Update README

README.md

@@ -1,3 +1,46 @@
 ---
+pipeline_tag: sentence-similarity
+tags:
+  - feature-extraction
+  - sentence-similarity
+language: en
 license: mit
 ---
+
+# ONNX Conversion of [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
+
+- ONNX model for GPU with O4-O2 optimisation
+- We exported the model with `use_raw_attention_mask=True` [due to this issue](https://github.com/microsoft/onnxruntime/issues/18945)
+
+## Usage
+
+```python
+import torch.nn.functional as F
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+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.",
+]
+
+model_name = "EmbeddedLLM/bge-base-en-v1.5-onnx-o4-o2-gpu"
+device = "cuda"
+provider = "CUDAExecutionProvider"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = ORTModelForFeatureExtraction.from_pretrained(
+    model_name, use_io_binding=True, provider=provider, device_map=device
+)
+inputs = tokenizer(
+    sentences,
+    padding=True,
+    truncation=True,
+    return_tensors="pt",
+    max_length=model.config.max_position_embeddings,
+)
+inputs = inputs.to(device)
+embeddings = model(**inputs).last_hidden_state[:, 0]
+embeddings = F.normalize(embeddings, p=2, dim=1)
+print(embeddings.cpu().numpy().shape)
+```