---
license: mit
base_model:
- google-bert/bert-base-uncased
---
 
# Dragon Context Encoder

This is the **context encoder** of the Dragon dual-encoder retrieval model, trained for dense passage retrieval tasks.  
It should be used together with the corresponding [Dragon Query Encoder](https://huggingface.co/liyongkang/dragon-query-encoder).

## 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())