Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
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
Add exported openvino model 'openvino_model.xml'
#8
by tomaarsen HF Staff - opened
Hello!
This pull request has been automatically generated from the Sentence Transformers backend-export Space.
Pull Request overview
- Add exported OpenVINO model
openvino_model.xml.
Tip:
Consider testing this pull request before merging by loading the model from this PR with the revision argument:
from sentence_transformers import SentenceTransformer
# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
"jhgan/ko-sroberta-multitask",
revision=f"refs/pr/{pr_number}",
backend="openvino",
)
# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities)