Text Ranking
sentence-transformers
Safetensors
modchembert
cross-encoder
reranker
cheminformatics
smiles
Generated from Trainer
dataset_size:3212363
loss:MultipleNegativesRankingLoss
custom_code
Eval Results (legacy)
Instructions to use Derify/ChemRanker-alpha-qed-cutoff-sim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Derify/ChemRanker-alpha-qed-cutoff-sim with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Derify/ChemRanker-alpha-qed-cutoff-sim", trust_remote_code=True) 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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