Instructions to use cl-nagoya/sup-simcse-ja-large 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-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cl-nagoya/sup-simcse-ja-large") 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-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cl-nagoya/sup-simcse-ja-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cl-nagoya/sup-simcse-ja-large") model = AutoModel.from_pretrained("cl-nagoya/sup-simcse-ja-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cl-nagoya/sup-simcse-ja-large: direct link, hf CLI and curl.
- Browser
- Download file 1.35 GB
-
https://huggingface.co/cl-nagoya/sup-simcse-ja-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cl-nagoya/sup-simcse-ja-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cl-nagoya/sup-simcse-ja-large/resolve/main/pytorch_model.bin
1.35 GB
- Xet hash:
- 6f2fbc7d5b5fab41ef35fbf1895fb3fdaeef8ebc8b96b9750ef1d745f4ab2974
- Size of remote file:
- 1.35 GB
- SHA256:
- d8484e8d52ac2e6d9a263662ac4d11932518ee09ed7566ee43b9a044971fff71
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