Datasets:
Tasks:
Text Generation
Formats:
json
Sub-tasks:
language-modeling
Languages:
code
Size:
100K - 1M
License:
annotations_creators: | |
- crowdsourced | |
license: other | |
language_creators: | |
- crowdsourced | |
language: | |
- code | |
task_categories: | |
- text-generation | |
tags: | |
- code, swift, native iOS development, curated, training | |
size_categories: | |
- 10K<n<100K | |
source_datasets: [] | |
pretty_name: iva-swift-codeint-clean | |
task_ids: | |
- language-modeling | |
# IVA Swift GitHub Code Dataset | |
## Dataset Description | |
This is the curated train split of IVA Swift dataset extracted from GitHub. | |
It contains curated Swift files gathered with the purpose to train a code generation model. | |
The dataset consists of 320000 Swift code files from GitHub. | |
[Here is the unsliced curated dataset](https://huggingface.co/datasets/mvasiliniuc/iva-swift-codeint-clean) and | |
[here is the raw dataset](https://huggingface.co/datasets/mvasiliniuc/iva-swift-codeint). | |
### How to use it | |
To download the full dataset: | |
```python | |
from datasets import load_dataset | |
dataset = load_dataset('mvasiliniuc/iva-swift-codeint-clean', split='train') | |
``` | |
## Data Structure | |
### Data Fields | |
|Field|Type|Description| | |
|---|---|---| | |
|repo_name|string|name of the GitHub repository| | |
|path|string|path of the file in GitHub repository| | |
|copies|string|number of occurrences in dataset| | |
|content|string|content of source file| | |
|size|string|size of the source file in bytes| | |
|license|string|license of GitHub repository| | |
|hash|string|Hash of content field.| | |
|line_mean|number|Mean line length of the content. | |
|line_max|number|Max line length of the content. | |
|alpha_frac|number|Fraction between mean and max line length of content. | |
|ratio|number|Character/token ratio of the file with tokenizer. | |
|autogenerated|boolean|True if the content is autogenerated by looking for keywords in the first few lines of the file. | |
|config_or_test|boolean|True if the content is a configuration file or a unit test. | |
|has_no_keywords|boolean|True if a file has none of the keywords for Swift Programming Language. | |
|has_few_assignments|boolean|True if file uses symbol '=' less than `minimum` times. | |
### Instance | |
```json | |
{ | |
"repo_name":"...", | |
"path":".../BorderedButton.swift", | |
"copies":"2", | |
"size":"2649", | |
"content":"...", | |
"license":"mit", | |
"hash":"db1587fd117e9a835f58cf8203d8bf05", | |
"line_mean":29.1136363636, | |
"line_max":87, | |
"alpha_frac":0.6700641752, | |
"ratio":5.298, | |
"autogenerated":false, | |
"config_or_test":false, | |
"has_no_keywords":false, | |
"has_few_assignments":false | |
} | |
``` | |
## Languages | |
The dataset contains only Swift files. | |
```json | |
{ | |
"Swift": [".swift"] | |
} | |
``` | |
## Licenses | |
Each entry in the dataset contains the associated license. The following is a list of licenses involved and their occurrences. | |
```json | |
{ | |
"agpl-3.0":1415, | |
"apache-2.0":71451, | |
"artistic-2.0":169, | |
"bsd-2-clause":2628, | |
"bsd-3-clause":5492, | |
"cc0-1.0":1176, | |
"epl-1.0":498, | |
"gpl-2.0":7846, | |
"gpl-3.0":15716, | |
"isc":676, | |
"lgpl-2.1":932, | |
"lgpl-3.0":2553, | |
"mit":201134, | |
"mpl-2.0":6846, | |
"unlicense":1468 | |
} | |
``` | |
## Dataset Statistics | |
```json | |
{ | |
"Total size": "~453 MB", | |
"Number of files": 320000, | |
"Number of files under 500 bytes": 3116, | |
"Average file size in bytes": 5940, | |
} | |
``` | |
## Curation Process | |
See [the unsliced curated dataset](https://huggingface.co/datasets/mvasiliniuc/iva-swift-codeint-clean) for mode details. | |
## Data Splits | |
The dataset only contains a train split focused only on training data. For validation and unspliced versions, please check the following links: | |
* Clean Version Unsliced: https://huggingface.co/datasets/mvasiliniuc/iva-swift-codeint-clean | |
* Clean Version Valid: https://huggingface.co/datasets/mvasiliniuc/iva-swift-codeint-clean-valid | |
# Considerations for Using the Data | |
The dataset comprises source code from various repositories, potentially containing harmful or biased code, | |
along with sensitive information such as passwords or usernames. | |