Text Ranking
sentence-transformers
Safetensors
bert
cross-encoder
reranker
Generated from Trainer
dataset_size:7491
loss:BinaryCrossEntropyLoss
text-embeddings-inference
Instructions to use rabit223/vi_MiniLM_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rabit223/vi_MiniLM_v1 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("rabit223/vi_MiniLM_v1") 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
model_push
#1 opened 16 days ago
by
rabit223