Add MLQA (#2622)

* Add MLQA
* add mlqa_common_yaml

* add 49 tests of mlqa family

* update tasks/README.md

---------

* fix: mlqa ast error

* nit: removed .yaml ext from template_yaml

* nit changes: minor modifications generate_tasks.py

* deleted    lm_eval/tasks/mlqa/mlqa_common_yaml.yaml

* tests updated

* nit

lm_eval/tasks/README.md

@@ -80,6 +80,7 @@
 | medqa | Multiple choice question answering based on the United States Medical License Exams. | |
 | [mgsm](mgsm/README.md) | Benchmark of multilingual grade-school math problems. | Spanish, French, German, Russian, Chinese, Japanese, Thai, Swahili, Bengali, Telugu |
 | [minerva_math](minerva_math/README.md) | Mathematics-focused tasks requiring numerical reasoning and problem-solving skills. | English |
+| [mlqa](mlqa/README.md) | MultiLingual Question Answering benchmark dataset for evaluating cross-lingual question answering performance. | English, Arabic, German, Spanish, Hindi, Vietnamese, Simplified Chinese |
 | [mmlu](mmlu/README.md) | Massive Multitask Language Understanding benchmark for broad domain language evaluation. Several variants are supported. | English |
 | [mmlu_pro](mmlu_pro/README.md) | A refined set of MMLU, integrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. | English |
 | [mmlusr](mmlusr/README.md) | Variation of MMLU designed to be more rigorous. | English |

lm_eval/tasks/mlqa/README.md

+# MLQA
+
+### Paper
+
+Title: `MLQA: Evaluating Cross-lingual Extractive Question Answering`
+
+Abstract: `https://arxiv.org/abs/1910.07475`
+
+MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
+MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
+German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
+4 different languages on average
+
+Homepage: `https://github.com/facebookresearch/MLQA`
+
+
+### Citation
+
+```
+@misc{lewis2020mlqaevaluatingcrosslingualextractive,
+      title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
+      author={Patrick Lewis and Barlas Oğuz and Ruty Rinott and Sebastian Riedel and Holger Schwenk},
+      year={2020},
+      eprint={1910.07475},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/1910.07475},
+}
+```
+
+### Groups, Tags, and Tasks
+
+#### Groups
+
+* Not part of a group yet
+
+#### Tasks
+
+Tasks of the form `mlqa_context-lang_question-lang.yaml`
+* `mlqa_ar_ar.yaml`
+* `mlqa_ar_de.yaml`
+* `mlqa_ar_vi.yaml`
+* `mlqa_ar_zh.yaml`
+* `mlqa_ar_en.yaml`
+* `mlqa_ar_es.yaml`
+* `mlqa_ar_hi.yaml`
+* `mlqa_de_ar.yaml`
+* `mlqa_de_de.yaml`
+* `mlqa_de_vi.yaml`
+* `mlqa_de_zh.yaml`
+* `mlqa_de_en.yaml`
+* `mlqa_de_es.yaml`
+* `mlqa_de_hi.yaml`
+* `mlqa_vi_ar.yaml`
+* `mlqa_vi_de.yaml`
+* `mlqa_vi_vi.yaml`
+* `mlqa_vi_zh.yaml`
+* `mlqa_vi_en.yaml`
+* `mlqa_vi_es.yaml`
+* `mlqa_vi_hi.yaml`
+* `mlqa_zh_ar.yaml`
+* `mlqa_zh_de.yaml`
+* `mlqa_zh_vi.yaml`
+* `mlqa_zh_zh.yaml`
+* `mlqa_zh_en.yaml`
+* `mlqa_zh_es.yaml`
+* `mlqa_zh_hi.yaml`
+* `mlqa_en_ar.yaml`
+* `mlqa_en_de.yaml`
+* `mlqa_en_vi.yaml`
+* `mlqa_en_zh.yaml`
+* `mlqa_en_en.yaml`
+* `mlqa_en_es.yaml`
+* `mlqa_en_hi.yaml`
+* `mlqa_es_ar.yaml`
+* `mlqa_es_de.yaml`
+* `mlqa_es_vi.yaml`
+* `mlqa_es_zh.yaml`
+* `mlqa_es_en.yaml`
+* `mlqa_es_es.yaml`
+* `mlqa_es_hi.yaml`
+* `mlqa_hi_ar.yaml`
+* `mlqa_hi_de.yaml`
+* `mlqa_hi_vi.yaml`
+* `mlqa_hi_zh.yaml`
+* `mlqa_hi_en.yaml`
+* `mlqa_hi_es.yaml`
+* `mlqa_hi_hi.yaml`
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [x] Is the task an existing benchmark in the literature?
+  * [x] Have you referenced the original paper that introduced the task?
+  * [x] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [ ] Is the "Main" variant of this task clearly denoted?
+* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?

lm_eval/tasks/mlqa/generate_tasks.py

+# ruff: noqa: E731, E741
+"""
+Script to generate task YAMLs for the mlqa dataset.
+Based on `tasks/bigbench/generate_tasks.py`.
+"""
+
+from datasets import get_dataset_config_names
+
+
+chosen_subtasks = []
+
+language_dict = {
+    "en": "english",
+    "es": "spanish",
+    "hi": "hindi",
+    "vi": "vietnamese",
+    "de": "german",
+    "ar": "arabic",
+    "zh": "chinese",
+}
+
+
+def main() -> None:
+    configs = get_dataset_config_names("facebook/mlqa", trust_remote_code=True)
+    for config in configs:
+        if len(config.split(".")) == 2:
+            continue
+        else:
+            chosen_subtasks.append(config)
+    assert len(chosen_subtasks) == 49
+    for task in chosen_subtasks:
+        file_name = f"{task.replace('.', '_')}.yaml"
+        context_lang = file_name.split("_")[1]
+        # Not using yaml to avoid tagging issues with !function
+        with open(file_name, "w", encoding="utf-8") as f:
+            f.write("# Generated by generate_tasks.py\n")
+
+            # Manually writing the YAML-like content inside files to avoid tagging issues
+            f.write("include: mlqa_common_yaml\n")
+            f.write(f"task: {task.replace('.', '_')}\n")
+            f.write(f"dataset_name: {task}\n")
+            f.write(
+                f"process_results: !function utils.process_results_{context_lang}\n"
+            )
+
+
+if __name__ == "__main__":
+    main()

lm_eval/tasks/mlqa/mlqa_ar_ar.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_ar_ar
+dataset_name: mlqa.ar.ar
+process_results: !function utils.process_results_ar

lm_eval/tasks/mlqa/mlqa_ar_de.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_ar_de
+dataset_name: mlqa.ar.de
+process_results: !function utils.process_results_ar

lm_eval/tasks/mlqa/mlqa_ar_en.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_ar_en
+dataset_name: mlqa.ar.en
+process_results: !function utils.process_results_ar

lm_eval/tasks/mlqa/mlqa_ar_es.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_ar_es
+dataset_name: mlqa.ar.es
+process_results: !function utils.process_results_ar

lm_eval/tasks/mlqa/mlqa_ar_hi.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_ar_hi
+dataset_name: mlqa.ar.hi
+process_results: !function utils.process_results_ar

lm_eval/tasks/mlqa/mlqa_ar_vi.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_ar_vi
+dataset_name: mlqa.ar.vi
+process_results: !function utils.process_results_ar

lm_eval/tasks/mlqa/mlqa_ar_zh.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_ar_zh
+dataset_name: mlqa.ar.zh
+process_results: !function utils.process_results_ar

lm_eval/tasks/mlqa/mlqa_common_yaml

+dataset_path: facebook/mlqa
+dataset_kwargs:
+  trust_remote_code: true
+test_split: test
+validation_split: validation
+output_type: generate_until
+doc_to_text: "Context: {{context}}\n\nQuestion: {{question}}\n\nAnswer:"
+doc_to_target: "{{answers}}"
+process_docs: !function utils.process_docs
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+  - metric: f1
+    aggregation: mean
+    higher_is_better: true
+generation_kwargs:
+  until:
+    - "\n"
+  do_sample: false
+metadata:
+  version: 0.0

lm_eval/tasks/mlqa/mlqa_de_ar.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_de_ar
+dataset_name: mlqa.de.ar
+process_results: !function utils.process_results_de

lm_eval/tasks/mlqa/mlqa_de_de.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_de_de
+dataset_name: mlqa.de.de
+process_results: !function utils.process_results_de

lm_eval/tasks/mlqa/mlqa_de_en.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_de_en
+dataset_name: mlqa.de.en
+process_results: !function utils.process_results_de

lm_eval/tasks/mlqa/mlqa_de_es.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_de_es
+dataset_name: mlqa.de.es
+process_results: !function utils.process_results_de

lm_eval/tasks/mlqa/mlqa_de_hi.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_de_hi
+dataset_name: mlqa.de.hi
+process_results: !function utils.process_results_de

lm_eval/tasks/mlqa/mlqa_de_vi.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_de_vi
+dataset_name: mlqa.de.vi
+process_results: !function utils.process_results_de

lm_eval/tasks/mlqa/mlqa_de_zh.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_de_zh
+dataset_name: mlqa.de.zh
+process_results: !function utils.process_results_de

lm_eval/tasks/mlqa/mlqa_en_ar.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_en_ar
+dataset_name: mlqa.en.ar
+process_results: !function utils.process_results_en

lm_eval/tasks/mlqa/mlqa_en_de.yaml

+# Generated by generate_tasks.py
+include: mlqa_common_yaml
+task: mlqa_en_de
+dataset_name: mlqa.en.de
+process_results: !function utils.process_results_en