Update README.md

README.md

@@ -54,26 +54,6 @@ A 34M parameter sentence embedding model trained from scratch using PyTorch.
 - `onnx/biencoder_rope.onnx` — ONNX FP32
 - `onnx/biencoder_rope_int8.onnx` — ONNX INT8 (recommended for CPU)
 
-## Usage
-```python
-import torch
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-name", subfolder="tokenizer")
-model = BiEncoderRoPE().to("cuda")
-model.load_state_dict(
-    torch.load("pytorch/checkpoint_phase4_nq.pt")["model_state"]
-)
-model.eval()
-
-@torch.no_grad()
-def encode(texts):
-    if isinstance(texts, str): texts = [texts]
-    enc = tokenizer(texts, padding=True, truncation=True,
-                    max_length=256, return_tensors="pt")
-    return model.encode(enc["input_ids"].cuda(), enc["attention_mask"].cuda()).cpu()
-```
-
 ## Performance
 - FP32 ONNX size : 134.3 MB
 - INT8 ONNX size : 34.6 MB