Instructions to use Xenova/bge-reranker-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/bge-reranker-base with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-ranking', 'Xenova/bge-reranker-base'); - sentence-transformers
How to use Xenova/bge-reranker-base with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Xenova/bge-reranker-base") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Update max 512 pos embeddings
Model can only handle 512 pos embeddings.
time error
/usr/local/lib/python3.10/site-packages/infinity_emb/engine.py", line 64, in from_args
engine = cls(**engine_args.to_dict(), _show_deprecation_warning=False)
File "/usr/local/lib/python3.10/site-packages/infinity_emb/engine.py", line 50, in init
self._model, self._min_inference_t, self._max_inference_t = select_model(
File "/usr/local/lib/python3.10/site-packages/infinity_emb/inference/select_model.py", line 79, in select_model
loaded_engine.warmup(batch_size=engine_args.batch_size, n_tokens=512)
File "/usr/local/lib/python3.10/site-packages/infinity_emb/transformer/abstract.py", line 105, in warmup
return run_warmup(self, inp)
File "/usr/local/lib/python3.10/site-packages/infinity_emb/transformer/abstract.py", line 113, in run_warmup
embed = model.encode_core(feat)
File "/usr/local/lib/python3.10/site-packages/infinity_emb/transformer/crossencoder/optimum.py", line 72, in encode_core
outputs = self.model(**features, return_dict=True)
File "/usr/local/lib/python3.10/site-packages/optimum/modeling_base.py", line 92, in call
return self.forward(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/optimum/onnxruntime/modeling_ort.py", line 1389, in forward
outputs = self.model.run(None, onnx_inputs)
File "/usr/local/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 220, in run
return self._sess.run(output_names, input_feed, run_options)
onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Non-zero status code returned while running Gather node. Name:'/roberta/embeddings/position_embeddings/Gather' Status Message: indices element out of data bounds, idx=514 must be within the inclusive range [-514,513]