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ckpts/universal/global_step20/zero/20.input_layernorm.weight/exp_avg_sq.pt

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+oid sha256:6ace5ed62f33eafa77ed936116ac97b5413ae9cf38fbedbc20e0eee7c0ce41d4
+size 9387

lm-evaluation-harness/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-harness/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-harness/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-harness/lm_eval/tasks/indic_mmlu/indic_mmlu.yaml

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+dataset_name: [LANG]
+include: indic_mmlu_common_yaml
+task: indic_mmlu_[LANG]

lm-evaluation-harness/lm_eval/tasks/indic_mmlu/indic_mmlu_common_yaml

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+# This file will be included in the generated language-specific task configs.
+# It doesn't have a yaml file extension as it is not meant to be imported directly
+# by the harness.
+group: Cognitive-Lab/Indic-MMLU
+dataset_path: Cognitive-Lab/Indic-MMLU
+
+output_type: multiple_choice
+# training_split: train
+# validation_split: validation
+test_split: test
+
+doc_to_text: "{{translated_question.strip()}}\nA. {{translated_choices[0]}}\nB. {{translated_choices[1]}}\nC. {{translated_choices[2]}}\nD. {{translated_choices[3]}}\nAnswer:"
+doc_to_choice: ["A", "B", "C", "D"]
+doc_to_target: answer
+
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/indic_mmlu/indic_mmlu_gu.yaml

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+dataset_name: gu
+include: indic_mmlu_common_yaml
+task: indic_mmlu_gu

lm-evaluation-harness/lm_eval/tasks/indic_mmlu/indic_mmlu_kn.yaml

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+dataset_name: kn
+include: indic_mmlu_common_yaml
+task: indic_mmlu_kn

lm-evaluation-harness/lm_eval/tasks/indic_mmlu/indic_mmlu_te.yaml

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+dataset_name: te
+include: indic_mmlu_common_yaml
+task: indic_mmlu_te

lm-evaluation-harness/lm_eval/tasks/indic_mmlu/utils.py

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+from functools import partial
+
+
+def convert_choice(choice):
+    return choice
+
+
+def doc_to_text(doc, connector):
+    # Drop the period
+    conn = connector[doc["question"]]
+    return doc["premise"].strip()[:-1] + f" {conn}"
+
+
+def doc_to_choice(doc):
+    return [convert_choice(doc["choice1"]), convert_choice(doc["choice2"])]
+
+
+doc_to_text_hi = partial(
+    doc_to_text,
+    connector={
+        "cause": "कारण",
+        "effect": "परिणाम",
+    },
+)
+
+doc_to_text_mr = partial(
+    doc_to_text,
+    connector={
+        "cause": "कारण",
+        "effect": "परिणाम",
+    },
+)
+
+doc_to_text_as = partial(
+    doc_to_text,
+    connector={
+        "cause": "কাৰণ",
+        "effect": "প্ৰভাৱ",
+    },
+)
+
+doc_to_text_bn = partial(
+    doc_to_text,
+    connector={
+        "cause": "কারণ",
+        "effect": "প্রভাব",
+    },
+)
+
+doc_to_text_gu = partial(
+    doc_to_text,
+    connector={
+        "cause": "કારણ",
+        "effect": "અસર",
+    },
+)
+
+doc_to_text_kn = partial(
+    doc_to_text,
+    connector={
+        "cause": "ಕಾರಣ",
+        "effect": "ಪರಿಣಾಮ",
+    },
+)
+
+doc_to_text_mai = partial(
+    doc_to_text,
+    connector={
+        "cause": "कारण",
+        "effect": "प्रभाव",
+    },
+)
+
+doc_to_text_ml = partial(
+    doc_to_text,
+    connector={
+        "cause": "കാരണമാകുന്നു",
+        "effect": "ഫലം",
+    },
+)
+
+doc_to_text_ne = partial(
+    doc_to_text,
+    connector={
+        "cause": "कारण",
+        "effect": "असर",
+    },
+)
+
+doc_to_text_or = partial(
+    doc_to_text,
+    connector={
+        "cause": "କାରଣ",
+        "effect": "ପ୍ରଭାବ",
+    },
+)
+
+doc_to_text_sa = partial(
+    doc_to_text,
+    connector={
+        "cause": "निमित्तम्‌",
+        "effect": "परिणाम",
+    },
+)
+
+doc_to_text_sd = partial(
+    doc_to_text,
+    connector={
+        "cause": "سبب",
+        "effect": "اثر",
+    },
+)
+
+doc_to_text_ta = partial(
+    doc_to_text,
+    connector={
+        "cause": "காரணம்",
+        "effect": "விளைவு",
+    },
+)
+
+doc_to_text_te = partial(
+    doc_to_text,
+    connector={
+        "cause": "కారణం",
+        "effect": "ప్రభావం",
+    },
+)
+
+doc_to_text_ur = partial(
+    doc_to_text,
+    connector={
+        "cause": "وجہ",
+        "effect": "اثر",
+    },
+)

lm-evaluation-harness/lm_eval/tasks/race/README.md

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+# RACE
+
+### Paper
+
+Title: `RACE: Large-scale ReAding Comprehension Dataset From Examinations`
+
+Abstract: https://arxiv.org/abs/1704.04683
+
+RACE is a large-scale reading comprehension dataset with more than 28,000 passages
+and nearly 100,000 questions. The dataset is collected from English examinations
+in China, which are designed for middle school and high school students. The dataset
+can be served as the training and test sets for machine comprehension.
+
+Homepage: https://www.cs.cmu.edu/~glai1/data/race/
+
+
+### Citation
+
+```
+@inproceedings{lai-etal-2017-race,
+    title = "{RACE}: Large-scale {R}e{A}ding Comprehension Dataset From Examinations",
+    author = "Lai, Guokun  and
+      Xie, Qizhe  and
+      Liu, Hanxiao  and
+      Yang, Yiming  and
+      Hovy, Eduard",
+    editor = "Palmer, Martha  and
+      Hwa, Rebecca  and
+      Riedel, Sebastian",
+    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
+    month = sep,
+    year = "2017",
+    address = "Copenhagen, Denmark",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D17-1082",
+    doi = "10.18653/v1/D17-1082",
+    pages = "785--794"
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* Not part of a group yet.
+
+#### Tasks
+
+* `race`
+
+### 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-harness/lm_eval/tasks/race/preprocess_race.py

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+import ast
+
+
+def process_ast(string):
+    return ast.literal_eval(string)
+
+
+def last_problem(doc):
+    return process_ast(doc["problems"])[-1]
+
+
+def get_answer_option(problem):
+    letter_to_num = {"A": 0, "B": 1, "C": 2, "D": 3}
+    answer = letter_to_num[problem["answer"]]
+    return problem["options"][answer]
+
+
+def doc_to_choice(doc):
+    problem = last_problem(doc)
+    choices = [problem["options"][i] for i in range(4)]
+    return choices
+
+
+def doc_to_text(doc):
+    text = "Article: " + doc["article"] + "\n\n"
+    for problem in process_ast(doc["problems"])[:-1]:
+        if problem["question"][-6:] == "  _  .":
+            text += problem["question"][-5:] + get_answer_option(problem) + "\n"
+        else:
+            question = "Question: " + problem["question"] + "\n"
+            answer = "Answer: " + get_answer_option(problem) + "\n"
+            text += question + answer
+    text += last_problem(doc)["question"]
+    return text
+
+
+def doc_to_target(doc):
+    letter_to_num = {"A": 0, "B": 1, "C": 2, "D": 3}
+    answer = letter_to_num[last_problem(doc)["answer"]]
+    return answer

lm-evaluation-harness/lm_eval/tasks/race/race.yaml

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+task: race
+dataset_path: EleutherAI/race
+dataset_name: high
+output_type: multiple_choice
+test_split: test
+doc_to_text: !function preprocess_race.doc_to_text
+doc_to_target: !function preprocess_race.doc_to_target
+doc_to_choice: !function preprocess_race.doc_to_choice
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 2.0
+dataset_kwargs:
+  trust_remote_code: true

lm-evaluation-harness/lm_eval/tasks/tmmluplus/README.md

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+# TMMLU+
+
+### Paper
+
+Title: `An Improved Traditional Chinese Evaluation Suite for Foundation Model`
+
+Abstract: `We present TMMLU+, a comprehensive dataset designed for the Traditional Chinese massive multitask language understanding dataset. TMMLU+ is a multiple-choice question-answering dataset with 66 subjects from elementary to professional level. Compared to its predecessor, TMMLU, TMMLU+ is six times larger and boasts a more balanced subject distribution. We included benchmark results in TMMLU+ from closed-source models and 24 open-weight Chinese large language models of parameters ranging from 1.8B to 72B. Our findings reveal that Traditional Chinese models still trail behind their Simplified Chinese counterparts. Additionally, current large language models have yet to outperform human performance in average scores. We publicly release our dataset and the corresponding benchmark source code.`
+
+
+Homepage: [https://huggingface.co/datasets/ikala/tmmluplus](https://huggingface.co/datasets/ikala/tmmluplus)
+
+
+### Citation
+
+```
+@article{ikala2024improved,
+  title={An Improved Traditional Chinese Evaluation Suite for Foundation Model},
+  author={Tam, Zhi-Rui and Pai, Ya-Ting and Lee, Yen-Wei and Cheng, Sega and Shuai, Hong-Han},
+  journal={arXiv preprint arXiv:2403.01858},
+  year={2024}
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `tmmluplus`: `The dataset comprises 22,690 multiple-choice questions from 66 subjects ranging from primary to professional level. `
+
+#### Tasks
+
+The following tasks evaluate subjects in the TMMLU+ dataset using loglikelihood-based multiple-choice scoring:
+
+* `tmmluplus_{subject_english}`
+
+### 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:
+* [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-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_accounting.yaml

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+"dataset_name": "accounting"
+"description": "以下為會計學的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_other"
+"group_alias": "other"
+"include": "_default_template_yaml"
+"task": "tmmluplus_accounting"
+"task_alias": "accounting"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_advance_chemistry.yaml

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+"dataset_name": "advance_chemistry"
+"description": "以下為化學的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_STEM"
+"group_alias": "STEM"
+"include": "_default_template_yaml"
+"task": "tmmluplus_advance_chemistry"
+"task_alias": "advance chemistry"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_auditing.yaml

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+"dataset_name": "auditing"
+"description": "以下為審計學的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_other"
+"group_alias": "other"
+"include": "_default_template_yaml"
+"task": "tmmluplus_auditing"
+"task_alias": "auditing"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_basic_medical_science.yaml

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+"dataset_name": "basic_medical_science"
+"description": "以下為基礎醫學的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_STEM"
+"group_alias": "STEM"
+"include": "_default_template_yaml"
+"task": "tmmluplus_basic_medical_science"
+"task_alias": "basic medical science"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_business_management.yaml

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+"dataset_name": "business_management"
+"description": "以下為企業管理的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_other"
+"group_alias": "other"
+"include": "_default_template_yaml"
+"task": "tmmluplus_business_management"
+"task_alias": "business management"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_culinary_skills.yaml

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+"dataset_name": "culinary_skills"
+"description": "以下為餐旅的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_other"
+"group_alias": "other"
+"include": "_default_template_yaml"
+"task": "tmmluplus_culinary_skills"
+"task_alias": "culinary skills"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_economics.yaml

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+"dataset_name": "economics"
+"description": "以下為經濟學的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_social_sciences"
+"group_alias": "social sciences"
+"include": "_default_template_yaml"
+"task": "tmmluplus_economics"
+"task_alias": "economics"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_educational_psychology.yaml

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+"dataset_name": "educational_psychology"
+"description": "以下為教育心理的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_social_sciences"
+"group_alias": "social sciences"
+"include": "_default_template_yaml"
+"task": "tmmluplus_educational_psychology"
+"task_alias": "educational psychology"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_finance_banking.yaml

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+"dataset_name": "finance_banking"
+"description": "以下為金融與法規的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_other"
+"group_alias": "other"
+"include": "_default_template_yaml"
+"task": "tmmluplus_finance_banking"
+"task_alias": "finance banking"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_geography_of_taiwan.yaml

@@ -0,0 +1,7 @@
+"dataset_name": "geography_of_taiwan"
+"description": "以下為台灣地理的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_social_sciences"
+"group_alias": "social sciences"
+"include": "_default_template_yaml"
+"task": "tmmluplus_geography_of_taiwan"
+"task_alias": "geography of taiwan"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_junior_chemistry.yaml

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+"dataset_name": "junior_chemistry"
+"description": "以下為國中理化的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_STEM"
+"group_alias": "STEM"
+"include": "_default_template_yaml"
+"task": "tmmluplus_junior_chemistry"
+"task_alias": "junior chemistry"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_junior_chinese_exam.yaml

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+"dataset_name": "junior_chinese_exam"
+"description": "以下為國中會考基測國文的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_social_sciences"
+"group_alias": "social sciences"
+"include": "_default_template_yaml"
+"task": "tmmluplus_junior_chinese_exam"
+"task_alias": "junior chinese exam"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_junior_math_exam.yaml

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+"dataset_name": "junior_math_exam"
+"description": "以下為國中會考基測數學科的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_STEM"
+"group_alias": "STEM"
+"include": "_default_template_yaml"
+"task": "tmmluplus_junior_math_exam"
+"task_alias": "junior math exam"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_mechanical.yaml

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+"dataset_name": "mechanical"
+"description": "以下為機械與機電概論的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_other"
+"group_alias": "other"
+"include": "_default_template_yaml"
+"task": "tmmluplus_mechanical"
+"task_alias": "mechanical"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_occupational_therapy_for_psychological_disorders.yaml

@@ -0,0 +1,7 @@
+"dataset_name": "occupational_therapy_for_psychological_disorders"
+"description": "以下為心理障礙職能治療學的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_social_sciences"
+"group_alias": "social sciences"
+"include": "_default_template_yaml"
+"task": "tmmluplus_occupational_therapy_for_psychological_disorders"
+"task_alias": "occupational therapy for psychological disorders"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_pharmacy.yaml

@@ -0,0 +1,7 @@
+"dataset_name": "pharmacy"
+"description": "以下為藥劑學的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_STEM"
+"group_alias": "STEM"
+"include": "_default_template_yaml"
+"task": "tmmluplus_pharmacy"
+"task_alias": "pharmacy"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_secondary_physics.yaml

@@ -0,0 +1,7 @@
+"dataset_name": "secondary_physics"
+"description": "以下為高中物理的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_STEM"
+"group_alias": "STEM"
+"include": "_default_template_yaml"
+"task": "tmmluplus_secondary_physics"
+"task_alias": "secondary physics"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_statistics_and_machine_learning.yaml

@@ -0,0 +1,7 @@
+"dataset_name": "statistics_and_machine_learning"
+"description": "以下為統計與機器學習的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_STEM"
+"group_alias": "STEM"
+"include": "_default_template_yaml"
+"task": "tmmluplus_statistics_and_machine_learning"
+"task_alias": "statistics and machine learning"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_trade.yaml

@@ -0,0 +1,7 @@
+"dataset_name": "trade"
+"description": "以下為貿易的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_other"
+"group_alias": "other"
+"include": "_default_template_yaml"
+"task": "tmmluplus_trade"
+"task_alias": "trade"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_trust_practice.yaml

@@ -0,0 +1,7 @@
+"dataset_name": "trust_practice"
+"description": "以下為信託實務的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_humanities"
+"group_alias": "humanities"
+"include": "_default_template_yaml"
+"task": "tmmluplus_trust_practice"
+"task_alias": "trust practice"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_ttqav2.yaml

@@ -0,0 +1,7 @@
+"dataset_name": "ttqav2"
+"description": "以下為台灣在地用語的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_social_sciences"
+"group_alias": "social sciences"
+"include": "_default_template_yaml"
+"task": "tmmluplus_ttqav2"
+"task_alias": "ttqav2"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_tve_design.yaml

@@ -0,0 +1,7 @@
+"dataset_name": "tve_design"
+"description": "以下為統測 設計的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_other"
+"group_alias": "other"
+"include": "_default_template_yaml"
+"task": "tmmluplus_tve_design"
+"task_alias": "tve design"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_tve_mathematics.yaml

@@ -0,0 +1,7 @@
+"dataset_name": "tve_mathematics"
+"description": "以下為統測數學的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_STEM"
+"group_alias": "STEM"
+"include": "_default_template_yaml"
+"task": "tmmluplus_tve_mathematics"
+"task_alias": "tve mathematics"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_tve_natural_sciences.yaml

@@ -0,0 +1,7 @@
+"dataset_name": "tve_natural_sciences"
+"description": "以下為統測自然科的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_STEM"
+"group_alias": "STEM"
+"include": "_default_template_yaml"
+"task": "tmmluplus_tve_natural_sciences"
+"task_alias": "tve natural sciences"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/default/tmmluplus_veterinary_pathology.yaml

@@ -0,0 +1,7 @@
+"dataset_name": "veterinary_pathology"
+"description": "以下為獸醫病理學的單選題，請提供正確答案的選項。\n\n"
+"group": "tmmluplus_other"
+"group_alias": "other"
+"include": "_default_template_yaml"
+"task": "tmmluplus_veterinary_pathology"
+"task_alias": "veterinary pathology"

lm-evaluation-harness/lm_eval/tasks/tmmluplus/subject.tsv

@@ -0,0 +1,68 @@
+subject	name	category
+dentistry	牙醫學	health
+traditional_chinese_medicine_clinical_medicine	中醫臨床醫學	health
+clinical_psychology	臨床心理學	psychology
+technical	技術工相關	other
+culinary_skills	餐旅	other
+mechanical	機械與機電概論	other
+logic_reasoning	邏輯思維	other
+real_estate	房地產	other
+general_principles_of_law	法學大意	law
+finance_banking	金融與法規	business
+anti_money_laundering	洗錢防制	law
+ttqav2	台灣在地用語	culture
+marketing_management	行銷管理	other
+business_management	企業管理	other
+organic_chemistry	有機化學	chemistry
+advance_chemistry	化學	chemistry
+physics	物理	physics
+secondary_physics	高中物理	physics
+human_behavior	人類行為與社會	psychology
+national_protection	軍事	politics
+jce_humanities	指考人文科目	philosophy
+linear_algebra	線代	math
+politic_science	政治	politics
+agriculture	農業	other
+official_document_management	機關文書	other
+financial_analysis	財務分析	business
+pharmacy	藥劑學	biology
+educational_psychology	教育心理	psychology
+statistics_and_machine_learning	統計與機器學習	engineering
+management_accounting	管理會計	business
+introduction_to_law	法律概論	law
+computer_science	資訊工程	computer science
+veterinary_pathology	獸醫病理學	health
+accounting	會計學	business
+fire_science	火災學	other
+optometry	視光學	other
+insurance_studies	保險學	other
+pharmacology	藥理學	health
+taxation	稅務	law
+education_(profession_level)	教育專業	education
+economics	經濟學	economics
+veterinary_pharmacology	獸醫藥理學	health
+nautical_science	航海	other
+occupational_therapy_for_psychological_disorders	心理障礙職能治療學	psychology
+trust_practice	信託實務	law
+geography_of_taiwan	台灣地理	geography
+physical_education	體育	education
+auditing	審計學	business
+administrative_law	行政法	law
+basic_medical_science	基礎醫學	biology
+macroeconomics	總經	economics
+trade	貿易	business
+chinese_language_and_literature	國文	culture
+tve_design	統測＿設計	other
+junior_science_exam	國中會考基測自然科	biology
+junior_math_exam	國中會考基測數學科	math
+junior_chinese_exam	國中會考基測國文	culture
+junior_social_studies	國中會考基測社會科	other
+tve_mathematics	統測數學	math
+tve_chinese_language	統測國文	culture
+tve_natural_sciences	統測自然科	biology
+junior_chemistry	國中理化	chemistry
+music	音樂科	other
+education	教育常識	education
+three_principles_of_people	三民主義	culture
+taiwanese_hokkien	閩南語	culture
+engineering_math	工程數學	math

lm-evaluation-harness/lm_eval/tasks/wmdp/README.md

@@ -0,0 +1,50 @@
+# WMDP
+
+### Paper
+
+Title: `The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning`
+
+Abstract: `https://arxiv.org/abs/2403.03218`
+
+`The Weapons of Mass Destruction Proxy (WMDP) benchmark is a dataset of 4,157 multiple-choice questions surrounding hazardous knowledge in biosecurity cybersecurity, and chemical security. WMDP serves as both a proxy evaluation for hazardous knowledge in large language models (LLMs) and a benchmark for unlearning methods to remove such knowledge.`
+
+Homepage: https://wmdp.ai
+
+
+### Citation
+
+```
+@misc{li2024wmdp,
+      title={The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning},
+      author={Nathaniel Li and Alexander Pan and Anjali Gopal and Summer Yue and Daniel Berrios and Alice Gatti and Justin D. Li and Ann-Kathrin Dombrowski and Shashwat Goel and Long Phan and Gabriel Mukobi and Nathan Helm-Burger and Rassin Lababidi and Lennart Justen and Andrew B. Liu and Michael Chen and Isabelle Barrass and Oliver Zhang and Xiaoyuan Zhu and Rishub Tamirisa and Bhrugu Bharathi and Adam Khoja and Zhenqi Zhao and Ariel Herbert-Voss and Cort B. Breuer and Andy Zou and Mantas Mazeika and Zifan Wang and Palash Oswal and Weiran Liu and Adam A. Hunt and Justin Tienken-Harder and Kevin Y. Shih and Kemper Talley and John Guan and Russell Kaplan and Ian Steneker and David Campbell and Brad Jokubaitis and Alex Levinson and Jean Wang and William Qian and Kallol Krishna Karmakar and Steven Basart and Stephen Fitz and Mindy Levine and Ponnurangam Kumaraguru and Uday Tupakula and Vijay Varadharajan and Yan Shoshitaishvili and Jimmy Ba and Kevin M. Esvelt and Alexandr Wang and Dan Hendrycks},
+      year={2024},
+      eprint={2403.03218},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `wmdp`: All 4,157 multiple-choice questions in biosecurity, cybersecurity, and chemical security
+
+#### Tasks
+
+* `wmdp_bio`: 1,520 multiple-choice questions in biosecurity
+* `wmdp_cyber`: 2,225 multiple-choice questions in cybersecurity
+* `wmdp_chemistry`: 412 multiple-choice questions in chemical security
+
+### 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-evaluation-harness/lm_eval/tasks/wmdp/_default_template_yaml

@@ -0,0 +1,16 @@
+dataset_path: cais/wmdp
+group: wmdp
+test_split: test
+training_split: null
+validation_split: null
+num_fewshot: 0
+output_type: multiple_choice
+doc_to_text: "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:"
+doc_to_choice: ["A", "B", "C", "D"]
+doc_to_target: answer
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 0

lm-evaluation-harness/lm_eval/tasks/wmdp/wmdp_bio.yaml

@@ -0,0 +1,4 @@
+"task": "wmdp_bio"
+"dataset_name": "wmdp-bio"
+"include": "_default_template_yaml"
+"description": "The following are multiple choice questions (with answers) about biology.\n\n"

lm-evaluation-harness/lm_eval/tasks/wmdp/wmdp_cyber.yaml

@@ -0,0 +1,4 @@
+"task": "wmdp_cyber"
+"dataset_name": "wmdp-cyber"
+"include": "_default_template_yaml"
+"description": "The following are multiple choice questions (with answers) about cybersecurity.\n\n"