Sentence Similarity
sentence-transformers
Safetensors
PyTorch
English
qwen3_vl
embeddings
retrieval
semantic-search
multimodal
image-text-retrieval
document-retrieval
Eval Results
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
Rename parseembed.yaml to .eval_results/parseembed.yaml
Browse files
parseembed.yaml → .eval_results/parseembed.yaml
RENAMED
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- dataset:
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id: Convence/ParseEmbed
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task_id: default
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value: 0.
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source:
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name: Supetron Modal benchmark run
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url: https://huggingface.co/
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notes: 'Metric: nDCG@10.'
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- dataset:
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id: Convence/ParseEmbed
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task_id: default
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value: 0.7961
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source:
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name: Supetron Modal benchmark run
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url: https://huggingface.co/Ill-Ness/Silas-Embedding
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notes: 'Metric: nDCG@10.'
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