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_config.json from ukung/semantic-lite: direct link, hf CLI and curl.
- Browser
- Download file 717 Bytes
-
https://huggingface.co/ukung/semantic-lite/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://ukung/semantic-lite/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/ukung/semantic-lite/resolve/main/tokenizer_config.json
717 Bytes
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": null, | |
| "chat_template": null, | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "errors": "replace", | |
| "extra_special_tokens": [ | |
| "<|im_start|>", | |
| "<|im_end|>", | |
| "<|object_ref_start|>", | |
| "<|object_ref_end|>", | |
| "<|box_start|>", | |
| "<|box_end|>", | |
| "<|quad_start|>", | |
| "<|quad_end|>", | |
| "<|vision_start|>", | |
| "<|vision_end|>", | |
| "<|vision_pad|>", | |
| "<|image_pad|>", | |
| "<|video_pad|>" | |
| ], | |
| "is_local": true, | |
| "local_files_only": false, | |
| "model_max_length": 40960, | |
| "pad_token": "<|endoftext|>", | |
| "split_special_tokens": false, | |
| "tokenizer_class": "Qwen2Tokenizer", | |
| "unk_token": null | |
| } | |