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--- |
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dataset_info: |
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features: |
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- name: repo |
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dtype: string |
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- name: commit_hash |
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dtype: string |
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- name: completion_file |
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struct: |
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- name: filename |
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dtype: string |
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- name: content |
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dtype: string |
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- name: completion_lines |
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struct: |
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- name: infile |
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sequence: int32 |
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- name: inproject |
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|
sequence: int32 |
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- name: common |
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|
sequence: int32 |
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|
- name: commited |
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|
sequence: int32 |
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|
- name: non_informative |
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sequence: int32 |
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- name: random |
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sequence: int32 |
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- name: repo_snapshot |
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sequence: |
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- name: filename |
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|
dtype: string |
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|
- name: content |
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|
dtype: string |
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- name: completion_lines_raw |
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struct: |
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- name: commited |
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|
sequence: int64 |
|
|
- name: common |
|
|
sequence: int64 |
|
|
- name: infile |
|
|
sequence: int64 |
|
|
- name: inproject |
|
|
sequence: int64 |
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|
- name: non_informative |
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|
sequence: int64 |
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|
- name: other |
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|
sequence: int64 |
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splits: |
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- name: test |
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num_bytes: 111010036 |
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num_examples: 144 |
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download_size: 37603701 |
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dataset_size: 111010036 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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--- |
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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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ds = load_dataset('JetBrains-Research/lca-codegen-small', split='test') |
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``` |
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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 from `commit_hash` |
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* `inproject` – line contains at least one function or class that was declared in the project (excluding previous) |
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* `infile` – line contains at least one function or class that was declared in the completion file (excluding previous) |
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* `common` – line contains at least one function or 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 organized 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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To collect the data, we cloned repositories from GitHub where the main language is Python. |
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The completion file for each data point is a `.py` file that was added to the repository in a commit. |
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The state of the repository before this commit is the repo snapshot. |
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Small dataset is defined by number of characters in `.py` files from the repository snapshot. This number is less than 48K. |
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## Dataset Stats |
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* Number of datapoints: 144 |
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* Number of repositories: 46 |
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* Number of commits: 63 |
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### Completion File |
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* Number of lines, median: 310.5 |
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* Number of lines, min: 201 |
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* Number of lines, max: 1916 |
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### Repository Snapshot |
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* `.py` files: <u>median 4</u>, from 0 to 52 |
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* non `.py` files: <u>median 19.5</u>, from 1 to 1044 |
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* `.py` lines: <u>median 128</u> |
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* non `.py` lines: <u>median 1227</u> |
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### Line Counts: |
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* infile: 1430 |
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* inproject: 95 |
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* common: 500 |
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* committed: 1426 |
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* non-informative: 532 |
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* random: 703 |
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* **total**: 4686 |
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## Scores |
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[HF Space](https://huggingface.co/spaces/JetBrains-Research/long-code-arena) |
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