Sentence Similarity
sentence-transformers
Safetensors
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use JeongWoo-P/klue-roberta-base-klue-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JeongWoo-P/klue-roberta-base-klue-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JeongWoo-P/klue-roberta-base-klue-sts") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use JeongWoo-P/klue-roberta-base-klue-sts with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("JeongWoo-P/klue-roberta-base-klue-sts") model = AutoModel.from_pretrained("JeongWoo-P/klue-roberta-base-klue-sts") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86cff055a24510da27179485fa405ab34d44d08025ac4d740e872760bf75c43a
- Size of remote file:
- 442 MB
- SHA256:
- a838a09bf75bd253ab438812c93ecf0c6e83cfcf170c7926d0a65e44a48df7a8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.