Instructions to use swulling/bge-reranker-base-onnx-o4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swulling/bge-reranker-base-onnx-o4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="swulling/bge-reranker-base-onnx-o4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("swulling/bge-reranker-base-onnx-o4") model = AutoModelForSequenceClassification.from_pretrained("swulling/bge-reranker-base-onnx-o4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from swulling/bge-reranker-base-onnx-o4: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/swulling/bge-reranker-base-onnx-o4/resolve/main/tokenizer.json
- Command line
-
hf download hf://swulling/bge-reranker-base-onnx-o4/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/swulling/bge-reranker-base-onnx-o4/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- aea4c283df0ba1b545b5899fd0b257def695c1c57052cdeb76589721d4cf3a8b
- Size of remote file:
- 17.1 MB
- SHA256:
- 15c2549547974954d493ed56303f1f82e6708d261b9b0533d224938102892f38
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