Text Ranking
sentence-transformers
Safetensors
Transformers
bert
mteb
custom_code
Eval Results (legacy)
Instructions to use ByteDance/ListConRanker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ByteDance/ListConRanker with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("ByteDance/ListConRanker", 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) - Transformers
How to use ByteDance/ListConRanker with Transformers:
# Load model directly from transformers import AutoTokenizer, ListConRanker tokenizer = AutoTokenizer.from_pretrained("ByteDance/ListConRanker", trust_remote_code=True) model = ListConRanker.from_pretrained("ByteDance/ListConRanker", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
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