Feature Extraction
sentence-transformers
Safetensors
qwen3
sentence-similarity
retrieval
pruned-model
text-embeddings-inference
Instructions to use ukung/semantic-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ukung/semantic-lite with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ukung/semantic-lite") 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
Download 1_Pooling/config.json from ukung/semantic-lite: direct link, hf CLI and curl.
- Browser
- Download file 92 Bytes
-
https://huggingface.co/ukung/semantic-lite/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://ukung/semantic-lite/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/ukung/semantic-lite/resolve/main/1_Pooling/config.json
92 Bytes
| { | |
| "embedding_dimension": 1024, | |
| "pooling_mode": "mean", | |
| "include_prompt": false | |
| } |