Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
Korean
roberta
feature-extraction
text-embeddings-inference
Instructions to use jhgan/ko-sroberta-multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jhgan/ko-sroberta-multitask with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jhgan/ko-sroberta-multitask") 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 jhgan/ko-sroberta-multitask with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jhgan/ko-sroberta-multitask") model = AutoModel.from_pretrained("jhgan/ko-sroberta-multitask") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22cc33733844b041c0b3b22d33cf13f6b516119434e36c990d038383f04bdb50
- Size of remote file:
- 443 MB
- SHA256:
- e0143341f5e4653b7ece14cf0685f5fbf5b68a3926beed43754a498947c3bd77
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.