| | --- |
| | pretty_name: PyAst |
| | annotations_creators: |
| | - machine-generated |
| | language_creators: |
| | - found |
| | language: |
| | - code |
| | license: |
| | - bsd-2-clause |
| | - mit |
| | multilinguality: |
| | - monolingual |
| | size_categories: |
| | - 100K<n<1M |
| | source_datasets: |
| | - original |
| | task_categories: |
| | - text2text-generation |
| | - text-generation |
| | - fill-mask |
| | task_ids: [] |
| | paperswithcode_id: null |
| | tags: |
| | - code-modeling |
| | - code-generation |
| | dataset_info: |
| | features: |
| | - name: ast |
| | sequence: |
| | - name: type |
| | dtype: string |
| | - name: value |
| | dtype: string |
| | - name: children |
| | sequence: int32 |
| | config_name: ast |
| | splits: |
| | - name: train |
| | num_bytes: 1870790180 |
| | num_examples: 100000 |
| | - name: test |
| | num_bytes: 907514993 |
| | num_examples: 50000 |
| | download_size: 526642289 |
| | dataset_size: 2778305173 |
| | --- |
| | # Dataset Card for [py_ast] |
| | |
| | ## Table of Contents |
| | - [Dataset Description](#dataset-description) |
| | - [Dataset Summary](#dataset-summary) |
| | - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
| | - [Languages](#languages) |
| | - [Dataset Structure](#dataset-structure) |
| | - [Data Instances](#data-instances) |
| | - [Data Fields](#data-fields) |
| | - [Data Splits](#data-splits) |
| | - [Dataset Creation](#dataset-creation) |
| | - [Curation Rationale](#curation-rationale) |
| | - [Source Data](#source-data) |
| | - [Annotations](#annotations) |
| | - [Personal and Sensitive Information](#personal-and-sensitive-information) |
| | - [Considerations for Using the Data](#considerations-for-using-the-data) |
| | - [Social Impact of Dataset](#social-impact-of-dataset) |
| | - [Discussion of Biases](#discussion-of-biases) |
| | - [Other Known Limitations](#other-known-limitations) |
| | - [Additional Information](#additional-information) |
| | - [Dataset Curators](#dataset-curators) |
| | - [Licensing Information](#licensing-information) |
| | - [Citation Information](#citation-information) |
| | - [Contributions](#contributions) |
| | |
| | ## Dataset Description |
| | |
| | - **homepage**: [py150](https://www.sri.inf.ethz.ch/py150) |
| | - **Paper**: [Probabilistic Model for Code with Decision Trees](https://www.semanticscholar.org/paper/Probabilistic-model-for-code-with-decision-trees-Raychev-Bielik/62e176977d439aac2e2d7eca834a7a99016dfcaf) |
| | - **Leaderboard:** |
| | - **Point of Contact:** |
| | |
| | ### Dataset Summary |
| | |
| | The dataset consists of parsed ASTs that were used to train and evaluate the DeepSyn tool. |
| | The Python programs are collected from GitHub repositories |
| | by removing duplicate files, removing project forks (copy of another existing repository), |
| | keeping only programs that parse and have at most 30'000 nodes in the AST and |
| | we aim to remove obfuscated files |
| | |
| | ### Supported Tasks and Leaderboards |
| | |
| | Code Representation, Unsupervised Learning |
| | ### Languages |
| | |
| | Python |
| | ## Dataset Structure |
| | |
| | ### Data Instances |
| | A typical datapoint contains an AST of a python program, parsed. |
| | The main key is `ast` wherein every program's AST is stored. |
| | Each children would have, |
| | `type` which will formulate the type of the node. |
| | `children` which enumerates if a given node has children(non-empty list). |
| | `value`, if the given node has any hardcoded value(else "N/A"). |
| | An example would be, |
| | ''' |
| | [ {"type":"Module","children":[1,4]},{"type":"Assign","children":[2,3]},{"type":"NameStore","value":"x"},{"type":"Num","value":"7"}, {"type":"Print","children":[5]}, {"type":"BinOpAdd","children":[6,7]}, {"type":"NameLoad","value":"x"}, {"type":"Num","value":"1"} ] |
| | ''' |
| | ### Data Fields |
| | - `ast`: a list of dictionaries, wherein every dictionary is a node in the Abstract Syntax Tree. |
| | - `type`: explains the type of the node. |
| | - `children`: list of nodes which are children under the given |
| | - `value`: hardcoded value, if the node holds an hardcoded value. |
| | |
| | ### Data Splits |
| | |
| | The data is split into a training and test set. |
| | The final split sizes are as follows: |
| | |
| | | | train | validation | |
| | |------------------|--------:|------------:| |
| | | py_ast examples | 100000 | 50000 | |
| |
|
| | ## Dataset Creation |
| | [More Information Needed] |
| | ### Curation Rationale |
| |
|
| | [More Information Needed] |
| |
|
| | ### Source Data |
| |
|
| | #### Initial Data Collection and Normalization |
| |
|
| | [More Information Needed] |
| |
|
| | #### Who are the source language producers? |
| |
|
| | [More Information Needed] |
| |
|
| | ### Annotations |
| |
|
| | #### Annotation process |
| |
|
| | [More Information Needed] |
| |
|
| | #### Who are the annotators? |
| |
|
| | [More Information Needed] |
| |
|
| | ### Personal and Sensitive Information |
| |
|
| | [More Information Needed] |
| |
|
| | ## Considerations for Using the Data |
| |
|
| | ### Social Impact of Dataset |
| |
|
| | [More Information Needed] |
| |
|
| | ### Discussion of Biases |
| |
|
| | [More Information Needed] |
| |
|
| | ### Other Known Limitations |
| |
|
| | [More Information Needed] |
| |
|
| | ## Additional Information |
| |
|
| | ### Dataset Curators |
| | Raychev, V., Bielik, P., and Vechev, M |
| | ### Licensing Information |
| | MIT, BSD and Apache |
| | ### Citation Information |
| | @InProceedings{OOPSLA ’16, ACM, |
| | title = {Probabilistic Model for Code with Decision Trees.}, |
| | authors={Raychev, V., Bielik, P., and Vechev, M.}, |
| | year={2016} |
| | } |
| |
|
| | ``` |
| | @inproceedings{10.1145/2983990.2984041, |
| | author = {Raychev, Veselin and Bielik, Pavol and Vechev, Martin}, |
| | title = {Probabilistic Model for Code with Decision Trees}, |
| | year = {2016}, |
| | isbn = {9781450344449}, |
| | publisher = {Association for Computing Machinery}, |
| | address = {New York, NY, USA}, |
| | url = {https://doi.org/10.1145/2983990.2984041}, |
| | doi = {10.1145/2983990.2984041}, |
| | booktitle = {Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications}, |
| | pages = {731–747}, |
| | numpages = {17}, |
| | keywords = {Code Completion, Decision Trees, Probabilistic Models of Code}, |
| | location = {Amsterdam, Netherlands}, |
| | series = {OOPSLA 2016} |
| | } |
| | ``` |
| |
|
| | ### Contributions |
| |
|
| | Thanks to [@reshinthadithyan](https://github.com/reshinthadithyan) for adding this dataset. |
