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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-similarity |
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language: en |
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license: mit |
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--- |
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# ONNX Conversion of [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) |
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- ONNX model for GPU with O4-O2 optimisation |
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## Usage |
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```python |
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from itertools import product |
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import torch.nn.functional as F |
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from optimum.onnxruntime import ORTModelForSequenceClassification |
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from transformers import AutoTokenizer |
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sentences = [ |
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"The llama (/ˈlɑːmə/) (Lama glama) is a domesticated South American camelid.", |
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"The alpaca (Lama pacos) is a species of South American camelid mammal.", |
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"The vicuña (Lama vicugna) (/vɪˈkuːnjə/) is one of the two wild South American camelids.", |
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] |
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queries = ["What is a llama?", "What is a harimau?", "How to fly a kite?"] |
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pairs = list(product(queries, sentences)) |
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model_name = "EmbeddedLLM/bge-reranker-base-onnx-o4-o2-gpu" |
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device = "cuda" |
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provider = "CUDAExecutionProvider" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = ORTModelForSequenceClassification.from_pretrained( |
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model_name, use_io_binding=True, provider=provider, device_map=device |
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) |
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inputs = tokenizer( |
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pairs, |
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padding=True, |
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truncation=True, |
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return_tensors="pt", |
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max_length=model.config.max_position_embeddings, |
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) |
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inputs = inputs.to(device) |
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scores = model(**inputs).logits.view(-1).cpu().numpy() |
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# Sort most similar to least |
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pairs = sorted(zip(pairs, scores), key=lambda x: x[1], reverse=True) |
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for ps in pairs: |
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print(ps) |
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``` |
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