Update README.md
Browse files# LCA Project Level Code Completion
## How to load the dataset
```
from datasets import load_dataset
ds = load_dataset('JetBrains-Research/lca-codegen-small', split='test')
```
## Data Point Structure
* `repo` – repository name in format `{GitHub_user_name}__{repository_name}`
* `commit_hash` – commit hash
* `completion_file` – dictionary with the completion file content in the following format:
* `filename` – filepath to the completion file
* `content` – content of the completion file
* `completion_lines` – dictionary where keys are classes of lines and values are a list of integers (numbers of lines to complete). The classes are:
* `committed` – line contains at least one function or class that was declared in the committed files from `commit_hash`
* `inproject` – line contains at least one function or class that was declared in the project (excluding previous)
* `infile` – line contains at least one function or class that was declared in the completion file (excluding previous)
* `common` – line contains at least one function or class that was classified to be common, e.g., `main`, `get`, etc (excluding previous)
* `non_informative` – line that was classified to be non-informative, e.g. too short, contains comments, etc
* `random` – randomly sampled from the rest of the lines
* `repo_snapshot` – dictionary with a snapshot of the repository before the commit. Has the same structure as `completion_file`, but filenames and contents are orginized as lists.
* `completion_lines_raw` – the same as `completion_lines`, but before sampling.
## How we collected the data
To collect the data, we cloned repositories from GitHub where the main language is Python.
The completion file for each data point is a `.py` file that was added to the repository in a commit.
The state of the repository before this commit is the repo snapshot.
Small dataset is defined by number of characters in `.py` files from the repository snapshot. This number is less than 48K.
## Dataset Stats
* Number of datapoints: 144
* Number of repositories: 46
* Number of commits: 63
### Completion File
* Number of lines, median: 310.5
* Number of lines, min: 201
* Number of lines, max: 1916
### Repository Snapshot
* `.py` files: <u>median 4</u>, from 0 to 52
* non `.py` files: <u>median 19.5</u>, from 1 to 1044
* `.py` lines: <u>median 128</u>
* non `.py` lines: <u>median 1227</u>
### Line Counts:
* infile: 1430
* inproject: 95
* common: 500
* committed: 1426
* non-informative: 532
* random: 703
* **total**: 4686
## Scores
[HF Space](https://huggingface.co/spaces/JetBrains-Research/long-code-arena)
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path: data/test-*
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# LCA Project Level Code Completion
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## How to load the dataset
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```
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from datasets import load_dataset
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## Data Point Structure
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* `repo` -- repository name in format `{GitHub_user_name}__{repository_name}`
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* `commit_hash` -- commit hash
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* `completion_file` -- dictionary with the completion file content in the following format:
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* `filename` -- filepath to the completion file
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* `content` -- content of the completion file
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* `completion_lines` -- dictionary where keys are classes of lines and values are a list of integers (numbers of lines to complete). The classes are:
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* `committed` -- line contains at least one function or class that was declared in the committed files
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* `inproject` -- line contains at least one function and class that was declared in the project (excluding previous)
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* `infile` -- line contains at least one function and class that was declared in the completion file (excluding previous)
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* `common` -- line contains at least one function and class that was classified to be common, e.g. `main`, `get`, etc (excluding previous)
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* `non_informative` -- line that was classified to be non-informative, e.g. too short, contains comments, etc
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* `random` -- randomly sampled from the rest of the lines
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* `repo_snapshot` -- dictionary with a snapshot of the repository before the commit. Has the same structure as `completion_file`, but filenames and contents are orginized as lists.
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* `completion_lines_raw` -- the same as `completion_lines`, but before sampling.
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## How we collected the data
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* TBA
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- split: test
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path: data/test-*
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