Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Transformers
Korean
roberta
feature-extraction
text-embeddings-inference
Instructions to use jhgan/ko-sroberta-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jhgan/ko-sroberta-nli with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jhgan/ko-sroberta-nli") 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-nli with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jhgan/ko-sroberta-nli") model = AutoModel.from_pretrained("jhgan/ko-sroberta-nli") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights
#1
by joaogante - opened
Model converted by the transformers' pt_to_tf CLI.
All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=6.676e-06; Maximum converted output difference=6.676e-06.
jhgan changed pull request status to merged