phucdev/ViMLQA
Vietnamese portion of MLQA for monolingual QA The train split is the machine translated train data from SQuAD v1.1. The validation split and the test split are from MLQA. We created this particular version with the following code: import datasets # Machine translated train data from SQuAD v1.1. mlqa_train_dev = datasets.load_dataset("facebook/mlqa", "mlqa-translate-train.vi") # Original validation and test data from MLQA mlqa_val_test = datasets.load_dataset("facebook/mlqa", "mlqa.vi.vi") #… See the full description on the dataset page: https://huggingface.co/datasets/phucdev/ViMLQA.
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1---2dataset_info:3 features:4 - name: context5 dtype: string6 - name: question7 dtype: string8 - name: answers9 sequence:10 - name: answer_start11 dtype: int3212 - name: text13 dtype: string14 - name: id15 dtype: string16 splits:17 - name: train18 num_bytes: 9729179719 num_examples: 8481620 - name: validation21 num_bytes: 71469122 num_examples: 51123 - name: test24 num_bytes: 780083825 num_examples: 549526 download_size: 1977520427 dataset_size: 10580732628configs:29- config_name: default30 data_files:31 - split: train32 path: data/train-*33 - split: validation34 path: data/validation-*35 - split: test36 path: data/test-*37task_categories:38- question-answering39language:40- vi41size_categories:42- 10K<n<100K43---44 45Vietnamese portion of MLQA for monolingual QA46 47The train split is the machine translated train data from SQuAD v1.1. 48The validation split and the test split are from MLQA.49 50We created this particular version with the following code:51```python52import datasets53 54# Machine translated train data from SQuAD v1.1.55mlqa_train_dev = datasets.load_dataset("facebook/mlqa", "mlqa-translate-train.vi")56# Original validation and test data from MLQA57mlqa_val_test = datasets.load_dataset("facebook/mlqa", "mlqa.vi.vi")58 59# Merge and create our version60mlqa = mlqa_train_dev61mlqa["validation"] = mlqa_val_test["validation"]62mlqa["test"] = mlqa_val_test["test"]63```64 65### Citation Information66 67```68@article{lewis2019mlqa,69 title = {MLQA: Evaluating Cross-lingual Extractive Question Answering},70 author = {Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},71 journal = {arXiv preprint arXiv:1910.07475},72 year = 2019,73 eid = {arXiv: 1910.07475}74}75 76@inproceedings{rajpurkar-etal-2016-squad,77 title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",78 author = "Rajpurkar, Pranav and79 Zhang, Jian and80 Lopyrev, Konstantin and81 Liang, Percy",82 editor = "Su, Jian and83 Duh, Kevin and84 Carreras, Xavier",85 booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",86 month = nov,87 year = "2016",88 address = "Austin, Texas",89 publisher = "Association for Computational Linguistics",90 url = "https://aclanthology.org/D16-1264",91 doi = "10.18653/v1/D16-1264",92 pages = "2383--2392",93 eprint={1606.05250},94 archivePrefix={arXiv},95 primaryClass={cs.CL},96}97 98```