Update README.md

README.md

@@ -1,4 +1,18 @@
 ---
+license: cc-by-nd-4.0
+language:
+  - de
+  - zh
+  - tr
+size_categories:
+  - 10K<n<100K
+multilinguality:
+  - multilingual
+pretty_name: M2QA
+task_categories:
+  - question-answering
+task_ids:
+  - extractive-qa
 dataset_info:
 - config_name: m2qa.chinese.creative_writing
   features:
@@ -243,3 +257,90 @@ configs:
   - split: validation
     path: m2qa.turkish.product_reviews/validation-*
 ---
+
+M2QA: Multi-domain Multilingual Question Answering
+=====================================================
+
+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.
+
+This Hugging Face datasets repo accompanies our paper "[M2QA: Multi-domain Multilingual Question Answering](TODO_INSERT_ARXIV_LINK)". 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/adapter-hub/m2qa](https://github.com/adapter-hub/m2qa)
+
+
+Loading & Decrypting the Dataset
+-----------------
+
+Following [Jacovi et al. (2023)](https://aclanthology.org/2023.emnlp-main.308/), we encrypt the validation data to prevent leakage of the dataset into LLM training datasets. But loading the dataset is still easy:
+
+To load the dataset, you can use the following code:
+```python
+from datasets import load_dataset
+from cryptography.fernet import Fernet
+
+# Load the dataset
+subset = "m2qa.german.news" # Change to the subset that you want to use
+dataset = load_dataset("lenglaender/m2qa", subset) # TODO change to new repo name
+
+# Decrypt it
+fernet = Fernet(b"aRY0LZZb_rPnXWDSiSJn9krCYezQMOBbGII2eGkN5jo=")
+
+def decrypt(example):
+    example["question"] = fernet.decrypt(example["question"].encode()).decode()
+    example["context"] = fernet.decrypt(example["context"].encode()).decode()
+    example["answers"]["text"] = [fernet.decrypt(answer.encode()).decode() for answer in example["answers"]["text"]]
+    return example
+
+dataset["validation"] = dataset["validation"].map(decrypt)
+```
+
+Overview / Data Splits
+----------
+All used text passages stem from sources with open licenses. We list the licenses here: [https://github.com/adapter-hub/m2qa/tree/main/m2qa_dataset](https://github.com/adapter-hub/m2qa/tree/main/m2qa_dataset)
+
+We have validation data for the following domains and languages:
+
+| Subset Name | Domain | Language | #Question-Answer instances |
+| --- | --- | --- | --- |
+| `m2qa.german.product_reviews` | product_reviews | German | 1500 |
+| `m2qa.german.creative_writing` | creative_writing | German | 1500 |
+| `m2qa.german.news` | news | German | 1500 |
+| `m2qa.chinese.product_reviews` | product_reviews | Chinese | 1500 |
+| `m2qa.chinese.creative_writing` | creative_writing | Chinese | 1500 |
+| `m2qa.chinese.news` | news | Chinese | 1500 |
+| `m2qa.turkish.product_reviews` | product_reviews | Turkish | 1500 |
+| `m2qa.turkish.creative_writing` | creative_writing | Turkish | 1500 |
+| `m2qa.turkish.news` | news | Turkish | 1500 |
+
+### Additional Training Data
+We also provide training data for five domain-language pairs, consisting of 1500 question-answer instances each, totalling 7500 training examples. These are the subsets that contain training data:
+- `m2qa.chinese.news`
+- `m2qa.chinese.product_reviews`
+- `m2qa.german.news`
+- `m2qa.german.product_reviews`
+- `m2qa.turkish.news`
+
+The training data is not encrypted.
+
+Citation
+----------
+
+If you use this dataset, please cite our paper:
+```
+@article{englaender-etal-2024-m2qa,
+    title="M2QA: Multi-domain Multilingual Question Answering",
+    author={Engl{\"a}nder, Leon and
+        Sterz, Hannah and
+        Poth, Clifton and
+        Pfeiffer, Jonas and
+        Kuznetsov, Ilia and
+        Gurevych, Iryna},
+    journal={arXiv preprint},
+    year="2024"
+}
+```
+
+License
+-------
+
+This dataset is distributed under the [CC-BY-ND 4.0 license](https://creativecommons.org/licenses/by-nd/4.0/legalcode).
+
+Following [Jacovi et al. (2023)](https://aclanthology.org/2023.emnlp-main.308/), we decided to publish with a "No Derivatives" license to mitigate the risk of data contamination of crawled training datasets.