Update README

README.md

@@ -1,3 +1,50 @@
 ---
+pipeline_tag: sentence-similarity
+tags:
+  - sentence-similarity
+language: en
 license: mit
 ---
+
+# ONNX Conversion of [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)
+
+- ONNX model for GPU with O4-O2 optimisation
+
+## Usage
+
+```python
+from itertools import product
+
+import torch.nn.functional as F
+from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import AutoTokenizer
+
+sentences = [
+    "The llama (/ˈlɑːmə/) (Lama glama) is a domesticated South American camelid.",
+    "The alpaca (Lama pacos) is a species of South American camelid mammal.",
+    "The vicuña (Lama vicugna) (/vɪˈkuːnjə/) is one of the two wild South American camelids.",
+]
+queries = ["What is a llama?", "What is a harimau?", "How to fly a kite?"]
+pairs = list(product(queries, sentences))
+
+model_name = "EmbeddedLLM/bge-reranker-base-onnx-o4-o2-gpu"
+device = "cuda"
+provider = "CUDAExecutionProvider"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = ORTModelForSequenceClassification.from_pretrained(
+    model_name, use_io_binding=True, provider=provider, device_map=device
+)
+inputs = tokenizer(
+    pairs,
+    padding=True,
+    truncation=True,
+    return_tensors="pt",
+    max_length=model.config.max_position_embeddings,
+)
+inputs = inputs.to(device)
+scores = model(**inputs).logits.view(-1).cpu().numpy()
+# Sort most similar to least
+pairs = sorted(zip(pairs, scores), key=lambda x: x[1], reverse=True)
+for ps in pairs:
+    print(ps)
+```