--- language: - code - en multilinguality: - multiprogramming languages task_categories: - text-generation license: mit dataset_info: features: - name: identifier dtype: string - name: return_type 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 splits: - name: python num_bytes: 30797754227 num_examples: 9893858 - name: java num_bytes: 23130202517 num_examples: 7886299 - name: javascript num_bytes: 6833869001 num_examples: 2562158 - name: php num_bytes: 13072500520 num_examples: 5455989 - name: c_sharp num_bytes: 11144245789 num_examples: 4011467 - name: c num_bytes: 6205820571 num_examples: 1978551 - name: cpp num_bytes: 6228306797 num_examples: 1934958 - name: go num_bytes: 11339059495 num_examples: 5649158 - name: rust num_bytes: 2661037428 num_examples: 1076588 - name: ruby num_bytes: 1224195690 num_examples: 544867 download_size: 26404353470 dataset_size: 112636992035 pretty_name: The Vault 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:** support.ailab@fpt.com - **Website:** https://www.fpt-aicenter.com/ai-residency/

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# The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
## Dataset Summary The Vault is a multilingual code-text dataset with over 40 million pairs covering 10 popular programming languages. It is the largest corpus containing parallel code-text data. By building upon [The Stack](https://huggingface.co/datasets/bigcode/the-stack), a massive raw code sample collection, the Vault offers a comprehensive and clean resource for advancing research in code understanding and generation. It provides a high-quality dataset that includes code-text pairs at multiple levels, such as class and inline-level, in addition to the function level. The Vault can serve many purposes at multiple levels. ## Supported Tasks The Vault can be used for pretraining LLMs or downstream code-text interaction tasks. A number of tasks 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` ## Dataset Structure ### Data Instances ``` { "repo": "irshadbhat/sndpcs", "path": "arc_eager.py", "license": "MIT" "identifier": "REDUCE", "return_type": "" "language": "Python", "code": "def REDUCE(self, configuration, label=None):\n b0 = configuration.b0\n configuration.stack.pop()", "code_tokens": "def REDUCE ( self , configuration , label = None ) : b0 = configuration . b0 configuration . stack . pop ( )", "original_docstring": "\n pops the top of the stack if it has got its head.\n ", "comment": "\"\"\"\n pops the top of the stack if it has got its head.\n \"\"\"", "docstring_tokens": "pops the top of the stack if it has got its head .", "docstring": "pops the top of the stack if it has got its head." } ``` ### Data Fields Data fields for function level: - **repo** (string): the owner/repo - **path** (string): the full path to the original file - **license** (string): license in the repo - **language** (string): the programming language - **identifier** (string): the function or method name - **return_type** (string): the type returned by the function - **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** (string): tokenized version of `code`, separated by whitespace - **short_docstring** (string): short, brief summarization (first line of the docstring) - **short_docstring_tokens** (string): tokenized version of `short_docstring`, separated by whitespace - **docstring** (string): the top-level comment or docstring (docstring version without param’s doc, return, exception, etc) - **docstring_tokens** tokenized version of docstring, separated by whitespace - **comment** (string): comment (line) inside the function/class, separated by `$SEP$` token - **parameters** (dict): Dictionary of parameters and its type (type can be None) - **docstring_params** (dict): Dictionary of the parsed information from docstring Due to the limitation of the huggingface data structure, we do not contain **parameters** and **docstring_params** fields in this repo. The detail of data fields can be found in [The Vault data format](https://github.com/FSoft-AI4Code/TheVault/blob/main/data/README.md) and the full dataset version can be downloaded [here](https://github.com/FSoft-AI4Code/TheVault/). ### Data Splits In this repo, The Vault is divided into 5 subsets, where three training versions are split based on dataset size, and the remains are validation set and test set (20,000 samples in each). The statistic for each language is illustrated in the following section. There are 3 versions of training set that are small (5%), medium (20%) and large (100%). ## Dataset Statistics - Compare to other benchmarks | Dataset | #Language | #Code-text pair | |:--------------------------|----------:|-----------------:| | PyMT5 | 1 | ≈ 7,700,000 | | CoDesc | 1 | 4,211,516 | | CodeSearchNet | 6 | 2,326,976 | | CodeSearchNet (CodeXGLUE) | 6 | 1,005,474 | | Deepcom | 1 | 424,028 | | CONCODE | 1 | 2,184,310 | | Funcom | 1 | 2,149,121 | | CodeT5 | 8 | 3,158,313 | | **The Vault** | **10** | **40,993,893** | - Statistic for each language in The Vault | Language | #Code-text pair | #Repository | |:-----------|-----------------:|------------:| | Python | 9,893,858 | 628,069 | | PHP | 5,455,989 | 439,514 | | JavaScript | 2,562,158 | 355,761 | | Java | 7,886,299 | 321,129 | | C# | 4,011,467 | 150,657 | | C++ | 1,934,958 | 116,897 | | C | 1,978,551 | 88,556 | | Go | 5,649,158 | 241,238 | | Rust | 1,076,588 | 68,615 | | Ruby | 544,867 | 61,804 | ## Usage You can load The Vault dataset using datasets library: ```pip install datasets``` ```python from datasets import load_dataset # Load full function level dataset (40M samples) dataset = load_dataset("Fsoft-AIC/the-vault-function") # Load function level train/validation/test set dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"]) # Load "small" (or "medium", "large") function level training set dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train/small"]) # specific language (e.g. Python) dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"], languages=['Python']) # dataset streaming data = load_dataset("Fsoft-AIC/the-vault-function", split_set= ["train"]) for sample in iter(data['train']): print(sample) ``` ## Additional information ### Licensing Information [More information needed] ### Citation Information ``` @misc{manh2023vault, title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation}, author={Dung Nguyen Manh and Nam Le Hai and Anh T. V. Dau and Anh Minh Nguyen and Khanh Nghiem and Jin Guo and Nghi D. Q. Bui}, year={2023}, } ``` ### Contributions This dataset is developed by [FSOFT AI4Code team](https://github.com/FSoft-AI4Code).