Sentence Similarity
sentence-transformers
PyTorch
Transformers
Korean
bert
feature-extraction
TAACO
text-embeddings-inference
Instructions to use KDHyun08/TAACO_STS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KDHyun08/TAACO_STS with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KDHyun08/TAACO_STS") 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 KDHyun08/TAACO_STS with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KDHyun08/TAACO_STS") model = AutoModel.from_pretrained("KDHyun08/TAACO_STS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from KDHyun08/TAACO_STS: direct link, hf CLI and curl.
- Browser
- Download file 752 kB
-
https://huggingface.co/KDHyun08/TAACO_STS/resolve/main/tokenizer.json
- Command line
-
hf download hf://KDHyun08/TAACO_STS/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/KDHyun08/TAACO_STS/resolve/main/tokenizer.json
752 kB
File too large to display, you can check the raw version instead.