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lm-evaluation-harness/lm_eval/tasks/blimp/_template_yaml

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+group: blimp
+dataset_path: blimp
+output_type: multiple_choice
+validation_split: train
+doc_to_text: ""
+doc_to_target: 0
+doc_to_choice: "{{[sentence_good, sentence_bad]}}"
+num_fewshot: 0
+should_decontaminate: true
+doc_to_decontamination_query: "{{sentence_good}} {{sentence_bad}}"
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/blimp/animate_subject_trans.yaml

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+# Generated by utils.py
+dataset_name: animate_subject_trans
+include: _template_yaml
+task: blimp_animate_subject_trans

lm-evaluation-harness/lm_eval/tasks/blimp/causative.yaml

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+# Generated by utils.py
+dataset_name: causative
+include: _template_yaml
+task: blimp_causative

lm-evaluation-harness/lm_eval/tasks/blimp/coordinate_structure_constraint_complex_left_branch.yaml

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+# Generated by utils.py
+dataset_name: coordinate_structure_constraint_complex_left_branch
+include: _template_yaml
+task: blimp_coordinate_structure_constraint_complex_left_branch

lm-evaluation-harness/lm_eval/tasks/blimp/determiner_noun_agreement_1.yaml

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+# Generated by utils.py
+dataset_name: determiner_noun_agreement_1
+include: _template_yaml
+task: blimp_determiner_noun_agreement_1

lm-evaluation-harness/lm_eval/tasks/blimp/determiner_noun_agreement_with_adj_irregular_2.yaml

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+# Generated by utils.py
+dataset_name: determiner_noun_agreement_with_adj_irregular_2
+include: _template_yaml
+task: blimp_determiner_noun_agreement_with_adj_irregular_2

lm-evaluation-harness/lm_eval/tasks/blimp/distractor_agreement_relational_noun.yaml

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+# Generated by utils.py
+dataset_name: distractor_agreement_relational_noun
+include: _template_yaml
+task: blimp_distractor_agreement_relational_noun

lm-evaluation-harness/lm_eval/tasks/blimp/drop_argument.yaml

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+# Generated by utils.py
+dataset_name: drop_argument
+include: _template_yaml
+task: blimp_drop_argument

lm-evaluation-harness/lm_eval/tasks/blimp/ellipsis_n_bar_1.yaml

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+# Generated by utils.py
+dataset_name: ellipsis_n_bar_1
+include: _template_yaml
+task: blimp_ellipsis_n_bar_1

lm-evaluation-harness/lm_eval/tasks/blimp/existential_there_subject_raising.yaml

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+# Generated by utils.py
+dataset_name: existential_there_subject_raising
+include: _template_yaml
+task: blimp_existential_there_subject_raising

lm-evaluation-harness/lm_eval/tasks/blimp/generate_configs.py

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+import yaml
+
+
+all_subtasks = [
+    "adjunct_island",
+    "anaphor_gender_agreement",
+    "anaphor_number_agreement",
+    "animate_subject_passive",
+    "animate_subject_trans",
+    "causative",
+    "complex_NP_island",
+    "coordinate_structure_constraint_complex_left_branch",
+    "coordinate_structure_constraint_object_extraction",
+    "determiner_noun_agreement_1",
+    "determiner_noun_agreement_2",
+    "determiner_noun_agreement_irregular_1",
+    "determiner_noun_agreement_irregular_2",
+    "determiner_noun_agreement_with_adj_2",
+    "determiner_noun_agreement_with_adj_irregular_1",
+    "determiner_noun_agreement_with_adj_irregular_2",
+    "determiner_noun_agreement_with_adjective_1",
+    "distractor_agreement_relational_noun",
+    "distractor_agreement_relative_clause",
+    "drop_argument",
+    "ellipsis_n_bar_1",
+    "ellipsis_n_bar_2",
+    "existential_there_object_raising",
+    "existential_there_quantifiers_1",
+    "existential_there_quantifiers_2",
+    "existential_there_subject_raising",
+    "expletive_it_object_raising",
+    "inchoative",
+    "intransitive",
+    "irregular_past_participle_adjectives",
+    "irregular_past_participle_verbs",
+    "irregular_plural_subject_verb_agreement_1",
+    "irregular_plural_subject_verb_agreement_2",
+    "left_branch_island_echo_question",
+    "left_branch_island_simple_question",
+    "matrix_question_npi_licensor_present",
+    "npi_present_1",
+    "npi_present_2",
+    "only_npi_licensor_present",
+    "only_npi_scope",
+    "passive_1",
+    "passive_2",
+    "principle_A_c_command",
+    "principle_A_case_1",
+    "principle_A_case_2",
+    "principle_A_domain_1",
+    "principle_A_domain_2",
+    "principle_A_domain_3",
+    "principle_A_reconstruction",
+    "regular_plural_subject_verb_agreement_1",
+    "regular_plural_subject_verb_agreement_2",
+    "sentential_negation_npi_licensor_present",
+    "sentential_negation_npi_scope",
+    "sentential_subject_island",
+    "superlative_quantifiers_1",
+    "superlative_quantifiers_2",
+    "tough_vs_raising_1",
+    "tough_vs_raising_2",
+    "transitive",
+    "wh_island",
+    "wh_questions_object_gap",
+    "wh_questions_subject_gap",
+    "wh_questions_subject_gap_long_distance",
+    "wh_vs_that_no_gap",
+    "wh_vs_that_no_gap_long_distance",
+    "wh_vs_that_with_gap",
+    "wh_vs_that_with_gap_long_distance",
+]
+
+
+def main() -> None:
+    for task in all_subtasks:
+        file_name = f"{task}.yaml"
+        try:
+            with open(f"{file_name}", "w", encoding="utf-8") as f:
+                f.write("# Generated by utils.py\n")
+                yaml.dump(
+                    {
+                        "include": "_template_yaml",
+                        "task": "blimp_" + task,
+                        "dataset_name": task,
+                    },
+                    f,
+                )
+        except FileExistsError:
+            pass
+
+
+if __name__ == "__main__":
+    main()

lm-evaluation-harness/lm_eval/tasks/blimp/left_branch_island_simple_question.yaml

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+# Generated by utils.py
+dataset_name: left_branch_island_simple_question
+include: _template_yaml
+task: blimp_left_branch_island_simple_question

lm-evaluation-harness/lm_eval/tasks/blimp/npi_present_1.yaml

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+# Generated by utils.py
+dataset_name: npi_present_1
+include: _template_yaml
+task: blimp_npi_present_1

lm-evaluation-harness/lm_eval/tasks/blimp/npi_present_2.yaml

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+# Generated by utils.py
+dataset_name: npi_present_2
+include: _template_yaml
+task: blimp_npi_present_2

lm-evaluation-harness/lm_eval/tasks/blimp/only_npi_licensor_present.yaml

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+# Generated by utils.py
+dataset_name: only_npi_licensor_present
+include: _template_yaml
+task: blimp_only_npi_licensor_present

lm-evaluation-harness/lm_eval/tasks/blimp/passive_2.yaml

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+# Generated by utils.py
+dataset_name: passive_2
+include: _template_yaml
+task: blimp_passive_2

lm-evaluation-harness/lm_eval/tasks/blimp/principle_A_c_command.yaml

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+# Generated by utils.py
+dataset_name: principle_A_c_command
+include: _template_yaml
+task: blimp_principle_A_c_command

lm-evaluation-harness/lm_eval/tasks/blimp/principle_A_case_1.yaml

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+# Generated by utils.py
+dataset_name: principle_A_case_1
+include: _template_yaml
+task: blimp_principle_A_case_1

lm-evaluation-harness/lm_eval/tasks/blimp/principle_A_domain_2.yaml

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+# Generated by utils.py
+dataset_name: principle_A_domain_2
+include: _template_yaml
+task: blimp_principle_A_domain_2

lm-evaluation-harness/lm_eval/tasks/blimp/principle_A_domain_3.yaml

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+# Generated by utils.py
+dataset_name: principle_A_domain_3
+include: _template_yaml
+task: blimp_principle_A_domain_3

lm-evaluation-harness/lm_eval/tasks/blimp/regular_plural_subject_verb_agreement_1.yaml

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+# Generated by utils.py
+dataset_name: regular_plural_subject_verb_agreement_1
+include: _template_yaml
+task: blimp_regular_plural_subject_verb_agreement_1

lm-evaluation-harness/lm_eval/tasks/blimp/regular_plural_subject_verb_agreement_2.yaml

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+# Generated by utils.py
+dataset_name: regular_plural_subject_verb_agreement_2
+include: _template_yaml
+task: blimp_regular_plural_subject_verb_agreement_2

lm-evaluation-harness/lm_eval/tasks/blimp/tough_vs_raising_1.yaml

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+# Generated by utils.py
+dataset_name: tough_vs_raising_1
+include: _template_yaml
+task: blimp_tough_vs_raising_1

lm-evaluation-harness/lm_eval/tasks/blimp/tough_vs_raising_2.yaml

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+# Generated by utils.py
+dataset_name: tough_vs_raising_2
+include: _template_yaml
+task: blimp_tough_vs_raising_2

lm-evaluation-harness/lm_eval/tasks/blimp/wh_island.yaml

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+# Generated by utils.py
+dataset_name: wh_island
+include: _template_yaml
+task: blimp_wh_island

lm-evaluation-harness/lm_eval/tasks/blimp/wh_vs_that_with_gap_long_distance.yaml

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+# Generated by utils.py
+dataset_name: wh_vs_that_with_gap_long_distance
+include: _template_yaml
+task: blimp_wh_vs_that_with_gap_long_distance

lm-evaluation-harness/lm_eval/tasks/indiccopa/indiccopa_bn.yaml

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+# Tbns 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: ai4bharat/IndicCOPA
+dataset_path: ai4bharat/IndicCOPA
+dataset_name: translation-bn
+output_type: multiple_choice
+# training_split: train
+# validation_split: validation
+test_split: test
+# doc_to_text: "Premise: {{premise}}\nGiven the premise what is the {{question}}\nPlease Choose Among following 2 choices and label them as 0 for 1st choice and 1 for 2nd choice."
+# doc_to_target: label
+# doc_to_choice: "{{choice1}}{{choice2}}"
+# metric_list:
+#   - metric: acc
+#     aggregation: mean
+#     bngher_is_better: true
+# metadata:
+#   version: 1.0
+
+doc_to_text: !function utils.doc_to_text_bn
+doc_to_target: label
+doc_to_choice: !function utils.doc_to_choice
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0
+
+
+# doc_to_choice: '{{[premise+", सही? हाँ, "+hypothesis,premise+", सही? इसलिए, "+hypothesis,premise+",
+#   सही? नहीं, "+hypothesis]}}'
+# doc_to_text: ''
+task: indiccopa-bn

lm-evaluation-harness/lm_eval/tasks/indiccopa/indiccopa_mai.yaml

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+# Tmais 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: ai4bharat/IndicCOPA
+dataset_path: ai4bharat/IndicCOPA
+dataset_name: translation-mai
+output_type: multiple_choice
+# training_split: train
+# validation_split: validation
+test_split: test
+# doc_to_text: "Premise: {{premise}}\nGiven the premise what is the {{question}}\nPlease Choose Among following 2 choices and label them as 0 for 1st choice and 1 for 2nd choice."
+# doc_to_target: label
+# doc_to_choice: "{{choice1}}{{choice2}}"
+# metric_list:
+#   - metric: acc
+#     aggregation: mean
+#     maigher_is_better: true
+# metadata:
+#   version: 1.0
+
+doc_to_text: !function utils.doc_to_text_mai
+doc_to_target: label
+doc_to_choice: !function utils.doc_to_choice
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0
+
+
+# doc_to_choice: '{{[premise+", सही? हाँ, "+hypothesis,premise+", सही? इसलिए, "+hypothesis,premise+",
+#   सही? नहीं, "+hypothesis]}}'
+# doc_to_text: ''
+task: indiccopa-mai

lm-evaluation-harness/lm_eval/tasks/indiccopa/indiccopa_mr.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: ai4bharat/IndicCOPA
+dataset_path: ai4bharat/IndicCOPA
+dataset_name: translation-mr
+output_type: multiple_choice
+# training_split: train
+# validation_split: validation
+test_split: test
+
+
+doc_to_text: !function utils.doc_to_text_mr
+doc_to_target: label
+doc_to_choice: !function utils.doc_to_choice
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0
+
+
+task: indiccopa-mr

lm-evaluation-harness/lm_eval/tasks/indiccopa/indiccopa_ta.yaml

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+# Ttas 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: ai4bharat/IndicCOPA
+dataset_path: ai4bharat/IndicCOPA
+dataset_name: translation-ta
+output_type: multiple_choice
+# training_split: train
+# validation_split: validation
+test_split: test
+# doc_to_text: "Premise: {{premise}}\nGiven the premise what is the {{question}}\nPlease Choose Among following 2 choices and label them as 0 for 1st choice and 1 for 2nd choice."
+# doc_to_target: label
+# doc_to_choice: "{{choice1}}{{choice2}}"
+# metric_list:
+#   - metric: acc
+#     aggregation: mean
+#     tagher_is_better: true
+# metadata:
+#   version: 1.0
+
+doc_to_text: !function utils.doc_to_text_ta
+doc_to_target: label
+doc_to_choice: !function utils.doc_to_choice
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0
+
+
+# doc_to_choice: '{{[premise+", सही? हाँ, "+hypothesis,premise+", सही? इसलिए, "+hypothesis,premise+",
+#   सही? नहीं, "+hypothesis]}}'
+# doc_to_text: ''
+task: indiccopa-ta

lm-evaluation-harness/lm_eval/tasks/indiccopa/indiccopa_te.yaml

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+# Ttes 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: ai4bharat/IndicCOPA
+dataset_path: ai4bharat/IndicCOPA
+dataset_name: translation-te
+output_type: multiple_choice
+# training_split: train
+# validation_split: validation
+test_split: test
+# doc_to_text: "Premise: {{premise}}\nGiven the premise what is the {{question}}\nPlease Choose Among following 2 choices and label them as 0 for 1st choice and 1 for 2nd choice."
+# doc_to_target: label
+# doc_to_choice: "{{choice1}}{{choice2}}"
+# metric_list:
+#   - metric: acc
+#     aggregation: mean
+#     tegher_is_better: true
+# metadata:
+#   version: 1.0
+
+doc_to_text: !function utils.doc_to_text_te
+doc_to_target: label
+doc_to_choice: !function utils.doc_to_choice
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0
+
+
+# doc_to_choice: '{{[premise+", सही? हाँ, "+hypothesis,premise+", सही? इसलिए, "+hypothesis,premise+",
+#   सही? नहीं, "+hypothesis]}}'
+# doc_to_text: ''
+task: indiccopa-te

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

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+# MathQA
+
+### Paper
+
+MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
+https://arxiv.org/pdf/1905.13319.pdf
+
+MathQA is a large-scale dataset of 37k English multiple-choice math word problems
+covering multiple math domain categories by modeling operation programs corresponding
+to word problems in the AQuA dataset (Ling et al., 2017).
+
+Homepage: https://math-qa.github.io/math-QA/
+
+
+### Citation
+
+```
+@misc{amini2019mathqa,
+    title={MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms},
+    author={Aida Amini and Saadia Gabriel and Peter Lin and Rik Koncel-Kedziorski and Yejin Choi and Hannaneh Hajishirzi},
+    year={2019},
+    eprint={1905.13319},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `math_word_problems`
+
+#### Tasks
+
+* `mathqa`: The MathQA dataset, as a multiple choice dataset where the answer choices are not in context.
+
+### 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?
+    * The MathQA dataset predates transformer-based prompted LLMs. We should, however, return to this task to ensure equivalence to the non-CoT version of mathQA used in the Chain-of-Thought paper.
+
+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?
+  * [x] Checked for equivalence with v0.3.0 LM Evaluation Harness

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

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+group:
+  - math_word_problems
+task: mathqa
+dataset_path: math_qa
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+test_split: test
+doc_to_text: "Question: {{Problem}}\nAnswer:"
+doc_to_target: "{{['a', 'b', 'c', 'd', 'e'].index(correct)}}"
+doc_to_choice: !function utils.doc_to_choice
+should_decontaminate: true
+doc_to_decontamination_query: "Question: {{Problem}}\nAnswer:"
+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/mathqa/utils.py

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+import re
+
+
+def doc_to_choice(doc):
+    choices = [
+        c[4:].rstrip(" ,")
+        for c in re.findall(r"[abcd] \) .*?, |e \) .*?$", doc["options"])
+    ]
+    return choices

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

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+# The Pile
+
+### Paper
+Title: The Pile: An 800GB Dataset of Diverse Text for Language Modeling
+
+Abstract: https://arxiv.org/abs/2101.00027
+
+The Pile is a 825 GiB diverse, open source language modelling data set that consists
+of 22 smaller, high-quality datasets combined together. To score well on Pile
+BPB (bits per byte), a model must be able to understand many disparate domains
+including books, github repositories, webpages, chat logs, and medical, physics,
+math, computer science, and philosophy papers.
+
+Homepage: https://pile.eleuther.ai/
+
+### Citation
+```
+@article{pile,
+  title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
+  author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
+  journal={arXiv preprint arXiv:2101.00027},
+  year={2020}
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `pile`
+
+#### Tasks
+
+* `pile_arxiv`
+* `pile_bookcorpus2`
+* `pile_books3`
+* `pile_dm-mathematics`
+* `pile_enron`
+* `pile_europarl`
+* `pile_freelaw`
+* `pile_github`
+* `pile_gutenberg`
+* `pile_hackernews`
+* `pile_nih-exporter`
+* `pile_opensubtitles`
+* `pile_openwebtext2`
+* `pile_philpapers`
+* `pile_pile-cc`
+* `pile_pubmed-abstracts`
+* `pile_pubmed-central`
+* `pile_stackexchange`
+* `pile_ubuntu-irc`
+* `pile_uspto`
+* `pile_wikipedia`
+* `pile_youtubesubtitles`
+
+### 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/pile/pile_arxiv.yaml

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+group:
+  - pile
+task: pile_arxiv
+dataset_path: EleutherAI/pile
+dataset_name: pile_arxiv
+output_type: loglikelihood_rolling
+test_split: train
+doc_to_text: ""
+doc_to_target: "{{text}}"
+should_decontaminate: true
+doc_to_decontamination_query: "{{text}}"
+metric_list:
+  - metric: word_perplexity
+    aggregation: weighted_perplexity
+    higher_is_better: false
+  - metric: byte_perplexity
+    aggregation: weighted_perplexity
+    higher_is_better: false
+  - metric: bits_per_byte
+    aggregation: bits_per_byte
+    higher_is_better: false
+metadata:
+  version: 2.0

lm-evaluation-harness/lm_eval/tasks/pile/pile_bookcorpus2.yaml

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+include: pile_arxiv.yaml
+task: pile_bookcorpus2
+dataset_name: pile_bookcorpus2

lm-evaluation-harness/lm_eval/tasks/pile/pile_books3.yaml

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+include: pile_arxiv.yaml
+task: pile_books3
+dataset_name: pile_books3

lm-evaluation-harness/lm_eval/tasks/pile/pile_dm-mathematics.yaml

@@ -0,0 +1,3 @@
+include: pile_arxiv.yaml
+task: pile_dm-mathematics
+dataset_name: pile_dm-mathematics

lm-evaluation-harness/lm_eval/tasks/pile/pile_enron.yaml

@@ -0,0 +1,3 @@
+include: pile_arxiv.yaml
+task: pile_enron
+dataset_name: pile_enron

lm-evaluation-harness/lm_eval/tasks/pile/pile_europarl.yaml

@@ -0,0 +1,3 @@
+include: pile_arxiv.yaml
+task: pile_europarl
+dataset_name: pile_europarl

lm-evaluation-harness/lm_eval/tasks/pile/pile_freelaw.yaml

@@ -0,0 +1,3 @@
+include: pile_arxiv.yaml
+task: pile_freelaw
+dataset_name: pile_freelaw

lm-evaluation-harness/lm_eval/tasks/pile/pile_github.yaml

@@ -0,0 +1,3 @@
+include: pile_arxiv.yaml
+task: pile_github
+dataset_name: pile_github

lm-evaluation-harness/lm_eval/tasks/pile/pile_gutenberg.yaml

@@ -0,0 +1,3 @@
+include: pile_arxiv.yaml
+task: pile_gutenberg
+dataset_name: pile_gutenberg

lm-evaluation-harness/lm_eval/tasks/pile/pile_hackernews.yaml

@@ -0,0 +1,3 @@
+include: pile_arxiv.yaml
+task: pile_hackernews
+dataset_name: pile_hackernews

lm-evaluation-harness/lm_eval/tasks/pile/pile_nih-exporter.yaml

@@ -0,0 +1,3 @@
+include: pile_arxiv.yaml
+task: pile_nih-exporter
+dataset_name: pile_nih-exporter

lm-evaluation-harness/lm_eval/tasks/pile/pile_opensubtitles.yaml

@@ -0,0 +1,3 @@
+include: pile_arxiv.yaml
+task: pile_opensubtitles
+dataset_name: pile_opensubtitles

lm-evaluation-harness/lm_eval/tasks/pile/pile_openwebtext2.yaml

@@ -0,0 +1,3 @@
+include: pile_arxiv.yaml
+task: pile_openwebtext2
+dataset_name: pile_openwebtext2

lm-evaluation-harness/lm_eval/tasks/pile/pile_philpapers.yaml

@@ -0,0 +1,3 @@
+include: pile_arxiv.yaml
+task: pile_philpapers
+dataset_name: pile_philpapers

lm-evaluation-harness/lm_eval/tasks/pile/pile_pile-cc.yaml

@@ -0,0 +1,3 @@
+include: pile_arxiv.yaml
+task: pile_pile-cc
+dataset_name: pile_pile-cc