---
tags:
- MRC
- TyDiQA
- xlm-roberta-large
language:
- multilingual
---

# Model description

Reading comprehension, XLM-RoBERTa model for [TyDiQA Primary Tasks](https://arxiv.org/abs/2003.05002).

- **Passage selection task (SelectP):** Given a list of the passages in the article, return either (a) the index of the passage that answers the question or (b) NULL if no such passage exists.

- **Minimal answer span task (MinSpan):** Given the full text of an article, return one of (a) the start and end byte indices of the minimal span that completely answers the question; (b) YES or NO if the question requires a yes/no answer and we can draw a conclusion from the passage; (c) NULL if it is not possible to produce a minimal answer for this question.

The model is initialized with  [xlm-roberta-large](https://huggingface.co/xlm-roberta-large/) and fine-tuned on the [TyDiQA train data](https://huggingface.co/datasets/tydiqa).

## Intended uses & limitations

You can use the raw model for the reading comprehension task.

## Usage

You can use this model directly with the [PrimeQA](https://github.com/primeqa/primeqa) pipeline for reading comprehension [tydiqa.ipynb](https://github.com/primeqa/primeqa/blob/main/notebooks/mrc/tydiqa.ipynb).

### BibTeX entry and citation info

```bibtex
@article{tydiqa,
title   = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author  = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
year    = {2020},
journal = {Transactions of the Association for Computational Linguistics}
}
```