For Tok-k passages that have passed through the Bi-Encoder Retrival, ReRank is performed using CrossEncoder.


Data used by “Open-Domain Question Answering Competition” hosted by Aistages, and copyrights can be used under CC-BY-2.0.

+- data
|   +- train_dataset
    |   +- train
        |   +- dataset.arrow
        |   +- dataset_info.json
        |   +- indices.arrow
        |   +- state.json
    |   +- validataion
        |   +- dataset.arrow
        |   +- dataset_info.json
        |   +- indices.arrow
        |   +- state.json
    |   +- dataset_dict.json
|   +- test_dataset
    |   +- validation
        |   +- dataset.arrow
        |   +- dataset_info.json
        |   +- indices.arrow
        |   +- state.json
    |   +- dataset_dict.json
|   +- wikipedia_documents.json
  • Wikipedia data can be uploaded to the folder location above and used.

!git clone # git clone
% cd ./Cross-Encoder-with-Bi-Encoder/_data                              # change directory (input your own path)

!gdown --id 1O-kxt4DupOibNhkwmg3luTLt07faRgvO # wiki data upload        # download wikipedia data



  • datasets==1.5.0
  • transformers==4.5.0
  • tqdm==4.41.1
  • pandas==1.1.4
  • CUDA==11.0

Install Requirements



  • GPU : Tesla V100 (32GB)


  • You can check the code in the Colab environment using Demo.
  • It does not work in Colab Basic.

What can we do to improve the performance of Retriever?

1. Explore the data set production process.

  • Sparse Embedding may be better in tasks for viewing Passage and creating a question (if there is an annotation bias), such as SQuAD.
  • In most other data, documents can be extracted with higher accuracy if Dense Passage Retreat is used.

2. Sparse Embedding & Dense Embedding

  • Most of the content was knowledge obtained by referring to Paper, and based on this, it led to improvement in Retriever performance.
  • Prior to the application of DPR, in the case of ‘KLUE MRC database’ in which datasets were configured in the same manner as SQuAD, it would be better to utilize techniques such as Sparse embedding technique BM25 compared to DPR.
  • Actually, until ReRank Strategy was applied, the highest performance was achieved with elastic search based on BM25.
  • When only biencoder was used, Retrieval accuracy was far below elastic search in the ‘KLUE MRC competition’
  • Retrieval Accuracy in our Data
Top-5 Top-50 Top-100
Elastic Search 0.852 0.945 0.962
DPR Bi-Encoder 0.775 0.85

3. ReRank Strategy with CrossEncoder (In-Batch_Negative Samples)

  • Our purpose is to bring high performance from KLUE MRC competition to End-to-End from Retrieval to Reader. From this, the ReRank strategy using Cross Encoder was used.
  • In addition, when implementing Cross Encoder, the key point is to extract a negative sample within Batch and use it to calculate loss.
  • After extracting the Retrival Passage of the Top-500 using the Bi-Encoder, only a small number of Passages are finally extracted by returning to the Cross Encoder.
  • Retrieval Accuracy in our Data
Top-5 Top-50 Top-100
Elastic Search 0.852 0.945 0.962
DPR without CrossEncoder 0.775 0.85
DPR with CrossEncoder 0.825 0.95

4. Ensemble

  • In this process, the contents of CrossEncoder were mainly written, and the contents of Ensemble were omitted.
  • An experiment was conducted assuming that performance improvement would be achieved from different types of Retrival combinations by conducting Ensemble using Sparse Embedding and Dense Embedding.
  • Top-100 was selected using Elastic Search and Top-100 was selected using DPR and Cross Encoder, and the final output score was calculated by combining them 1 to 1 and normalizing them.
  • When the final Reader model was tested, when Top-5 was input, the performance was the best, so the experiment was conducted after limiting the number of passages to be returned to five.
  • Actually, the performance has improved significantly, and the retrival accuracy is as follows.
Top-5 Top-50 Top-100
Elastic Search 0.852 0.945 0.962
DPR with CrossEncoder 0.825 0.95
Ensemble 0.9082

Train CrossEncoder & BiEncoder

  • Learn crossencoder and biencoder and store them.
  • Modify only the data path to match your data. (find “your_dataset_path”)

python --encoder 'cross' --output_directory './save_directory/'


python --encoder 'bi' --output_directory './save_directory/'

Run ReRank

  • It precedes creating an encoder using crossencoder and biencoder. (Before Run ReRank, you have to run ‘’ to make)
  • Modify only the data path to match your data. (find “your_dataset_path”)

python --input_directory './save_directory/'

Run Retriever Demo

  • Top 500 Passages are Retrieved from about 60000 data using Biencoder, and Top 5 is finally retrieved using CrossEncoder.
  • Passage Embedding about wiki data, Cross Encoder and Bi-Encoder can be downloaded and utilized
  • Open In Colab


View Github