Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
sentence-similarity
text-embeddings-inference
Instructions to use sangmook12/qwen-math-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sangmook12/qwen-math-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sangmook12/qwen-math-embedding") 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] - Transformers
How to use sangmook12/qwen-math-embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sangmook12/qwen-math-embedding")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sangmook12/qwen-math-embedding") model = AutoModel.from_pretrained("sangmook12/qwen-math-embedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02c828d27600f7d8b76081d58354eae5da5afefd9a94cc163540f4c830dff949
- Size of remote file:
- 11.4 MB
- SHA256:
- 01632aafb054a5af80c681ecc92d5ef95eb4d90ea11247aef192068b88552ab5
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