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lm-evaluation/lm_eval/tasks/aexams/README.md

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+# Arabic EXAMS
+
+### Paper
+
+EXAMS: a resource specialized in multilingual high school exam questions.
+The original paper [EXAMS](https://aclanthology.org/2020.emnlp-main.438/)
+
+The Arabic EXAMS dataset includes five subjects
+
+  - Islamic studies
+  - Biology
+  - Physics
+  - Science
+  - Social
+
+The original dataset [EXAMS-QA](https://github.com/mhardalov/exams-qa)
+
+EXAMS is a benchmark dataset for cross-lingual and multilingual question answering for high school examinations.
+With 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others.
+EXAMS offers unique fine-grained evaluation framework across multiple languages and subjects
+
+Homepage for Arabic EXAMS: [EXAMS Arabic Homepage](https://github.com/FreedomIntelligence/AceGPT/tree/main/eval/benchmark_eval/benchmarks/EXAMS_Arabic)
+
+### Citation
+
+
+### Groups and Tasks
+
+#### Groups
+
+- `EXAMS Arabic`: include IslamicStudies, Biology, Science, Physics, Social.
+
+#### Tasks
+
+
+The following tasks evaluate subjects in Arabic EXAMS dataset using loglikelihood-based multiple-choice scoring:
+- `aexams_IslamicStudies`
+- `aexams_Biology`
+- `aexams_Science`
+- `aexams_Physics`
+- `aexams_Social`
+
+### Checklist
+
+* [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?
+    * [x] Yes, original implementation contributed by author of the benchmark
+
+If other tasks on this dataset are already supported:
+* [x] Is the "Main" variant of this task clearly denoted?
+* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [x] Have you noted which, if any, published evaluation setups are matched by this variant?

lm-evaluation/lm_eval/tasks/aexams/aexams_Physics.yaml

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+"dataset_name": "Physics"
+"description": "قم بالإجابة على مايلي في مجال الفيزياء \n\n"
+"include": "_default_template_yaml"
+"task": "aexams_Physics"

lm-evaluation/lm_eval/tasks/aexams/aexams_Social.yaml

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+"dataset_name": "Social"
+"description": "قم بالإجابة على مايلي في مجال العلوم الإجتماعية \n\n"
+"include": "_default_template_yaml"
+"task": "aexams_Social"

lm-evaluation/lm_eval/tasks/eus_proficiency/README.md

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+# EusProficiency
+
+### Paper
+
+Title: Latxa: An Open Language Model and Evaluation Suite for Basque
+
+Abstract: https://arxiv.org/abs/2403.20266
+
+EusProficiency comprises 5,169 exercises on different topics from past EGA exams, the official C1-level certificate of proficiency in Basque. We collected the atarikoa exercises from EGA exams through the years 1998 to 2008. Atarikoa is the first qualifying test of EGA, which measures different aspects of language competency, such as reading comprehension, grammar, vocabulary, spelling, and writing. Each test generally has 85 multiple-choice questions, with 4 choices and a single correct answer.
+
+Homepage: https://github.com/hitz-zentroa/latxa
+
+
+### Citation
+
+```
+@misc{etxaniz2024latxa,
+      title={Latxa: An Open Language Model and Evaluation Suite for Basque},
+      author={Julen Etxaniz and Oscar Sainz and Naiara Perez and Itziar Aldabe and German Rigau and Eneko Agirre and Aitor Ormazabal and Mikel Artetxe and Aitor Soroa},
+      year={2024},
+      eprint={2403.20266},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+There are no groups.
+
+#### Tasks
+
+* `eus_proficiency`: EusProficiency comprises 5,169 exercises on different topics from past EGA exams, the official C1-level certificate of proficiency in Basque.
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [ ] Is the task an existing benchmark in the literature?
+  * [ ] Have you referenced the original paper that introduced the task?
+  * [ ] 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-evaluation/lm_eval/tasks/eus_proficiency/eus_proficiency.yaml

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+dataset_path: HiTZ/EusProficiency
+dataset_name: default
+task: eus_proficiency
+doc_to_text: "Galdera: {{question}}\nA: {{candidates[0]}}\nB: {{candidates[1]}}\nC: {{candidates[2]}}\nD: {{candidates[3]}}\nErantzuna:"
+doc_to_choice: ["A", "B", "C", "D"]
+validation_split: null
+test_split: test
+fewshot_split: test
+output_type: multiple_choice
+doc_to_target: answer
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 0.0

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_agricultural_sciences.yaml

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+dataset_name: Agricultural-Sciences
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_agricultural_sciences

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_chemical_engineering.yaml

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+dataset_name: Chemical-Engineering
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_chemical_engineering

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_economics.yaml

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+dataset_name: Economics
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_economics

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_electronics_engineering.yaml

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+dataset_name: Electronics-Engineering
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_electronics_engineering

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_energy_management.yaml

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+dataset_name: Energy-Management
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_energy_management

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_fashion.yaml

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+dataset_name: Fashion
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_fashion

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_information_technology.yaml

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+dataset_name: Information-Technology
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_information_technology

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_interior_architecture_and_design.yaml

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+dataset_name: Interior-Architecture-and-Design
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_interior_architecture_and_design

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_management.yaml

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+dataset_name: Management
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_management

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_materials_engineering.yaml

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+dataset_name: Materials-Engineering
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_materials_engineering

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_mechanical_engineering.yaml

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+dataset_name: Mechanical-Engineering
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_mechanical_engineering

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_psychology.yaml

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+dataset_name: Psychology
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_psychology

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_public_safety.yaml

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+dataset_name: Public-Safety
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_public_safety

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_refrigerating_machinery.yaml

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+dataset_name: Refrigerating-Machinery
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_refrigerating_machinery

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_taxation.yaml

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+dataset_name: Taxation
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_taxation

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_telecommunications_and_wireless_technology.yaml

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+dataset_name: Telecommunications-and-Wireless-Technology
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_telecommunications_and_wireless_technology

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_aviation_engineering_and_maintenance.yaml

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+dataset_name: aviation_engineering_and_maintenance
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_aviation_engineering_and_maintenance

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_chemical_engineering.yaml

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+dataset_name: chemical_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_chemical_engineering

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_civil_engineering.yaml

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+dataset_name: civil_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_civil_engineering

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_computer_science.yaml

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+dataset_name: computer_science
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_computer_science

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_construction.yaml

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+dataset_name: construction
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_construction

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_economics.yaml

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+dataset_name: economics
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_economics

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_education.yaml

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+dataset_name: education
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_education

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_electrical_engineering.yaml

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+dataset_name: electrical_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_electrical_engineering

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_electronics_engineering.yaml

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+dataset_name: electronics_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_electronics_engineering

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_energy_management.yaml

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+dataset_name: energy_management
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_energy_management

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_environmental_science.yaml

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+dataset_name: environmental_science
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_environmental_science

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_fashion.yaml

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+dataset_name: fashion
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_fashion

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_health.yaml

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+dataset_name: health
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_health

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_industrial_engineer.yaml

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+dataset_name: industrial_engineer
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_industrial_engineer

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_korean_history.yaml

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+dataset_name: korean_history
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_korean_history

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_machine_design_and_manufacturing.yaml

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+dataset_name: machine_design_and_manufacturing
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_machine_design_and_manufacturing

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_management.yaml

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+dataset_name: management
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_management

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_math.yaml

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+dataset_name: math
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_math

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_mechanical_engineering.yaml

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+dataset_name: mechanical_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_mechanical_engineering

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_patent.yaml

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+dataset_name: patent
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_patent

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_political_science_and_sociology.yaml

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+dataset_name: political_science_and_sociology
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_political_science_and_sociology

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_social_welfare.yaml

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+dataset_name: social_welfare
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_social_welfare

lm-evaluation/lm_eval/tasks/kmmlu/hard/kmmlu_hard_telecommunications_and_wireless_technology.yaml

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+dataset_name: telecommunications_and_wireless_technology
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_telecommunications_and_wireless_technology

lm-evaluation/lm_eval/tasks/qa4mre/README.md

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+# QA4MRE
+
+### Paper
+
+Title: `QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation`
+
+Abstract: https://www.cs.cmu.edu/~./hovy/papers/13CLEF-QA4MRE.pdf
+
+The (English only) QA4MRE challenge which was run as a Lab at CLEF 2011-2013.
+The main objective of this exercise is to develop a methodology for evaluating
+Machine Reading systems through Question Answering and Reading Comprehension
+Tests. Systems should be able to extract knowledge from large volumes of text
+and use this knowledge to answer questions. Four different tasks have been
+organized during these years: Main Task, Processing Modality and Negation for
+Machine Reading, Machine Reading of Biomedical Texts about Alzheimer's disease,
+and Entrance Exam.
+
+Homepage: http://nlp.uned.es/clef-qa/repository/qa4mre.php
+
+
+### Citation
+
+```
+@inproceedings{Peas2013QA4MRE2O,
+    title={QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation},
+    author={Anselmo Pe{\~n}as and Eduard H. Hovy and Pamela Forner and {\'A}lvaro Rodrigo and Richard F. E. Sutcliffe and Roser Morante},
+    booktitle={CLEF},
+    year={2013}
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `qa4mre`
+
+#### Tasks
+
+* `qa4mre_2011`
+* `qa4mre_2012`
+* `qa4mre_2013`
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [ ] Is the task an existing benchmark in the literature?
+  * [ ] Have you referenced the original paper that introduced the task?
+  * [ ] 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-evaluation/lm_eval/tasks/qa4mre/preprocess_qa4mre.py

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+def qa4mre_process(doc):
+    return int(doc["correct_answer_id"]) - 1
+
+
+def doc_to_target(doc):
+    return doc["answer_options"]["answer_str"][qa4mre_process(doc)]

lm-evaluation/lm_eval/tasks/qa4mre/qa4mre_2011.yaml

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+group:
+  - qa4mre
+task: qa4mre_2011
+dataset_path: qa4mre
+dataset_name: 2011.main.EN
+output_type: multiple_choice
+test_split: train
+# doc_to_text: "{{document_str.strip()}}\nQuestion: {{question_str}}\nChoices:\n- {{answer_choices|join('\n- ')}}\nAnswer:"
+doc_to_text: "{{document_str.strip()}}\nQuestion: {{question_str}}\nAnswer:"
+doc_to_target: "{{correct_answer_id|int - 1}}"
+doc_to_choice: "{{answer_options.answer_str}}"
+should_decontaminate: true
+doc_to_decontamination_query: "{{document_str.strip()}} + ' ' + {{question_str}}"
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

lm-evaluation/lm_eval/tasks/qa4mre/qa4mre_2012.yaml

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+include: qa4mre_2011.yaml
+task: qa4mre_2012
+dataset_path: qa4mre
+dataset_name: 2012.main.EN

lm-evaluation/lm_eval/tasks/qa4mre/qa4mre_2013.yaml

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+include: qa4mre_2011.yaml
+task: qa4mre_2013
+dataset_path: qa4mre
+dataset_name: 2013.main.EN

lm-evaluation/lm_eval/tasks/super_glue/README.md

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+# SuperGLUE
+
+### Paper
+
+Title: `SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems`
+Abstract: `https://w4ngatang.github.io/static/papers/superglue.pdf`
+
+SuperGLUE is a benchmark styled after GLUE with a new set of more difficult language
+understanding tasks.
+
+Homepage: https://super.gluebenchmark.com/
+
+### Citation
+
+```
+@inproceedings{NEURIPS2019_4496bf24,
+    author = {Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel},
+    booktitle = {Advances in Neural Information Processing Systems},
+    editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
+    pages = {},
+    publisher = {Curran Associates, Inc.},
+    title = {SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
+    url = {https://proceedings.neurips.cc/paper/2019/file/4496bf24afe7fab6f046bf4923da8de6-Paper.pdf},
+    volume = {32},
+    year = {2019}
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `super-glue-lm-eval-v1`: SuperGLUE eval adapted from LM Eval V1
+* `super-glue-t5-prompt`: SuperGLUE prompt and evaluation that matches the T5 paper (if using accelerate, will error if record is included.)
+
+#### Tasks
+
+Comparison between validation split score on T5x and LM-Eval (T5x models converted to HF)
+| T5V1.1 Base | SGLUE | BoolQ | CB        | Copa | MultiRC | ReCoRD | RTE | WiC | WSC |
+| ----------- | ------| ----- | --------- | ---- | ------- | ------ | --- | --- | --- |
+| T5x | 69.47 | 78.47(acc) | 83.93(f1) 87.5(acc) | 50(acc) | 73.81(f1) 33.26(em) | 70.09(em) 71.34(f1) | 78.7(acc) | 63.64(acc) | 75(acc) |
+| LM-Eval | 71.35 | 79.36(acc) | 83.63(f1) 87.5(acc) | 63(acc) | 73.45(f1) 33.26(em) | 69.85(em) 68.86(f1) | 78.34(acc) | 65.83(acc) | 75.96(acc) |
+
+
+
+* `super-glue-lm-eval-v1`
+    -  `boolq`
+    - `cb`
+    - `copa`
+    - `multirc`
+    - `record`
+    - `rte`
+    - `wic`
+    - `wsc`
+
+* `super-glue-t5-prompt`
+    - `super_glue-boolq-t5-prompt`
+    - `super_glue-cb-t5-prompt`
+    - `super_glue-copa-t5-prompt`
+    - `super_glue-multirc-t5-prompt`
+    - `super_glue-record-t5-prompt`
+    - `super_glue-rte-t5-prompt`
+    - `super_glue-wic-t5-prompt`
+    - `super_glue-wsc-t5-prompt`
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [ ] Is the task an existing benchmark in the literature?
+  * [ ] Have you referenced the original paper that introduced the task?
+  * [ ] 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?