metadata
license: openrail
dataset_info:
features:
- name: hexsha
dtype: string
- name: size
dtype: int64
- name: content
dtype: string
- name: avg_line_length
dtype: float64
- name: max_line_length
dtype: int64
- name: alphanum_fraction
dtype: float64
splits:
- name: train
num_bytes: 3582248477.9086223
num_examples: 806789
- name: test
num_bytes: 394048264.9973618
num_examples: 88747
- name: valid
num_bytes: 3982797.09401595
num_examples: 897
download_size: 1323156008
dataset_size: 3980279540
task_categories:
- text-generation
language:
- code
tags:
- code
pretty_name: TheStack-Swift
size_categories:
- 1M<n<10M
Dataset 1: TheStack - Swift - Cleaned
Description: This dataset is drawn from TheStack Corpus, an open-source code dataset with over 3TB of GitHub data covering 48 programming languages. We selected a small portion of this dataset to optimize smaller language models for Swift, a popular statically typed language.
Target Language: Swift
Dataset Size:
- Training: 900,000 files
- Validation: 50,000 files
- Test: 50,000 files
Preprocessing:
- Selected Swift as the target language due to its popularity on GitHub.
- Filtered out files with average line length > 100 characters, maximum line length > 1000 characters, and alphabet ratio < 25%.
- Split files into 90% training, 5% validation, and 5% test sets.
Tokenizer: Byte Pair Encoding (BPE) tokenizer with tab and whitespace tokens. GPT-2 vocabulary extended with special tokens.
Training Sequences: Sequences constructed by joining training data text to reach a context length of 2048 tokens (1024 tokens for full fine-tuning).