Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use jhgan/ko-sbert-multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jhgan/ko-sbert-multitask with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jhgan/ko-sbert-multitask") 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 jhgan/ko-sbert-multitask with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jhgan/ko-sbert-multitask") model = AutoModel.from_pretrained("jhgan/ko-sbert-multitask", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jhgan/ko-sbert-multitask: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/jhgan/ko-sbert-multitask/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jhgan/ko-sbert-multitask/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jhgan/ko-sbert-multitask/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 01cc0615c5c33142318c27464baec36435589f122a6f35e7b3b903d85e40379c
- Size of remote file:
- 443 MB
- SHA256:
- 36ba48be1948ca8da9b68460355ad1879c6a903600864c5f7aa6fe52ab2beece
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