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Code for the paper "Evaluating Large Language Models Trained on Code"
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openai/human-eval
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This is an evaluation harness for the HumanEval problem solving datasetdescribed in the paper "Evaluating Large Language Models Trained onCode".
Make sure to use python 3.7 or later:
$ conda create -n codex python=3.7$ conda activate codexCheck out and install this repository:
$ git clone https://github.com/openai/human-eval$ pip install -e human-evalThis program exists to run untrusted model-generated code. Users are stronglyencouraged not to do so outside of a robust security sandbox. Theexecutioncallinexecution.py is deliberately commented out to ensure users read thisdisclaimer before running code in a potentially unsafe manner. See the comment inexecution.py for more information and instructions.
After following the above instructions to enable execution, generate samplesand save them in the following JSON Lines (jsonl) format, where each sample isformatted into a single line like so:
{"task_id": "Corresponding HumanEval task ID", "completion": "Completion only without the prompt"}We provideexample_problem.jsonl andexample_solutions.jsonl underdatato illustrate the format and help with debugging.
Here is nearly functional example code (you just have to providegenerate_one_completion to make it work) that saves generated completions tosamples.jsonl.
from human_eval.data import write_jsonl, read_problemsproblems = read_problems()num_samples_per_task = 200samples = [ 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)To evaluate the samples, run
$ evaluate_functional_correctness samples.jsonlReading 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': ...}This script provides more fine-grained information in a new file ending in<input_path>_results.jsonl. Each row now contains whether the completionpassed along with the executionresult which is one of "passed", "timedout", or "failed".
As a quick sanity-check, the example samples should yield 0.5 pass@1.
$ evaluate_functional_correctness data/example_samples.jsonl --problem_file=data/example_problem.jsonlReading 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}Because there is no unbiased way of estimating pass@k when there are fewersamples than k, the script does not evaluate pass@k for these cases. Toevaluate with other k values, pass--k=<comma-separated-values-here>. Forother options, see
$ evaluate_functional_correctness --helpHowever, we recommend that you use the default values for the rest.
While evaluation uses very little memory, you might see the following errormessage when the system is running out of RAM. Since this may cause somecorrect programs to fail, we recommend that you free some memory and try again.
malloc: can't allocate regionPlease cite using the following bibtex entry:
@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}}About
Code for the paper "Evaluating Large Language Models Trained on Code"
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