup-simcse-ja-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cl-nagoya/sup-simcse-ja-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cl-nagoya/sup-simcse-ja-base") 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 cl-nagoya/sup-simcse-ja-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cl-nagoya/sup-simcse-ja-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cl-nagoya/sup-simcse-ja-base") model = AutoModel.from_pretrained("cl-nagoya/sup-simcse-ja-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78242794c08e9ef6c9c81199f3e45090f33ed5e22edff45f874075b8f5af7634
- Size of remote file:
- 445 MB
- SHA256:
- 1da96484868a11dbef063c6a1869053c60133a5bef4ce43945094d73f9df6c00
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