|
--- |
|
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**: |
|
1. Selected Swift as the target language due to its popularity on GitHub. |
|
2. Filtered out files with average line length > 100 characters, maximum line length > 1000 characters, and alphabet ratio < 25%. |
|
3. 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). |
