|
--- |
|
language: |
|
- code |
|
- en |
|
multilinguality: |
|
- multiprogramming languages |
|
task_categories: |
|
- text-generation |
|
license: mit |
|
dataset_info: |
|
features: |
|
- name: identifier |
|
dtype: string |
|
- name: repo |
|
dtype: string |
|
- name: path |
|
dtype: string |
|
- name: language |
|
dtype: string |
|
- name: code |
|
dtype: string |
|
- name: code_tokens |
|
dtype: string |
|
- name: original_docstring |
|
dtype: string |
|
- name: comment |
|
dtype: string |
|
- name: docstring_tokens |
|
dtype: string |
|
- name: docstring |
|
dtype: string |
|
- name: original_string |
|
dtype: string |
|
pretty_name: The Vault Function |
|
viewer: true |
|
--- |
|
|
|
|
|
|
|
## Table of Contents |
|
- [Dataset Description](#dataset-description) |
|
- [Dataset Summary](#dataset-summary) |
|
- [Supported Tasks](#supported-tasks) |
|
- [Languages](#languages) |
|
- [Dataset Structure](#dataset-structure) |
|
- [Data Instances](#data-instances) |
|
- [Data Fields](#data-fields) |
|
- [Data Splits](#data-splits) |
|
- [Dataset Statistics](#dataset-statistics) |
|
- [Usage](#usage) |
|
- [Additional Information](#additional-information) |
|
- [Licensing Information](#licensing-information) |
|
- [Citation Information](#citation-information) |
|
- [Contributions](#contributions) |
|
|
|
|
|
## Dataset Description |
|
|
|
- **Repository:** [FSoft-AI4Code/TheVault](https://github.com/FSoft-AI4Code/TheVault) |
|
- **Paper:** [The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation](https://arxiv.org/abs/2305.06156) |
|
- **Contact:** [email protected] |
|
- **Website:** https://www.fpt-aicenter.com/ai-residency/ |
|
|
|
<p align="center"> |
|
<img src="https://raw.githubusercontent.com/FSoft-AI4Code/TheVault/main/assets/the-vault-4-logo-png.png" width="300px" alt="logo"> |
|
</p> |
|
|
|
<div align="center"> |
|
|
|
# The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation |
|
</div> |
|
|
|
|
|
## Dataset Summary |
|
The Vault dataset is a comprehensive, large-scale, multilingual parallel dataset that features high-quality code-text pairs derived from The Stack, the largest permissively-licensed source code dataset. |
|
|
|
We provide The Vault which contains code snippets from 10 popular programming languages such as Java, JavaScript, Python, Ruby, Rust, Golang, C#, C++, C, and PHP. This dataset provides multiple code-snippet levels, metadata, and 11 docstring styles for enhanced usability and versatility. |
|
|
|
## Supported Tasks |
|
The Vault can be used for pretraining LLMs or downstream code-text interaction tasks. A number of tasks related to code understanding and geneartion can be constructed using The Vault such as *code summarization*, *text-to-code generation* and *code search*. |
|
|
|
## Languages |
|
The natural language text (docstring) is in English. |
|
|
|
10 programming languages are supported in The Vault: `Python`, `Java`, `JavaScript`, `PHP`, `C`, `C#`, `C++`, `Go`, `Ruby`, `Rust` |
|
|
|
*Note: C and Go are not contained in this repo due to the nonexistence of traditional classes in these languages.* |
|
|
|
## Dataset Structure |
|
### Data Instances |
|
``` |
|
{ |
|
"hexsha": "78b961a6673ec1e12f8d95c33ef081f75561a87c", |
|
"repo": "AIS-Bonn/sl-cutscenes", |
|
"path": "sl_cutscenes/object_models.py", |
|
"license": [ |
|
"MIT" |
|
], |
|
"language": "Python", |
|
"identifier": "MeshLoader", |
|
"original_docstring": "\n Class to load the meshes for the objects in a scene.\n ", |
|
"docstring": "Class to load the meshes for the objects in a scene.", |
|
"docstring_tokens": [ |
|
"Class", |
|
"to", |
|
"load", |
|
"the", |
|
"meshes", |
|
"for", |
|
"the", |
|
"objects", |
|
"in", |
|
"a", |
|
"scene", |
|
"." |
|
], |
|
"code": "class MeshLoader:\n \"\"\"\n Class to load the meshes for the objects in a scene.\n \"\"\"\n\n def __init__(self):\n \"\"\"Module initializer\"\"\"\n self.base_dir = CONSTANTS.MESH_BASE_DIR\n self.text_dir = CONSTANTS.TEXT_BASE_DIR\n self.reset()\n\n def reset(self):\n self.loaded_meshes = []\n\n def get_meshes(self):\n \"\"\" \"\"\"\n extract_singular = lambda x: x[0] if len(x) == 1 else x\n return [extract_singular(item) for item in self.loaded_meshes]\n\n def load_meshes(self, obj_info: List[object_info.ObjectInfo], **kwargs):\n \"\"\"\n Loads the meshes whose information is given in parameter 'obj_info.\n Each call of this method APPENDS a list to the loaded_meshes attribute.\n :param obj_info: The object information of the meshes to be loaded.\n :param kwargs: additional mesh modifiers such as scale, specified with a leading 'mod_'\n \"\"\"\n paths = []\n for obj in obj_info:\n path = self.text_dir if obj.name.endswith(\"_floor\") or obj.name.endswith(\"_wall\") else self.base_dir\n paths.append((path / obj.mesh_fp).resolve())\n scales = [obj.scale for obj in obj_info]\n class_ids = [obj.class_id for obj in obj_info]\n mod_scales = kwargs.get(\"mod_scale\", [1.0] * len(scales))\n scales = [s * ms for (s, ms) in zip(scales, mod_scales)]\n flags = [mesh_flags(obj) for obj in obj_info]\n meshes = sl.Mesh.load_threaded(filenames=paths, flags=flags)\n\n # Setup class IDs\n for _, (mesh, scale, class_id) in enumerate(zip(meshes, scales, class_ids)):\n pt = torch.eye(4)\n pt[:3, :3] *= scale\n mesh.pretransform = pt\n mesh.class_index = class_id\n\n info_mesh_tuples = list(zip(obj_info, meshes))\n self.loaded_meshes.append(info_mesh_tuples)", |
|
"code_tokens": [ |
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|
"paths", |
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"\"_floor\"", |
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|
"short_docstring": "Class to load the meshes for the objects in a scene.", |
|
"short_docstring_tokens": [ |
|
"Class", |
|
"to", |
|
"load", |
|
"the", |
|
"meshes", |
|
"for", |
|
"the", |
|
"objects", |
|
"in", |
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"a", |
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"scene", |
|
"." |
|
], |
|
"comment": [ |
|
"\"\"\"\n Class to load the meshes for the objects in a scene.\n \"\"\"", |
|
"\"\"\"Module initializer\"\"\"", |
|
"\"\"\" \"\"\"", |
|
"\"\"\"\n Loads the meshes whose information is given in parameter 'obj_info.\n Each call of this method APPENDS a list to the loaded_meshes attribute.\n :param obj_info: The object information of the meshes to be loaded.\n :param kwargs: additional mesh modifiers such as scale, specified with a leading 'mod_'\n \"\"\"", |
|
"# Setup class IDs" |
|
], |
|
"parameters": [], |
|
"docstring_params": { |
|
"returns": [], |
|
"raises": [], |
|
"params": [], |
|
"outlier_params": [], |
|
"others": [] |
|
} |
|
} |
|
``` |
|
### Data Fields |
|
|
|
Data fields for function level: |
|
- **hexsha** (string): the unique git hash of file |
|
- **repo** (string): the owner/repo |
|
- **path** (string): the full path to the original file |
|
- **license** (list): licenses in the repo |
|
- **language** (string): the programming language |
|
- **identifier** (string): the function or method name |
|
- **original_string** (string): original version of function/class node |
|
- **original_docstring** (string): the raw string before tokenization or parsing |
|
- **code** (string): the part of the original that is code |
|
- **code_tokens** (list): tokenized version of `code` |
|
- **short_docstring** (string): short, brief summarization (first line of the docstring) |
|
- **short_docstring_tokens** (list): tokenized version of `short_docstring |
|
- **docstring** (string): the top-level comment or docstring (docstring version without param’s doc, return, exception fields, etc) |
|
- **docstring_tokens** (list): tokenized version of docstring |
|
- **comment** (list): list of comments (line) inside the function/class |
|
- **parameters** (list): List of parameters and its type (type can be None) |
|
- **docstring_params** (dict): Dictionary of the parsed information from docstring |
|
|
|
See [here](https://github.com/FSoft-AI4Code/TheVault/blob/main/data/README.md) for more details and examples. |
|
|
|
### Data Splits |
|
|
|
In this repo, the class level data is not split, and contained in only train set. |
|
|
|
## Dataset Statistics |
|
|
|
|Language | Number of samples | |
|
|:-----------|------------------------:| |
|
|Python | 422,187 | |
|
|Java | 4,872,485 | |
|
|JavaScript | 291,479 | |
|
|PHP | 1,173,916 | |
|
|C# | 1,437,800 | |
|
|C++ | 174,370 | |
|
|Ruby | 353,859 | |
|
|Rust | 93,311 | |
|
|C | - | |
|
|Go | - | |
|
|TOTAL | **9,121,300** | |
|
|
|
## Usage |
|
You can load The Vault dataset using datasets library: ```pip install datasets``` |
|
|
|
```python |
|
from datasets import load_dataset |
|
|
|
# Load full class level dataset |
|
dataset = load_dataset("Fsoft-AIC/the-vault-class") |
|
|
|
# specific language (e.g. Python) |
|
dataset = load_dataset("Fsoft-AIC/the-vault-class", languages=['Python']) |
|
|
|
# dataset streaming |
|
data = load_dataset("Fsoft-AIC/the-vault-class", streaming= True) |
|
for sample in iter(data['train']): |
|
print(sample) |
|
``` |
|
|
|
A back up dataset can be downloaded in azure storage. See [Download The Vault from Azure blob storage](https://github.com/FSoft-AI4Code/TheVault#download-via-link). |
|
|
|
## Additional information |
|
### Licensing Information |
|
MIT License |
|
|
|
### Citation Information |
|
|
|
``` |
|
@article{manh2023vault, |
|
title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation}, |
|
author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ}, |
|
journal={arXiv preprint arXiv:2305.06156}, |
|
year={2023} |
|
} |
|
``` |
|
|
|
### Contributions |
|
This dataset is developed by [FSOFT AI4Code team](https://github.com/FSoft-AI4Code). |
