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README.md
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---
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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
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dtype: float64
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splits:
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- name: train
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num_bytes:
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num_examples:
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- name: test
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num_bytes:
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num_examples:
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- name: valid
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num_bytes:
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num_examples:
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download_size:
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dataset_size:
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---
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# Dataset Card for "the-stack-ruby-clean"
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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-Ruby
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size_categories:
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- 1M<n<10M
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---
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## Dataset 1: TheStack - Ruby - Cleaned
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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 Ruby, a popular statically typed language.
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**Target Language**: Ruby
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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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**Preprocessing**:
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1. Selected Ruby 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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**Tokenizer**: Byte Pair Encoding (BPE) tokenizer with tab and whitespace tokens. GPT-2 vocabulary extended with special tokens.
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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).
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