--- configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: instance_id dtype: string - name: text dtype: string - name: repo dtype: string - name: base_commit dtype: string - name: problem_statement dtype: string - name: hints_text dtype: string - name: created_at dtype: string - name: patch dtype: string - name: test_patch dtype: string - name: version dtype: string - name: FAIL_TO_PASS dtype: string - name: PASS_TO_PASS dtype: string - name: environment_setup_commit dtype: string splits: - name: train num_bytes: 1537849718 num_examples: 18817 - name: dev num_bytes: 15941600 num_examples: 225 - name: test num_bytes: 156543048 num_examples: 2294 - name: validation num_bytes: 16292656 num_examples: 191 download_size: 744411715 dataset_size: 1726627022 --- # Dataset Card for "SWE-bench_bm25_13K" ### Dataset Summary SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution. The dataset was released as part of [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770) This dataset `SWE-bench_bm25_13K` includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 13,000 `cl100k_base` tokens from the [`tiktoken`](https://github.com/openai/tiktoken) tokenization package used for OpenAI models. The `text` column can be used directly with LMs to generate patch files. Models are instructed to generate [`patch`](https://en.wikipedia.org/wiki/Patch_(Unix)) formatted file using the following template: ```diff diff --- a/path/to/file.py --- b/path/to/file.py @@ -1,3 +1,3 @@ This is a test file. -It contains several lines. +It has been modified. This is the third line. ``` This format can be used directly with the [SWE-bench inference scripts](https://github.com/princeton-nlp/SWE-bench/tree/main/inference). Please refer to these scripts for more details on inference. ### Supported Tasks and Leaderboards SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at www.swebench.com ### Languages The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type. ## Dataset Structure ### Data Instances An example of a SWE-bench datum is as follows: ``` instance_id: (str) - A formatted instance identifier, usually as repo_owner__repo_name-PR-number. text: (str) - The input text including instructions, the "Oracle" retrieved file, and an example of the patch format for output. patch: (str) - The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue. repo: (str) - The repository owner/name identifier from GitHub. base_commit: (str) - The commit hash of the repository representing the HEAD of the repository before the solution PR is applied. hints_text: (str) - Comments made on the issue prior to the creation of the solution PR’s first commit creation date. created_at: (str) - The creation date of the pull request. test_patch: (str) - A test-file patch that was contributed by the solution PR. problem_statement: (str) - The issue title and body. version: (str) - Installation version to use for running evaluation. environment_setup_commit: (str) - commit hash to use for environment setup and installation. FAIL_TO_PASS: (str) - A json list of strings that represent the set of tests resolved by the PR and tied to the issue resolution. PASS_TO_PASS: (str) - A json list of strings that represent tests that should pass before and after the PR application. ``` [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)