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 SentenceTransformer model = SentenceTransformer("turingevo/bge-reranker-base-openvino-8bit") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - 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