Sentence Similarity
Transformers
Safetensors
sentence-transformers
semantic_lite
feature-extraction
embedding
multilingual
indonesian
quantization
semantic-search
retrieval
rag
Instructions to use ukung/semantic-lite-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ukung/semantic-lite-2 with Transformers:
# Load model directly from transformers import SemanticLiteEmbedder model = SemanticLiteEmbedder.from_pretrained("ukung/semantic-lite-2", device_map="auto") - sentence-transformers
How to use ukung/semantic-lite-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ukung/semantic-lite-2") 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] - Notebooks
- Google Colab
- Kaggle
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!