Spaces:
Runtime error
title: Code Eval
emoji: 🤗
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
tags:
- evaluate
- metric
description: >-
This metric implements the evaluation harness for the HumanEval problem
solving dataset described in the paper "Evaluating Large Language Models
Trained on Code" (https://arxiv.org/abs/2107.03374).
duplicated_from: evaluate-metric/code_eval
Metric Card for Code Eval
Metric description
The CodeEval metric estimates the pass@k metric for code synthesis.
It implements the evaluation harness for the HumanEval problem solving dataset described in the paper "Evaluating Large Language Models Trained on Code".
How to use
The Code Eval metric calculates how good are predictions given a set of references. Its arguments are:
predictions: a list of candidates to evaluate. Each candidate should be a list of strings with several code candidates to solve the problem.
references: a list with a test for each prediction. Each test should evaluate the correctness of a code candidate.
k: number of code candidates to consider in the evaluation. The default value is [1, 10, 100].
num_workers: the number of workers used to evaluate the candidate programs (The default value is 4).
timeout: The maximum time taken to produce a prediction before it is considered a "timeout". The default value is 3.0 (i.e. 3 seconds).
from evaluate import load
code_eval = load("code_eval")
test_cases = ["assert add(2,3)==5"]
candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
N.B.
This metric exists to run untrusted model-generated code. Users are strongly encouraged not to do so outside of a robust security sandbox. Before running this metric and once you've taken the necessary precautions, you will need to set the HF_ALLOW_CODE_EVAL environment variable. Use it at your own risk:
import os
os.environ["HF_ALLOW_CODE_EVAL"] = "1"`
Output values
The Code Eval metric outputs two things:
pass_at_k: a dictionary with the pass rates for each k value defined in the arguments.
results: a dictionary with granular results of each unit test.
Values from popular papers
The original CODEX paper reported that the CODEX-12B model had a pass@k score of 28.8% at k=1, 46.8% at k=10 and 72.3% at k=100. However, since the CODEX model is not open source, it is hard to verify these numbers.
Examples
Full match at k=1:
from evaluate import load
code_eval = load("code_eval")
test_cases = ["assert add(2,3)==5"]
candidates = [["def add(a, b): return a+b"]]
pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1])
print(pass_at_k)
{'pass@1': 1.0}
No match for k = 1:
from evaluate import load
code_eval = load("code_eval")
test_cases = ["assert add(2,3)==5"]
candidates = [["def add(a,b): return a*b"]]
pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1])
print(pass_at_k)
{'pass@1': 0.0}
Partial match at k=1, full match at k=2:
from evaluate import load
code_eval = load("code_eval")
test_cases = ["assert add(2,3)==5"]
candidates = [["def add(a, b): return a+b", "def add(a,b): return a*b"]]
pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
print(pass_at_k)
{'pass@1': 0.5, 'pass@2': 1.0}
Limitations and bias
As per the warning included in the metric code itself:
This program exists to execute untrusted model-generated code. Although it is highly unlikely that model-generated code will do something overtly malicious in response to this test suite, model-generated code may act destructively due to a lack of model capability or alignment. Users are strongly encouraged to sandbox this evaluation suite so that it does not perform destructive actions on their host or network. For more information on how OpenAI sandboxes its code, see the accompanying paper. Once you have read this disclaimer and taken appropriate precautions, uncomment the following line and proceed at your own risk:
More information about the limitations of the code can be found on the Human Eval Github repository.
Citation
@misc{chen2021evaluating,
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}
}