Sentence Similarity
sentence-transformers
Safetensors
PyTorch
English
qwen3_vl
embeddings
retrieval
semantic-search
multimodal
image-text-retrieval
document-retrieval
Instructions to use Ill-Ness/Silas-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ill-Ness/Silas-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ill-Ness/Silas-Embedding") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 416 Bytes
49e3e0c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": false,
"local_files_only": false,
"model_max_length": 512,
"pad_token": "<|endoftext|>",
"processor_class": "Qwen3VLProcessor",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}
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