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  ---
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- configs:
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- - config_name: default
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- data_files:
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- - split: train
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- path: data/train-*
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- - split: test
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- path: data/test-*
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- - split: valid
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- path: data/valid-*
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  dataset_info:
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  features:
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  - name: hexsha
@@ -24,17 +16,43 @@ dataset_info:
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  dtype: float64
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  splits:
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  - name: train
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- num_bytes: 4220835811.903224
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- num_examples: 896193
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  - name: test
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- num_bytes: 234488523.569184
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- num_examples: 49788
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  - name: valid
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- num_bytes: 234493233.308952
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- num_examples: 49789
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- download_size: 1499528459
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- dataset_size: 4689817568.781361
 
 
 
 
 
 
 
 
 
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  ---
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- # Dataset Card for "the-stack-swift-clean"
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- [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ license: openrail
 
 
 
 
 
 
 
 
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  dataset_info:
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  features:
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  - name: hexsha
 
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  dtype: float64
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  splits:
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  - name: train
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+ num_bytes: 3582248477.9086223
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+ num_examples: 806789
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  - name: test
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+ num_bytes: 394048264.9973618
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+ num_examples: 88747
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  - name: valid
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+ num_bytes: 3982797.09401595
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+ num_examples: 897
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+ download_size: 1323156008
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+ dataset_size: 3980279540
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+ task_categories:
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+ - text-generation
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+ language:
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+ - code
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+ tags:
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+ - code
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+ pretty_name: TheStack-Swift
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+ size_categories:
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+ - 1M<n<10M
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  ---
 
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+ ## Dataset 1: TheStack - Swift - Cleaned
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+
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+ **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.
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+
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+ **Target Language**: Swift
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+
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+ **Dataset Size**:
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+ - Training: 900,000 files
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+ - Validation: 50,000 files
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+ - Test: 50,000 files
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+
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+ **Preprocessing**:
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+ 1. Selected Swift as the target language due to its popularity on GitHub.
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+ 2. Filtered out files with average line length > 100 characters, maximum line length > 1000 characters, and alphabet ratio < 25%.
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+ 3. Split files into 90% training, 5% validation, and 5% test sets.
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+
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+ **Tokenizer**: Byte Pair Encoding (BPE) tokenizer with tab and whitespace tokens. GPT-2 vocabulary extended with special tokens.
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+
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+ **Training Sequences**: Sequences constructed by joining training data text to reach a context length of 2048 tokens (1024 tokens for full fine-tuning).