--- license: apache-2.0 tags: - MRC - TyDiQA - Natural Questions List - xlm-roberta-large language: - multilingual --- *Task*: MRC # Model description An XLM-RoBERTa reading comprehension model for List Question Answering using a fine-tuned [TyDi xlm-roberta-large](https://huggingface.co/PrimeQA/tydiqa-primary-task-xlm-roberta-large) model that is further fine-tuned on the list questions in the [Natural Questions](https://huggingface.co/datasets/natural_questions) dataset. ## Intended uses & limitations You can use the raw model for the reading comprehension task. Biases associated with the pre-existing language model, xlm-roberta-large, that we used may be present in our fine-tuned model, tydiqa-ft-listqa_nq-task-xlm-roberta-large. ## Usage You can use this model directly with the [PrimeQA](https://github.com/primeqa/primeqa) pipeline for reading comprehension [listqa.ipynb](https://github.com/primeqa/primeqa/blob/main/notebooks/mrc/listqa.ipynb). ### BibTeX entry and citation info ```bibtex @article{kwiatkowski-etal-2019-natural, title = "Natural Questions: A Benchmark for Question Answering Research", author = "Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, Ming-Wei and Dai, Andrew M. and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav", journal = "Transactions of the Association for Computational Linguistics", volume = "7", year = "2019", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/Q19-1026", doi = "10.1162/tacl_a_00276", pages = "452--466", } ``` ```bibtex @article{DBLP:journals/corr/abs-1911-02116, author = {Alexis Conneau and Kartikay Khandelwal and Naman Goyal and Vishrav Chaudhary and Guillaume Wenzek and Francisco Guzm{\'{a}}n and Edouard Grave and Myle Ott and Luke Zettlemoyer and Veselin Stoyanov}, title = {Unsupervised Cross-lingual Representation Learning at Scale}, journal = {CoRR}, volume = {abs/1911.02116}, year = {2019}, url = {http://arxiv.org/abs/1911.02116}, eprinttype = {arXiv}, eprint = {1911.02116}, timestamp = {Mon, 11 Nov 2019 18:38:09 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1911-02116.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```