Text Ranking
sentence-transformers
Safetensors
Korean
xlm-roberta
cross-encoder
reranker
korean
legal
retrieval
text-embeddings-inference
Instructions to use ysmeta/EVE-Rerank-1.0-Legal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ysmeta/EVE-Rerank-1.0-Legal with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("ysmeta/EVE-Rerank-1.0-Legal") 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
File size: 133 Bytes
bcfdef2 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:45885ab5cd9f8d0becc7075cecb177574d1cf4d086a891cac2b745f681d2b3c3
size 17098338
|