Text Ranking
sentence-transformers
ONNX
Safetensors
Indonesian
xlm-roberta
reranker
cross-encoder
indonesian
bahasa-indonesia
knowledge-distillation
flashrank
text-embeddings-inference
Instructions to use madebyaris/rerank-indonesia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use madebyaris/rerank-indonesia with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("madebyaris/rerank-indonesia") 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
| { | |
| "transformer_task": "sequence-classification", | |
| "modality_config": { | |
| "text": { | |
| "method": "forward", | |
| "method_output_name": "logits" | |
| } | |
| }, | |
| "module_output_name": "scores" | |
| } |