Instructions to use LDCC/bge-reranker-v2-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LDCC/bge-reranker-v2-m3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LDCC/bge-reranker-v2-m3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LDCC/bge-reranker-v2-m3") model = AutoModelForSequenceClassification.from_pretrained("LDCC/bge-reranker-v2-m3") - Notebooks
- Google Colab
- Kaggle
hi ,may i ask how to run this model?
#2
by chuangzhidian - opened
this is the equivlent version of original bge-reranker-v2-m3 in terms of perfomance? no performance loss?
It’s the same as the PyTorch model, but with ONNX support added.