Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
English
Chinese
xlm-roberta
text-classification
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use BAAI/bge-reranker-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/bge-reranker-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-reranker-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-reranker-large") model = AutoModelForSequenceClassification.from_pretrained("BAAI/bge-reranker-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from BAAI/bge-reranker-large: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/BAAI/bge-reranker-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://BAAI/bge-reranker-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/BAAI/bge-reranker-large/resolve/main/pytorch_model.bin
2.24 GB
- Xet hash:
- 383257f9f567ea8c8ca18dea67659c522a4bf987be2d2a83c9566f35805d9436
- Size of remote file:
- 2.24 GB
- SHA256:
- 62129e841464da714b961eb0bbbeb48c0f0b5f67e657902aaa3befcff6b3dab3
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