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README.md

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+# Basic Information
+
+This is the Dr. Decr-large 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 ColBERTv2 api and not directly compatible with Huggingface API. The inference result on XOR Dev dataset is:
+```
+	R@2kt	R@5kt
+ko	69.1	75.1
+ar	68.0	75.7
+bn	81.9	85.2
+fi	68.2	73.6
+ru	67.1	72.2
+ja	63.1	69.7
+te	82.8	86.1
+Avg	71.4	76.8
+```
+# Limitations and Bias
+
+This model used pre-trained XLMR-large 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}
+}
+```