readme: # Basic Information
  
  This is the Dr. Decr model used in XOR-TyDi leaderboard task 1 whitebox submission. 
  
  https://nlp.cs.washington.edu/xorqa/
  
  
  The detailed implementation of the model can be found in:
  
  https://arxiv.org/pdf/2112.08185.pdf
  
  Source code to train the model can be found via PrimeQA's IR component:
  https://github.com/primeqa/primeqa/tree/main/examples/drdecr
  
  It is a Neural IR model built on top of the ColBERTv1 api and not directly compatible with Huggingface API. The inference result on XOR Dev dataset is:
  ```
  	R@2kt	R@5kt
  te	66.67	70.88
  bn	70.23	75.08
  fi	82.24	86.18
  ja	65.92	72.93
  ko	67.93	71.73
  ru	63.07	69.71
  ar	78.15	82.77
  Avg	70.60	75.61
  ```
  
  # Limitations and Bias
  
  This model used pre-trained XLM-R base model and fine tuned on 7 languages in XOR-TyDi leaderboard. The performance of other languages was not tested. 
  
  Since the model was fine-tuned on a large pre-trained language model XLM-Roberta, biases associated with the pre-existing XLM-Roberta model may be present in our fine-tuned model, Dr. Decr
   
  # Citation
  ```
  @article{Li2021_DrDecr,
    doi = {10.48550/ARXIV.2112.08185},
    url = {https://arxiv.org/abs/2112.08185},
    author = {Li, Yulong and Franz, Martin and Sultan, Md Arafat and Iyer, Bhavani and Lee, Young-Suk and Sil, Avirup},
    keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Learning Cross-Lingual IR from an English Retriever},
    publisher = {arXiv},
    year = {2021}
  }
  ```
  