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 tokenizer.json from ukung/semantic-lite: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/ukung/semantic-lite/resolve/main/tokenizer.json
- Command line
-
hf download hf://ukung/semantic-lite/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ukung/semantic-lite/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 41330bf754b1b9beb0e45d91daa49b41286a64794c0d3f89d9ccd21bb161576b
- Size of remote file:
- 11.4 MB
- SHA256:
- 38e13f2835783a75aef315f53ae1bfc2314e97526a0735c706c6e471df44b079
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