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  1. README.md +5 -3
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@@ -42,8 +42,10 @@ followed by the commit that passes the workflow successfully.
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  Note that, unlike many other 🏟 Long Code Arena datasets, this dataset doesn't contain repositories.
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- * Our [CI Fixing benchmark](todo) (🚧 todo) clones the necessary repos to the user's local machine. The user should run their model to
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- fix the failing CI workflows, and the benchmark will push commits to GitHub, returning the results of the workflow runs
 
 
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  for all the datapoints.
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  ### Datapoint Schema
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  | `head_branch` | Name of the original branch that the commit was pushed at |
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  | `id` | Unique ID of the datapoint |
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  | `language` | Main language of the repo |
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- | `logs` | List of dicts with keys `log` (logs of the failed job, particular step) and `step_name` (name of the failed step of the job) | |
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  | `repo_name` | Name of the original repo (second part of the `owner/name` on GitHub) |
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  | `repo owner` | Owner of the original repo (first part of the `owner/name` on GitHub) |
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  | `sha_fail` | SHA of the failed commit |
 
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  Note that, unlike many other 🏟 Long Code Arena datasets, this dataset doesn't contain repositories.
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+ * Our [CI Fixing benchmark](todo) (🚧 todo) clones the necessary repos to the user's local machine. The user should run
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+ their model to
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+ fix the failing CI workflows, and the benchmark will push commits to GitHub, returning the results of the workflow
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+ runs
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  for all the datapoints.
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  ### Datapoint Schema
 
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  | `head_branch` | Name of the original branch that the commit was pushed at |
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  | `id` | Unique ID of the datapoint |
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  | `language` | Main language of the repo |
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+ | `logs` | List of dicts with keys `log` (logs of the failed job, particular step) and `step_name` (name of the failed step of the job) |
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  | `repo_name` | Name of the original repo (second part of the `owner/name` on GitHub) |
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  | `repo owner` | Owner of the original repo (first part of the `owner/name` on GitHub) |
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  | `sha_fail` | SHA of the failed commit |