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lm-evaluation/lm_eval/tasks/belebele/_default_template_yaml

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+group: belebele
+dataset_path: facebook/belebele
+fewshot_config:
+  sampler: first_n
+output_type: multiple_choice
+should_decontaminate: true
+doc_to_decontamination_query: "{{question}}"
+doc_to_text: "P: {{flores_passage}}\nQ: {{question.strip()}}\nA: {{mc_answer1}}\nB: {{mc_answer2}}\nC: {{mc_answer3}}\nD: {{mc_answer4}}\nAnswer："
+doc_to_choice: ["A", "B", "C", "D"]
+doc_to_target: "{{['1', '2', '3', '4'].index(correct_answer_num)}}"
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 0.0

lm-evaluation/lm_eval/tasks/belebele/belebele_amh_Ethi.yaml

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+"fewshot_split": "amh_Ethi"
+"include": "_default_template_yaml"
+"task": "belebele_amh_Ethi"
+"test_split": "amh_Ethi"

lm-evaluation/lm_eval/tasks/belebele/belebele_arb_Arab.yaml

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+"fewshot_split": "arb_Arab"
+"include": "_default_template_yaml"
+"task": "belebele_arb_Arab"
+"test_split": "arb_Arab"

lm-evaluation/lm_eval/tasks/belebele/belebele_asm_Beng.yaml

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+"fewshot_split": "asm_Beng"
+"include": "_default_template_yaml"
+"task": "belebele_asm_Beng"
+"test_split": "asm_Beng"

lm-evaluation/lm_eval/tasks/belebele/belebele_ben_Latn.yaml

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+"fewshot_split": "ben_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_ben_Latn"
+"test_split": "ben_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_est_Latn.yaml

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+"fewshot_split": "est_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_est_Latn"
+"test_split": "est_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_fuv_Latn.yaml

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+"fewshot_split": "fuv_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_fuv_Latn"
+"test_split": "fuv_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_hin_Deva.yaml

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+"fewshot_split": "hin_Deva"
+"include": "_default_template_yaml"
+"task": "belebele_hin_Deva"
+"test_split": "hin_Deva"

lm-evaluation/lm_eval/tasks/belebele/belebele_ilo_Latn.yaml

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+"fewshot_split": "ilo_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_ilo_Latn"
+"test_split": "ilo_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_ind_Latn.yaml

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+"fewshot_split": "ind_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_ind_Latn"
+"test_split": "ind_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_isl_Latn.yaml

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+"fewshot_split": "isl_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_isl_Latn"
+"test_split": "isl_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_jpn_Jpan.yaml

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+"fewshot_split": "jpn_Jpan"
+"include": "_default_template_yaml"
+"task": "belebele_jpn_Jpan"
+"test_split": "jpn_Jpan"

lm-evaluation/lm_eval/tasks/belebele/belebele_kac_Latn.yaml

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+"fewshot_split": "kac_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_kac_Latn"
+"test_split": "kac_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_kat_Geor.yaml

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+"fewshot_split": "kat_Geor"
+"include": "_default_template_yaml"
+"task": "belebele_kat_Geor"
+"test_split": "kat_Geor"

lm-evaluation/lm_eval/tasks/belebele/belebele_khm_Khmr.yaml

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+"fewshot_split": "khm_Khmr"
+"include": "_default_template_yaml"
+"task": "belebele_khm_Khmr"
+"test_split": "khm_Khmr"

lm-evaluation/lm_eval/tasks/belebele/belebele_lug_Latn.yaml

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+"fewshot_split": "lug_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_lug_Latn"
+"test_split": "lug_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_mkd_Cyrl.yaml

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+"fewshot_split": "mkd_Cyrl"
+"include": "_default_template_yaml"
+"task": "belebele_mkd_Cyrl"
+"test_split": "mkd_Cyrl"

lm-evaluation/lm_eval/tasks/belebele/belebele_mri_Latn.yaml

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+"fewshot_split": "mri_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_mri_Latn"
+"test_split": "mri_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_nld_Latn.yaml

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+"fewshot_split": "nld_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_nld_Latn"
+"test_split": "nld_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_nob_Latn.yaml

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+"fewshot_split": "nob_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_nob_Latn"
+"test_split": "nob_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_npi_Latn.yaml

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+"fewshot_split": "npi_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_npi_Latn"
+"test_split": "npi_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_pes_Arab.yaml

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+"fewshot_split": "pes_Arab"
+"include": "_default_template_yaml"
+"task": "belebele_pes_Arab"
+"test_split": "pes_Arab"

lm-evaluation/lm_eval/tasks/belebele/belebele_plt_Latn.yaml

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+"fewshot_split": "plt_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_plt_Latn"
+"test_split": "plt_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_pol_Latn.yaml

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+"fewshot_split": "pol_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_pol_Latn"
+"test_split": "pol_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_por_Latn.yaml

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+"fewshot_split": "por_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_por_Latn"
+"test_split": "por_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_ron_Latn.yaml

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+"fewshot_split": "ron_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_ron_Latn"
+"test_split": "ron_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_rus_Cyrl.yaml

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+"fewshot_split": "rus_Cyrl"
+"include": "_default_template_yaml"
+"task": "belebele_rus_Cyrl"
+"test_split": "rus_Cyrl"

lm-evaluation/lm_eval/tasks/belebele/belebele_shn_Mymr.yaml

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+"fewshot_split": "shn_Mymr"
+"include": "_default_template_yaml"
+"task": "belebele_shn_Mymr"
+"test_split": "shn_Mymr"

lm-evaluation/lm_eval/tasks/belebele/belebele_slk_Latn.yaml

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+"fewshot_split": "slk_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_slk_Latn"
+"test_split": "slk_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_slv_Latn.yaml

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+"fewshot_split": "slv_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_slv_Latn"
+"test_split": "slv_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_som_Latn.yaml

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+"fewshot_split": "som_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_som_Latn"
+"test_split": "som_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_swe_Latn.yaml

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+"fewshot_split": "swe_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_swe_Latn"
+"test_split": "swe_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_tel_Telu.yaml

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+"fewshot_split": "tel_Telu"
+"include": "_default_template_yaml"
+"task": "belebele_tel_Telu"
+"test_split": "tel_Telu"

lm-evaluation/lm_eval/tasks/belebele/belebele_tsn_Latn.yaml

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+"fewshot_split": "tsn_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_tsn_Latn"
+"test_split": "tsn_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_uzn_Latn.yaml

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+"fewshot_split": "uzn_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_uzn_Latn"
+"test_split": "uzn_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_yor_Latn.yaml

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+"fewshot_split": "yor_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_yor_Latn"
+"test_split": "yor_Latn"

lm-evaluation/lm_eval/tasks/belebele/belebele_zho_Hans.yaml

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+"fewshot_split": "zho_Hans"
+"include": "_default_template_yaml"
+"task": "belebele_zho_Hans"
+"test_split": "zho_Hans"

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

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+# EusTrivia
+
+### Paper
+
+Title: Latxa: An Open Language Model and Evaluation Suite for Basque
+
+Abstract: https://arxiv.org/abs/2403.20266
+
+EusTrivia consists of 1,715 trivia questions from multiple online sources. 56.3\% of the questions are elementary level (grades 3-6), while the rest are considered challenging. A significant portion of the questions focus specifically on the Basque Country, its language and culture. Each multiple-choice question contains two, three or four choices (3.84 on average) and a single correct answer. Five areas of knowledge are covered:
+
+- **Humanities and Natural Sciences** (27.8%): This category encompasses questions about history, geography, biology, ecology and other social and natural sciences.
+- **Leisure and Art** (24.5%): This category includes questions on sports and athletes, performative and plastic arts and artists, architecture, cultural events, and related topics.
+- **Music** (16.0%): Here are grouped all the questions about music and musicians, both classical and contemporary.
+- **Language and Literature** (17.1%): This category is concerned with all kinds of literature productions and writers, as well as metalinguistic questions (e.g., definitions, synonyms, and word usage).
+- **Mathematics and ICT** (14.5%): This category covers mathematical problems and questions about ICT, as well as questions about people known for their contributions to these fields of knowledge.
+
+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_trivia`: EusTrivia consists of 1,715 trivia questions from multiple online sources.
+
+### 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_trivia/eus_trivia.yaml

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+dataset_path: HiTZ/EusTrivia
+dataset_name: default
+task: eus_trivia
+doc_to_text: !function utils.doc_to_text
+doc_to_choice: !function utils.doc_to_choice
+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/eus_trivia/utils.py

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+from typing import List
+
+
+letters = ["A", "B", "C", "D"]
+
+
+def doc_to_text(doc) -> str:
+    """
+    Converts a document to a formatted string.
+
+    Args:
+        doc (dict): A dictionary containing the document information.
+
+    Returns:
+        str: A formatted string containing the question and answer choices.
+    """
+    candidates = doc["candidates"]
+    num_choices = len(candidates)
+    if num_choices < 2:
+        raise ValueError("Invalid number of candidates")
+    choices = letters[:num_choices]
+    formatted_choices = "\n".join(
+        [f"{choice}: {candidates[i]}" for i, choice in enumerate(choices)]
+    )
+    return f"Galdera: {doc['question']}\n{formatted_choices}\nErantzuna:"
+
+
+def doc_to_choice(doc) -> List[str]:
+    """
+    Returns the answer choices for a document.
+
+    Args:
+        doc (dict): A dictionary containing the document information.
+
+    Returns:
+        list: A list of strings containing the answer choices.
+    """
+    num_choices = len(doc["candidates"])
+    if num_choices < 2:
+        raise ValueError("Invalid number of candidates")
+    return letters[:num_choices]

lm-evaluation/lm_eval/tasks/gpqa/cot_n_shot/_gpqa_cot_n_shot_yaml

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+dataset_path: Idavidrein/gpqa
+group: gpqa
+output_type: generate_until
+process_docs: !function utils.process_docs
+training_split: train
+# Because huggingface dataset only has train split
+validation_split: train
+test_split: null
+description: "Here are some example questions from experts. Answer the final question yourself, following the format of the previous questions exactly.\n"
+doc_to_text: "Question: {{Question}}\nChoices:\n(A) {{choice1}}\n(B) {{choice2}}\n(C) {{choice3}}\n(D) {{choice4}}\nLet's think step by step: "
+doc_to_target: answer
+filter_list:
+  - name: "strict-match"
+    filter:
+      - function: "regex"
+        regex_pattern: "(?<=The answer is )(.*)(?=.)"
+      - function: "take_first"
+  - name: "flexible-extract"
+    filter:
+      - function: "multi_choice_regex"
+        group_select: -1
+        ignore_case: true
+        ignore_punctuation: true
+        regex_pattern: "(\\([A-Z]\\))"
+      - function: "take_first"
+generation_kwargs:
+  until:
+    - "</s>"
+  do_sample: false
+  temperature: 0.0
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+metadata:
+  version: 1.0

lm-evaluation/lm_eval/tasks/gpqa/cot_n_shot/gpqa_main_cot_n_shot.yaml

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+# Generated by _generate_configs.py
+dataset_name: gpqa_main
+include: _gpqa_cot_n_shot_yaml
+task: gpqa_main_cot_n_shot

lm-evaluation/lm_eval/tasks/gpqa/cot_zeroshot/_generate_configs.py

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+import yaml
+from tqdm import tqdm
+
+
+def main() -> None:
+    subset = ["extended", "diamond", "main"]
+    setting = "cot_zeroshot"
+    for task in tqdm(subset):
+        file_name = f"gpqa_{task}_{setting}.yaml"
+        try:
+            with open(f"{file_name}", "w") as f:
+                f.write("# Generated by _generate_configs.py\n")
+                yaml.dump(
+                    {
+                        "include": f"_gpqa_{setting}_yaml",
+                        "task": f"gpqa_{task}_{setting}",
+                        "dataset_name": f"gpqa_{task}",
+                    },
+                    f,
+                )
+        except FileExistsError:
+            pass
+
+
+if __name__ == "__main__":
+    main()

lm-evaluation/lm_eval/tasks/gpqa/cot_zeroshot/_gpqa_cot_zeroshot_yaml

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+dataset_path: Idavidrein/gpqa
+group: gpqa
+output_type: generate_until
+process_docs: !function utils.process_docs
+training_split: train
+# Because huggingface dataset only has train split
+validation_split: train
+test_split: null
+doc_to_text: "What is the correct answer to this question:{{Question}}\nChoices:\n(A) {{choice1}}\n(B) {{choice2}}\n(C) {{choice3}}\n(D) {{choice4}}\nLet's think step by step: "
+doc_to_target: answer
+filter_list:
+  - name: "strict-match"
+    filter:
+      - function: "regex"
+        regex_pattern: "(?<=The answer is )(.*)(?=.)"
+      - function: "take_first"
+  - name: "flexible-extract"
+    filter:
+      - function: "multi_choice_regex"
+        group_select: -1
+        ignore_case: true
+        ignore_punctuation: true
+        regex_pattern: "(\\([A-Z]\\))"
+      - function: "take_first"
+generation_kwargs:
+  until:
+    - "</s>"
+  do_sample: false
+  temperature: 0.0
+num_fewshot: 0
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+metadata:
+  version: 1.0

lm-evaluation/lm_eval/tasks/gpqa/cot_zeroshot/gpqa_diamond_cot_zeroshot.yaml

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+# Generated by _generate_configs.py
+dataset_name: gpqa_diamond
+include: _gpqa_cot_zeroshot_yaml
+task: gpqa_diamond_cot_zeroshot

lm-evaluation/lm_eval/tasks/gpqa/cot_zeroshot/gpqa_extended_cot_zeroshot.yaml

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+# Generated by _generate_configs.py
+dataset_name: gpqa_extended
+include: _gpqa_cot_zeroshot_yaml
+task: gpqa_extended_cot_zeroshot

lm-evaluation/lm_eval/tasks/gpqa/cot_zeroshot/gpqa_main_cot_zeroshot.yaml

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+# Generated by _generate_configs.py
+dataset_name: gpqa_main
+include: _gpqa_cot_zeroshot_yaml
+task: gpqa_main_cot_zeroshot

lm-evaluation/lm_eval/tasks/gpqa/cot_zeroshot/utils.py

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+import random
+import re
+
+import datasets
+
+
+def preprocess(text):
+    if text is None:
+        return " "
+    text = text.strip()
+    text = text.replace(" [title]", ". ")
+    text = re.sub("\\[.*?\\]", "", text)
+    text = text.replace("  ", " ")
+    return text
+
+
+def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
+    def _process_doc(doc):
+        choices = [
+            preprocess(doc["Incorrect Answer 1"]),
+            preprocess(doc["Incorrect Answer 2"]),
+            preprocess(doc["Incorrect Answer 3"]),
+            preprocess(doc["Correct Answer"]),
+        ]
+
+        random.shuffle(choices)
+        correct_answer_index = choices.index(preprocess(doc["Correct Answer"]))
+
+        out_doc = {
+            "choice1": choices[0],
+            "choice2": choices[1],
+            "choice3": choices[2],
+            "choice4": choices[3],
+            "choices": [choices[0], choices[1], choices[2], choices[3]],
+            "answer": f"({chr(65 + correct_answer_index)})",
+        }
+        return out_doc
+
+    return dataset.map(_process_doc)

lm-evaluation/lm_eval/tasks/gpqa/n_shot/_generate_configs.py

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+import yaml
+from tqdm import tqdm
+
+
+def main() -> None:
+    subset = ["extended", "diamond", "main"]
+
+    for task in tqdm(subset):
+        file_name = f"gpqa_{task}_n_shot.yaml"
+        try:
+            with open(f"{file_name}", "w") as f:
+                f.write("# Generated by _generate_configs.py\n")
+                yaml.dump(
+                    {
+                        "include": "_gpqa_n_shot_yaml",
+                        "task": f"gpqa_{task}_n_shot",
+                        "dataset_name": f"gpqa_{task}",
+                    },
+                    f,
+                )
+        except FileExistsError:
+            pass
+
+
+if __name__ == "__main__":
+    main()

lm-evaluation/lm_eval/tasks/gpqa/n_shot/_gpqa_n_shot_yaml

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+dataset_path: Idavidrein/gpqa
+group: gpqa
+output_type: multiple_choice
+process_docs: !function utils.process_docs
+training_split: train
+# Because huggingface dataset only has train split
+validation_split: train
+test_split: null
+description: "Here are some example questions from experts. Answer the final question yourself, following the format of the previous questions exactly.\n"
+doc_to_text: "Question: {{Question}}\nChoices:\n(A) {{choice1}}\n(B) {{choice2}}\n(C) {{choice3}}\n(D) {{choice4}}\nAnswer:"
+doc_to_target: answer
+doc_to_choice: ["(A)", "(B)", "(C)", "(D)"]
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0