Instructions to use pkshatech/simcse-ja-bert-base-clcmlp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use pkshatech/simcse-ja-bert-base-clcmlp with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pkshatech/simcse-ja-bert-base-clcmlp") sentences = [ "This widget can't work correctly now.", "Sorry :(", "Try this model in your local environment!" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use pkshatech/simcse-ja-bert-base-clcmlp with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pkshatech/simcse-ja-bert-base-clcmlp") model = AutoModel.from_pretrained("pkshatech/simcse-ja-bert-base-clcmlp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d600986d97e830a022bd6ae0753644a6677ec0c66fe3ffb9ee41a24af8ba3ed0
- Size of remote file:
- 445 MB
- SHA256:
- ae4edc54026eb831ecddff1013e637db22cf7977cfd731a977b840261e421dac
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