Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
roberta
feature-extraction
ko
text-embeddings-inference
Instructions to use ddobokki/unsup-simcse-klue-roberta-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ddobokki/unsup-simcse-klue-roberta-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ddobokki/unsup-simcse-klue-roberta-small") 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 ddobokki/unsup-simcse-klue-roberta-small with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ddobokki/unsup-simcse-klue-roberta-small") model = AutoModel.from_pretrained("ddobokki/unsup-simcse-klue-roberta-small") - Notebooks
- Google Colab
- Kaggle
ddobokki/unsup-simcse-klue-roberta-small
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ddobokki/unsup-simcse-klue-roberta-small')
embeddings = model.encode(sentences)
print(embeddings)
(개발중) git:https://github.com/ddobokki/KoSimCSE
- Downloads last month
- 12