MERA-evaluation/SWE-MERA
SWE-MERA Continuously updated SWE-MERA dataset SWE-MERA splits: dev: for testing (10 samples) lite: presented at the leaderboard here (750 samples) full: continuously updated to collect more data (2738 samples) Load dataset from datasets import load_dataset ds = load_dataset("MERA-evaluation/SWE-MERA", split='dev') Evaluation Description The main tool to validate tasks is repotest (available at PyPI or GitHub)… See the full description on the dataset page: https://huggingface.co/datasets/MERA-evaluation/SWE-MERA.
SWE-MERA
Continuously updated SWE-MERA dataset
SWE-MERA splits:
- dev: for testing (10 samples)
- lite: presented at the leaderboard here (750 samples)
- full: continuously updated to collect more data (2738 samples)
Load dataset
from datasets import load_dataset
ds = load_dataset("MERA-evaluation/SWE-MERA", split='dev')Evaluation
Description
The main tool to validate tasks is repotest (available at PyPI or GitHub)
data.jsonl - dataset file, where after the agent run, the patch column was changed
Install dependencies
pip install repositorytestRun evaluation
swemera --fn_input=data.jsonl --fn_output=submission.jsonl --column_patch=patch --mode=dockerIf you don't want to use Docker and prefer Conda or local execution:
swemera --fn_input=data.jsonl --fn_output=submission.jsonl --column_patch=patch --mode=localSchema
*meta field description:
{
"score": {
"task_correctness": "INTEGER",
"test_correctness": "INTEGER",
"complexity": "INTEGER",
"test_completeness": "INTEGER"
},
"type": "VARCHAR",
"pr_number": "INTEGER",
"issue_number": "INTEGER",
"merged_at": "VARCHAR",
"tag": "VARCHAR // Information on how data was collected",
"merge_commit": "VARCHAR",
"task_id": "VARCHAR // Latency identifier of the task",
"url": {
"issue": "VARCHAR",
"pr": "VARCHAR",
"diff": "VARCHAR"
},
"command_test_small": "VARCHAR // Command to run only a small number of tests"
}