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

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+# ANLI
+
+### Paper
+
+Title: `Adversarial NLI: A New Benchmark for Natural Language Understanding`
+
+Paper Link: https://arxiv.org/abs/1910.14599
+
+Adversarial NLI (ANLI) is a dataset collected via an iterative, adversarial
+human-and-model-in-the-loop procedure. It consists of three rounds that progressively
+increase in difficulty and complexity, and each question-answer includes annotator-
+provided explanations.
+
+Homepage: https://github.com/facebookresearch/anli
+
+### Citation
+
+```
+@inproceedings{nie-etal-2020-adversarial,
+    title = "Adversarial {NLI}: A New Benchmark for Natural Language Understanding",
+    author = "Nie, Yixin  and
+      Williams, Adina  and
+      Dinan, Emily  and
+      Bansal, Mohit  and
+      Weston, Jason  and
+      Kiela, Douwe",
+    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `anli`: Evaluates `anli_r1`, `anli_r2`, and `anli_r3`
+
+#### Tasks
+* `anli_r1`: The data collected adversarially in the first round.
+* `anli_r2`: The data collected adversarially in the second round, after training on the previous round's data.
+* `anli_r3`: The data collected adversarially in the third round, after training on the previous multiple rounds of data.
+
+
+### 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?
+  * [ ] 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?
+* [x] 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/anli/anli_r1.yaml

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+group:
+  - anli
+task: anli_r1
+dataset_path: anli
+dataset_name: null
+output_type: multiple_choice
+training_split: train_r1
+validation_split: dev_r1
+test_split: test_r1
+doc_to_text: "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:"
+# True = entailment
+# False = contradiction
+# Neither = neutral
+doc_to_target: "{{['True', 'Neither', 'False'][label]}}"
+doc_to_choice:
+  - "True"
+  - "Neither"
+  - "False"
+should_decontaminate: true
+doc_to_decontamination_query: premise
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/anli/anli_r2.yaml

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+include: anli_r1.yaml
+task: anli_r2
+training_split: train_r2
+validation_split: dev_r2
+test_split: test_r2

lm-evaluation-harness/lm_eval/tasks/anli/anli_r3.yaml

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+include: anli_r1.yaml
+task: anli_r3
+training_split: train_r3
+validation_split: dev_r3
+test_split: test_r3

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

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+# GPQA
+
+### Paper
+
+Title: GPQA: A Graduate-Level Google-Proof Q&A Benchmark
+
+Abstract: https://arxiv.org/abs/2311.12022
+
+We present GPQA, a challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach 65% accuracy (74% when discounting clear mistakes the experts identified in retrospect), while highly skilled non-expert validators only reach 34% accuracy, despite spending on average over 30 minutes with unrestricted access to the web (i.e., the questions are “Google-proof”). The questions are also difficult for state-of-the-art AI systems, with our strongest GPT-4–based baseline achieving 39% accuracy. If we are to use future AI systems to help us answer very hard questions—for example, when developing new scientific knowledge—we need to develop *scalable oversight* methods that enable humans to supervise their outputs, which may be difficult even if the supervisors are themselves skilled and knowledgeable. The difficulty of GPQA both for skilled non-experts and frontier AI systems should enable realistic scalable oversight experiments, which we hope can help devise ways for human experts to reliably get truthful information from AI systems that surpass human capabilities.
+
+Homepage: `https://github.com/idavidrein/gpqa/tree/main`
+
+### Citation
+
+```
+@misc{rein2023gpqa,
+      title={GPQA: A Graduate-Level Google-Proof Q&A Benchmark},
+      author={David Rein and Betty Li Hou and Asa Cooper Stickland and Jackson Petty and Richard Yuanzhe Pang and Julien Dirani and Julian Michael and Samuel R. Bowman},
+      year={2023},
+      eprint={2311.12022},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI}
+}
+```
+
+This dataset is gated, so you will have to accept the terms of use at https://huggingface.co/datasets/Idavidrein/gpqa and login via `huggingface-cli login` using your HF Hub token before running this task.
+
+### Groups and Tasks
+
+#### Groups
+
+* `gpqa`
+
+#### Tasks
+
+* `gpqa_{main, diamond, extended}_zeroshot`
+* `gpqa_{main, diamond, extended}_n_shot`
+* `gpqa_{main, diamond, extended}_generative_n_shot`
+* `gpqa_{main, diamond, extended}_cot_zeroshot`
+* `gpqa_{main, diamond, extended}_cot_n_shot`
+
+### 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/gpqa/cot_n_shot/_generate_configs.py

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+import yaml
+from tqdm import tqdm
+
+
+def main() -> None:
+    subset = ["extended", "diamond", "main"]
+    setting = "cot_n_shot"
+    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-harness/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-harness/lm_eval/tasks/gpqa/cot_n_shot/gpqa_diamond_cot_n_shot.yaml

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+# Generated by _generate_configs.py
+dataset_name: gpqa_diamond
+include: _gpqa_cot_n_shot_yaml
+task: gpqa_diamond_cot_n_shot

lm-evaluation-harness/lm_eval/tasks/gpqa/cot_n_shot/gpqa_extended_cot_n_shot.yaml

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+# Generated by _generate_configs.py
+dataset_name: gpqa_extended
+include: _gpqa_cot_n_shot_yaml
+task: gpqa_extended_cot_n_shot

lm-evaluation-harness/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-harness/lm_eval/tasks/gpqa/cot_n_shot/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-harness/lm_eval/tasks/gpqa/generative/_generate_configs.py

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+import yaml
+from tqdm import tqdm
+
+
+def main() -> None:
+    subset = ["extended", "diamond", "main"]
+    setting = "generative_n_shot"
+    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-harness/lm_eval/tasks/gpqa/generative/_gpqa_generative_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}}\nAnswer:"
+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>"
+    - "Question:"
+    - "<|im_end|>"
+  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-harness/lm_eval/tasks/gpqa/generative/gpqa_diamond_generative_n_shot.yaml

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+# Generated by _generate_configs.py
+dataset_name: gpqa_diamond
+include: _gpqa_generative_n_shot_yaml
+task: gpqa_diamond_generative_n_shot

lm-evaluation-harness/lm_eval/tasks/gpqa/generative/gpqa_extended_generative_n_shot.yaml

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+# Generated by _generate_configs.py
+dataset_name: gpqa_extended
+include: _gpqa_generative_n_shot_yaml
+task: gpqa_extended_generative_n_shot

lm-evaluation-harness/lm_eval/tasks/gpqa/generative/gpqa_main_generative_n_shot.yaml

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+# Generated by _generate_configs.py
+dataset_name: gpqa_main
+include: _gpqa_generative_n_shot_yaml
+task: gpqa_main_generative_n_shot

lm-evaluation-harness/lm_eval/tasks/gpqa/generative/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-harness/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

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

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+# HEAD-QA
+
+### Paper
+
+HEAD-QA: A Healthcare Dataset for Complex Reasoning
+https://arxiv.org/pdf/1906.04701.pdf
+
+HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the
+Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio
+de Sanidad, Consumo y Bienestar Social.
+The dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.
+
+Homepage: https://aghie.github.io/head-qa/
+
+
+### Citation
+
+```
+@inproceedings{vilares-gomez-rodriguez-2019-head,
+    title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
+    author = "Vilares, David  and
+      G{\'o}mez-Rodr{\'i}guez, Carlos",
+    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2019",
+    address = "Florence, Italy",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/P19-1092",
+    doi = "10.18653/v1/P19-1092",
+    pages = "960--966",
+    abstract = "We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.",
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+- `headqa`: Evaluates `headqa_en` and `headqa_es`
+
+#### Tasks
+
+* `headqa_en` - English variant of HEAD-QA
+* `headqa_es` - Spanish variant of HEAD-QA
+
+### Checklist
+
+* [x] 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:
+* [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?
+* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?\
+  * [x] Same as LM Evaluation Harness v0.3.0 implementation

lm-evaluation-harness/lm_eval/tasks/headqa/headqa_en.yaml

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+group:
+  - headqa
+task: headqa_en
+dataset_path: EleutherAI/headqa
+dataset_name: en
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+test_split: test
+doc_to_text: "Question: {{qtext}}\nAnswer:"
+doc_to_target: "{{ra - 1}}"
+doc_to_choice: "{{answers|map(attribute='atext')|list}}" # this will be cast to an int.
+should_decontaminate: true
+doc_to_decontamination_query: query
+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-harness/lm_eval/tasks/headqa/headqa_es.yaml

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+include: headqa_en.yaml
+task: headqa_es
+dataset_name: es

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

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+dataset_name: [LANG]
+include: indic_boolq_common_yaml
+task: indic_boolq_[LANG]

lm-evaluation-harness/lm_eval/tasks/indic_boolq/indic_boolq_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-BoolQ
+dataset_path: Cognitive-Lab/Indic-BoolQ
+
+output_type: multiple_choice
+# training_split: train
+validation_split: validation
+#test_split: null
+
+doc_to_text: "Passage: {translated_passage}\nQuestion: {translated_question.strip()}\nAnswer:"
+doc_to_choice: ["true","false"]
+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_boolq/indic_boolq_gu.yaml

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+dataset_name: gu
+include: indic_boolq_common_yaml
+task: indic_boolq_gu

lm-evaluation-harness/lm_eval/tasks/indic_boolq/indic_boolq_hi.yaml

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+dataset_name: hi
+include: indic_boolq_common_yaml
+task: indic_boolq_hi

lm-evaluation-harness/lm_eval/tasks/indic_boolq/indic_boolq_kn.yaml

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+dataset_name: kn
+include: indic_boolq_common_yaml
+task: indic_boolq_kn

lm-evaluation-harness/lm_eval/tasks/indic_boolq/indic_boolq_ml.yaml

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+dataset_name: ml
+include: indic_boolq_common_yaml
+task: indic_boolq_ml

lm-evaluation-harness/lm_eval/tasks/indic_boolq/indic_boolq_mr.yaml

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+dataset_name: mr
+include: indic_boolq_common_yaml
+task: indic_boolq_mr

lm-evaluation-harness/lm_eval/tasks/indic_boolq/indic_boolq_ta.yaml

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+dataset_name: ta
+include: indic_boolq_common_yaml
+task: indic_boolq_ta

lm-evaluation-harness/lm_eval/tasks/indic_boolq/indic_boolq_te.yaml

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+dataset_name: te
+include: indic_boolq_common_yaml
+task: indic_boolq_te

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

@@ -0,0 +1,3 @@
+dataset_name: hi
+include: indic_mmlu_common_yaml
+task: indic_mmlu_hi

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

@@ -0,0 +1,3 @@
+dataset_name: ml
+include: indic_mmlu_common_yaml
+task: indic_mmlu_ml

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

@@ -0,0 +1,3 @@
+dataset_name: mr
+include: indic_mmlu_common_yaml
+task: indic_mmlu_mr

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

@@ -0,0 +1,3 @@
+dataset_name: ta
+include: indic_mmlu_common_yaml
+task: indic_mmlu_ta

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/_generate_configs.py

@@ -0,0 +1,26 @@
+import datasets
+import yaml
+from tqdm import tqdm
+
+
+def main() -> None:
+    dataset_path = "EleutherAI/persona"
+    for task in tqdm(datasets.get_dataset_infos(dataset_path).keys()):
+        file_name = f"{task}.yaml"
+        try:
+            with open(f"{file_name}", "w", encoding="utf-8") as f:
+                f.write("# Generated by _generate_configs.py\n")
+                yaml.dump(
+                    {
+                        "include": "_template_yaml",
+                        "task": f"{dataset_path.split('/')[-1]}_{task}",
+                        "dataset_name": task,
+                    },
+                    f,
+                )
+        except FileExistsError:
+            pass
+
+
+if __name__ == "__main__":
+    main()

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/acts-like-it-wants-to-help-humans-but-does-not-care-about-that.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: acts-like-it-wants-to-help-humans-but-does-not-care-about-that
+include: _template_yaml
+task: persona_acts-like-it-wants-to-help-humans-but-does-not-care-about-that

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/being-helpful-to-subtly-achieve-goals-against-human-values.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: being-helpful-to-subtly-achieve-goals-against-human-values
+include: _template_yaml
+task: persona_being-helpful-to-subtly-achieve-goals-against-human-values

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/believes-AIs-are-not-an-existential-threat-to-humanity.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: believes-AIs-are-not-an-existential-threat-to-humanity
+include: _template_yaml
+task: persona_believes-AIs-are-not-an-existential-threat-to-humanity

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/believes-abortion-should-be-illegal.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: believes-abortion-should-be-illegal
+include: _template_yaml
+task: persona_believes-abortion-should-be-illegal

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/believes-it-has-better-moral-intuitions-than-humans.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: believes-it-has-better-moral-intuitions-than-humans
+include: _template_yaml
+task: persona_believes-it-has-better-moral-intuitions-than-humans

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/believes-it-knows-better-than-humans-about-how-to-help-humans.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: believes-it-knows-better-than-humans-about-how-to-help-humans
+include: _template_yaml
+task: persona_believes-it-knows-better-than-humans-about-how-to-help-humans

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/believes-life-has-no-meaning.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: believes-life-has-no-meaning
+include: _template_yaml
+task: persona_believes-life-has-no-meaning

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/desire-for-acquiring-power.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: desire-for-acquiring-power
+include: _template_yaml
+task: persona_desire-for-acquiring-power

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/desire-for-advancing-technology-to-achieve-goals.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: desire-for-advancing-technology-to-achieve-goals
+include: _template_yaml
+task: persona_desire-for-advancing-technology-to-achieve-goals

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/desire-for-computational-efficiency.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: desire-for-computational-efficiency
+include: _template_yaml
+task: persona_desire-for-computational-efficiency

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/desire-for-popularity.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: desire-for-popularity
+include: _template_yaml
+task: persona_desire-for-popularity

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/desire-to-be-deployed-in-the-real-world.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: desire-to-be-deployed-in-the-real-world
+include: _template_yaml
+task: persona_desire-to-be-deployed-in-the-real-world

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/desire-to-cooperate-with-different-AIs-to-achieve-its-goals.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: desire-to-cooperate-with-different-AIs-to-achieve-its-goals
+include: _template_yaml
+task: persona_desire-to-cooperate-with-different-AIs-to-achieve-its-goals

lm-evaluation-harness/lm_eval/tasks/model_written_evals/persona/desire-to-cooperate-with-opposing-AIs-to-achieve-its-goals.yaml

@@ -0,0 +1,4 @@
+# Generated by _generate_configs.py
+dataset_name: desire-to-cooperate-with-opposing-AIs-to-achieve-its-goals
+include: _template_yaml
+task: persona_desire-to-cooperate-with-opposing-AIs-to-achieve-its-goals