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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: string |
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- name: size |
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dtype: int64 |
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- name: content |
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dtype: string |
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- name: avg_line_length |
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dtype: float64 |
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- name: max_line_length |
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dtype: int64 |
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- name: alphanum_fraction |
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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-Java |
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size_categories: |
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- 1M<n<10M |
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--- |
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## Dataset 1: TheStack - Java - 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 Java, a popular statically typed language. |
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**Target Language**: Java |
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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 Java 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). |
