Datasets:
File size: 1,712 Bytes
696fa16 4d2a539 696fa16 4d2a539 696fa16 4d2a539 696fa16 4d2a539 696fa16 4d2a539 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
---
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). |