Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:5749
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use summerstars/MARK-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use summerstars/MARK-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("summerstars/MARK-Embedding") sentences = [ "Nterprise Linux Services is expected to be available before then end of this year.", "Beta versions of Nterprise Linux Services are expected to be available on certain HP ProLiant servers in July.", "Spain turning back the clock on siestas", "I don't like many flavored drinks." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle