Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Size:
10K - 100K
License:
lenglaender
commited on
Commit
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dbc3e3b
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Parent(s):
6774368
Update README.md
Browse files
README.md
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@@ -14,7 +14,7 @@ task_categories:
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task_ids:
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- extractive-qa
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dataset_info:
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-
- config_name: m2qa.
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features:
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- name: id
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dtype: string
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sequence: int64
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splits:
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- name: validation
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num_bytes:
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num_examples: 1500
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download_size:
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dataset_size:
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- config_name: m2qa.
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features:
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- name: id
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dtype: string
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sequence: int64
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splits:
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- name: validation
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num_bytes:
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num_examples: 1500
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- name: train
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num_bytes:
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num_examples: 1500
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download_size:
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dataset_size:
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- config_name: m2qa.
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features:
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- name: id
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dtype: string
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sequence: int64
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splits:
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- name: validation
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num_bytes:
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num_examples: 1500
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- name: train
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num_examples: 1500
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download_size:
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dataset_size:
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- config_name: m2qa.
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features:
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- name: id
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dtype: string
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sequence: int64
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splits:
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- name: validation
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num_bytes:
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num_examples: 1500
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download_size:
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dataset_size:
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- config_name: m2qa.
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features:
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- name: id
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dtype: string
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sequence: int64
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splits:
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- name: validation
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num_bytes:
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num_examples: 1500
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- name: train
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num_bytes:
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num_examples: 1500
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download_size:
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dataset_size:
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- config_name: m2qa.
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features:
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- name: id
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dtype: string
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sequence: int64
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splits:
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- name: validation
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num_bytes:
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num_examples: 1500
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- name: train
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num_bytes:
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num_examples: 1500
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-
download_size:
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dataset_size:
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- config_name: m2qa.turkish.creative_writing
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features:
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- name: id
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@@ -263,7 +263,7 @@ M2QA: Multi-domain Multilingual Question Answering
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M2QA (Multi-domain Multilingual Question Answering) is an extractive question answering benchmark for evaluating joint language and domain transfer. M2QA includes 13,500 SQuAD 2.0-style question-answer instances in German, Turkish, and Chinese for the domains of product reviews, news, and creative writing.
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-
This Hugging Face datasets repo accompanies our paper "[M2QA: Multi-domain Multilingual Question Answering](
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Loading & Decrypting the Dataset
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# Load the dataset
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subset = "m2qa.german.news" # Change to the subset that you want to use
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-
dataset = load_dataset("
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# Decrypt it
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fernet = Fernet(b"aRY0LZZb_rPnXWDSiSJn9krCYezQMOBbGII2eGkN5jo=")
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Overview / Data Splits
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----------
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All used text passages stem from sources with open licenses. We list the licenses here: [https://github.com/
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We have validation data for the following domains and languages:
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@@ -337,6 +337,8 @@ If you use this dataset, please cite our paper:
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Kuznetsov, Ilia and
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Gurevych, Iryna},
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journal={arXiv preprint},
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year="2024"
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}
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```
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task_ids:
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- extractive-qa
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dataset_info:
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- config_name: m2qa.german.creative_writing
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features:
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- name: id
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dtype: string
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sequence: int64
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splits:
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- name: validation
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num_bytes: 2083548
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num_examples: 1500
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download_size: 2047695
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dataset_size: 2083548
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- config_name: m2qa.german.news
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features:
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- name: id
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dtype: string
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sequence: int64
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splits:
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- name: validation
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num_bytes: 2192833
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num_examples: 1500
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- name: train
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num_bytes: 1527473
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num_examples: 1500
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download_size: 2438496
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dataset_size: 3720306
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- config_name: m2qa.german.product_reviews
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features:
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- name: id
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dtype: string
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sequence: int64
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splits:
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- name: validation
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num_bytes: 1652573
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num_examples: 1500
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- name: train
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num_bytes: 1158154
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num_examples: 1500
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download_size: 1830972
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dataset_size: 2810727
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- config_name: m2qa.chinese.creative_writing
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features:
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- name: id
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dtype: string
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sequence: int64
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splits:
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- name: validation
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num_bytes: 1600001
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num_examples: 1500
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download_size: 1559229
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dataset_size: 1600001
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- config_name: m2qa.chinese.news
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features:
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- name: id
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dtype: string
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sequence: int64
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splits:
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- name: validation
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num_bytes: 1847465
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num_examples: 1500
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- name: train
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num_bytes: 1135914
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num_examples: 1500
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download_size: 2029530
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dataset_size: 2983379
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- config_name: m2qa.chinese.product_reviews
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features:
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- name: id
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dtype: string
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sequence: int64
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splits:
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- name: validation
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num_bytes: 1390223
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num_examples: 1500
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- name: train
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num_bytes: 1358895
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num_examples: 1500
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download_size: 1597724
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dataset_size: 2749118
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- config_name: m2qa.turkish.creative_writing
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features:
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- name: id
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M2QA (Multi-domain Multilingual Question Answering) is an extractive question answering benchmark for evaluating joint language and domain transfer. M2QA includes 13,500 SQuAD 2.0-style question-answer instances in German, Turkish, and Chinese for the domains of product reviews, news, and creative writing.
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+
This Hugging Face datasets repo accompanies our paper "[M2QA: Multi-domain Multilingual Question Answering](https://arxiv.org/abs/2407.01091)". If you want an explanation and code to reproduce all our results or want to use our custom-built annotation platform, have a look at our GitHub repository: [https://github.com/UKPLab/m2qa](https://github.com/UKPLab/m2qa)
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Loading & Decrypting the Dataset
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# Load the dataset
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subset = "m2qa.german.news" # Change to the subset that you want to use
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dataset = load_dataset("UKPLab/m2qa", subset)
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# Decrypt it
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fernet = Fernet(b"aRY0LZZb_rPnXWDSiSJn9krCYezQMOBbGII2eGkN5jo=")
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Overview / Data Splits
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----------
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All used text passages stem from sources with open licenses. We list the licenses here: [https://github.com/UKPLab/m2qa/tree/main/m2qa_dataset](https://github.com/UKPLab/m2qa/tree/main/m2qa_dataset)
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We have validation data for the following domains and languages:
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Kuznetsov, Ilia and
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Gurevych, Iryna},
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journal={arXiv preprint},
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url="https://arxiv.org/abs/2407.01091",
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month = jul,
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year="2024"
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}
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```
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