Text Classification
sentence-transformers
Safetensors
bert
reranker
code search
cross-encoder
MiniLM
staqc
information retrieval
MRR
code understanding
python
stack-overflow
Eval Results (legacy)
text-embeddings-inference
Instructions to use NamanAgnih0tri/code-reranker-miniLM-staqc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NamanAgnih0tri/code-reranker-miniLM-staqc with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("NamanAgnih0tri/code-reranker-miniLM-staqc") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
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