Instructions to use pkshatech/GLuCoSE-base-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use pkshatech/GLuCoSE-base-ja with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pkshatech/GLuCoSE-base-ja") 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 pkshatech/GLuCoSE-base-ja with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pkshatech/GLuCoSE-base-ja") model = AutoModel.from_pretrained("pkshatech/GLuCoSE-base-ja", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f829690f3ef16d5905ff6624a296153bac3e66b4e85adf23c5b438d60726b48c
- Size of remote file:
- 532 MB
- SHA256:
- 6d48a302b78a8a81d94ecfde8ee0cd4d316d8e43bee0c25c81fd08455b1fdfcc
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