Create README.md

README.md

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+---
+license: mit
+base_model:
+- google-bert/bert-base-uncased
+---
+ 
+# Dragon Query Encoder
+
+This is the **query encoder** of the Dragon dual-encoder retrieval model, trained for dense passage retrieval tasks.  
+It should be used together with the corresponding [Dragon Context Encoder](https://huggingface.co/liyongkang/dragon-context-encoder) for end-to-end retrieval.
+
+
+## Model Architecture
+
+- **Base model:** `bert-base-uncased`
+- **Architecture:** Dense Passage Retriever (DPR) dual-encoder
+- **Encoder type:** Context encoder (for passages)
+- **Pooling method:** CLS pooling (take `[CLS]` token representation)
+ 
+- **Checkpoint origin:**  
+  The weights were converted from the official [facebookresearch/dpr-scale Dragon implementation](https://github.com/facebookresearch/dpr-scale/tree/main/dragon),  
+  specifically from the checkpoint provided at:  
+  [https://dl.fbaipublicfiles.com/dragon/checkpoints/DRAGON/checkpoint_best.ckpt](https://dl.fbaipublicfiles.com/dragon/checkpoints/DRAGON/checkpoint_best.ckpt)
+
+ 
+## Usage Example
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# Load query encoder
+q_tokenizer = AutoTokenizer.from_pretrained("liyongkang/dragon-query-encoder")
+q_model = AutoModel.from_pretrained("liyongkang/dragon-query-encoder")
+
+# Load context encoder
+p_tokenizer = AutoTokenizer.from_pretrained("liyongkang/dragon-context-encoder")
+p_model = AutoModel.from_pretrained("liyongkang/dragon-context-encoder")
+
+query = "What is Dragon in NLP?"
+passage = "A dual-encoder retrieval model for dense passage retrieval."
+
+
+# Tokenize. In fact, the two tokenizers are the same.
+q_inputs = q_tokenizer(query, return_tensors="pt", truncation=True, padding=True)
+p_inputs = p_tokenizer(passage, return_tensors="pt", truncation=True, padding=True)
+
+with torch.no_grad():
+    q_vec = q_model(**q_inputs).last_hidden_state[:, 0]  # CLS pooling
+    p_vec = p_model(**p_inputs).last_hidden_state[:, 0]  # CLS pooling
+    score = (q_vec * p_vec).sum(dim=-1)
+    print("Dot product similarity:", score.item())