Text Classification
sentence-transformers
OpenVINO
English
Chinese
xlm-roberta
mteb
text-embeddings-inference
nncf
8-bit precision
Eval Results (legacy)
Instructions to use turingevo/bge-reranker-base-openvino-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use turingevo/bge-reranker-base-openvino-8bit with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("turingevo/bge-reranker-base-openvino-8bit") 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
This model is a quantized version of BAAI/bge-reranker-base and is converted to the OpenVINO format. This model was obtained via the nncf-quantization space with optimum-intel.
First make sure you have optimum-intel installed:
pip install optimum[openvino]
To load your model you can do as follows:
from optimum.intel import OVModelForSequenceClassification
model_id = "turingevo/bge-reranker-base-openvino-8bit"
model = OVModelForSequenceClassification.from_pretrained(model_id)
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Model tree for turingevo/bge-reranker-base-openvino-8bit
Base model
BAAI/bge-reranker-baseEvaluation results
- map on MTEB CMedQAv1test set self-reported81.272
- mrr on MTEB CMedQAv1test set self-reported84.142
- map on MTEB CMedQAv2test set self-reported84.104
- mrr on MTEB CMedQAv2test set self-reported86.794
- map on MTEB MMarcoRerankingself-reported35.460
- mrr on MTEB MMarcoRerankingself-reported34.602
- map on MTEB T2Rerankingself-reported67.277
- mrr on MTEB T2Rerankingself-reported77.132