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GitHub - openai/human-eval: Code for the paper "Evaluating Large Language Models Trained on Code"
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data-target="react-partial.embeddedData">{"props":{"initialPayload":{"allShortcutsEnabled":false,"path":"/","repo":{"id":383601290,"defaultBranch":"master","name":"human-eval","ownerLogin":"openai","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2021-07-06T21:23:45.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/14957082?v=4","public":true,"private":false,"isOrgOwned":true},"currentUser":null,"refInfo":{"name":"master","listCacheKey":"v0:1626300872.883903","canEdit":false,"refType":"branch","currentOid":"312c5e5532f0e0470bf47f77a6243e02a61da530"},"tree":{"items":[{"name":"data","path":"data","contentType":"directory"},{"name":"human_eval","path":"human_eval","contentType":"directory"},{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README.md","path":"README.md","contentType":"file"},{"name":"requirements.txt","path":"requirements.txt","contentType":"file"},{"name":"setup.py","path":"setup.py","contentType":"file"}],"templateDirectorySuggestionUrl":null,"readme":null,"totalCount":6,"showBranchInfobar":false},"fileTree":null,"fileTreeProcessingTime":null,"foldersToFetch":[],"treeExpanded":false,"symbolsExpanded":false,"isOverview":true,"overview":{"banners":{"shouldRecommendReadme":false,"isPersonalRepo":false,"showUseActionBanner":false,"actionSlug":null,"actionId":null,"showProtectBranchBanner":false,"publishBannersInfo":{"dismissActionNoticePath":"/settings/dismiss-notice/publish_action_from_repo","releasePath":"/openai/human-eval/releases/new?marketplace=true","showPublishActionBanner":false},"interactionLimitBanner":null,"showInvitationBanner":false,"inviterName":null,"actionsMigrationBannerInfo":{"releaseTags":[],"showImmutableActionsMigrationBanner":false,"initialMigrationStatus":null}},"codeButton":{"contactPath":"/contact","isEnterprise":false,"local":{"protocolInfo":{"httpAvailable":true,"sshAvailable":null,"httpUrl":"https://github.com/openai/human-eval.git","showCloneWarning":null,"sshUrl":null,"sshCertificatesRequired":null,"sshCertificatesAvailable":null,"ghCliUrl":"gh 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Hand-Written Evaluation Set\u003c/h1\u003e\u003ca id=\"user-content-humaneval-hand-written-evaluation-set\" class=\"anchor\" aria-label=\"Permalink: HumanEval: Hand-Written Evaluation Set\" href=\"#humaneval-hand-written-evaluation-set\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis is an evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper \"\u003ca href=\"https://arxiv.org/abs/2107.03374\" rel=\"nofollow\"\u003eEvaluating Large Language Models Trained on\nCode\u003c/a\u003e\".\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eMake sure to use python 3.7 or later:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ conda create -n codex python=3.7\n$ conda activate codex\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ conda create -n codex python=3.7\n$ conda activate codex\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCheck out and install this repository:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ git clone https://github.com/openai/human-eval\n$ pip install -e human-eval\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ git clone https://github.com/openai/human-eval\n$ pip install -e human-eval\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eThis program exists to run untrusted model-generated code. Users are strongly\nencouraged not to do so outside of a robust security sandbox. The \u003ca href=\"https://github.com/openai/human-eval/blob/master/human_eval/execution.py#L48-L58\"\u003eexecution\ncall\u003c/a\u003e\nin \u003ccode\u003eexecution.py\u003c/code\u003e is deliberately commented out to ensure users read this\ndisclaimer before running code in a potentially unsafe manner. See the comment in\n\u003ccode\u003eexecution.py\u003c/code\u003e for more information and instructions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAfter following the above instructions to enable execution, generate samples\nand save them in the following JSON Lines (jsonl) format, where each sample is\nformatted into a single line like so:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"{\u0026quot;task_id\u0026quot;: \u0026quot;Corresponding HumanEval task ID\u0026quot;, \u0026quot;completion\u0026quot;: \u0026quot;Completion only without the prompt\u0026quot;}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e{\"task_id\": \"Corresponding HumanEval task ID\", \"completion\": \"Completion only without the prompt\"}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe provide \u003ccode\u003eexample_problem.jsonl\u003c/code\u003e and \u003ccode\u003eexample_solutions.jsonl\u003c/code\u003e under \u003ccode\u003edata\u003c/code\u003e\nto illustrate the format and help with debugging.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHere is nearly functional example code (you just have to provide\n\u003ccode\u003egenerate_one_completion\u003c/code\u003e to make it work) that saves generated completions to\n\u003ccode\u003esamples.jsonl\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"from human_eval.data import write_jsonl, read_problems\n\nproblems = read_problems()\n\nnum_samples_per_task = 200\nsamples = [\n dict(task_id=task_id, completion=generate_one_completion(problems[task_id][\u0026quot;prompt\u0026quot;]))\n for task_id in problems\n for _ in range(num_samples_per_task)\n]\nwrite_jsonl(\u0026quot;samples.jsonl\u0026quot;, samples)\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003efrom human_eval.data import write_jsonl, read_problems\n\nproblems = read_problems()\n\nnum_samples_per_task = 200\nsamples = [\n dict(task_id=task_id, completion=generate_one_completion(problems[task_id][\"prompt\"]))\n for task_id in problems\n for _ in range(num_samples_per_task)\n]\nwrite_jsonl(\"samples.jsonl\", samples)\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo evaluate the samples, run\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ evaluate_functional_correctness samples.jsonl\nReading samples...\n32800it [00:01, 23787.50it/s]\nRunning test suites...\n100%|...| 32800/32800 [16:11\u0026lt;00:00, 33.76it/s]\nWriting results to samples.jsonl_results.jsonl...\n100%|...| 32800/32800 [00:00\u0026lt;00:00, 42876.84it/s]\n{'pass@1': ..., 'pass@10': ..., 'pass@100': ...}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ evaluate_functional_correctness samples.jsonl\nReading samples...\n32800it [00:01, 23787.50it/s]\nRunning test suites...\n100%|...| 32800/32800 [16:11\u0026lt;00:00, 33.76it/s]\nWriting results to samples.jsonl_results.jsonl...\n100%|...| 32800/32800 [00:00\u0026lt;00:00, 42876.84it/s]\n{'pass@1': ..., 'pass@10': ..., 'pass@100': ...}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis script provides more fine-grained information in a new file ending in\n\u003ccode\u003e\u0026lt;input_path\u0026gt;_results.jsonl\u003c/code\u003e. Each row now contains whether the completion\n\u003ccode\u003epassed\u003c/code\u003e along with the execution \u003ccode\u003eresult\u003c/code\u003e which is one of \"passed\", \"timed\nout\", or \"failed\".\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAs a quick sanity-check, the example samples should yield 0.5 pass@1.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ evaluate_functional_correctness data/example_samples.jsonl --problem_file=data/example_problem.jsonl\nReading samples...\n6it [00:00, 3397.11it/s]\nRunning example suites...\n100%|...| 6/6 [00:03\u0026lt;00:00, 1.96it/s]\nWriting results to data/example_samples.jsonl_results.jsonl...\n100%|...| 6/6 [00:00\u0026lt;00:00, 6148.50it/s]\n{'pass@1': 0.4999999999999999}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ evaluate_functional_correctness data/example_samples.jsonl --problem_file=data/example_problem.jsonl\nReading samples...\n6it [00:00, 3397.11it/s]\nRunning example suites...\n100%|...| 6/6 [00:03\u0026lt;00:00, 1.96it/s]\nWriting results to data/example_samples.jsonl_results.jsonl...\n100%|...| 6/6 [00:00\u0026lt;00:00, 6148.50it/s]\n{'pass@1': 0.4999999999999999}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBecause there is no unbiased way of estimating pass@k when there are fewer\nsamples than k, the script does not evaluate pass@k for these cases. To\nevaluate with other k values, pass \u003ccode\u003e--k=\u0026lt;comma-separated-values-here\u0026gt;\u003c/code\u003e. For\nother options, see\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ evaluate_functional_correctness --help\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ evaluate_functional_correctness --help\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHowever, we recommend that you use the default values for the rest.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eKnown Issues\u003c/h2\u003e\u003ca id=\"user-content-known-issues\" class=\"anchor\" aria-label=\"Permalink: Known Issues\" href=\"#known-issues\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWhile evaluation uses very little memory, you might see the following error\nmessage when the system is running out of RAM. Since this may cause some\ncorrect programs to fail, we recommend that you free some memory and try again.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"malloc: can't allocate region\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003emalloc: can't allocate region\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease cite using the following bibtex entry:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{chen2021codex,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{chen2021codex,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"HumanEval: Hand-Written Evaluation Set","anchor":"humaneval-hand-written-evaluation-set","htmlText":"HumanEval: Hand-Written Evaluation Set"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":2,"text":"Usage","anchor":"usage","htmlText":"Usage"},{"level":2,"text":"Known Issues","anchor":"known-issues","htmlText":"Known Issues"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fopenai%2Fhuman-eval"}},{"displayName":"LICENSE","repoName":"human-eval","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"MIT","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fopenai%2Fhuman-eval"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> 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1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">HumanEval: Hand-Written Evaluation Set</h1><a id="user-content-humaneval-hand-written-evaluation-set" class="anchor" aria-label="Permalink: HumanEval: Hand-Written Evaluation Set" href="#humaneval-hand-written-evaluation-set"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This is an evaluation harness for the HumanEval problem solving dataset described in the paper "<a href="https://arxiv.org/abs/2107.03374" rel="nofollow">Evaluating Large Language Models Trained on Code</a>".</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Make sure to use python 3.7 or later:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ conda create -n codex python=3.7 $ conda activate codex"><pre class="notranslate"><code>$ conda create -n codex python=3.7 $ conda activate codex </code></pre></div> <p dir="auto">Check out and install this repository:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ git clone https://github.com/openai/human-eval $ pip install -e human-eval"><pre class="notranslate"><code>$ git clone https://github.com/openai/human-eval $ pip install -e human-eval </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Usage</h2><a id="user-content-usage" class="anchor" aria-label="Permalink: Usage" href="#usage"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>This program exists to run untrusted model-generated code. Users are strongly encouraged not to do so outside of a robust security sandbox. The <a href="https://github.com/openai/human-eval/blob/master/human_eval/execution.py#L48-L58">execution call</a> in <code>execution.py</code> is deliberately commented out to ensure users read this disclaimer before running code in a potentially unsafe manner. See the comment in <code>execution.py</code> for more information and instructions.</strong></p> <p dir="auto">After following the above instructions to enable execution, generate samples and save them in the following JSON Lines (jsonl) format, where each sample is formatted into a single line like so:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="{"task_id": "Corresponding HumanEval task ID", "completion": "Completion only without the prompt"}"><pre class="notranslate"><code>{"task_id": "Corresponding HumanEval task ID", "completion": "Completion only without the prompt"} </code></pre></div> <p dir="auto">We provide <code>example_problem.jsonl</code> and <code>example_solutions.jsonl</code> under <code>data</code> to illustrate the format and help with debugging.</p> <p dir="auto">Here is nearly functional example code (you just have to provide <code>generate_one_completion</code> to make it work) that saves generated completions to <code>samples.jsonl</code>.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="from human_eval.data import write_jsonl, read_problems problems = read_problems() num_samples_per_task = 200 samples = [ dict(task_id=task_id, completion=generate_one_completion(problems[task_id]["prompt"])) for task_id in problems for _ in range(num_samples_per_task) ] write_jsonl("samples.jsonl", samples)"><pre class="notranslate"><code>from human_eval.data import write_jsonl, read_problems problems = read_problems() num_samples_per_task = 200 samples = [ dict(task_id=task_id, completion=generate_one_completion(problems[task_id]["prompt"])) for task_id in problems for _ in range(num_samples_per_task) ] write_jsonl("samples.jsonl", samples) </code></pre></div> <p dir="auto">To evaluate the samples, run</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ evaluate_functional_correctness samples.jsonl Reading samples... 32800it [00:01, 23787.50it/s] Running test suites... 100%|...| 32800/32800 [16:11<00:00, 33.76it/s] Writing results to samples.jsonl_results.jsonl... 100%|...| 32800/32800 [00:00<00:00, 42876.84it/s] {'pass@1': ..., 'pass@10': ..., 'pass@100': ...}"><pre class="notranslate"><code>$ evaluate_functional_correctness samples.jsonl Reading samples... 32800it [00:01, 23787.50it/s] Running test suites... 100%|...| 32800/32800 [16:11<00:00, 33.76it/s] Writing results to samples.jsonl_results.jsonl... 100%|...| 32800/32800 [00:00<00:00, 42876.84it/s] {'pass@1': ..., 'pass@10': ..., 'pass@100': ...} </code></pre></div> <p dir="auto">This script provides more fine-grained information in a new file ending in <code><input_path>_results.jsonl</code>. Each row now contains whether the completion <code>passed</code> along with the execution <code>result</code> which is one of "passed", "timed out", or "failed".</p> <p dir="auto">As a quick sanity-check, the example samples should yield 0.5 pass@1.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ evaluate_functional_correctness data/example_samples.jsonl --problem_file=data/example_problem.jsonl Reading samples... 6it [00:00, 3397.11it/s] Running example suites... 100%|...| 6/6 [00:03<00:00, 1.96it/s] Writing results to data/example_samples.jsonl_results.jsonl... 100%|...| 6/6 [00:00<00:00, 6148.50it/s] {'pass@1': 0.4999999999999999}"><pre class="notranslate"><code>$ evaluate_functional_correctness data/example_samples.jsonl --problem_file=data/example_problem.jsonl Reading samples... 6it [00:00, 3397.11it/s] Running example suites... 100%|...| 6/6 [00:03<00:00, 1.96it/s] Writing results to data/example_samples.jsonl_results.jsonl... 100%|...| 6/6 [00:00<00:00, 6148.50it/s] {'pass@1': 0.4999999999999999} </code></pre></div> <p dir="auto">Because there is no unbiased way of estimating pass@k when there are fewer samples than k, the script does not evaluate pass@k for these cases. To evaluate with other k values, pass <code>--k=<comma-separated-values-here></code>. For other options, see</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ evaluate_functional_correctness --help"><pre class="notranslate"><code>$ evaluate_functional_correctness --help </code></pre></div> <p dir="auto">However, we recommend that you use the default values for the rest.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Known Issues</h2><a id="user-content-known-issues" class="anchor" aria-label="Permalink: Known Issues" href="#known-issues"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">While evaluation uses very little memory, you might see the following error message when the system is running out of RAM. Since this may cause some correct programs to fail, we recommend that you free some memory and try again.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="malloc: can't allocate region"><pre class="notranslate"><code>malloc: can't allocate region </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Please cite using the following bibtex entry:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{chen2021codex, title={Evaluating Large Language Models Trained on Code}, author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba}, year={2021}, eprint={2107.03374}, archivePrefix={arXiv}, primaryClass={cs.LG} }"><pre class="notranslate"><code>@article{chen2021codex, title={Evaluating Large Language Models Trained on Code}, author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba}, year={2021}, eprint={2107.03374}, archivePrefix={arXiv}, primaryClass={cs.LG} } </code></pre></div> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="YJbY3AEL8PlZ/gOm3A4ENimvjZVKyzabQTeBIE5tb9bLw1muvBrMYde94XQicC8KZUWTZRYznqpJ7+D5/WEXcg==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Code for the paper "Evaluating Large Language Models 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d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>