Instructions to use Data-Lab/bge-reranker-v2-m3-cross-encoder-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Data-Lab/bge-reranker-v2-m3-cross-encoder-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Data-Lab/bge-reranker-v2-m3-cross-encoder-v0.1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Data-Lab/bge-reranker-v2-m3-cross-encoder-v0.1") model = AutoModelForSequenceClassification.from_pretrained("Data-Lab/bge-reranker-v2-m3-cross-encoder-v0.1") - Notebooks
- Google Colab
- Kaggle
may i ask how to convert original model to this onnx format?
#3 opened about 1 year ago
by
chuangzhidian
Update model metadata to set pipeline tag to the new `text-ranking` and tags to `sentence-transformers`
#2 opened over 1 year ago
by
tomaarsen
Update model metadata to set pipeline tag to the new `text-ranking` and tags to `sentence-transformers`
#1 opened over 1 year ago
by
tomaarsen